Population stress, growth deficit, and degenerative joint disease inforagers from South Africa’s Later Stone Age
by
L. Elizabeth Doyle
A thesis submitted in conformity with the requirementsfor the degree of Doctor of PhilosophyGraduate Department of Anthropology
University of Toronto
© Copyright 2015 by L. Elizabeth Doyle
Abstract
Population stress, growth deficit, and degenerative joint disease in foragers from South
Africa’s Later Stone Age
L. Elizabeth Doyle
Doctor of Philosophy
Graduate Department of Anthropology
University of Toronto
2015
Harsh conditions during development may alter the human adult phenotype in ways that
affect vulnerability to disease and death. This study’s objectives are A) to explore the utility
of neural canal size and appendicular osteoarthritis as prospective indicators of developmental
stress; B) to test for developmental stress effects in a foraging population with no significant
socioeconomic stratification; and C) to explore temporal variation in neuroskeletal size and
joint degeneration.
The study sample consists of 143 Later Stone Age foragers (M=75, F=64, I=4) from the
Cape Floristic Region of southern Africa. 135 cases have radiocarbon dates between 9100 and
560 uncalibrated years BP. Osteoarthritis was quantified with an ordinal scoring procedure.
Relationships among 14C date, measures of body and neural canal size, OA, and age at death
were explored using logistic and ordinary least squares regression, independence tests, and
means contrasts. Age, sex, and body size were controlled where appropriate.
A positive relationship is observed between age at death and both body size and ML NC
diameter, but reaches statistical significance only in the latter case (OR=1.74, 95% CI=1.08–
2.82). The effect is detected in both sexes, but odds ratios are greater and p values smaller in
females (Male OR=1.50, 95% CI=0.82–2.73)5; Female OR=2.14, 95% CI=1.02–4.50). Age at
death is the only significant predictor for both presence and severity of osteoarthritis. No signif-
icant relationship is observed between age and anteroposterior diameter. Average mediolateral
diameter of the neural canal declines between 3000–2000BP and increases slightly afterwards
(β1 = −0.83, β2 = 0.68, adjusted R2 = 0.06, SEE=0.99). This quadratic curve is consistent
i
with published accounts of temporal change in average body size. No temporal pattern is
identified in osteoarthritis.
The neural canal shows promise as a bioarchaeological stress indicator, though osteoarthritis
does not. Mediolateral diameter, which ceases growth in childhood and adolescence, may be
more plastic to developmental stress than anteroposterior diameter, which ceases growing by
early childhood. The former’s growth schedule overlaps with that of the femur and so may yield
correlative as well as independent information about growth.
ii
This thesis is dedicated to my mother, Dr. Veronica Doyle. Wish you were here to read this,
Mom.
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Acknowledgements
A great many people have provided support, encouragement, and guidance in the long journey
toward the completion of this thesis. First and foremost, I thank the curators and curatorial
staff who gave me the opportunity to study the skeletal remains that are the basis of this
research: Dr Sven Ouzman of Iziko South African Museums, Dr. James Brink of National
Museum Bloemfontein, and Prof. Judith Sealy and Prof. Alan Morris of the University of
Cape Town Departments of Archaeology and Human Biology. Curatorial and research staff
members, Ms Wilhelmina Seconna of Iziko, Dr Jacquie Friedling of UCT, and Ms Sharon Holt
of National Museum Bloemfontein, all went above and beyond in providing ground support
during my research. Prof. Sealy and Prof. Morris have also generously shared insights about
southern African archaeology and continue to be important and generous colleagues. My re-
search in South Africa was supported by funding from the University of Toronto Department of
Anthropology and Massey College, and a large part of my programme has been supported by
the Social Sciences and Humanities Research Council through scholarship funding and through
a research grant to Prof. Susan Pfeiffer.
I am grateful to the members of my core committee, Prof. Susan Pfeiffer, Prof. Michael
Schillaci, and Prof. Esteban Parra, for their guidance and encouragement in shaping this
project. Extra thanks are due to Susan Pfeiffer, who first accepted me to the University of
Toronto, provided financial support for my studies, and guided this project from start to finish.
Susan’s enthusiastic and exacting approach to scientific biological anthropology represents a
high standard of scholarship to which I aspire, and her incisive and patient guidance represents
a model of academic mentorship that I strive to match in my own teaching endeavours.
I am, of course, grateful to current and former members of the Pfeiffer Group for compan-
ionship and support during our shared experience of graduate school: Dr. Catherine Merritt
and Prof. Lesley Harrington, remarkable role models and women much wiser than their years;
Elizabeth Sawchuk for general hilarity, and for allowing me to help out with a project that
quite unexpectedly took me to Kenya; Jarred Heinrich and Michelle Cameron, for impromptu
laboratory discussions of R or indeed any topic that took our interest; Amy Beresheim and
Thivvya Vairamuthu, who joined the Pfeiffer Group in my last years and whom I will enjoy
iv
meeting again at many future conferences. I hope that I gave back to you all as much as you
gave to me.
And finally, I am ever thankful for my extended family, a network of people who have been
a source of wise words, inspiration, and support throughout this long journey. My godmothers
Jacqui Aubuchon and Eileen King, lifelong educators. My assortment of aunties, uncles, and
cousins by blood and by affection: Fran and Bill, Ellen and Drew, Kit and Donelle, Will and
Linda Ross, Sarah West and Bill Ethier. My good friend Karen McAthy is my soul sister and
mentor and a more brilliant intellectual than I will ever be. My Judo family, chiefly Tami Dacks,
Dave Miller, Jorge Comrie, and Ben Ganss among many others, brought me onto the mats and
helped keep me healthy through graduate school. And of course my partner Raymond Goerke,
who has shared the last several years of this journey with me with compassion and grace, and
his family, Judi, Len, and Caity, who welcomed me so warmly and thoroughly into their own
family circle. All of you have made these years so much richer and I am so grateful to have you
in my life.
v
Contents
1 Introduction 1
1.1 Developmental Stress and the Origins of Health and Disease . . . . . . . . . . . . 1
1.1.1 Epidemiological Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Bioarchaeological Perspectives . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Early stress and developmental programming 5
2.1 Stress, deprivation, and development . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Stress and disease: the physiological links . . . . . . . . . . . . . . . . . . 6
2.1.2 Stress and the disruption of development . . . . . . . . . . . . . . . . . . 8
2.1.3 Prenatal exposure and intergenerational inertia . . . . . . . . . . . . . . . 10
2.1.4 Exposure in infancy, childhood, and adolescence . . . . . . . . . . . . . . 18
2.2 Developmental stress and the programming of adulthood outcomes: epidemio-
logical and evolutionary perspectives . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1 Evolutionary hypotheses for developmental programming . . . . . . . . . 21
2.2.2 Empirical approaches to evolutionary programming hypotheses . . . . . . 25
2.2.3 Growth constraint and adulthood outcomes in a foraging context: argu-
ment for a bioarchaeological perspective . . . . . . . . . . . . . . . . . . . 32
3 The bioarchaeology of stress and growth disruption 33
3.0.1 Growth markers and early stress in bioarchaeology . . . . . . . . . . . . . 34
3.0.2 Skeletal indicators of growth process and adulthood outcome . . . . . . . 35
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3.1 Osteological indicators of physiological degeneration: tracking non-lethal adult
outcomes in skeletal material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.1 Aetiologies of ostearthritis . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.1.2 The role of cardiometabolic factors in OA pathobiology . . . . . . . . . . 46
3.1.3 Evidence for the influence of growth conditions on risk of OA . . . . . . . 47
3.2 Bioarchaeological perspectives on osteoarthritis . . . . . . . . . . . . . . . . . . . 48
3.2.1 Identifying OA in skeletal remains . . . . . . . . . . . . . . . . . . . . . . 49
3.3 Palaeoepidemiological theory and method: Application to the bioarchaeology of
stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3.1 Sampling strategy and study design . . . . . . . . . . . . . . . . . . . . . 51
3.3.2 Estimating the probability of outcome . . . . . . . . . . . . . . . . . . . . 53
4 Coastal foragers of the Southern African Later Stone Age 58
4.1 The Later Stone Age and contemporary KhoeSan ethnography: continuity and
distinctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2 Ecogeographic context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.1 Holocene ecology of the Cape Floristic Region . . . . . . . . . . . . . . . . 61
4.2.2 Subsistence in the Cape Floristic Region . . . . . . . . . . . . . . . . . . . 63
4.3 Holocene dynamics of land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4 Coastal Later Stone Age people as a test case for developmental stress effects in
a prehistoric foraging population . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.4.1 Causes of mortality and morbidity . . . . . . . . . . . . . . . . . . . . . . 71
4.4.2 Social and environmental determinants of resource access and risk exposure 73
4.5 The coastal Later Stone Age collection as a palaeoepidemiological sample . . . . 74
4.5.1 Sample structure and provenience . . . . . . . . . . . . . . . . . . . . . . 75
5 Research Questions and Hypotheses 78
6 Materials 81
6.1 Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.1.1 Geographical context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
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6.1.2 Temporal context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.1.3 Subsistence context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Sample composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.2.1 Osteological inclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.2 Ecogeographic and temporal characteristics . . . . . . . . . . . . . . . . . 85
6.3 Osteological Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3.1 Methodological Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3.2 Sex estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.3.3 Age at death estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.3.4 Summary age phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3.5 Osteological measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.3.6 Joint Degeneration and Osteoarthritis . . . . . . . . . . . . . . . . . . . 93
7 Quantitative Methods 100
7.1 Preliminary diagnostic analyses and data management . . . . . . . . . . . . . . . 100
7.1.1 Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.1.2 Principal Components Analysis for Neural Canal Measurements . . . . . . 101
7.1.3 Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2 Osteological Measurement Error and Reliability . . . . . . . . . . . . . . . . . . . 107
7.2.1 Osteological Measurement Error . . . . . . . . . . . . . . . . . . . . . . 107
7.2.2 Comparison of Neural Canal Variation . . . . . . . . . . . . . . . . . . . 108
7.3 Descriptive Statistics and Preliminary Diagnostic Analyses . . . . . . . . . . . . 108
7.3.1 Sex-based Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.3.2 Central Tendency and Distribution . . . . . . . . . . . . . . . . . . . . . 109
7.3.3 Correlations and Collinearity . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.4 Statistical Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.5 Summary of Statistical Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.5.1 Hypothesis I: Skeletal growth outcome relative to age at death . . . . . 115
7.5.2 Hypothesis II: Presence and severity of joint degeneration relative to
skeletal growth outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
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7.5.3 Hypothesis III: Temporal variation in skeletal growth outcomes . . . . . 117
7.5.4 Hypothesis IV: Temporal variation in joint degeneration . . . . . . . . . 119
8 Results: Descriptive Statistics and Diagnostic Analyses 120
8.0.1 Sample Demographic Composition . . . . . . . . . . . . . . . . . . . . . . 120
8.0.2 Sample Temporal and Ecogeographic Composition . . . . . . . . . . . . . 120
8.0.3 Marine Dietary Content . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.0.4 Osteological Measurement Error . . . . . . . . . . . . . . . . . . . . . . . 125
8.0.5 Comparison of Neural Canal Variance . . . . . . . . . . . . . . . . . . . . 128
8.1 Descriptive Statistics and Preliminary Diagnostic Analyses . . . . . . . . . . . . 131
8.1.1 Sexual Dimorphism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8.1.2 Other Demographic Confounders . . . . . . . . . . . . . . . . . . . . . . 137
8.1.3 Central Tendency and Distribution . . . . . . . . . . . . . . . . . . . . . 138
8.1.4 Homogeneity of Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8.1.5 Correlations and Collinearity . . . . . . . . . . . . . . . . . . . . . . . . . 139
9 Results: Hypothesis Testing 143
9.1 Hypothesis I: Skeletal growth outcome relative to age at death . . . . . . . . . 143
9.2 Means comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
9.3 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
9.3.1 Binary Age Group as an outcome of Body Size . . . . . . . . . . . . . . 148
9.3.2 Binary Age Group as an outcome of Neural Canal Size . . . . . . . . . . 148
9.3.3 Testing alternative age divisions: comparing Very Young Adults, Young
Adults, and Mature-Elderly Adults . . . . . . . . . . . . . . . . . . . . . 154
9.4 Effect size, power, and sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . 156
9.4.1 Power analysis of means contrasts . . . . . . . . . . . . . . . . . . . . . . 156
9.4.2 Power analysis of binary logistic regression . . . . . . . . . . . . . . . . . 156
9.5 Hypothesis test I summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
9.6 Hypothesis II: Presence and severity of joint degeneration relative to skeletal
growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
9.6.1 Tests of independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
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9.6.2 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
9.6.3 OA as an outcome of Neural Canal Size . . . . . . . . . . . . . . . . . . . 167
9.7 Effect size, power, and sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . 170
9.8 Hypothesis test II summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
9.9 Hypothesis III: Temporal variation in skeletal growth outcomes . . . . . . . . . 171
9.10 Means comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
9.11 OLS regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
9.12 Supplementary replication with MI datasets . . . . . . . . . . . . . . . . . . . . 174
9.12.1 Effect size, power, and sensitivity . . . . . . . . . . . . . . . . . . . . . . 176
9.12.2 Hypothesis test III summary . . . . . . . . . . . . . . . . . . . . . . . . 179
9.13 Hypothesis IV: Temporal variation in joint degeneration . . . . . . . . . . . . . 181
9.13.1 Tests of independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
9.13.2 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
9.13.3 Effect size, power, and sensitivity . . . . . . . . . . . . . . . . . . . . . . 183
9.13.4 Hypothesis test IV summary . . . . . . . . . . . . . . . . . . . . . . . . 184
10 Discussion and Conclusion 185
10.1 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
10.1.1 Sample demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
10.1.2 Measurement error and reliability . . . . . . . . . . . . . . . . . . . . . . . 185
10.1.3 Distribution, homogeneity of variance, and collinearity testing . . . . . . . 186
10.1.4 Hypothesis testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
10.1.5 Power and sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
10.2 Pathways between growth deficits and early death in the Later Stone Age context188
10.3 Temporal variation in skeletal growth outcomes: the neural canal versus body size193
10.4 Joint degeneration as an osteological indicator of early stress and allostatic disease198
10.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
10.5.1 Exploring the neural canal and degenerative joint disease as candidate
indicators of developmental stress . . . . . . . . . . . . . . . . . . . . . . . 199
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10.5.2 Applying the Developmental Origins of Health and Disease Hypothesis
to Later Stone Age foragers . . . . . . . . . . . . . . . . . . . . . . . . . . 201
A Appendix 203
B Appendix 207
C Appendix 214
Bibliography 224
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List of Tables
6.1 Chronological age-estimation methods . . . . . . . . . . . . . . . . . . . . . . . . 89
6.2 Summary of groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.3 Scoring criteria for joint modification . . . . . . . . . . . . . . . . . . . . . . . . . 96
7.1 Size ranks based on Harrell-Davis quantiles . . . . . . . . . . . . . . . . . . . . . 104
7.2 Summary of variables used in analysis . . . . . . . . . . . . . . . . . . . . . . . . 105
8.1 Distribution of cases across age phases, stratified by sex . . . . . . . . . . . . . . 121
8.2 Demographic, temporal, and ecogeographic composition of sample . . . . . . . . 121
8.3 Osteological observer error analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 127
8.4 Comparison of neural canal variance in three samples . . . . . . . . . . . . . . . . 132
8.5 Descriptive Statistics: Osteological Measurements . . . . . . . . . . . . . . . . . . 133
8.6 Descriptive statistics of transformed femoral (FXL and FXH) and neural canal
measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
8.7 Descriptive Statistics: Osteoarthritis and joint modification case frequency . . . . 135
8.8 Descriptive Statistics: Osteoarthritis case frequency in upper and lower limbs . . 136
8.9 Descriptive Statistics: Osteoarthritis Severity (OA.Sev) . . . . . . . . . . . . . . 136
8.10 Summary of Principal Components Analyses for neural canal measurements . . . 137
8.11 Pooled Pearson correlation coefficients for the full imputed dataset . . . . . . . . 140
8.12 Partial correlations for the imputed dataset . . . . . . . . . . . . . . . . . . . . . 141
8.13 Partial correlations for the imputed dataset, sexes separated . . . . . . . . . . . . 142
9.1 t tests for difference of means between binary age phases . . . . . . . . . . . . . . 145
9.2 Hypothesis I: Results of Binary Logistic Regression . . . . . . . . . . . . . . . . . 149
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9.3 t tests for difference of means between three age groups (VYA, YA and MA/EA) 154
9.4 Hypothesis I power analysis for means contrasts . . . . . . . . . . . . . . . . . . . 157
9.5 Hypothesis I power analysis for Binary Logistic Regressions (Full Sample) . . . . 159
9.6 Hypothesis II tests of conditional independence for OA relative to ranked skeletal
size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
9.7 Hypothesis II logistic regression models of OA as an outcome of age and skeletal
size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
9.8 Hypothesis II power analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
9.9 Comparison of skeletal size by time period . . . . . . . . . . . . . . . . . . . . . . 173
9.10 Hypothesis III Regression models for skeletal size and radiocarbon date . . . . . 175
9.11 Hypothesis III power analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
9.12 Hypothesis IV tests of conditional independence for OA and Time Period . . . . 182
9.13 Hypothesis IV logistic regression models for OA and Time Period . . . . . . . . . 184
9.14 Power analyses for conditional independence tests for Hypothesis IV . . . . . . . 184
B.1 Demographic, geographic, and temporal variables of the full sample (page 1) . . 208
B.2 Demographic, geographic, and temporal variables of the full sample (page 2) . . 209
B.3 Demographic, geographic, and temporal variables of the full sample (page 3) . . 210
B.4 Osteological measurements and joint modification variables of the full sample
(page 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
B.5 Osteological measurements and joint modification variables of the full sample
(page 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
B.6 Osteological measurements and joint modification variables of the full sample
(page 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
C.1 Principal Components Analyses for Neural Canal Measurements . . . . . . . . . 215
C.2 Full descriptive statistics of five imputed datasets . . . . . . . . . . . . . . . . . . 216
C.3 Full descriptive statistics of five imputed datasets . . . . . . . . . . . . . . . . . . 217
C.4 Demographic distribution of joint-modification severity scores . . . . . . . . . . . 218
C.5 Zero-order correlations for the original datasets: full sample . . . . . . . . . . . . 219
C.6 Zero-order correlations for the original and imputed datasets in the full sample . 220
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C.7 Zero-order correlations for the original and imputed datasets in Males . . . . . . 221
C.8 Zero-order correlations for the original and imputed datasets in Females . . . . . 222
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List of Figures
3.1 Growth patterns of the lumbar and thoracic neural canals . . . . . . . . . . . . . 39
4.1 Map of the major plant communities of the Southern African Cape . . . . . . . . 63
4.2 Frequency distribution of radiocarbon dates from the full LSA collection . . . . . 71
6.1 Map of the study range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Dimensions of the Neural Canal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.1 Distribution of radiocarbon dates in study sample . . . . . . . . . . . . . . . . . 122
8.2 Scatterplot of stable carbon against nitrogen dietary isotopes . . . . . . . . . . . 123
8.3 Scatterplot of stable carbon against nitrogen dietary isotopes . . . . . . . . . . . 125
8.4 Interobserver comparison of anteroposterior and mediolateral neural canal mea-
surements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
8.5 Inter-population comparison of neural canal variability and mean size . . . . . . 130
9.1 Young Adult versus Mature-Elderly Adult skeletal size in the full sample . . . . . 146
9.2 Young Adult versus Mature-Elderly Adult skeletal size in males and females . . . 147
9.3 Comparing skeletal size among Very Young Adults, Young Adults, and Mature-
Elderly Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
9.4 Bar graph of OA Severity category relative to FXL Rank . . . . . . . . . . . . . 161
9.5 Hypothesis II effect plots for OA and Age . . . . . . . . . . . . . . . . . . . . . . 165
9.6 Hypothesis II effect plots for OA and body size . . . . . . . . . . . . . . . . . . . 167
9.7 Box plots of skeletal size across time periods . . . . . . . . . . . . . . . . . . . . . 176
9.8 Scatter plots of skeletal size across time periods . . . . . . . . . . . . . . . . . . . 177
xv
9.9 Scatter plots of skeletal size across time periods . . . . . . . . . . . . . . . . . . . 178
9.10 Hypothesis IV effects plot for OA and Time Period . . . . . . . . . . . . . . . . . 183
A.1 Field Datasheet page 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
A.2 Field Datasheet page 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
A.3 Field Datasheet page 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
C.1 Hypothesis II and IV conditional means plots . . . . . . . . . . . . . . . . . . . . 223
xvi
Chapter 1
Introduction
1.1 Developmental Stress and the Origins of Health and Disease
Conditions in prenatal and infant life affect adult phenotype through plastic developmental
pathways. Enduring, severe stress or deprivation during development may profoundly alter
outcome phenotype both morphologically — in reduced linear growth, lean tissue, and bone
mass — and physiologically, in altered respiratory function, metabolic capacity, and immune
response. The hypothesis that adult disease risks are partly determined by early-life condi-
tions is referred to as the Developmental Origins of Health and Disease hypothesis (DOHaD)
(Gluckman and Hanson, 2006a).
1.1.1 Epidemiological Perspectives
Developmental stress effects are relatively well demonstrated in contemporary populations:
individuals who were ill or disadvantaged in early life tend to exhibit elevated risk markers,
and experience greater probability of various illnesses and of early death, particularly from
cardiometabolic causes (Barker, 2007; Benyshek, 2013; Cottrell and Seckl, 2009; Drake and
Liu, 2010; Godfrey et al., 2010; Monaghan, 2008; Wells, 2011; Van IJzendoorn et al., 2007; Ziol-
Guest et al., 2012). The longer and more severe the early-life stress period, the more profound
the potential long-term effects.
Adulthood conditions are important mediators of developmental stress effects. Endoge-
nous, developmentally determined frailty is recognised as one point in a greater matrix of
1
Chapter 1. Introduction 2
risk factors that cumulatively contribute to epidemiological outcomes. Existing evidence sug-
gests that the size of developmental stress effects is typically small in comparison with that
of proximate exposures, especially adult body composition, nutrition, and lifestyle factors (e.g.
Victora et al., 2008). Evolutionary arguments derived from the DOHaD hypothesis (Bateson
et al., 2004; Gluckman and Hanson, 2006a; Gluckman et al., 2009a; Godfrey et al., 2010) are
as yet underdeveloped; indeed, those that attribute a direct adaptive benefit to early growth
restriction have been challenged on several points (Hayward and Lummaa, 2013; Kuzawa and
Quinn, 2009; Monaghan, 2008; Rickard and Lummaa, 2007; Wells, 2007, 2011). The problem
of under-development is partly rooted in the difficulty of ing thorough, high-quality data from
small-scale, non-urban, non-industrial peoples (cf., Hayward and Lummaa, 2013).
1.1.2 Bioarchaeological Perspectives
Bioarchaeologists have conducted a parallel line of inquiry into childhood stress and its im-
plications for disease and death, investigating associations between osteological indicators of
non-specific ill health in childhood — notably enamel hypoplasias, Harris lines, and various
measures of stature — and a limited set of palaeodemographic or palaeoepidemiologic param-
eters such as mortality hazard, average age at death, or skeletal disease markers (Cohen and
Armelagos, 1984; Cohen and Crane-Kramer, 2007; Clark et al., 1986; DeWitte, 2014; DeWitte
and Bekvalac, 2010; Dewitte and Hughes-Morey, 2012; DeWitte and Wood, 2008; Eshed et al.,
2004; Goodman and Armelagos, 1988; Gunnell et al., 2001; Kemkes-Grottenthaler, 2005; Klaus
and Tam, 2009; Klaus et al., 2009; Klaus, 2014; Larsen, 1997; Lieverse et al., 2007a; Redfern
and Dewitte, 2011; Ribot and Roberts, 1996; Starling and Stock, 2007; Steckel et al., 2002;
Steckel, 2005, 1995; Temple et al., 2013; Temple and Goodman, 2014; Wilson, 2014).
Bioarchaeological investigations of developmental stress effects have mostly focussed on
populations with food-producing (usually agricultural or horticultural) economies, often with
diachronic or synchronic nonagricultural comparators. For example, accumulated results show
that average stature tends to decrease after a subsistence transition (Larsen, 1997; Mummert
et al., 2011). It has been argued that this could be evidence of lower early selective mortality
resulting in more stunted individuals who survive to adulthood (Wood et al., 1992b); however,
the observed association between signs of early constraint and increased age-specific mortality
Chapter 1. Introduction 3
hazard (Boldsen, 2007; Dewitte and Hughes-Morey, 2012; DeWitte and Wood, 2008; Gunnell
et al., 2001; Kemkes-Grottenthaler, 2005; Steckel, 2005; Temple and Goodman, 2014; Wilson,
2014) supports the simpler interpretation that smaller mean stature reflects increased exposure
to causes of morbidity at the population scale (Cohen et al., 1994; Cohen and Armelagos, 1984;
Gage and DeWitte, 2009; Larsen, 1997; Larsen and Crosby, 2002; Mummert et al., 2011; Steckel,
1979, 1995; Stock and Pinhasi, 2011). Yet, in most in vivo contexts, morbidity and mortality
are strongly mediated by socioenvironmental conditions, some of which influence stress and
mortality in both early and adult life. The role of developmental stress in determining adult
mortality in the past is difficult to pinpoint.
How strong a role do developmental stress effects play, independent of other exposures,
in determining health outcomes in the absence of contemporary health risks and historical
socioeconomic stratification? Under non-marginal conditions, hunter-gatherer economies are
not more prone to famine than pastoral or agricultural economies (Berbesque et al., 2014; Huss-
Ashmore, 1997). Their epidemiological contexts, however, are both diverse and frequently very
different from those of contemporary populations, both urban and rural. In the context of
past hunter-gatherer populations, a study of developmental stress effects could provide a lens
on selective pressure and adaptive response: evidence that people were suffering stunting and
dying young could be indicate that something was wrong, and the population was undergoing
stress that induced change.
1.2 Research Objectives
The main objectives of this thesis are: A) to explore the utility of the diameter of the adult
neural canal and appendicular osteoarthritis as prospective indicators of developmental stress;
B) to test for developmental stress effects in a population with a mobile, immediate-return
foraging subsistence pattern and no evidence of socioeconomic stratification; and C) to explore
temporal variation in neuroskeletal size and joint degeneration in the context of that same
foraging population.
The first two objectives concern a bioarchaeological test of the hypothesis that developmen-
tal conditions, as inferred from adult stature, body mass, and neural canal size, are related to
Chapter 1. Introduction 4
adult risk of mortality and degenerative morbidity in a small-scale population that maintained
a mobile, immediate-return foraging economy throughout much of its history and whose archae-
ological signature records little evidence of socioeconomic stratification. The study focusses on
three variables that are either known to be, or are prospectively affected by developmental de-
rangement: femur size, a conventional measure of overall adult stature and body mass; neural
canal size (NC), a prospective marker of growth during infancy and childhood (Armelagos et al.,
2009; Clark, 1988; Holland, 2013; Watts, 2011, 2013a); and osteoarthritis of the synovial joints
(OA), a candidate marker of susceptibility to degeneration in systemic homeostasis. The study
seeks to determine whether the latter two variables could contribute information about stress
in bioarchaeological contexts additional to that of conventional indicators such as stature.
The third objective is to contribute to a bioarchaeological narrative about the population
history of the Later Stone Age peoples who inhabited Africa’s southern-most coasts during
the Holocene. Previous genetic, archaeological and bioarchaeological studies indicate that the
Later Stone Age population of this region grew at a steady, moderate pace for a long period of
time (Cox et al., 2009) until the latter half of the Holocene, when resource exploitation appears
to have increased and average body sizes appear to have declined(Barham and Mitchell, 2008;
Mitchell, 2002). An apparent peak of foraging intensity between 3000 and 2000 uncalibrated
radiocarbon years before present (BP) was followed by a period of more moderate activity
(Ginter, 2011; Pfeiffer, 2013; Pfeiffer and Sealy, 2006; Sealy and Pfeiffer, 2000). Average body
sizes appear to have been dynamic over this period, as is dietary emphasis on marine versus
terrestrial foods (Ginter, 2008, 2011; Kurki et al., 2012; Jerardino, 1998; Pfeiffer and Sealy,
2006; Sealy, 2006; Sealy et al., 1992; Sealy and Pfeiffer, 2000), and some evidence for occasional
interpersonal violence, mostly concentrated in the South-West coastal region, suggests that the
foragers of that time occasionally had to cope with hard times, but did so within the context of
their foraging life-way (Doyle, 2015; Morris, 2012; Morris and Parkington, 1982; Pfeiffer et al.,
1999; Pfeiffer and van der Merwe, 2004; Pfeiffer and Sealy, 2006; Pfeiffer, 2012b). This study
will add to the bioarchaeological aspect of this narrative by exploring temporal variation in
neuroskeletal size and in the frequency and severity of appendicular joint degeneration.
Chapter 2
Early stress and developmental
programming
The observation that psychosocial and nutritional stresses correlate with poor growth, high
morbidity, and early mortality has deep historical roots, but the idea that these associations
are linked by biological processes rooted in development, rather than being driven entirely by
common environmental exposures, was proposed relatively recently as the “thrifty phenotype”
hypothesis (Barker et al., 1989; Hales and Barker, 1992, 2001; Forsdahl, 1977) and has been syn-
thesized as the Developmental Origins of Health and Disease hypothesis (DOHaD) (Gluckman
and Hanson, 2006a). Evolutionary hypotheses, adaptationist and neutral, have been posited
to explain the history of such effects (Gluckman and Hanson, 2006a; Gluckman et al., 2009a;
Godfrey et al., 2010; Kuzawa, 2005; Kuzawa and Quinn, 2009; Monaghan, 2008; Rickard and
Lummaa, 2007; Wells, 2007). The recent gain in the momentum of epigenetic research has since
greatly advanced understanding of the mechanisms behind programming effects and the means
of their transmission across generations.
2.1 Stress, deprivation, and development
Stress, a term employed here to collectively indicate environmental, nutritional, and psychoso-
cial hardship, affects phenotype and disease risk differently according to the timing, type, and
severity of exposure. While changes to body size and composition are at the focus of most prior
5
Chapter 2. Early stress and developmental programming 6
and ongoing research because they can be measured easily, cheaply, and with minimal physical
invasion, regulatory changes “upstream” are now recognised as the common underpinning of
many correlated effects, from gross morphology to specific organ function. With the recent ad-
vances in field data collection and analytical technologies, direct investigation of physiological
markers is becoming more accessible and therefore more frequent.
2.1.1 Stress and disease: the physiological links
Stress is a biocultural phenomenon, meaning that both causes and effects may be psychological,
social, behavioural, or physiological; endogenous or exogenous, and can interact and feed back
on one another in complex ways that ultimately erode homeostasis and enhance vulnerability
to illness and death. Simple caloric deficiency is a prominent factor, as are deficiencies in
both macronutrients and micronutrients, all of which can be caused directly by inadequate
food quantity or diversity, and indirectly by disease load, notably enteric diarrhoeal disease and
parasitic infestation (DeBoer et al., 2012; Calder et al., 2006; Little, 1997; Ulijaszek et al., 1998).
Severe underweight (wasting) and restricted linear growth (stunting) are consistently associated
with higher mortality risk relative to normal growth across contemporary populations (Caulfield
et al., 2004; Ulijaszek et al., 1998).
Undernutrition impairs immune system maintenance, immunological response, and synthe-
sis of the many proteins and other biomolecules involved in all cellular processes from digestion
to wound healing, and even the ability to thermoregulate (Snodgrass, 2012; Caulfield et al.,
2004; Demas, 2004). Reducing skeletal muscle mass in the context of low energy availability,
although less demanding in terms of tissue maintenance costs, also increases the relative costs
of everyday activity, including food procurement, immune defence, and wound recovery (Snod-
grass, 2012). Conversely, frequent immunological insults (as in a pathogen-rich environment)
will induce energetic stress even without initial undernutrition because of the high energetic
costs of mounting an immunological defense, compounded by dehydration and suppression of
appetite (DeBoer et al., 2012).
Psychosocial stress, often a compounding factor in cases of famine, war, political instability,
or disease, is also an independent predictor of morbidity and mortality in populations that are
comparatively affluent. Common stressors in these contexts often do include poverty, food inse-
Chapter 2. Early stress and developmental programming 7
curity, violence, and societal marginalization and discrimination; but more innocuous domestic
demands, workplace environments, lack of sleep, and lack of social integration are also impli-
cated (Braveman et al., 2011; Dow et al., 2010; Frankish et al., 2005; Mcewen and Gianaros,
2010; Ziol-Guest et al., 2012).
The body’s response to stress is itself implicated in exacerbating the processes that can lead
to disease. Two systems are involved in the stress response: the neuroendocrine hypothalamic-
pituitary-adrenal (HPA) axis and the sympathetic autonomic nervous system (SNS), both of
which are controlled by the hypothalamus. Broadly speaking, the SNS stimulates the instan-
taneous “fight-or-flight” response by directly inducing the adrenal medulla and sympathetic
nerve terminals to release epinephrine and other catecholamines, which promote alertness, raise
blood pressure, and mobilize lipids and glucose for immediate fuel, while suspending higher
cognitive activity and critical homeostatic processes such as innate immune system function-
ing. The HPA axis response acts through the bloodstream (humoral system), meaning that it
is initiated somewhat more slowly, but acts over a longer period of time: in response to acute
stress, the hypothalamus stimulates the anterior pituitary lobe to release adrenocorticotropic
hormone (ACTH) into the bloodstream. ACTH, in turn, induces the adrenal cortex to release
glucocorticoids, notably cortisol, which act on a variety of target tissues to ready the body to
cope with acute insult: for example, cortisol acts on vascular tissues to increase blood pressure;
provides a ready supply of fuel for the brain and heart by stimulating glucose output by the
liver and suppressing uptake by muscle and adipose tissues; and clears the way for a rapid
cell-mediated immune response by pulling leukocytes and other immune cells out of circulation
and moving them to the skin, saliva, and lymph nodes (Lewitus et al., 2010; Sabban, 2009).
The HPA axis operates on a negative feedback loop: the adrenal cortex continues to release
glucocorticoids for up to two hours after initial stimulation, until blood concentrations reach a
threshold that signals the anterior pituitary to cease producing ACTH. After an acute stress
episode, glucocorticoid concentrations return to normal within a few hours; however, a chronic
stress regimen produces higher steady-state levels of glucocorticoids and epinephrine — because
the adrenal medulla is also stimulated by the HPA axis — while dampening circadian and acute
surges of glucocorticoids, particularly cortisol (Shapira, 2010).
Chronic activation of the stress response can eventually erode a number of allostatic main-
Chapter 2. Early stress and developmental programming 8
tenance processes, notably immunity, inflammation control, wound healing, glucose regulation,
and metabolism in numerous tissues. For example, the thymus, a gland responsible for matura-
tion and activation of T-lymphocytes, exhibits wasting and the number of circulating lympho-
cytes declines. Antibody production is suppressed along with other agents of adaptive immunity.
Adrenal hypertrophy occurs, resulting in chronic elevation of steady-state epinephrine and other
catecholamines, which in turn induce chronic upregulation of proinflammatory cytokines (IL-6
and TNF-α) and granulocytes, all ultimately resulting in increased susceptibility to infection
and slower wound healing in humans (Segerstrom and Miller, 2004; Shapira, 2010). While
physical deprivation will elicit these changes under chronic conditions, that long-standing psy-
chosocial stress will also degrade immunity has been demonstrated in a wide range of cultural
contexts (Blackwell et al., 2010; McDade, 2002; Shapira, 2010; Segerstrom and Miller, 2004;
Sorensen et al., 2009). The high energy cost of immunity suggests that degradation of active
immune maintenance under conditions of chronic stress are part of a generalized trade-off of
costly first-line defensive functions in favour of last-line functions (Segerstrom, 2010; Segerstrom
and Miller, 2004). This trade-off is also reflected in the preferential storage of excess energy in
central adipose depots at the expense of lean tissue and peripheral adipose depots, a process
that is also mediated by the stress axis (Shapira, 2010; Sharp et al., 2013).
2.1.2 Stress and the disruption of development
Early-life exposure to stress, either directly or in utero, may epigenetically program the adult-
hood stress axis in ways that increase vulnerability to disease (Calder et al., 2006; Drake and Liu,
2010; Drake et al., 2005; Gluckman et al., 2009b; Monaghan, 2008; Rutherford, 2009). For exam-
ple, one study of healthy young adults (age 20) from high- and low-socioeconomic-status (SES)
backgrounds showed altered gene expression and activity of leukocyte, glucocorticoid receptor,
cortisol, and proinflammatory interleukin-6, all pointing towards greater stress-response and
immune reactivity in young people who had experienced deprivation in childhood (Miller et al.,
2009). Another study, in over a thousand working-age American adults (30-41 years), childhood
poverty was associated with immune-mediated chronic conditions, including osteoarthritis and
hypertension, as well as decreased work productivity in adulthood, regardless of adult economic
status (Ziol-Guest et al., 2012).
Chapter 2. Early stress and developmental programming 9
Epigenetic modifications to stress-response pathways can attenuate the inhibitory response
to blood cortisol, upregulate activation of proinflammatory pathways, and dampen anti-inflammatory
mechanisms (Miller et al., 2009). Anxiety, depression, and other neurodevelopmental disorders
linked to dysregulation of stress responses are also more common in individuals with low birth-
weight, growth stunting, and other markers of prenatal and childhood stress (Cottrell et al.,
2012; McGowan et al., 2009; Soreq et al., 2010; Schlotz et al., 2008). Altered expression of
specific glucocorticoid receptors has been demonstrated in affective disorders, child abuse, and
even prenatal exposure to maternal war stress (McGowan et al., 2009; Mulligan et al., 2012;
Rodney and Mulligan, 2014). As psychosocial stress rarely occurs in isolation, and is often
accompanied by violence, disease or deprivation, modification of the stress axis may simply
compound the developmental effects of other stresses experienced in ontogeny.
Early stress exposure and critical periods
The concept of critical periods is a useful framework for understanding the relationship between
timing of stress exposure and the nature and intensity of its developmental effect. Much of
vertebrate development proceeds in a scheduled manner, with particular events occurring in
sequence and at intervals controlled by regulatory gene complexes that are highly conserved.
This is particularly the case during gestation, a time when nearly all organ systems develop
to functional maturity (Bogin et al., 2012; Ulijaszek et al., 1998). Phases of rapid anatomical
or physiological development represent critical periods of ontogeny during which disruption,
retardation, or acceleration may result in profound changes to the outcome phenotype (Cameron
and Demerath, 2002). A developmental insult sustained at a particularly sensitive phase of
development may result in spontaneous abortion, preterm birth, unrecoverable growth failure,
or permanent changes in organ function, while a similar insult sustained at a different phase
may merely slow growth temporarily, with minimal long-term consequences (Clarkin, 2008;
Cameron, 2007; Cohen et al., 2004; Dancause et al., 2012; Godfrey et al., 2010; Van IJzendoorn
et al., 2007; Wells, 2010).
Anthropometric measures of growth in head circumference, stature, mass, body propor-
tions and composition have been the primary indices of well-being and developmental success
throughout ontogeny because of their efficiency, low cost, and relative non-invasiveness. They
Chapter 2. Early stress and developmental programming 10
continue to be crucial measures in contemporary research, although it is increasingly recog-
nized that birth weight, for example, may be a relatively insensitive surrogate for intrauterine
programming (Eriksson, 2006; Gluckman et al., 2009b; Kuzawa and Quinn, 2009). Methods
for field collection and analysis of epigenetic and biomolecular data are rapidly advancing and
becoming cost-effective; however, discussions of programming effects still largely depend on
measures of growth as the main predictor. This will be reflected here.
2.1.3 Prenatal exposure and intergenerational inertia
The proportional energetic demands of the developing human body are very high during late
pregnancy, a time when growth velocity is high and when all major organ systems are in the
process of maturation: for example, the third trimester of pregnancy is a period of crucial
development in several physiological systems, notably the respiratory system, the circulatory
system, and the urinary system (Ulijaszek et al., 1998). Kidney development is also highlighted
in developmental stress research because, although usually functional in neonates with prenatal
growth restriction, the organs themselves are smaller and have fewer nephrons, making the
individual susceptible to hypertension in later life (Bassan et al., 2000; Hinchliffe et al., 1992;
Lampl et al., 2002; Manalich et al., 2000). Indelible changes to growth and development can
result from constraint during this period (Cameron and Demerath, 2002; Gluckman et al.,
2009b; Henry and Ulijaszek, 1996).
Restriction of nutrient and energy availability in utero, often concomitant with long-standing
maternal undernutrition, is the best-known direct cause of prenatal growth restriction. This
effect is so well-known that “eating down” — voluntary fasting by expectant mothers — is a
widely observed strategy to reduce the chance of obstetric complications by reducing the size of
the foetus (Christian et al., 2006; Rush, 2000; Martorell and Zongrone, 2012). However, other
sources of maternal stress are also increasingly recognised as having influence on foetal growth
and birth outcomes, including maternal illness (Heinke and Kuzawa, 2008), low socioeconomic
status (Chung and Kuzawa, 2014; Kuzawa et al., 2011) and war violence (Rodney and Mulligan,
2014).
Chapter 2. Early stress and developmental programming 11
Prenatal effects: body size and composition
Size and body proportions at birth are the best-documented measures of prenatal exposure to
stressors in humans. However, exposure to toxicants, trauma, and maternal illness will impact
neonate size and body composition via processes that include restricted nutrient transmission
across the placenta and alteration of foetal developmental trajectory by maternal glucocorticoids
(Cottrell and Seckl, 2009; Drake et al., 2005; Heinke and Kuzawa, 2008; Monaghan, 2008;
Mulligan et al., 2012; Rodney and Mulligan, 2014). Allocation of maternal resources elsewhere,
for example to growth in very young mothers, results in modifications to foetal phenotype
(Kramer et al., 2009; Monaghan, 2008; Henry and Ulijaszek, 1996). Even temporary alterations
in diet and eating rhythms may induce phenotypic changes: in a Tunisian hospital cohort the
infants of mothers who were themselves in utero during the Ramadan fast were found to be
substantially smaller than those whose mothers were not; furthermore, those babies who were
prenatal during Ramadan were smaller still (Alwasel et al., 2013). Adult stature correlates
positively with birth-weight and length (Adair et al., 2013; Araújo de França et al., 2014;
Kuzawa et al., 2011; Victora et al., 2008).
Prenatal restriction can also substantially alter offspring body composition by inducing
development of a phenotype with reduced lean mass (Adair et al., 2013; Gluckman and Hanson,
2006a; Baker et al., 2009; Kuzawa et al., 2011; Salonen et al., 2011; Thomas et al., 2012; Victora
et al., 2008). Functionally, this translates into reduced muscularity, reduced strength, and lower
physical work capacity, independent of other factors (Huss-Ashmore, 1997; Salonen et al., 2011;
Thomas et al., 2012). A compounding factor appears to be a greater tendency toward central
adiposity in stunted children and adults, another key risk factor for later-life cardiometabolic
disease (Araújo de França et al., 2014; Pomeroy et al., 2014; Thomas et al., 2012).
Prenatal effects: metabolic and cardiovascular allostasis
Metabolic and cardiovascular allostasis are the programming phenomena most widely investi-
gated in epidemiological contexts (Barker and Bagby, 2005; Chen and Zhou, 2007; Dancause
et al., 2012; Eriksson et al., 2014; Kyle and Pichard, 2006; Roseboom et al., 2000; Song, 2013;
Sotomayor, 2012; Syddall et al., 2005). The thrifty phenotype and predictive adaptive response
Chapter 2. Early stress and developmental programming 12
hypotheses of developmental programming focus predominantly on this association (Gluckman
and Hanson, 2006a; Hales and Barker, 2001).
Birth weight is negatively associated with insulin resistance; higher blood pressure is also
observed in low birth weight cohorts from low-income countries, although full-blown hyperten-
sion is rarely reported e.g. (Sotomayor, 2012; Thomas et al., 2012; Victora et al., 2008). In
middle and high-income countries, hypertension and cardiovascular mortality are also consis-
tently associated with low birth weight (Barker et al., 1989, 2002; Chen et al., 2012; Koletzko
and Brands, 2011; Risnes et al., 2011; Uauy et al., 2008).
The association between birth size and cardiometabolic risk is often reported to be U-
shaped rather than linear, in that those who were born as very large babies, particularly if
they have obese or diabetic mothers, also have a much greater risk of disease than those who
fall into the normal range of birth weight (Benyshek, 2013; Cameron and Demerath, 2002;
Victora et al., 2008). As is true with most prenatal effects, postnatal growth is a significant
contributing factor: greater stature in childhood is associated with greater lean mass (Kuzawa
et al., 2011) and cardiorespiratory fitness (Salonen et al., 2011); while rapid postnatal weight
gain is associated both with greater adiposity and vulnerability to cardiometabolic disease in
later life (Adair et al., 2013; Barker et al., 2002; Kuzawa et al., 2011; Norris et al., 2012).
Modifications to metabolic and cardiovascular regulation have wide-ranging implications.
For example, osteoarthritis (OA), a disease of inflammatory and mechanical deterioration of the
joint cartilage, is linked to glucose metabolism and to cardiovascular health (Katz et al., 2010;
Velasquez and Katz, 2010). Hyperglycemia is associated with decrease of insulin-like growth
factor-1 (IGF-1) response, and might thereby contribute to cartilage degeneration (Trippel,
2004). Sturmer et al. (2001) observe a mild propensity to OA in the contralateral joint in
osteoarthritic hip and knee arthroplasty patients with non-insulin-dependent diabetes, although
this effect did not extend to OA in the hand. Dahaghin et al. (2007) find that diabetes increases
the risk for hand OA independent of BMI, with the strongest effect in young diabetes patients.
Kornaat et al. (2009) and Suri et al. (2010) report associations between arterial wall thickness
and OA, while Gandhi et al. (2010) note an independent association between hypertension and
progression of hip OA. Although obesity is widely recognised as a major risk factor for OA,
obesity-independent effects are reported as well. Cohort studies have even directly identified
Chapter 2. Early stress and developmental programming 13
low birth-weight and early-life disadvantage as risks for OA (Clynes et al., 2014; Jordan et al.,
2005; Sayer et al., 2003; Syddall et al., 2005; Ziol-Guest et al., 2012). Observations in free-
living animal models support the link between early deprivation and early joint degeneration,
although the precise physiological pathways involved have not been identified (Peterson, 1988;
Peterson et al., 2010).
Prenatal effects: Immune function
Immune development and response in adolescence and adulthood may be compromised by both
prenatal and postnatal restriction. In the classic and best-known cohort study of this topic,
adults from the Gambia who were born during the annual rainy season — when infectious dis-
ease load is high and food availability low —- are observed to have much a higher risk of death
from infectious disease than those born during the dry harvest season (N=3102, N deaths=1077)
(Moore et al., 1997, 1999). Prospective studies that actually capture adult mortality risk are
rare, but numerous other epidemiological cohorts in diverse regions of the world are accumulat-
ing results that indicate the formidable complexity of candidate biological mechanisms that may
link adulthood immunity to early-life conditions. In a more recent birth cohort of 138 Gam-
bian infants, those born in the rainy season had significantly higher cord-blood and postnatal
lymphocyte counts than those born in the harvest season, congruent with greater maternal
exposure to infectious agents during gestation; those same rainy-season infants also had signifi-
cantly smaller thymuses than their harvest-season counterparts for up to 52 weeks postpartum
(Collinson et al., 2003). However, postnatal factors were found to be strong mediators with
this cohort: season of measurement impacted thymus size far more strongly than season of
birth, even when infants’ current weight was controlled (Collinson et al., 2003). Similar ob-
servations were made on a three-cohort sample of Tsimané and Pumé forager-horticulturalist
infants: both the immediate nutritional condition of the infants and significant variability in
maternal condition were implicated in variability of thymus size and the curve of thymic devel-
opment in the first years of life (Veile et al., 2012). Philippine adolescents with low birthweight
and persistent low BMI were also found to have reduced thymic function and, additionally,
were less likely to mount an immune response to a typhoid vaccine – indicating that postnatal
growth was a significant additive risk factor to small birth size (McDade et al., 2001b,a). Proin-
Chapter 2. Early stress and developmental programming 14
flammatory markers (C-reactive protein) in those Filipino adolescents who exhibited a blunted
immune response were found to be considerably higher in young adulthood (aged 20–21 years)
than in those with a strong immune response (Mcdade et al., 2011). Inflammatory regulation
was also found to be sensitive to birth weight in Bangladeshi children: those with low birth-
weight exhibited higher C-reactive protein levels, higher T-cell turnover, and lower interleukin-7
concentrations than those of normal birthweight; the authors interpreted this as evidence of
an overstretched system coping with immune activation at the expense of homeostasis (Raqib
et al., 2007). Despite reduced thymus size in association with early-life exposure, T-cell func-
tion may recover despite deleterious early conditions: comparative studies of vaccine response
in birth cohorts in The Gambia and Lahore (Moore et al., 1997, 2004) compared the typhoid
vaccine, which drives a T-cell-independent immune response, to a rabies vaccine that drives a
T-cell-dependent response, and found that birth weight mediated response in the former case,
but not in the latter. The birth-weight effect observed in the typhoid-vaccine response was
maintained after a second dose at 3-year follow-up (Moore et al., 2007).
The duration of breastfeeding and exposure to infectious agents in early life are mediating
factors to early-life immune development in the Filipino cohort (McDade et al., 2001b). In the
Gambia, as well, findings strongly suggest that the rainy season’s dual stressors of infectious
disease and hunger have compounding effects on infant growth and immune development (Moore
et al., 2007). Women in the latter population exhibit long lactation intervals and very high milk
output despite caloric deficits, apparently as an adaptation to the predictable seasonal stresses
in their environment (Huss-Ashmore and Ulijaszek, 1997). However, deficits in interleukin-7
and other immune factors in milk collected from women during the rainy season imply that
maternal buffering capacities are limited (Ngom et al., 2004).
So far, programming of the adult immune system by early stress exposure has been difficult
to characterize and validate (Victora et al., 2008). Accumulated evidence suggests that depri-
vation in utero and in infancy will deleteriously affect immunocompetence, but the acquired
and innate arms of the immune system may be affected differently, multiple mediating factors
are involved, and the effects may be neither linear nor immutable. However, one likely inter-
vening variable that is not explicitly addressed in most such studies is stress status: given the
well-documented immunosuppressive effect of chronic stress (Shapira, 2010; Segerstrom, 2010),
Chapter 2. Early stress and developmental programming 15
and evidence for a culturally situated effect of maternal stress on birth size (Mulligan et al.,
2012) an interaction between early-life exposure, programming of the HPA axis, and adulthood
stress exposure may well be implicated in the reported inconsistencies.
Prenatal effects: Respiratory function
Pulmonary structure and function begin to develop relatively late in gestation and the lungs
continue to differentiate postnatally, adding alveoli to increase surface area, for some time
after birth; they do not achieve full respiratory function until several years of age (Ulijaszek
et al., 1998). Prenatal growth restriction has a well-documented effect on lung development,
particularly by disrupting alveolar formation and production of surfactant, a process that is also
mediated by glucocorticoids (Harding et al., 2006; Seckl and Holmes, 2007). Repeat exposure
to elevated maternal glucocorticoids has been linked to early-life respiratory distress (Seckl and
Holmes, 2007). Low birth-weight for gestational age – the most commonly analyzed index of
prenatal growth restriction – is related to pulmonary capacity at various stages of adulthood
in samples from both low- and high-income countries (Harding et al., 2006; Stein et al., 1997;
Victora et al., 2005). Independent post-natal effects on pulmonary function are less well-
documented, but reduced pulmonary capacity is a known correlate of wasting and stunting
(Harding et al., 2006), and extant malnutrition reduces the efficacy of several lung tissue defence
mechanisms in both humans and animal models, including B-, T-, and macrophage cell activity,
and mucociliary operation (Bellanti et al., 1997). As with other programming effects, adulthood
respiratory effects are strongly linked with underlying socioenvironmental conditions, notably
exposure to environmental toxicants, familial smoking, maternal health, and postnatal growth
success (Victora et al., 2005). Postnatal growth itself is a prominent compounding variable:
after adjustment for adult height, reported associations between lung capacity and birth weight
are markedly attenuated in meta-analysis (Lawlor et al., 2005; Victora et al., 2005). Pre-term
birth – a cause of low birth-weight that is generally but not universally controlled in lung
function studies – is strongly associated with low respiratory function even when intra-uterine
growth is normal (Harding et al., 2006; Lawlor et al., 2005). The mechanism is far more
direct in these cases, as early birth effectively disrupts pulmonary development. It is likely,
however, that there is an interaction between maternal stress, prenatal exposure, and pre-term
Chapter 2. Early stress and developmental programming 16
birth even when size-for-gestational-age appears normal: in addition to intrauterine growth
restriction, pre-term birth is more likely in mothers with low body weight and mothers suffering
illness or severe nutritional or psychosocial stress (Han et al., 2011; Heinke and Kuzawa, 2008;
Lampl et al., 2008). This is reflected today in worldwide prevalence statistics: pre-term births
represent up to 25% of births in some low-income countries today, in contrast with 5–10% in
high-income countries (Pike, 2005). This particular interaction, between intrauterine growth,
adverse maternal environment, and risk of pre-term birth is at the centre of much discussion
about the evolutionary trade-offs inherent in the long human gestation period and relatively
large size of human neonates (Pike, 2005; Wells et al., 2012).
The role of the stress axis in programming prenatal effects
The HPA axis appears to have an important role in mediating the developmental response
to in utero stress signals (Worthman and Kuzara, 2005). The intrauterine environment helps
to buffer the foetus from most exogenous stressors and from many maternal experiences and
processes that could disrupt its development, notably the maternal immune system and envi-
ronmental pathogens and toxicants. The placenta is instrumental in shielding the developing
foetus but can be altered in states of severe maternal stress. The placental enzyme 11-β-
hydroxysteroid dehydrogenase 2 (11β–HSD2) normally blocks maternal glucocorticoids from
the foetus, but is downregulated in the context of maternal undernutrition or stress, allowing
maternal glucocorticoids to cross the placental barrier and alter foetal development (Cottrell
and Seckl, 2009; Seckl and Holmes, 2007). Experimental rodent models also indicate that
upregulation of the foetal HPA axis occurs in response to maternal undernutrition via other
mechanisms, independently of placental 11B-HSD2 activity (Cottrell et al., 2012). The third
trimester is a particularly sensitive time for this effect, and the placenta of male foetuses may
be more susceptible to 11β–HSD2 under-expression than that of the female (e.g. Cottrell et al.,
2012; Thayer et al., 2011). For example, the basal cortisol levels of Philippine mothers are
inversely related to the birth weight of their sons but not their daughters in a large study
(Thayer et al., 2011). Elevated glucocorticoids in utero alter the expression of glucocorticoid
receptors in the brain, and in other glucocorticoid-responsive tissues such as the lungs, liver,
vascular, kidney, and adipose tissues. In offspring with this phenotype, stress responses may be
Chapter 2. Early stress and developmental programming 17
both over-responsive to upregulation and under-responsive to downregulation (Benyshek, 2013;
Cottrell et al., 2012; Cottrell and Seckl, 2009; Fall et al., 2002; Levitt et al., 2000; Seckl and
Holmes, 2007; Schlotz et al., 2013; Thayer and Kuzawa, 2014; Ward, 2004). This is associated
with changes in glucose metabolism, appetite regulation, body composition, and potentially
immune function, described above (Victora et al., 2008). The outcome phenotype is one that
has been described as “primed” for an environment with scarce resources and heightened risk
of violence, but which predisposes the offspring to the long-term allostatic consequences of
over-preparation and over-reaction (Benyshek, 2013; Bogin et al., 2007; Gluckman and Hanson,
2006a; Bateson et al., 2004; Miller et al., 2009). Those allostatic consequences include predis-
position to obesity, cardiovascular disease, glucose dysregulation, osteoarthritis, anxiety, and
cognitive disorders (Cottrell and Seckl, 2009).
Intergenerational programming
Inter-generational effects are also increasingly investigated as a contributing factor: low birth-
weight (BW) and reduced outcome growth in mothers and grandmothers is consistently and
strongly linked to reduced birthweight and other outcomes in offspring and grand-offspring
(Alwasel et al., 2011, 2013; Azcorra et al., 2015; Benyshek, 2013; Chung and Kuzawa, 2014;
Drake and Liu, 2010; Kuzawa and Bragg, 2012; Thayer et al., 2011; Victora et al., 2008; Wells,
2010). Maternal postnatal growth is also implicated in offspring size: in a low-income rural
sample, maternal leg length — a measure that is particularly plastic to childhood resources (see
below) — was found to be a strong independent predictor of offspring BW even after accounting
for another strong predictor, placental size (Chung and Kuzawa, 2014). Under experimental
conditions, intergenerational effects are observed to tail off incrementally at each generation,
and are usually undetectable by the F3 (great-grandmaternal) generation unless renewed by
environmental and psychosocial conditions (Drake and Liu, 2010). It is proposed that maternal
and grandmaternal effects occur through direct exposure of the foetus to gestational factors,
and through exposure of the foetal germline to the following generation’s own programmed
phenotype (Drake and Liu, 2010; Monaghan, 2008). Observational and experimental evidence
is accumulating that paternal health markers also influence offspring phenotype through epi-
genetic pathways, although the precise mechanisms involved are still under investigation. It
Chapter 2. Early stress and developmental programming 18
is clear that, although prenatal programming effects are clearly documented in experimental
models and controlled cohort studies, they have relatively small independent effects.
2.1.4 Exposure in infancy, childhood, and adolescence
In free-living human populations, prenatal effects are usually compounded by postnatal hard-
ship, which itself can have profound consequences. The long-term implications are related to
the severity, timing, and duration of stressors: for example, in a meta-analysis of children who
were adopted from orphanages in low-income countries, those who were adopted before the age
of 1 exhibited rapid rebound in stature, body composition, and head circumference, while those
who had survived for longer under orphanage conditions exhibited much more severe stunting
and much less complete catch-up, particularly in head circumference (Van IJzendoorn et al.,
2007). A similar pattern was detected in a five-cohort study of low and middle-income coun-
tries: although patterns of growth faltering were variable throughout pre-maturity, the most
consistent predictor of stunted adulthood stature was growth failure prior to the age of 1 year
(Stein et al., 2010). Laotian refugees who had been displaced during infancy had significantly
shorter leg length than those who had not been displaced; however, among the many who had
been displaced multiple times during childhood and adolescence, reduction in leg length was
linearly correlated with the number of displacements (Clarkin, 2008, 2012).
In contrast to the far-reaching effects of physiological stress experienced in infancy, growth
trajectories in childhood and adolescence are less sensitive and more resilient to episodic dis-
ruption (Stinson, 2012). Here, childhood refers to the phase between the end of infancy and the
onset of puberty; this terminology subsumes the juvenile stage defined by Bogin et al. (2012) as
falling between adrenarche and puberty. Childhood is a time of intense social learning, as well
as the final stages of growth for the brain and development of the respiratory system (Ulijaszek
et al., 1998). Mortality and morbidity are typically rarer during this phase than in infancy
because growth velocity and its concomitant energy demands are much lower, and the immune
system has developed fully (Ulijaszek et al., 1998). Growth rate is slow during childhood and
speeds up after the onset of puberty; both the rate and duration of growth in childhood and
adolescence are more resilient than at earlier phases of ontogeny (Bogin et al., 2012). Physical
insults tend to have a smaller effect on growth outcomes, and any improvement in conditions
Chapter 2. Early stress and developmental programming 19
will allow at least some recovery before maturation (Cameron et al., 2005; Cameron, 2007;
Ghattas et al., 2007; Martorell and Zongrone, 2012; Stinson, 2012; Victora et al., 2008). For
example, children born into slavery in the American South suffered extreme stunting and on
average fell into the 0.5 percentile of 20th century height-for-age until late childhood, when slave
children were expected to begin plantation work but also received a supplementary food ration.
Growth rate increased dramatically at that phase, so that average adult statures reached the
20th percentile (Steckel, 1995). Nevertheless, the child’s ongoing cognitive and psychosocial
development and relative dependence on caregivers for food and care mean that severe or ongo-
ing hardship can still inflict long-lasting damage (Bogin et al., 2012; Cottrell and Seckl, 2009;
Ghosh et al., 2015; Van IJzendoorn et al., 2007; Stinson, 2012; Woo et al., 2010). In Ama-
zonian Tsimané children, for example, greater C-reactive protein was associated with reduced
prospective growth between the ages of 2 and 4; from 2-10 years of age, inflammatory markers,
mediated by low fat reserves, were also associated with reduced growth (McDade et al., 2008).
The same effect is reported for immunoglobulin E in Shuar forager-horticulturalist children
(Blackwell et al., 2010).
2.2 Developmental stress and the programming of adulthood
outcomes: epidemiological and evolutionary perspectives
Much of the physiological detail regarding the developmental consequences of multi-generational
deprivation and of various intervention strategies is provided by observational studies of people
in regions where undernutrition, infectious disease, and social ills are endemic e.g. (Adair
et al., 2013; Azcorra et al., 2015; Blackwell et al., 2010; Cameron, 1991; DeBoer et al., 2012;
Huss-Ashmore and Ulijaszek, 1997; Moore et al., 2007; Mulligan et al., 2012; Nikitovic and
Bogin, 2013; Pomeroy et al., 2012; Raqib et al., 2007; Victora et al., 2008). The earlier that
poor conditions are introduced, the more severe they are, and the longer they persist, the
more profound the phenotypic and long-term health effects (Cameron, 2007; Cohen et al.,
2004; Van IJzendoorn et al., 2007). Mounting evidence indicates that trans-generational effects
occur through epigenetic modifications that occur prenatally (Drake and Liu, 2010; Monaghan,
2008). In most of these studies, however, the social and economic context preclude analytical
Chapter 2. Early stress and developmental programming 20
separation of prenatal from postnatal effects.
Much of the evidence regarding developmental programming in humans derives from ob-
servational studies of cohorts who were exposed in utero to profound social disruptions, such
as the civilian famines of Leningrad and Amsterdam (Lumey et al., 2011; Roseboom et al.,
2000), the Chinese Famine of 1959-1961 (Chen and Zhou, 2007; Song, 2013), and the violence
of World War II (Eriksson et al., 2014; Gunnell et al., 1998; Syddall et al., 2005). More recent
cohort studies have been initiated to research the long-term effects of natural disasters such
as the 1998 Montreal Ice Storm (Dancause et al., 2011, 2012) and the 2011 Great East Japan
Earthquake and Tsunami (Catalano et al., 2013). These studies yield evidence that exposure to
severe stress in utero and in early life can significantly alter development and increase the risk of
neurological and cardiometabolic disorders in later life (Benyshek, 2013; Cameron, 2007; Eriks-
son et al., 2014; Gunnell et al., 1998; Syddall et al., 2005; Roseboom et al., 2000; Sotomayor,
2012). However, collectively the results of these cohort studies indicate that a typically afflu-
ent environment interrupted by transient severe stress, as in the Dutch Hunger Winter, the
Montreal Ice Storm, and the Great East Japan Earthquake, significantly attenuates program-
ming effects. The strongest evidence continues to point to chronic, multigenerational stress as
the strongest and most consistent cause of long-lasting programming effects in humans (Chen
and Zhou, 2007; Kuzawa, 2005; Kuzawa and Quinn, 2009; Lumey et al., 2011; Martorell and
Zongrone, 2012; Song, 2013).
The adulthood consequences of developmental stress are often subtle, and the methodologi-
cal challenges of working with epidemiological datasets are widely discussed e.g. (Wells, 2009).
Effect size estimates vary widely, but recent meta-analyses identify reliable associations between
developmental stress exposure and several deleterious adulthood outcomes. Lung function, cog-
nition, and markers of immune response correlate positively with birth weight (McDade et al.,
2001b; Mcdade et al., 2011; Raqib et al., 2007; Victora et al., 2008); the costs of maintaining
and mounting immune defence are also found to be part of a trade-off with somatic growth in
childhood (McDade et al., 2008). In all cases, prenatal effects, when detectable, are often ex-
acerbated by continued restriction in infancy and childhood, and can be modified by postnatal
growth. Poor glucose homeostasis, high blood lipids, and hypertension, the foci of many pro-
gramming studies, are all associated with both low birth-weight and rapid postnatal rebound
Chapter 2. Early stress and developmental programming 21
(Adair et al., 2013; Cameron, 2007; Victora et al., 2008; Martorell and Zongrone, 2012). In
sum, the longer and more severe the exposure, the more likely that effects will persist into
adulthood, and the more likely they are to affect adulthood outcomes.
2.2.1 Evolutionary hypotheses for developmental programming
The evolutionary implications of developmental programming are the topic of ongoing discussion
in human biology and public health. Two primary models have taken shape under various
names: the adaptationist predictive model and the constraint model. The adaptationist model
is most widely referred to as the thrifty phenotype hypothesis, the Barker Hypothesis (Barker
et al., 1989) or the predictive adaptive response hypothesis (Gluckman and Hanson, 2006b,c).
In the predictive adaptive response model, a stressed mother’s body conveys signals to her
gestating foetus that direct its phenotypic development along a “thrifty” trajectory, producing
a physiology that is “pre-adapted” to resource limitation and thereby better equipped to survive
to adulthood than a full-sized, energy-demanding offspring (Gluckman and Hanson, 2006a,b;
Godfrey et al., 2010; Kuzawa and Quinn, 2009; Wells, 2010).
Gluckman and colleagues propose that the thrifty phenotype benefits offspring fitness as
long as environmental conditions remain harsh, with low energy availability (Monaghan, 2008).
Characteristics such as HPA axis sensitivity, central adiposity, hypertension, and insulin resis-
tance have been proposed to be adaptive traits when the postnatal environment is a harsh one
(Baker et al., 2009; Bateson et al., 2004; Gluckman et al., 2009b,a; Kuzawa and Quinn, 2009).
When adulthood conditions do not match the low-energy environment predicted by in-
trauterine signals, however, the selective benefit is exchanged for a long-term cost – namely,
susceptibility to obesity, diabetes, and cardiovascular disease. Most discussions of the adapta-
tionist programming model are primarily concerned with cardiometabolic risk. Relatively little
discussion has concerned other sequelae, such as reductions in lean mass, respiratory function, or
immune function, each of which could be detrimental to survival in a challenging environment.
The predictive adaptive evolutionary model has been critiqued (Bogin et al., 2007; Kuzawa
and Quinn, 2009; Rickard and Lummaa, 2007; Wells, 2007, 2011) on the basis that, in human
biological studies, small babies are more vulnerable to mortality, and small adults have lower
survival and lower reproductive success (Monaghan, 2008; Hill and Hurtado, 1996; Sear et al.,
Chapter 2. Early stress and developmental programming 22
2003; Walker and Hamilton, 2008). Furthermore, the experiences of a mother only during
her pregnancy are unlikely to be accurate predictors of the offspring’s lifelong environmental
conditions, particularly in a long-lived species like humans (Kuzawa, 2005; Kuzawa and Quinn,
2009; Rickard and Lummaa, 2007; Wells, 2007; Wells et al., 2012).
The constraint model, otherwise dubbed the “Maternal Capital” model (Benyshek, 2013;
Bogin et al., 2007; Monaghan, 2008; Wells, 2010), posits that the energetic, hormonal and im-
munological milieu that a mother provides to her developing foetus is directly influenced by the
mother’s own “capital” in the form of accessible energetic resources (Wells et al., 2012). The
maternal capital budget imposes constraints upon the developing foetus, both directly by lim-
iting resource availability, but also indirectly by signalling the foetal developmental trajectory
to prioritize brain development at the expense of somatic growth; thus, the thrifty phenotype
that develops is more likely to be a response to constraint signals during gestation and infancy
rather than a predictive adaptation expected adulthood conditions (Kuzawa, 2005; Kuzawa and
Quinn, 2009; Wells, 2010, 2011). Elevated foetal levels of corticotropin-releasing hormone and
glucocorticoids observed in the context of maternal stress and foetal nutrient restriction, are
associated not only with reduced foetal size and HPA axis programming, but also with shorter
gestation (Cottrell and Seckl, 2009). This is one possible pathway by which a mother with a
low capital budget may restrict or even terminate investment in the foetus.
During infancy, a glucose-sensitive, insulin-intolerant, low-muscle phenotype may convey
some benefits: instead of muscle, most pre-weaning somatic growth is allocated to building
up adipose tissue reserves, which help to buffer the infant from the energetic and immunolog-
ical stresses of weaning (Kuzawa, 1998). Reducing peripheral insulin activity has the effect of
maximizing available blood glucose for brain activity (brain glucose uptake is non-insulin depen-
dent), while preferential formation of metabolically active visceral adipose tissue helps to main-
tain energy reserves in an easily-mobilizable depot while optimizing investment in costly lean
tissue (Benyshek, 2013; Kuzawa, 1998; Kuzawa and Quinn, 2009). Once set, these metabolic
trajectories persist, possibly yielding a selective benefit for the stressed offspring through the
energetically expensive and vulnerable stages of birth and infancy, but sensitizing them to allo-
static overload leading to disease in life (Wells et al., 2012). Wells (Wells, 2012) conceptualizes
this relationship as a mismatch between metabolic load (adulthood somatic size) relative to
Chapter 2. Early stress and developmental programming 23
somatic capacity (caused by underdeveloped regulatory organs).
Tests of the predictive model in small-scale contexts have yielded equivocal results. Baker
et al. (2009), for example, report significant positive associations between mothers’ average
birth interval and truncal adioposity in their children among Ache horticulturalist-foragers,
which they interpret as evidence that poorer maternal conditions (leading to greater birth
intervals) prompted children to preferentially deposit subcutaneous fat in the central region,
where it is reportedly more immediately available to fuel activity in times of low food availability
(Baker et al., 2009). In contrast, Hayward and Lummaa (2013) report that poor environmental
conditions at birth are significantly detrimental to survival in childhood and adulthood, and
confer no benefit to female reproductive success regardless of adulthood environment, in a large
preindustrial Finnish census dataset.
To date, the best-fitting evolutionary model of DOHaD for both palaeopathological and
epidemiological observations is the constraint model (Bogin et al., 2007; Wells, 2011), which
contends that the developing offspring is forced to grow within the restrictions imposed by its
developmental milieu. Hence, brain-sparing and fat-sparing occurs, and expensive yet “non-
essential” processes are constrained. These non-essential processes would include immune de-
fence, which is buffered in utero and during infancy by acquired immunity, as well as muscle,
bone, and metabolically important organs like liver, kidney and pancreas. Reduced investment
in these systems leads to greater vulnerability to allostatic load — the homeostatic degradation
that comes with continuous adjustment. The constraint model suggests that stunting does not
convey a net benefit to postnatal survival or reproduction survival in childhood despite the re-
duced growth demands that the programmed phenotype makes on scarce environmental energy
resources.
Though the simple adaptationist model of programming is not well supported, a recent
synthetic argument posits that programming may play an indirect role in the evolution of hu-
man life history through the intergenerational transfer of phenotypic information (Kuzawa and
Bragg, 2012). The concept of intergenerational inertia encapsulates the persistence of certain
programmed traits – notably susceptibility to obesity and cardiometabolic disease – across gen-
erations, even when deprived conditions begin to alleviate (Kuzawa, 2005). It contributes to the
“dual burden” of cardiometabolic risk and undernutrition that is often observed in populations
Chapter 2. Early stress and developmental programming 24
transitioning from poverty to affluence, with high frequencies of both over- and under-nutrition
(Doak et al., 2005; Pomeroy et al., 2012; Wells et al., 2012). The inertial effect is hypothesized
to arise from the fact that the gestational environment provided by a mother is a product both
of her lifelong circumstances and of her own development, itself influenced by her own mother’s
condition (Drake and Liu, 2010; Kuzawa, 2005; Kuzawa and Bragg, 2012).
The maternal signal may convey information about average conditions rather than about
those that apply strictly during her pregnancy (Kuzawa, 2005; Kuzawa and Quinn, 2009; Wells,
2007). This is particularly relevant to the evolution of small body size in many healthy human
populations. Small size in healthy populations is not equivalent to stunting in stressed ones
(Stinson, 2012): in stunting, growth slows and maturation is delayed, while stable small body
size is often associated with faster growth and earlier maturation (Migliano and Guillon, 2012;
Perry and Dominy, 2009; Walker et al., 2006; Walker and Hamilton, 2008). However, skeletal
data from the Later Stone Age foragers of southern Africa (Pfeiffer and Harrington, 2011) and
anthropometric data from contemporary Hadza (Blurton Jones et al., 1992; Walker and Hamil-
ton, 2008) demonstrate that some small-bodied populations exhibit very little secular trend in
stature despite changing nutritional conditions (Becker et al., 2010; Pfeiffer and Harrington,
2011; Hausman and Wilmsen, 1985), indicating that small body size need not be a sign of
growth failure. As high risks of early mortality, high residential density, and prevalent energetic
stress are associated with stunting and poverty in some populations and with stable small body
size and early maturation in others (Migliano and Guillon, 2012; Walker et al., 2006; Walker and
Hamilton, 2008), Kuzawa and Bragg propose that intergenerational phenotypic programming
for reduced growth investment (and small body size) is a possible preliminary phase in a shift
of life history.
Phenotypic plasticity enables organisms to respond to environmental conditions on a generation-
by-generation time scale and eventually drives natural selection of genotypes that “stabilize”
the beneficial phenotype (Kuzawa and Bragg, 2012). Under this model, the human phenotype
that develops under harsh conditions – characterized by small stature, altered HPA sensitivity,
reduced lean mass and metabolic capacity – can be viewed as an interim stage in this process.
As human growth in deprivation is typically longer and slower, and involves lower mean fertility,
any early survival advantage that programming conveys in early life may be offset by reduc-
Chapter 2. Early stress and developmental programming 25
tion in reproductive success unless external mortality risks are consistently high. Consequently,
Kuzawa and Bragg hypothesize that a population that is consistently exposed to high mortal-
ity over many generations will exhibit selection for faster maturation and smaller body size,
resulting in the correlation of life history with population mortality rates observed across many
traditional societies (Kuzawa and Bragg, 2012; Migliano and Guillon, 2012; Perry and Dominy,
2009; Walker et al., 2006; Walker and Hamilton, 2008). They predict that a population living
at high density, without access to medical interventions, and experiencing high rates of both
energetic restriction and pre-adult mortality, would favour a shift in life history toward earlier
growth cessation and early maturation, with concomitant effects on adult body size. Their
model implies that health emergencies associated with widespread stunting and high mortality
risk in contemporary contexts represent selective events in action that, in the absence of public
health interventions, could promote shifts in life history phenotype over evolutionary time scales
(Kuzawa and Bragg, 2012). To date, empirical evidence to support this model is scarce: al-
though genome-wide scans of several very small-bodied indigenous populations reveal evidence
of selection for alleles of some growth- and sexual maturation-related genes, the identities of
those genes are highly heterogeneous among regions, suggesting that “pygmy” phenotypes may
have evolved in response to a variety of selective pressures worldwide (Migliano et al., 2013).
2.2.2 Empirical approaches to evolutionary programming hypotheses
Evolutionary hypotheses of developmental programming rely mainly on epidemiological ob-
servations of disease in contemporary populations whose nutritional ecologies contrast strongly
with those of any small-scale agricultural or hunting-gathering human society e.g. (Eaton et al.,
1988; Cordain et al., 2000, 2005; Konner and Eaton, 2010; Milton, 2000). In contemporary hu-
man societies, in which most famines are caused by political or economic forces, most chronic
hunger occurs in the context of social marginalization and violence, malnutrition associated with
obesity occurs alongside that associated with underweight, and most communicable diseases are
caused by density-dependent pathogens (Lozano et al., 2012; Stinson, 2012; Wells et al., 2012).
Current evidence shows clearly that the adulthood consequences of early-life stress depend on
its duration, timing, and intensity, but also on socioenvironmental context (Ice and James,
2012; Van IJzendoorn et al., 2007; Stinson, 2012). While chronic growth constraint will result
Chapter 2. Early stress and developmental programming 26
in permanent stunting and reduced performance across several dimensions of human capital,
the most commonly documented effects are both caused by, and observed in the context of,
significant socioeconomic inequality (Adair et al., 2013; Victora et al., 2008; Ziol-Guest et al.,
2012). Access to good nutrition and education, strong social supports, and freedom from vi-
olence and oppression are all strong, independent predictors of both ontogenetic factors and
life outcomes (Braveman et al., 2011). Many are also highly situational: it is not clear, for
example, that reduced educational achievement, a well-documented correlate of growth restric-
tion in contemporary contexts (Cameron, 1991; Lumey et al., 2011; Victora et al., 2008) would
translate into an analogous sequela in a small-scale context. Conversely, the greater morbidity
and mortality rates observed at all stages of the life course in small-scale societies could well
increase the observability and fitness implications of certain programmed phenomena simply by
increasing the variability of early-life conditions and adulthood mortality risk, which are low in
many epidemiological cohorts (Gurven and Kaplan, 2007).
The classic human life-history pattern, with its extended period of slow pre-adolescent
growth facilitated by parental and alloparental support, and considerable resilience to distur-
bance, buffers human growth under most circumstances (Bogin et al., 2007; Hill and Hurtado,
2009; Little, 1997; Sellen, 2007). Except under very severe and protracted constraint, human
growth is likely to rebound by extending growth and delaying maturation (e.g. Cameron, 1991,
2007; Steckel, 1979, 1995; Wells and Stock, 2007). Furthermore, as Berbesque et al. (2014)
show, when latitude is controlled, groups following a hunting-gathering subsistence strategy
experience fewer and less severe episodes of famine than either pastoralists or agricultural-
ists. While the phenomena of developmental programming are highly relevant to contemporary
public-health intiatives, how many of their adulthood consequences would be likely to apply if
contemporary social, political, and ecological factors were translated to forms more consistent
with prehistoric, non-mechanized, non-urban, small-scale societies?
The potential for programming adult outcomes in small-scale non-industrial soci-
eties
If socioeconomic inequality, overnutrition, medical intervention, and contemporary life ex-
pectancies were excluded — in other words, under morbidity, mortality, and life-history con-
Chapter 2. Early stress and developmental programming 27
ditions closer to those that applied for much of our evolutionary history — how might devel-
opmental programming be expected to influence survival and fitness? Collectively, the cohort
data from contemporary agricultural, pastoral, and hunting-gathering subsistence economies
provide several indications:
First, people living in small-scale societies often have smaller children, slower growth, and
shorter stature than contemporary norms. We know that many small-scale, subsistence-level
groups have somewhat small babies, and that their children grow slowly relative to contempo-
rary growth standards (Foster et al., 2005; Godoy et al., 2010b; Cameron, 1991; Howell, 2010;
Little, 1997; Walker et al., 2006). For example, short stature is common among South American
small-scale indigenous groups, with up to and above 50% of children falling below the US 5th
percentile (Foster et al., 2005). This is attributed to high disease load and inadequate nutrition
in childhood; however, wasting (low weight-for-height) and low lean mass are far less common,
occurring at prevalences of 6% or less across six studies of South American rural indigenous
groups (Foster et al., 2005; Godoy et al., 2006). Foster and colleagues rarely observed wasting
in Tismané children, but found that many were stunted and had low weight-for-age relative to
US standards (52%). Nevertheless, their percentage of lean mass was at or slightly below the
US 50th percentile (Foster et al., 2005). As with most non-urban, non-mechanized societies,
chronic noncommunicable diseases are rare. The southern African KhoeSan-speaking peoples
stand out in comparative population models that show faster growth, earlier maturation, and
smaller adult size being associated with increased early mortality among small-scale societies
(Migliano et al., 2007; Migliano and Guillon, 2012; Walker and Hamilton, 2008; Walker et al.,
2006). Despite their small adult stature, they grow slowly, mature late, and have had, at least
during the ethnographic era, relatively low early mortality (Walker et al., 2006). Furthermore,
growth tempo among Later Stone Age KhoeSan children shows little evidence of systemati-
cally lagging growth (Harrington and Pfeiffer, 2008) and comparison of statural measures for
contemporary KhoeSan-speaking foragers with skeletal estimates from Later Stone Age an-
cestors indicates fairly consistent mean statures despite highly variable nutritional conditions
between the early Holocene and the historic periods (Cameron and Pfeiffer, 2014; Pfeiffer and
Harrington, 2011; Hausman and Wilmsen, 1985; Pfeiffer and Sealy, 2006). This relatively loose
association between stature and other health predictors – such as muscle mass – in hunter-
Chapter 2. Early stress and developmental programming 28
gatherers implies that long-term health outcomes may track fairly loosely relative to growth
schedules and outcomes when compared at large inter-population or diachronic scales – meaning
that slower growth and smaller adult stature may not reliably predict survival or other markers
of health.
Second, survival is generally lower among small-scale traditional societies than in contempo-
rary urban affluent societies and causes of death are generally acute and allopathic rather than
degenerative (Blurton Jones et al., 1992; Early and Headland, 1998; Gurven and Kaplan, 2007;
Howell, 2000; Hill et al., 2007; Hill and Hurtado, 1996; Little, 1997). Injury causing transient
or permanent disability is a common hazard of any rigorous lifestyle and peripartum death is
a consistent risk for women, particularly in populations with high birth-rates (Ronsmans and
Graham, 2006; Rush, 2000; Wells et al., 2012). Among some small-scale groups, including the
Hiwi, Yanomami, and Ju’/hoansi, interpersonal violence occurs at varying levels of intensity
and can result in deaths (Hill et al., 2007; Hill and Hurtado, 1996; Howell, 2000; Lee, 1979;
Wrangham et al., 2006). Disease and parasite loads are often high and account for a significant
share of mortality in many hunting and gathering groups (Early and Headland, 1998; Gurven
and Kaplan, 2007) and many infectious diseases and parasites are known to have long histories
of coevolution with our species (Harper and Armelagos, 2013). The latter observation must be
qualified, however: many deaths in contemporary hunter-gatherer cohorts are attributed to in-
fectious diseases with relatively short histories among those peoples (Gurven and Kaplan, 2007;
Hill and Hurtado, 1996; Howell, 2000). Given the profound impact of introduced pathogens
such as smallpox, measles, whooping cough, and tuberculosis on the infectious disease profiles
of many indigenous peoples from the historic era onward, infectious disease morbidity and mor-
tality rates observed ethnographically may not accurately represent prehistoric epidemiology.
Third, some of the epidemiological outcomes that are reliably linked to early-life stress,
such as psychological disorders and cardiometabolic dysregulation may have a relatively small
effect on overall survival and reproduction compared to the stronger and more acute challenges
described above. This is especially likely in a small-scale society with relatively early mortality,
in which survival into late elderhood is relatively unusual (Coale and P, 1966; Séguy and Buchet,
2013) because most such conditions are highly age-correlated and are unlikely to develop early
in an active, non-Western habitual ecology (Eaton et al., 1988; Gurven and Kaplan, 2007; Little,
Chapter 2. Early stress and developmental programming 29
1997). Furthermore, although many psychological outcomes could be significantly difficult to
cope with, they are situational and under-studied among small-scale peoples, making their
influence difficult to predict. Vulnerability to late-life degenerative disease may affect quality
of life, and possibly could affect elderhood survival, but without much fitness effect. Though
later-life activity undoubtedly contributes significantly to group and kin fitness (Blurton Jones
et al., 2002; Hill and Hurtado, 2009), studies of the net fitness benefit gained by grandmothers
and grandfathers who help to raise grandchildren suggest that it is real, but weak (Sear and
Mace, 2008).
In the context of humanity’s evolutionary history, programming effects that directly affect
the pre-adult and peak reproductive years are most likely to be relevant. Proximate stresses —
injury, conflict, seasonal hunger, parasite load — can exert immediate downward pressure on
both survival and human capital among people living a rigorous life. If an individual’s baseline
immune status and work capacity are already reduced by developmental factors, they could
be more vulnerable to environmental challenges than otherwise. However, the magnitude of
influence that early stress effects, in themselves, might have on adulthood outcomes in such
contects is unclear. Small adult body size is a correlate of lower reproductive success across
human populations (Walker et al., 2006). In women, small maternal size also increases the risk of
obstetric complications (Brabin et al., 2002; Liston, 2003; Sokal et al., 1991; Wells et al., 2012).
Smaller size, paired with lower relative lean mass and work capacity, can also impact individuals’
foraging success, travel endurance, and ability to survive trauma or violence (Huss-Ashmore,
1997). In certain social circumstances, body size and physical capability could significantly
affect marriageability or otherwise attractiveness as a sexual partner (Huss-Ashmore, 1997;
Kaplan et al., 2000). Reduced immune function could significantly increase susceptibility to
infection or parasitism (Howell, 2000; McDade et al., 2001b,a; Moore et al., 1999; Tanner et al.,
2009). Reduced pulmonary function could affect vulnerability to respiratory infection and
its ill effects, and could be exacerbated by prolonged exposure to crowded, poorly ventilated
habitations, particularly when heated by open hearths (e.g. Boocock et al., 1995; Merrett,
2003; Nasanen-Gilmore et al., 2015). High frequencies of skeletal tuberculosis, observed among
longhouse-dwelling Iroquoian people, also illustrate the potential impact of respiratory disease in
communal living conditions (Roberts and Buikstra, 2003; Pfeiffer, 1984). Respiratory capacity
Chapter 2. Early stress and developmental programming 30
would also influence efficiency and endurance in performing high-intensity tasks, which could
impact an individual’s work capacity, their status, and potentially their reproductive fitness
(Gurven and Hill, 2009; Sear and Mace, 2008).
The prevalence of allopathic causes of death suggests that physiological frailty is salient to
mortality risk in small-scale contexts. If developmental programming affects vulnerability to
disease, violence, or trauma, then its influence could cause detectable effects in past populations.
Several common causes of morbidity and mortality are likely candidates to cause such an effect:
Immune function may be impacted by stress and deprivation, as shown by a number of studies
from diverse populations (Blackwell et al., 2010; McDade et al., 2001b,a, 2008; Moore et al.,
1997; Tanner et al., 2009). Maternal mortality is likely to be exacerbated by stunted body size
in small-scale, prehistoric societies just as it is in subsistence-level economies today (Brabin
et al., 2002; Hogan et al., 2010; Rush, 2000).
The potential role of early stress in selective mortality in non-industrial, prehistoric
contexts
Selective mortality is undoubtedly relevant in small-scale human societies. So, too, is human
capital, a term normally used to refer to economic outcomes, like attained schooling or income
e.g. (Adair et al., 2013), but which serves here to collectively describe foraging productivity,
physical work capacity, knowledge and skill base, inherited or attained social status, and other
physical and social traits that contribute to social outcomes and thus can affect both reproduc-
tive success and survival. Programming effects that influence dimensions of human capital, such
as capacity to procure and share food or participate in alloparenting, may influence fitness via
indirect effects on kin survival, individual social status, and group cohesion (Hill and Hurtado,
2009; Kaplan et al., 2000; Sear and Mace, 2008; Sellen, 2007; Wells and Stock, 2007). The
direct impact of programming effects on fitness outcomes (individual or kin-level) is difficult to
identify, however. Case reports of hunting and gathering groups supporting chronically disabled
members (Blurton Jones et al., 2002; Hawkey, 1998; Dettwyler, 1991; Hill et al., 2007; Hill and
Hurtado, 1996) and robust evidence of biocultural strategies for buffering overall health in the
face of allopathic stressors e.g. (Godoy et al., 2010a; Tanner et al., 2009) suggest that reduced
physical capacity may not significantly depress a single individual’s fitness in the context of a
Chapter 2. Early stress and developmental programming 31
healthy and resilient group. Programming effects strong enough to have a measurable effect on
individual or group fitness would likely have to be both prevalent and chronic, so as to depress
the group’s ability to cope and survive cumulative challenges.
Controlled studies of early stress and adult outcomes in living small-scale, traditional soci-
eties — particularly hunter gatherers — have been difficult to carry out. Although researchers
doing ethnographic, demographic, and epidemiological research with contemporary peoples take
pains to minimize errors of interpretation, many datasets struggle with challenges, such as small
group sizes, imprecise age structure, difficulties in follow-up, technological limitations, and re-
liance on recall to document retrospective data, which constrain fine-grained hypothesis tests
about stress and survival (e.g. Becker et al., 2010; Blurton Jones et al., 1992, 2002; Howell,
2000; Kaplan et al., 2000). Large, reliable, cradle-to-grave datasets of the kind that have been
used in most epidemiological studies of early-life stress and programming, are difficult to come
by, although ongoing large-scale studies among some groups — notably the TAPS (Tsimané
Amazonian Panel Study) and the SLHP (Shuar Life History Project), underway in Bolivia and
Ecuador respectively — are beginning to build an epigenetic picture of transitioning hunter-
gatherer communities. An ongoing and inescapable challenge is that most small-scale groups
under study have begun to adopt elements of cash economies, have access to market goods
such as sugar, flour, alcohol, and tobacco, and are often in the process of epidemiological and
demographic transitions that can markedly alter the health profile of a traditional population
(Cameron, 1991; Hausman and Wilmsen, 1985; Headland, 1989; Hill and Hurtado, 1996; Hill
et al., 2007; Howell, 2010; Tanner et al., 2014; Thomas, 1997). Such transitions bring contempo-
rary hunter-gatherers closer to the “dual burden” epidemiological pattern characteristic of low-
and middle-income industrialized populations (Adair et al., 2013; Norris et al., 2012; Pomeroy
et al., 2014; Wells et al., 2012) and challenge efforts to distinguish early-life stress effects from
the contextual health risks inherent in such health environments.
Linking early stress with survival and well-being in small-scale hunter-gatherer contexts
could provide an independent test of the hypothesis that growth constraints may exert an
independent effect on survival in a non-agricultural, non-industrial, high-mobility context — a
set of social and environmental conditions much closer to those of early anatomically modern
human ancestors in Southern Africa (Klein, 1974; Marean, 2010).
Chapter 2. Early stress and developmental programming 32
2.2.3 Growth constraint and adulthood outcomes in a foraging context: ar-
gument for a bioarchaeological perspective
Ethnographically described small-scale societies – groups who depend largely on non-mechanized
food production or non-domesticated resources for subsistence – are at the root of evolutionary
scenarios for developmental programming and its consequent health effects. The proximate
causes of most health problems associated with developmental disruption are tied to marginal-
ization and poverty, generally in the context of industrialized, urbanized, and in particular
economically stratified societies. Contemporary small-scale societies tend to live in marginal
territories and to have livelihoods and epidemiological profiles deeply affected by encroach-
ing historic processes. The question of whether developmental disruption is linked to health
outcomes in societies without these characteristics is still not resolved.
Would a foraging population, existing in an epidemiologic matrix characterized by acute
trauma, environmental pathogens, and nutritional stresses — one very different from those
observed in most contemporary populations — exhibit heterogeneity of adult outcomes linked
with early-life conditions? One way of directly addressing this question is to compare the
distribution of a developmental disruption indicator with a proxy for later life outcome, such as
age at death, in the skeletons of foragers who lived prior to urbanization and colonization. This
approach would apply epidemiological analysis to a carefully selected bioarchaeological sample,
one that approximates, as closely as possible, a cross-sectional profile of deaths in the focal
population over a given time span. While subject to constraints of its own (Boldsen and Milner,
2012; Jackes, 2011; Love and Muller, 2002; Paine and Boldsen, 2002; Pinhasi and Bourbou, 2008;
Pinhasi and Turner, 2008; Wood et al., 1992b), the palaeoepidemiological approach enables the
study of human populations that are not well represented today. It facilitates comparisons that
encompass large spatial and temporal scales that may reveal broad, subtle variation across time
or space.
Chapter 3
The bioarchaeology of stress and
growth disruption
Bioarchaeologists have for decades addressed questions of physiological stress and frailty over
the life course. A cornerstone of much bioarchaeological study of environmental crises, sub-
sistence transitions, and other large-scale cultural changes in the human past, for example, is
the relationship between differential exposure to hardship and, correspondingly, non-random
patterning in the osteological signature of such exposures e.g. (Cohen and Armelagos, 1984;
Eshed et al., 2010; Klaus and Tam, 2009; Larsen, 1997; Stock and Pinhasi, 2011). For the
most part, hunting and gathering societies are represented as the neutral or control group in
comparative analyses whose aim is to determine the effect of broad cultural changes on human
well-being (Cohen and Armelagos, 1984; Cook and Buikstra, 1979; Eshed et al., 2010; Goodman
and Armelagos, 1988, 1989; Klaus and Tam, 2009; Larsen, 1997; Lieverse et al., 2007a; Mum-
mert et al., 2011; Steckel, 2005; Temple, 2010; Wilson, 2014). Temple and Goodman (2014)
has produced an excellent analysis of microstructural enamel hypoplasia and mortality hazard
in a small sample of juveniles and young adults from the Jomon coastal foraging population
of northern Japan; however, this is one of a limited number of studies that focus explicitly on
developmental stress within hunting and gathering contexts.
33
Chapter 3. The bioarchaeology of stress and growth disruption 34
3.0.1 Growth markers and early stress in bioarchaeology
The study of growth outcomes and their relationship to mortality is not a new topic in bioar-
chaeology. Multiple osteological measures of developmental conditions have been found to cor-
respond to younger age at death and reduced survival in the past and worse health outcomes in
the present (Boldsen, 2007; Clark et al., 1986, 1988, 1989; Cook and Buikstra, 1979; DeWitte,
2014; DeWitte and Wood, 2008; DeWitte and Bekvalac, 2010; Goodman and Armelagos, 1988,
1989; Redfern and Dewitte, 2011; Redfern et al., 2015; Temple and Goodman, 2014; Watts, 2011,
2013b; Wilson, 2014). Recent studies using probabilistic modelling techniques rather than sim-
ple contrasts have demonstrated that decreased adulthood survival can be associated with early
stress lesions (Boldsen, 2005, 2007; DeWitte, 2014; Dewitte and Hughes-Morey, 2012; DeWitte
and Wood, 2008; Redfern and Dewitte, 2011; Temple and Goodman, 2014; Usher, 2000; Wilson,
2014). This is illustrated by studies of selective mortality in a catastrophic death assemblage,
the London East Smithfield plague cemetery, which showed that epidemic mortality was still
significantly selective, although much less so than in a traditional attritional death assemblage
(DeWitte and Wood, 2008).
A correlative link between early stress and later survival does not, of course, necessarily
imply a direct causal link. Goodman and Armelagos (1988) articulate three hypotheses to
explain the association between osteological evidence of early stress and age at death: first, that
the osteological indicator itself may be a symptom of pre-existing or endogenous frailty that
ultimately contributes to early death; second, that the original cause of restriction is also directly
responsible for subsequent frailty, in other words that early stress causes later frailty 1988,
p.941; or, third, that both the cause of the lesion and the cause of death are symptomatic of an
underlying risk factor such as socioeconomic inequality, or “differential cultural buffering” 1989,
p.942. In both epidemiological and palaeoepidemiological settings, socioenvironmental factors
still present a significant risk of confounding. In their critical paper The Osteological Paradox,
Wood and colleagues (Wood et al., 1992a) point out that a convergence of intra-population
heterogeneity in fertility and physical robustness could produce a spurious association between
adulthood age at death and developmental stress lesions in skeletal assemblages: if a living
population consists of an advantaged subset that enjoys better childhood survival and better
Chapter 3. The bioarchaeology of stress and growth disruption 35
fertility than a second, disadvantaged subset, that division could yield a younger mean age-at-
death among those dead who had developed stress lesions (Wood et al., 1992b). In the case of
DeWitte and Wood’s study of prospective plague cases, for example, it is likely that poverty —
and its constraints on citizens’ ability to escape plague-ridden London — contributed to both
the presence of lesions and probability of dying in the plague (Dewitte and Hughes-Morey,
2012; DeWitte and Wood, 2008). Corroborative evidence from a population without significant
socioeconomic stratification would provide important support for the hypothesis that selective
mortality and other adulthood outcomes are influenced by developmental constraints in early
life.
3.0.2 Skeletal indicators of growth process and adulthood outcome
Bioarchaeological studies of developmental disruption rely on a kit of stress indicators, skeletal
traits, both discrete and continuous, that are influenced by conditions during growth and de-
velopment. Some, notably growth arrest lines seen on X-ray (Harris lines), bone lesions such
as cribra orbitalia, and pre-adult long bone lengths, have limited application to the question
of adulthood survival because they are vulnerable to erasure by bone remodelling, or, in the
case of juvenile bone lengths, simply cannot be directly observed in adults (Pinhasi, 2008).
Others, notably enamel hypoplastic defects, are highly useful (Armelagos et al., 2009) but can
be costly to measure reliably and, in hunter-gatherers, are often erased by dental wear in older
individuals (Hillson, 2014). Dimensions of skeletal size, on the other hand, can be measured
reliably, are observable in all individuals with adequate skeletal preservation, and are effective
indicators of variability in growth conditions within populations.
.
Body size: stature and mass
Body size, and particularly stature, is the most direct osteological analogue of a standard
anthropometric measure. Body size and proportions are plastic to climatic variation, gene
flow, and changes in life history among populations and across both wide and narrow time
spans (Bogin and Baker, 2012; Kuzawa and Bragg, 2012; Migliano and Guillon, 2012; Nikitovic
and Bogin, 2013; Ruff, 2002; Stinson, 2012; Walker et al., 2006; Walker and Hamilton, 2008).
Chapter 3. The bioarchaeology of stress and growth disruption 36
Within populations, and over shorter time intervals, however, heterogeneity of living conditions
during growth is the dominant interpretive paradigm for variability of stature (Cameron, 2007;
Cardoso, 2007; Stinson, 2012; Ulijaszek, 1997).
Growth and development prioritize the maturation of crucial organs over somatic growth,
such that the linear growth of the appendages may be more plastic to conditions of undernu-
trition than other structures, including the neuroskeleton and torso. Leg length in particular is
notably more plastic than trunk length or full stature to growth conditions because of develop-
mental canalization of the appendages is less stringent relative to that of the torso (Hallgrims-
son, 1999; Stinson, 2012). Individuals who grow up under conditions of chronic undernutrition
frequently display proportionately shorter limbs than those raised with nutritional adequacy
(Bogin and Baker, 2012; Bogin and Varela-Silva, 2010; Bogin and Rios, 2003; Boldsen, 1998;
Chung and Kuzawa, 2014; Nikitovic and Bogin, 2013; Pomeroy et al., 2012). The effect even
exhibits a gradient along the limb, with the distal segments displaying greater growth restriction
than the proximal segments (Pomeroy et al., 2012).
Stature and body mass can be reconstructed reliably from the skeleton using population-
appropriate regression equations (Ruff, 2002). The more complete the skeletal assembly, the
more precise and accurate the size estimate because error related to variability in body pro-
portions can be reduced (Auerbach and Ruff, 2004; Ruff, 2002). In the best-case scenario for
stature estimation, a skeleton’s full length is re-assembled from the heights of every bone from
the calcaneus to the cranium, and the regression equation is used to estimate the missing con-
tribution of soft tissue to the living stature (Raxter et al., 2006). Similarly, the best estimates
of body mass are derived from regression equations that incorporate both leg length and bi-iliac
width – indices of stature and body breadth, both of which substantially influence overall body
mass and vary systematically worldwide (Auerbach and Ruff, 2004; Ruff, 2002). Although these
methods yield the most accurate and precise estimates of size (Auerbach and Ruff, 2004), the
fact that they require relatively complete skeletal remains affects their applicability in archae-
ological samples.
Stature can also be estimated from the length of the femur, as it represents a major compo-
nent of total body length. Body mass can also be estimated from the diameter of the femoral
head. These methods require population-appropriate regression equations to account for vari-
Chapter 3. The bioarchaeology of stress and growth disruption 37
ability in body proportions, but they are preferable for most bioarchaeological investigations
because the femur frequently preserves well and can be measured very accurately and repro-
ducibly (Auerbach and Ruff, 2004; Kurki et al., 2010; Scheuer and Black, 2000).
In contemporary North American populations, femur length and head size complete their
growth by approximately 18 years of age in females and 20 years in males (Ruff et al., 1991;
Scheuer and Black, 2000), although some populations with slower average growth have a later
age of completion. As the size of the femur head is developmentally tied to that of the acetab-
ulum and thus to the pelvic girdle as a unit, while the length of the shaft is influenced more
directly by the endogenous and exogenous determinants of linear size, femur length and head
measurements may capture overlapping but slightly divergent information about an individual’s
growth.
Because linear growth in the limbs is sensitive to growth retardation, femur length has been
reliably used as an index of developmental stress in a wide variety of stress-mortality studies.
It has been demonstrated to have a positive association with age at death in a variety of past
populations (Boldsen, 1998; DeWitte and Wood, 2008; Kemkes-Grottenthaler, 2005; Steckel,
2005, 1995; Steckel and Rose, 2002). As numerous growth studies have shown, linear growth is
resilient, and delay in maturation and prolonging of the growth period can allow full recovery
of adulthood stature even under conditions of chronic caloric constraint (Bogin and Baker,
2012; Cameron, 1991, 2007; Cameron et al., 2005; Little, 1997). From a bioarchaeological point
of view, the likelihood that developmental disruption will be obscured by adolescent catch-
up growth means that incorporating complementary stress indicators may help to capture
variability in growth conditions (Armelagos et al., 2009; Boldsen, 1998; Clark et al., 1986).
The vertebral neural canal
A promising, but relatively under-researched indicator of developmental stress, is the diameter
of the vertebral neural canal (NC) in the thoracolumbar spinal column.
The morphogenesis of the vertebral neural canal is linked closely to the growth of the
central nervous system (Roth et al., 1976). By necessity, the canal must grow in area in order
to accommodate the spinal cord and nerves; stenosis of the lumbar canal, particularly in the
sagittal axis, is a well-documented cause of debilitating pain and nerve damage (Binder et al.,
Chapter 3. The bioarchaeology of stress and growth disruption 38
2002; Eisenstein, 1977, 1983; Hinck et al., 1966; Papp et al., 1994, 1997; Porter et al., 1980;
Roth et al., 1976; Saifuddin et al., 2000; Verbiest, 1955). During development, the neural
tissue strongly influences the hyperplasia and hypertrophy of the associated skeletal tissue in
order to preserve a safe margin of space for the spinal cord (Roth et al., 1976). Because
this protective field generated by the neural tissue imposes a minimum on the canal’s size,
but does not impose a maximum, there is some room for plasticity in canal size that can
accommodate the need for additional size imposed by the mechanical loads of body weight and
muscle attachments, particularly in the lower, weight-bearing segments. As an infant grows,
the neurological developmental signal must compete with the integrated requirements of the
skeleton and the body it supports (Roth et al., 1976), meaning that the growth schedule of the
thoracic and lumbar vertebral canals is influenced by both the neurological and the somatic
growth trajectories (Bogin et al., 2012; Scheuer and Black, 2000). Consequently, adult size is
achieved earliest in the cervical column, and latest in the caudalmost lumbar column (Scheuer
and Black, 2000) . Canal size in the upper cervical column reaches adult size by early childhood
(between 3 and 4 years of age) (Scheuer and Black, 2000), concurrent with the end of central
nervous system growth, while the lower lumbar canals continue to grow slowly into adolescence
(Hinck et al., 1966; Papp et al., 1994, 1997; Porter et al., 1987a,b; Ursu et al., 1996; Watts,
2013a).
The primary ossification centres that make up the neural arches begin to fuse at the posterior
synchondrosis (the later location of the spinous process) within the first postnatal year (Scheuer
and Black, 2000, p.196). Fusion of the posterior synchondrosis begins in the lower thoracic and
upper lumbar arches, and proceeds both cranially and caudally, but at different rates, so that
cervical arches have fused posteriorly by the end of the second year, and but the fifth lumbar
vertebral arch fuses posteriorly around age 5 years. Fusion of the arch with the centrum occurs
at the neurocentral junction between the ages of 2 and 6 year, beginning in the lumbar and
cervical and followed by the thoracic region; all fusion has normally completed in the cervical
region between years 3 and 4, in the thoracic region by the end of year 6, and in the caudalmost
lumbar by the end of year 5 (Scheuer and Black, 2000).
Growth of the neural canal tracks closely with that of head circumference as shown in
Figure 3.1. Most growth occurs before the onset of early childhood, and thereafter is relatively
Chapter 3. The bioarchaeology of stress and growth disruption 39
Figure 3.1: Growth of the neural canal in the anteroposterior (A) and mediolateral (B) planes, cranial circumfer-ence, and maximum femur length, expressed as a precentage of adult size. Juvenile AP measurements are fromlumbar vertebrae and are collated from Papp et al. (1994) and Ursu et al. (1996). Juvenile ML measurementsare from both lumbar and thoracic vertebrae and are collated from Hinck et al. (1966) and Ursu et al. (1996).Percentage of adult size is estimated relative to adult mean values in Hinck et al. (1966) and Ursu et al. (1996).Percentage of adult cranial circumference at birth and 1 year is derived from Scheuer and Black (2000, p.43);percentage of adult size after 3 years of age is derived from Epstein (1986). Percentage of adult femur length ismodified from LSA growth curves published by Pfeiffer and Harrington (2011)
slow and slight, terminating ultimately in adolescence(Epstein, 1986; Scheuer and Black, 2000;
Stoch et al., 1963). Endochondral growth is the most rapid growth process involved in growth
of canal diameter, and ceases when neurocentral fusion occurs, meaning that the most rapid
increase of canal size occurs quite early in life. The midsagittal diameter (MS) in particular
does not appear to increase significantly after early childhood; while the transverse diameter
(T) continues to increase slowly by subperiosteal remodelling alongside the growing vertebral
body for up to several years following neurocentral fusion, particularly in the weight-bearing
lower spinal column (Scheuer and Black, 2000). Sagittal diameters in the lumbar region reach
approximately 70% of average adult size by the end of gestation (Ursu et al., 1996) and reach
90% to 100% of adult size in early childhood (Hinck et al., 1966) (Figure 3.1). Transverse
lumbar diameters are at approximately 70% at birth (Ursu et al., 1996) and, at L5, reach 90%
by approximately 9 years of age (Hinck et al., 1966). In both thoracic and lumbar regions,
less than 20% of transverse growth occurs between the end of infancy and adulthood (Hinck
et al., 1966). In contrast, the femur, which reaches approximately 30% of the adult mean
length by the age of 4 years, reaches 70% by age 8 years, and 90% by approximately 15 years
of age (Harrington and Pfeiffer, 2008; Humphrey, 1998). This growth schedule means that,
like the cranial vault and other structures that follow the neural growth schedule more than
the general, the vertebral neural canal has very limited capacity for catch-up growth after
early childhood. Overall, the lower limit of canal size is determined by the neural tissue itself,
while the upper limit of transverse canal diameter, particularly in the lower, weight-bearing
Chapter 3. The bioarchaeology of stress and growth disruption 40
segments, is likely influenced by overall body size. Neural canals that are small relative to the
population average in the sagittal dimension may be caused in some cases by failure of the
neuroprotective field mechanism, or by global constraint severe enough to impede growth of the
central nervous system itself (Roth et al., 1976). Smallness in the transverse dimension may
be caused by both of the above factors, but may also reflect body-wide growth faltering, most
likely between childhood and the juvenile period. Because growth slows and then ceases quite
early in both dimensions, there is less capacity for catch-up growth in the later juvenile period
and in adolescence.
These traits of early growth and limited prospective catch-up inspired the exploration of
neural canal size as a potential indicator of the growth conditions from gestation through
childhood (Armelagos et al., 2009; Clark et al., 1986, 1988, 1989; Jeffrey et al., 2003; Papp
et al., 1995, 1997; Porter et al., 1987a,b; Porter and Oakshot, 1994; Watts, 2011, 2013a,a). To
date, bioarchaeological and clinical research has focussed on the thoracic and lumbar regions.
This is likely because stenotic canals in the thoracolumbar spine are most commonly associated
with back pain and other morbidities, which has led to considerable clinical observations and
anatomical work to characterize the extent of normal variation in their size and shape, and the
potential developmental causes of stenosis (Binder et al., 2002; Eisenstein, 1977, 1983; Hinck
et al., 1966; Papp et al., 1994, 1997; Porter et al., 1980; Roth et al., 1976; Saifuddin et al., 2000;
Verbiest, 1955).
The work of Clark and colleagues 1985; 1986; 1988; 1989 introduced the neural canal as
an indicator of non-specific stress with potential bioarchaeological utility by linking smaller
thoracolumbar canal size to earlier age-at-death and to adoption of maize horticulture in adults
from the Dickson Mounds cemetery site, and to reduced thymic activity in a small longitudinal
study of living men. Their work emphasizes the importance of using indicators that reliably
reflect the neural and thymolymphatic growth curves (Clark et al., 1986, p.146) and tests the
neural canal as a potential addition to the suite of conventional indicators — including cranial
size, dental development, and birth-weight — on the basis of prior research by Porter et al.
(1980), Hinck et al. (1966) and others, that neural canal growth follows a schedule closer to that
of the central nervous system than do other anthropometrics, such as stature or body weight.
Their rationale focusses on the proposed role of the thymus, a gland with an important
Chapter 3. The bioarchaeology of stress and growth disruption 41
role in immune development, in determining adulthood vulnerability to infection (Clark et al.,
1989). They conducted studies of neural canal and other vertebral variables, as well as other
anthropometrics, in both the archaeological Dickson Mounds collection (Clark et al., 1986) and
a small longitudinal sample of young men (Clark et al., 1988, 1989). In the Dickson Mounds
sample (N=90), they compare individuals who died prior to the age of 35 years to those who
survived longer. They report that NC stunting is systemic (consistent within vertebral column),
that small NC is associated with greater vertebral bone loss and with earlier age at death
regardless of sex, age at death, or cultural period. Furthermore, they report that thoracic NC,
which matures earlier than lumbar NC, is a more reliable predictor of early age at death than
lumbar NC (Clark et al., 1986). In their small longitudinal study, TSN-a (thymosin alpha 1)
is measured as proxy for immune function. Healthy male adults (N=16) with reduced thymus
function (lower TSN-a) had smaller NC and greater sitting height ratio than normative reference
values (Clark et al., 1988), and stepwise multiple regression showed that TSN-a is inversely
correlated with sitting height ratio, and positively correlated with NC (Clark et al., 1989).
Though significant variation in thymic development is linked with variability in nutritional
and pathogenic contexts, the precise role of poor early immune development in determining
adulthood susceptibility to infection has proved difficult to positively identify in the context of
epidemiological cohort studies e.g. (Collinson et al., 2003; Ghattas et al., 2007; McDade et al.,
2001b; Moore et al., 1997, 1999; Ngom et al., 2004; Raqib et al., 2007; Veile et al., 2012). Porter
and Oakshot (1994) expanded on the cohort approach of (Clark et al., 1988, 1989) and found
that both cardiovascular and gastrointestinal problems were more common in a cohort of living
people with small canal sizes than in their controls, a phenomenon that they attributed to
neural canal stunting being a signal of developmental disruption in a number of other biological
systems.
Clark and colleagues’ assertion that neural canals, in general, complete their growth by age
4 years, has been refined by detailed growth studies (Hinck et al., 1966; Holland, 2013; Papp
et al., 1994; Scheuer and Black, 2000; Watts, 2013b). However, their findings, linking neural
canal size with reduced adult physical and immunological capacity, are generally supported by
other studies of neural canals in both living and archaeological samples. Although growth in the
lower lumbar region overlaps with the adolescent growth spurt, the period of peak velocity in
Chapter 3. The bioarchaeology of stress and growth disruption 42
the neural canal occurs much earlier than in the limbs, so that there is a much narrower margin
for catch-up during adolescence, similar to the permanent stunting often seen in the cranial
circumference of children with early growth failure (Brandt et al., 2003; Van IJzendoorn et al.,
2007; Westwood et al., 1983; Winick and Rosso, 1969). In a cohort of 161 living children, for
example, Jeffrey et al. (2003) demonstrate that those who were born small for their gestational
age had significantly smaller neural canals at age 10; furthermore, those whose families were of
low socioeconomic status also had smaller canals than those who were of higher status. Porter
et al. (1987a), in a study of skeletal assemblages from Anglo-Saxon Raunds and Romano-
British Poundbury, observed that adults with linear enamel hypoplasias or Harris lines had
significantly smaller neural canals than those without. The most recent comparable work has
reported similar results to those of Clark et al. (Clark et al., 1986) in two separate British
archaeological assemblages (Watts, 2011, 2013a,a). Eisenstein (1983) measured the midsagittal
and transverse (maximum interpedicular) diameters of all five lumbar vertebrae in 485 cadaver
skeletons from the Raymond Dart Collection: of his sample, 372 of the skeletons belonged to
black South Africans of Zulu or Sotho ethnic identity; 113 belonged to white South Africans.
This study found little sexual dimorphism of canal size in either dimension within assigned
racial categories, but marked size differences between them, regardless of sex (Eisenstein, 1983).
Though Eisenstein’s interest was in characterizing racial variation in canal size that might have
been informative about susceptibility to canal stenosis, he inadvertently captured substantially
different socioeconomic strata in his samples. As with many anatomy collections that were
instituted in the late 19th and early 20th centuries, the Dart Collection has a high proportion
of unclaimed bodies from area hospitals, most of whom would have been black and poor (Dayal
et al., 2009). Many, if not most, black South Africans included in Eisenstein’s sample would
have experienced poor nutrition and living conditions throughout much of their lives. Notably,
mean vertebral body width was also substantially smaller in the Zulu and Sotho sample subsets
than in the white South African subsets, indicating that they experienced generally reduced
skeletal growth.
Though the neural canal appears to have considerable potential as a developmental stress
indicator, several methodological issues must be addressed. First, the similarity between the
lumbar canal and general somatic growth trajectories suggests that body size is a factor that
Chapter 3. The bioarchaeology of stress and growth disruption 43
needs to be considered (Clark et al., 1986; Hinck et al., 1966; Papp et al., 1994, 1997). If lin-
ear growth is prolonged and maturation delayed under deprivation, it is reasonable to expect
that the same process affects the neural canal, particularly in the transverse dimension and
the lumbar region. Second, in all of the fore-running cases, cultural buffering in the form of
socioeconomic status or subsistence strategy, is a factor that may well contribute to hidden
heterogeneity in mortality risk (Wood et al., 1992a). The third issue is analytical: with rare
exceptions (Jeffrey et al., 2003), each of the fore-running studies relies predominantly on means
comparisons with t-tests or ANOVA, and most test each vertebral level separately. This fre-
quentist approach is vulnerable to statistical and multiple-testing error and is likely unnecessary,
as suggested by the high internal consistency observed within the thoracic and lumbar regions
reported by Clark et al. (Clark et al., 1986). Furthermore, means comparisons, while useful for
the small samples and simple comparisons that are common in bioarchaeology, only estimate
average size differences between broad age groups groups; they do not measure the strength
of the relationship between age-at-death and the metric concerned. Recent methodological
advances in palaeoepidemiological research will be discussed below.
3.1 Osteological indicators of physiological degeneration: track-
ing non-lethal adult outcomes in skeletal material
Much interest in the implications of developmental programming for contemporary public health
arises from concerns about noncommunicable conditions that are ultimately rooted in degenera-
tion of physiological processes: emotional and cognitive disorders, cardiovascular and metabolic
disease, autoimmune dysregulation, and cancers (Benyshek, 2013; Cottrell and Seckl, 2009;
Gluckman and Hanson, 2006c; Liu et al., 2010; Martorell and Zongrone, 2012; Victora et al.,
2008). These noncommunicable conditions, many of which are aetiologically rooted in lifelong
poverty and marginalization, are coming to dominate twenty-first century epidemiological land-
scapes as a result of changing lifestyles and a gradual worldwide decline in infectious disease
(Lozano et al., 2012). Though degenerative diseases represent a minor component of the disease
load among contemporary small-scale peoples (Eaton et al., 1988; Howell, 2000), recent imaging
studies that demonstrate the presence of atherosclerosis and other degenerative sequelae among
Chapter 3. The bioarchaeology of stress and growth disruption 44
ancient peoples, including the Bronze Age man dubbed Otzi (Clarke et al., 2014; Thompson
et al., 2013) indicate that they should be considered when discussing developmental stress and
adulthood outcomes in the past. If developmental programming alters factors such as blood
pressure, inflammatory response, and glucose control even among people living non-Western
lifestyles, then its potential to influence degenerative conditions in ancient peoples should be
tested before being dismissed.
Cardiometabolic status is mostly ephemeral from an osteological standpoint, apart from
occasional case reports of calcified atherosclerotic deposits in burial matrix or mummified tissues
(Clarke et al., 2014; Thompson et al., 2013); however, osteoarthritis (OA), the most commonly
observed condition in palaeopathology (Waldron, 2009) is pathobiologically linked to a number
of systemic physiological processes that are implicated in cardiometabolic disease (Abramson
and Attur, 2009; Dahaghin et al., 2007; Kapoor et al., 2011; Katz et al., 2010; Kornaat et al.,
2009; Puenpatom and Victor, 2009; Stürmer et al., 2001; Suri et al., 2010). Several studies
have even reported associations between OA and early life conditions (Clynes et al., 2014;
Jordan et al., 2005; Peterson, 1988; Peterson et al., 2010; Sayer et al., 2003; Ziol-Guest et al.,
2012). Several epidemiological cohort studies from the United Kingdom have identified obesity-
independent associations between OA and both low birth weight and slow postnatal growth
(Clynes et al., 2014; Jordan et al., 2005; Sayer et al., 2003; Syddall et al., 2005) and a longitudinal
study of the moose population on Isle Royale found that individuals born during a period of
very high population density and therefore low food supplies were likely to be small adults, to
die early, and to have high rates of severe osteoarthritis in the limbs (Peterson, 1988; Peterson
et al., 2010). Palaeopathological work by scholars who have attempted to investigate non-
activity-related aspects of osteoarthritis in palaeopathology, have also yielded results suggesting
that joint disease might be related to skeletal size and robustness (Dequeker et al., 2003; Rogers
et al., 2004; Weiss, 2006; Weiss and Jurmain, 2007). Growth and development of the articular
cartilage is closely associated with growth and development of the skeleton and is driven by the
same growth factors and growth schedule (Archer et al., 1999); thus, stunting and wasting would
also have implications for the development of the articular cartilage. These factors identify OA
as a prospective osteological indicator of physiological degeneration.
Chapter 3. The bioarchaeology of stress and growth disruption 45
3.1.1 Aetiologies of ostearthritis
The primary risk factor for OA, after age, is derangement of a joint’s structure or habitual
loading pattern by either direct trauma or development (Buckwalter and Brown, 2004). For
this reason, OA has been long studied by bioarchaeologists seeking to reconstruct activity
patterns and work loads among past peoples (Bridges, 1991; Cohen and Crane-Kramer, 2007;
Sofaer Derevenski, 2000; Jurmain, 1991; Klaus et al., 2009; Lieverse et al., 2007b; Ortner,
2003; Watkins, 2012; Webb, 1995). However, poverty, malnutrition, auto-immune disorders,
and obesity are also significant independent predictors of joint degeneration (Abramson and
Attur, 2009; Engström et al., 2009; Kerkhof et al., 2010; Kornaat et al., 2009; Sayer et al., 2003;
Ziol-Guest et al., 2012). The likelihood that a degenerative lesion will develop in any given
joint is also influenced by the articular cartilage’s architecture, thickness, and physiological
environment, which are influenced by individual systemic physiology (Archer et al., 1999; Jones
et al., 2003).
Articular cartilage possesses a multi-layered histological architecture that gives it extraordi-
nary stiffness and resilience to the continual cyclical compression generated by joint movement
(Adams, 2006; Buckwalter JA and Hunziker, 1999). The outermost layer comprises a thin zone
of transversely arrayed collagen bundles which produce a tough, shear-resistant outer surface.
Deep to the transverse horizon lies the radial horizon, a columnar layer composed of extracel-
lular matrix made up of large and small aggregated proteoglycans and bundled collagen fibres,
surrounding chondrocytes, the cells responsible for depositing and maintaining hyaline carti-
lage in this distinctive structure. The combination of hygroscopic proteoglycans with bundles
of rigid collagen gives the radial layer both elasticity and stiffness, and makes it principally
responsible for resistance to compression (Buckwalter JA and Hunziker, 1999). The deepest
horizon is the interface between the radial horizon and the subchondral bone surface, a cor-
rugated, mineralised layer that interdigitates with the subchondral bone, providing a strong,
shear-resistant base (Adams, 2006; Buckwalter JA and Hunziker, 1999).
The life-span and reparative qualities of articular cartilage are directly limited by the life-
span and activity of chondrocytes. As chondroblasts convert to chondrocyte at the end of the
active growth period, they undergo a physiological shift in which they retain their ability to
Chapter 3. The bioarchaeology of stress and growth disruption 46
synthesize hyaline cartilage but reduce their capacity to reproduce the stress-resistant archi-
tecture of articular cartilage (Goldring and Goldring, 2010). In an adult joint, perforation of
the articular cartilage surface stimulates proliferation of loosely organised hyaline cartilage by
chondrocytes (Archer et al., 1999), but the architectural properties of the cartilage surface are
not restored. Acute mechanical stress induces progressive chondrocyte death at the point of
impact, which then radiates in the surrounding tissue for several hours afterward (Clements
et al., 2001; Goldring and Goldring, 2010; Szczodry et al., 2009). The locus of cartilage de-
generation in later life is often the site of old trauma (Drawer, 2001; Englund and Lohmander,
2004; Lohmander et al., 2007; Roos, 2005).
3.1.2 The role of cardiometabolic factors in OA pathobiology
Cardiovascular disease has a consistent epidemiological association with incident and progressive
OA, to the extent that the presence of OA is a predictor for risk of cardiovascular death
(Conaghan et al., 2005; Katz et al., 2010). In addition, metabolic dysregulation – particularly
of glucose homeostasis – is emerging as a factor in articular cartilage degeneration. Even when
obesity is controlled, these relationships are attenuated but persistent (Conaghan et al., 2005;
Katz et al., 2010; Puenpatom and Victor, 2009). Age- and BMI-independent associations have
reported between OA (diagnosed by the presence of JSN or osteophyte) of the vertebral facet
joints and aortic arterial calcification (Suri et al., 2010) and between generalised symptomatic
OA and popliteal artery wall thickness (Kornaat et al., 2009).
Lipid regulation, vascular function, glucose homeostasis, and inflammation are all implicated
in the onset and progression of OA (Abramson and Attur, 2009; Katz et al., 2010; Yoshimura
et al., 2011). Given the elaborate cellular architecture and rigorous mechanical life of articular
cartilage, even minor perturbations in cellular metabolism may depress matrix production and
repair, attenuating the resilience, and, ultimately, the lifetime, of the tissue (Katz et al., 2010;
Sowers and Karvonen-Gutierrez, 2010). Atheromatous vascular disease may cause ischaemia
of the subchondral bone, causing necrosis, cartilage decline, and subsequent inflammation that
also stimulates osteophyte development (Conaghan et al., 2005; Suri et al., 2010). Based on
the available evidence, some authors advance the hypothesis that osteoarthritis shares a com-
mon pathway with some aspects of metabolic syndrome, a strong multifactorial predictor for
Chapter 3. The bioarchaeology of stress and growth disruption 47
degenerative disease and mortality (Conaghan et al., 2005; Katz et al., 2010; Puenpatom and
Victor, 2009; Suri et al., 2010; Velasquez and Katz, 2010).
Although evidence for a direct relationship, independent of obesity, is still equivocal (En-
gström et al., 2009), mounting evidence suggests that vascular dysfunction and glucose home-
ostasis may indeed have a connection to OA. Hyperglycemia is an important factor in chondro-
cyte anabolic response through its role as an inhibitor of growth factors: high circulating glucose
is associated with suppression of the insulin-like growth factor-1 (IGF-1) response, and might
thereby contribute to cartilage degeneration (Trippel, 2004). Stürmer et al. (2001) observe a
mild propensity to OA in the contralateral joint in osteoarthritic hip and knee arthroplasty
patients with non-insulin-dependent diabetes, although this effect did not extend to OA in the
hand. Dahaghin et al. (2007) note that diabetes increases the risk for hand OA independent of
BMI, particularly among younger diabetes patients.
3.1.3 Evidence for the influence of growth conditions on risk of OA
The links between OA, immune function, and metabolic function suggest that osteoarthritis
may be understood as a disease of interaction between physical wear and tear, and dysregulation
in one or more systemic pathways over the life course (Conaghan et al., 2005; Katz et al., 2010;
Sandell, 2012; Sowers and Karvonen-Gutierrez, 2010). While traumatic joint injury is indicated
as a major contributor, it seems to act as a trigger, and the force of its effect on later joint health
is mediated by underlying physiology (Buckwalter and Brown, 2004; Lotz, 2010; Szczodry et al.,
2009; Valderrabano et al., 2009). The onset, severity, and progression of osteoarthritis may be
influenced by developmental processes long before active joint deterioration begins (Buckwalter
et al., 2004; Peterson, 1988; Peterson et al., 2010; Sandell, 2012; Sayer et al., 2003).
The potential contribution of developmental stress to osteoarthritis aetiology has not been
widely investigated, but some studies have reported associations between OA and poor growth
in early life. For example, Sayer et al. (2003) note a significant association between radiographic
hand OA and low birthweight in males for the prospective Medical Research Council National
Survey of Health and Development in Britain: the effect of birthweight was independent of adult
weight, but exacerbated by adult overweight. Jordan et al. (Jordan et al., 2005) report a link
between the severity and presence of lumbar OA and low birthweight in males in a retrospective
Chapter 3. The bioarchaeology of stress and growth disruption 48
study. In both studies, females did not show a corresponding association, although the number
of women in the latter study was small.
Osteological studies also provide evidence that suggests a developmental component to OA.
An inverse correlation between OA frequency and adult body size in skeletal samples has been
noted (Jurmain, 1991; Weiss, 2005, 2006). A longitudinal population study of skeletal remains
from moose links stunted early growth and early appearance of osteoarthritis with nutritional
stress in early life (Peterson, 1988; Peterson et al., 2010). A rise in the frequency of skeletons
with OA has been noted during the historic transition in North and South America, a time in
which enforced workload increased and living conditions deteriorated (Klaus et al., 2009). In
an ancient human population, therefore, variance in adult OA may not reflect wear and tear so
much as variance in childhood well-being.
3.2 Bioarchaeological perspectives on osteoarthritis
Palaeopathological research has not explicitly tested whether exposure to physiological stress
over the life course may be a contributing factor to OA in past populations. Many studies
interpret temporal and spatial variation in OA frequency and severity as evidence of exposure
to different types and intensities of physical activity in the affected samples. For example,
socioeconomic and cultural disparities in physical labour are often at the focus of such inter-
pretive exercises (Bridges, 1991; Sofaer Derevenski, 2000; Jurmain, 1977a, 1991; Klaus et al.,
2009; Larsen et al., 2007; Lieverse et al., 2007b; Watkins, 2012; Webb, 1995). However, there
is a widely acknowledged potential for confounding by intrinsic variables, including genetic and
epigenetic variation in aspects of phenotype that affect propensity to joint injury and cartilage
deterioration (Crubézy et al., 2002; Jurmain, 1977b, 1999; Jurmain et al., 2012; Larsen, 1997;
Rogers et al., 2004; Waldron, 2009; Weiss, 2005; Weiss and Jurmain, 2007).
Heritability of OA is widely documented but is not well-characterized in part because it
involves numerous physiological pathways and with numerous physical manifestations (Sandell,
2012; Valdes and Spector, 2008). The relative contribution of genes versus shared environment
and intergenerational epigenetic effects is not precisely characterized, although collectively they
are known to play a substantial role in the timing, location, and progress of OA (Sandell, 2012).
Chapter 3. The bioarchaeology of stress and growth disruption 49
The complexity of osteoarthritis, along with the confounding influence of age and heredity,
means that the simple presence of lesions in a given skeleton are mostly not useful from the
standpoint of interpreting that person’s individual exposure to extrinsic risk factors like nutri-
tional deprivation or physical injury. However, patterns of OA at the population level may be
informative about the force and prevalence of some such extrinsic factors when analysed using
a probabilistic approach with appropriate control of known confounders Waldron (2009). In
any skeletal sample, for example, it is reasonable to assume that all individuals who die with
OA are susceptible to some extent, as even post-traumatic OA is partly determined by intrinsic
factors including heredity as well as simple age (Englund et al., 2004a,b). At any given age,
all individuals without skeletal evidence of OA represent two possible states: those who are
susceptible but died before OA could substantially affect the skeleton; and those with a low-
susceptibility phenotype, who would have been unlikely to develop skeletal OA regardless of
lifespan. The probability of the first state declines with advancing age: by late adulthood, most
susceptible individuals will have developed OA. In young and middle adulthood, incidence and
prevalence of OA may be more plastic to mediating factors, both mechanical and physiological
(Dahaghin et al., 2007; Jordan et al., 2005). Given a relatively uniform distribution of age and
behavioural factors, a cohort with a higher burden of a given extrinsic OA-inducing risk factor
may have a higher burden of OA, particularly at a younger age, compared to one without such a
stress burden. This probabilistic analytical approach is already widely applied in contemporary
epidemiology, and in the branch of palaeopathology that has been dubbed palaeopidemiology
(Boldsen and Milner, 2012; Pinhasi and Turner, 2008).
3.2.1 Identifying OA in skeletal remains
Joint degeneration is known to be common among past human populations regardless of lifestyle
or time period (Jurmain et al., 2012); however, because osteoarthritis is diagnosed clinically
using perceptual criteria (pain, swelling, crepitus) in addition to non-skeletal radiological criteria
(joint capsule thickening; joint space narrowing), the correspondence between osteologically
observable modification and clinical symptoms is imprecise (Fukui et al., 2010; Rogers and
Dieppe, 1994; Waldron, 2009). Of the various osteological features that are identified as part
of osteoarthritic disease, only two are clearly reflected in clinical criteria: eburnation (sclerosis
Chapter 3. The bioarchaeology of stress and growth disruption 50
of subchondral bone in accordance with loss of joint space) and marginal osteophyte (bony
growths around the margin of the articular surface) (Rogers and Dieppe, 1994; Waldron, 2009).
The other common osteological identifiers like pitting and superficial new bone formation, are
likely linked with disease processes. Pitting, for example, may be caused by vascularization of
the normally avascular articular cartilage, spurred by proangiogenic factors released by inflamed
synovium, and by osteoclastic activity, which creates channels between the subchondral bone
and cartilage layers that facilitate blood vessel penetration from below (Goldring and Goldring,
2010; Murata et al., 2008). Superficial new bone is likely produced by processes related to those
that produce osteophyte: vascular invasion of the cartilage layer from the subchondral bone is
accompanied by shifts in cellular activity which results in localized ossification of cartilage and
capsule tissue (Mapp and Walsh, 2012). With the exception of eburnation, however, individual
processes of alteration, even if observed on a dry bone, may not necessarily represent a painful
or “diseased” joint (Rogers and Dieppe, 1994).
The operational definition posed by Waldron (2009) is intended to maximize capture of
osteoarthritis cases while minimizing inclusion of joints that may not have been perceived as
painful by their owners in life. Under the operational schema, OA is diagnosed in joints that
have eburnation or at least two of the other bony manifestations of osteoarthritic change (osteo-
phyte, pitting, superficial new bone, or alteration of joint shape) (Waldron, 2009). In practical
terms, the balance between specificity and sensitivity afforded by this operational definition of
osteoarthritis is optimal for a correlative study design because it balances the risks of achieving
too many false positives against those of too many false negatives, both of which would have the
effect of obscuring correlative relationships between OA and predictors. Waldron’s definition
rests on the distinction between individual pathological processes, which may proceed without
causing perceptible disease, and true disease that would have been perceived as painful by the
living person to whom the joint belonged. For this reason, this thesis will hereafter refer to
individual disease processes as “modification forms” and reserve the term osteoarthritis for only
those cases that fit the operational definition.
Chapter 3. The bioarchaeology of stress and growth disruption 51
3.3 Palaeoepidemiological theory and method: Application to
the bioarchaeology of stress
Palaeoepidemiology is an extension of palaeopathology — the study of disease in ancient people
— to the population level (Boldsen and Milner, 2012). Its focus is less on diagnosis of individual
cases than on reconstruction of the dynamics of disease in ancient populations. This involves
estimating the prevalence of both exposure and disease outcomes in the living population based
on observations made on the sample of deaths represented by a recovered skeletal assemblage.
The validity of such an approach depends on consideration and control of several critical factors.
The skeletal sample must approximate, as closely as possible, an average profile of deaths that
occurred in the living population. The environmental, social, and mortuary context must
be reasonably well characterized so that likely confounders can be identified and mitigated
(Boldsen and Milner, 2012). Furthermore, palaeoepidemiological research requires an analytical
methodology that can characterize the probability of disease outcomer given indication of an
exposure.
3.3.1 Sampling strategy and study design
A significant part of any biological study design is the identification of an appropriate sample
and control of potential sources of sampling error. A palaeoepidemiological design must address
the intrinsic weaknesses of archaeological samples as well as those distinct to epidemiological
samples discussed in detail by (Boldsen and Milner, 2012; DiGangi and Moore, 2012; Jackes,
2011; Milner et al., 2008; Pinhasi and Turner, 2008; Sattenspiel and Harpending, 1983; Wood
et al., 1992b; Wright and Yoder, 2003). As this study proposes to test a biological effect that,
if present, is expected to affect growth and later survival, the design must ensure that the most
likely confounding variables are controlled by the sampling and analytical methods; that the
sample itself represents the wider population from which it is derived; and that major sources
of observer error are accurately characterized and controlled (Baxter, 2003; Quinn and Keough,
2002).
A valid epidemiological sample approximates an accurate cross-section of the wider pop-
ulation of people who are at risk of exposure to a given stressor and must have no hidden
Chapter 3. The bioarchaeology of stress and growth disruption 52
heterogeneity in exposure or susceptibility (Quinn and Keough, 2002). The same principles
must be upheld in a palaeoepidemiological sample (Boldsen and Milner, 2012). Ideally, a
palaeoepidemiological sample should be drawn from a single, well-characterized cultural con-
text with a stable if not uniform demographic structure across space and time (Coale, 1972).
Most importantly, there must be a reasonable degree of confidence that the recovered collection
— the sample available for study —- actually does represent the wider range of deaths that
occurred in the population of interest. It must be biased neither by culturally nor environmen-
tally mediated structure in fertility, mortality, and risk exposures, nor by taphonomic over- or
under-representation of certain subsets of the mortality population. Systemic stratification in
the social and demographic structure of the living population must be controlled. If mortuary
and other contextual data do not suggest egalitarian social structure, it must be possible to
identify and isolate those subsets of the study sample that belong to distinct societal strata.
Palaeoepidemiological studies that depend on osteological evidence of active injury or illness
at the time of decease – must also contend with the so-called osteological paradox, the mis-
classification of individuals who are frail and die quickly, and those who are robust and survive
long enough to develop active lesions (Wood et al., 1992b). Even although recent findings by
DeWitte indicate that osteological manifestations of active morbidity actually are associated
with increased frailty 2014, this particular source of error is not a concern here. The proposed
study design avoids this problem by making assumptions about neither the specific type of ex-
posure nor its relationship with cause of death. By focussing instead on episodes of morbidity
that occurred long before death, all individuals who live to adulthood – and are thus eligible
for study – are considered survivors regardless of whether they were affected by the childhood
morbidity (Armelagos et al., 2009; Wright and Yoder, 2003).
Finally, the study design must also contend with the problem of inaccurate and imprecise
age estimates, a challenge that is common to anthropological research involving skeletal human
remains (Hoppa and Vaupel, 2002; Wood et al., 1992b; Wright and Yoder, 2003). While new
methods of age estimation using Bayesian probability models rather than phase-based inter-
vals do improve the accuracy and bias in chronological age estimates (Boldsen et al., 2002;
Konigsberg and Frankenberg, 2002), another common strategy is to circumvent the problem by
dividing the adult sample into broad age strata that reflect phases of the life course, such as
Chapter 3. The bioarchaeology of stress and growth disruption 53
“younger adults” who died in their peak reproductive years, and “older adults” who survived
into their mature and elder years (Roksandic and Armstrong, 2011). This broad age strati-
fication method is commonly used in both bioarchaeology and epidemiology and is useful for
bioarchaeological models because it avoids the problem of taphonomic under-representation of
individuals with less robust bone quality, such as late adolescents and very old adults. While
the stratified sampling model does reduce the likelihood of detecting more subtle age-related
effects (Harrell, 2001; Quinn and Keough, 2002), it is less vulnerable to systematic under- or
over-estimation, small sample effects, and taphonomic effects on the mortuary sample’s age
profile.
3.3.2 Estimating the probability of outcome
Palaeodemographers have urged the use of probabilistic methods in place of simple contrasts
for testing the relationships between markers of morbidity and mortality in skeletal assemblages
(Boldsen and Milner, 2012; Gage, 1988; Konigsberg and Frankenberg, 2002; Temple et al., 2014;
Wood et al., 1992a; Wright and Yoder, 2003). Rather than framing lesion frequencies as a direct
proxy for morbidity prevalence (something that cannot be directly tested and is problematic,
given that not all those who experience an insult at a given time point will survive, and not all
those who survive will develop a lesion), this approach instead quantifies the prevalence of two
states, such as lesion presence and age at death, then generates an estimate about the likelihood
of one state given the presence of the other (Temple et al., 2014).
Though many recent studies still follow a fundamentally frequentist approach e.g. (Eshed
et al., 2010; Holland, 2013; Lieverse et al., 2007a; Starling and Stock, 2007; Temple, 2008,
2010; Temple et al., 2013; Watts, 2011, 2013b), probabilistic methodologies are becoming more
common e.g. (Boldsen, 2005, 2007; DeWitte, 2014; DeWitte and Bekvalac, 2010; Dewitte and
Hughes-Morey, 2012; DeWitte and Wood, 2008; Nikita et al., 2013; Redfern and Dewitte, 2011;
Redfern et al., 2015; Temple and Goodman, 2014; Usher, 2000; Wilson, 2014).
Logistic regression methods
Logistic regression methods are conceptually related to conventional regression and frequency-
based methods. Logistic methods deal explicitly in probabilities and are able to produce both
Chapter 3. The bioarchaeology of stress and growth disruption 54
partial and full regression coefficients to describe total and bivariate effects. Models are built
using maximum likelihood techniques, a class of estimation methods which iteratively assess all
possible estimates of a given parameter or set of parameters until identifying that which max-
imizes the likelihood of the observed data. Unlike OLS-based regression methods, maximum
likelihood estimation is relatively robust to deviations from linearity, normality and homoscedas-
ticity, instead using a probability distribution, specified a priori, that represents the observed
probability of each category of the response variable (coded 0 and 1 in conventional binary
logistic regression). In its simplest form, the logistic regression procedure fits a model that best
describes the change in the probability of one outcome (y1) versus another (y0) for a given
change in one or more predictors (x1) in which the model coefficient is equal to the log-odds
of outcome y1 for predictor x1 (Quinn and Keough, 2002, p. 359). Logistic regression shares
many core assumptions with conventional linear regression in addition to several of its own.
As with conventional linear methods, datapoints must not be derived from paired or repeated
observations; error terms are also assumed to be independent of one another; residuals are
assumed to be normally distributed; and there must be little or no multicollinearity among the
predictors. However, unlike conventional regression, the response and predictor variables are
not required to be linearly correlated; rather, predictors must be linearly related to the log odds
of the response, or may be broken into ordinal factors if nonlinear associations emerge (Harrell,
2001). The probability distribution of the response variable is assumed to be consistent with
the distribution chosen for the random component of the model; for binary models, a binomial
distribution is normally appropriate (Quinn and Keough, 2002). Finally, sample size can be a
concern: maximum-likelihood models, while less constrained by parametric assumptions than
OLS-based models, are nevertheless somewhat less powerful. Statistical references recommend
minimum sample sizes between 10 and 30 cases per independent variable (cite Harrell; Quinn
and Keough).
Ordered proportional-odds, or ordinal logistic regression (OLR), is a useful extension of
multinomial logistic regression in that it permits more than two outcome categories and as-
sumes intrinsic ranking amongst them. An OLR model describes the probabilistic associations
between a latent, continuous outcome variable – the cut-points at which it is divided into levels
are identified by threshold values – and one or more factorial or continuous predictors. Like
Chapter 3. The bioarchaeology of stress and growth disruption 55
conventional logistic regression, OLR models are built iteratively using a maximum-likelihood
method; however, rather than modelling the relative probability of dichotomous events, OLR
models the cumulative probabilities of one event and all the others that follow it in ordinal
ranking. The logit link function was deemed appropriate for all models and was corroborated
by examining parallel slopes and goodness-of-fit criteria (Quinn and Keough, 2002). OLR in-
cludes an additional assumption to those made by binary logistic regression: it assumes that
the relationship between a predictor and outcome is proportional across all levels of the out-
come variable. This is known as the assumption of proportional odds or parallel slopes, and
is the basis for the ordered regression output, which produces one set of coefficients for each
predictor. If the assumption of proportional odds does not then separate coefficients are needed
to adequately describe the change in outcome between each pair of predictor levels (Harrell,
2001).
In a valid OLR where the assumption of proportional odds is met and the logit link function
is used, the coefficient at each level x of the outcome variable is analogous to a binary logistic
regression coefficient where x and all levels below it are coded 0, all levels above x are coded 1,
and all other predictors are held at a fixed value of 0 (Harrell, 2001). Similarly, the coefficient
of a continuous predictor represents the ordered log odds of a change in the outcome level
with each unit increase in the predictor value; while, for a categorical predictor, the coefficient
represents the ordered log odds of change in outcome level for each level of the predictor relative
to the reference level, again while all other predictors are held constant. The exponentiated
coefficient approximates the expected odds ratio of the same.
Power and effect size
The power of a statistical test is characterized as its probability of correctly rejecting the null
hypothesis (Quinn and Keough, 2002); in other words, the probability of detecting an effect
that actually exists. It is commonly expressed as 1−β, where β is the probability of incorrectly
failing to reject the null hypothesis (achieving a false negative result or Type II error). It
is inversely related to α, the probability of Type I error (false negative), which is typically
represented by p, the probability that a result as extreme as that observed could occur by
chance – in other words, the probability that the null hypothesis is true. The more restrictive
Chapter 3. The bioarchaeology of stress and growth disruption 56
the chance of Type I error, the greater the risk of committing a Type II error (Cohen, 1988).
In practice, power is determined by the size of the effect under investigation, by the sample
size (N ), by the significance threshold used (α), and by the variance existing in the population at
large. A small effect, small sample size, stringent α threshold, and high degree of background
variance all contribute to low power. Sample size is often constrained by preservation and
study resources, while α value is constrained by the researcher’s willingness to risk a Type I
error (Cohen, 1988). Power analysis quantifies the probability of type I error for a given effect
and sample size; sensitivity (the minimum observable effect size given a set sample size and
power threshold); and the minimum sample size required to achieve adequate power, given
an a priori specified effect size. This both aids in planning a research design and resource
requirements beforehand, and in evaluating the accuracy and reliability of the results after the
fact.
Several different types of power analysis can be performed at various stages of the research
to evaluate different aspects of confidence in the results see (Quinn and Keough, 2002). A
priori power analysis is strongly recommended in the research development phase, as it helps
to determine target sample size, required research budgets, and other logistics. A priori analysis
requires estimates of the population variance and of the potential size of the effect, which are
often od through pilot research or examining published results. In some cases, however, a
priori analysis is impractical: in bioarchaeology, for example, sample size is often affected by
preservation and by curatorial and excavatory practices. As detailed above, sampling in this
case relied on the completeness of remains, and on the availability of travel resources, and
the final sample, which could not be predicted precisely ahead of time, is as large as it could
reasonably be.
Though a priori analysis is the most robust use of power tests, post hoc analysis nevertheless
has significant benefits. It can be used to evaluate a priori predictions about the population
effect size and to quantify the reliability of a nonsignificant result. A valid post hoc estimate of
power requires an independent estimate of effect size for the underlying population just as an
a priori test does (Quinn and Keough, 2002). Conventional operational scales of effect size can
be used to establish the minimum threshold at which violation of the null hypothesis is likely
to be detected by the methods that have been applied (Faul et al., 2007).
Chapter 3. The bioarchaeology of stress and growth disruption 57
A third option for power analysis is the sensitivity test, which is used in evaluating the
confidence of final results: unlike a conventional post hoc power test, sensitivity analysis does
not assume an existing population effect size, but rather generates an estimate of the minimum
effect size that could be detected with acceptable power and Type I error control, given the
N and method of the hypothesis test (Faul et al., 2007). Sensitivity analysis is a particularly
useful complement to post hoc power testing in the context of this study and other research in
which sampling is markedly influenced by external, uncontrollable factors such as taphonomy,
and where neither sample size nor ES can be identified confidently beforehand.
Chapter 4
Coastal foragers of the Southern
African Later Stone Age
The Later Stone Age (LSA) foraging peoples of the Cape present an excellent opportunity to
investigate potential relationships among skeletal growth outcomes, mortality, and degenera-
tive disease in a small-scale mobile foraging population. They represent a regionally isolated
population with little socioeconomic stratification and a flexible, broad-spectrum subsistence
strategy, who inhabited a landscape with a relatively benign climate and a rich variety of marine
and terrestrial food sources. In other words, the LSA peoples represent an excellent example of
a prehistoric foraging population who were well adapted to their environment and whose life-
ways exposed them to few of the structural inequalities, systemic violence, periodic nutritional
deprivation and other hardships that may link early-life stress with adult outcomes in many
living groups.
4.1 The Later Stone Age and contemporary KhoeSan ethnog-
raphy: continuity and distinctions
Molecular, dental, and craniometric indicators tie the coastal foragers of the Later Stone Age
to the biological population that today includes the /Gwi, Ju’/hoansi, and other living peoples
who are included in the morphologically distinct yet culturally diverse KhoeSan ethnolinguistic
58
Chapter 4. Coastal foragers of the Southern African Later Stone Age 59
group (Barnard, 1992; Lee, 1979; Silberbauer, 1981).
The ancestors of living KhoeSan groups have occupied a wide variety of habitats across
much of southern Africa for as long as 100 – 150,000 years (Henn et al., 2012; Kim et al.,
2014; Schlebusch et al., 2012). Diverse lines of evidence, from linguistics to archaeology, have
helped to characterize the population history of KhoeSan-speaking peoples of Southern Africa.
The history that has emerged is one of isolation, stability, and long-standing continuity of
genetic identity and material culture, but also one of considerable local and short-term adaptive
flexibility (Barbieri et al., 2013; Barnard, 1992; Breton et al., 2014; Ginter, 2008; Henn et al.,
2012; Irish et al., 2014; Kim et al., 2014; Macholdt et al., 2014; Morris, 2002; Morris et al.,
2014; Pickrell et al., 2012; Schlebusch et al., 2012; Schuster et al., 2010; Stynder et al., 2007a,b;
Scheinfeldt et al., 2010; Tishkoff et al., 2007; Wood et al., 2005).
Admixture with outside populations has been minimal until recent centuries and has re-
mained quite limited (Schlebusch et al., 2012; Schuster et al., 2010). Principal sources of
prehistoric gene flow into the Cape are associated with the introduction of herding – thought
to be associated with the arrival of livestock and herders from East Africa some 2000 years
ago – and, in the Eastern Cape, with the arrival of Bantu-speaking farmers nearly a thousand
years afterwards. Living KhoeSan speakers who belong to foraging groups tend to separate out
from KhoeSan-speaking pastoralists groups; the latter tend to carry genetic signals of longer
and more extensive admixture with other groups (Breton et al., 2014; Kim et al., 2014; Ma-
choldt et al., 2014; Morris, 2014; Pickrell et al., 2012; Schlebusch et al., 2012). Ancient DNA
research is beginning to demonstrate direct genetic continuity between coastal LSA foragers
and living Kalahari-based foraging peoples, but the LSA genetic database is still small and
under development (Morris et al., 2014).
Many of the behavioural adaptations that have been documented among KhoeSan-speakers
in recent centuries appear to have been part of the foraging way of life in southern Africa for a
very long time (Barham and Mitchell, 2008; Mitchell, 2002). Subsistence, for example, appears
to have been mostly driven by immediate-return strategies rather than logistical collection or
food production, with the exception of the late Holocene introduction of herding. Characteristic
microlithic techniques and technologies such as the poisoned projectile, compound hafted tools,
bow and arrow, and digging stick occur as far back as the early Later Stone Age, some 20,000
Chapter 4. Coastal foragers of the Southern African Later Stone Age 60
years ago and in some cases extend back into the Middle Stone Age D’Errico et al. (2012);
Lombard (2007); Lombard et al. (2012). Ochre and ostrich eggshell were ubiquitous elements of
personal adornment and mortuary practice (Inskeep, 1986; Lombard et al., 2012). Accumulation
of material wealth does not appear to have been of much social importance but regional networks
of reciprocal gift exchange (hxaro) have been inferred in some times and places (Hall and
Binneman, 1987; Hall, 2000; Wadley, 1987). However, in contrast to these long-running themes,
archaeological evidence shows that, at different times and in different places, people made use
of logistical storage technologies (Hall, 2000), intensively harvested shellfish (Jerardino, 2010),
adopted herding (Orton et al., 2013; Sadr, 2003; Sealy, 2010; Stynder, 2009), and buried their
dead simply and individually or in cemetery-like clusters, with grave goods or without (Dewar,
2010; Hall, 2000; Inskeep, 1986; Manhire, 1993; Morris, 1987; Pfeiffer, 2013; Sealy and Pfeiffer,
2000).
The Kalahari ethnographies and historical accounts describe a way of life characterized by
very dispersed regional populations and subsistence strategies that are adapted to marginal
environments, yet genomic reconstructions of demographic history indicate that the wider an-
cestral KhoeSan population has been both numerous and stable for a very long time. Pleistocene
ancestors of living KhoeSan maintained an effective population size ranging from the tens to
hundreds of thousands for millennia, possibly as a direct result of their access to the rich marine
food web available along the coasts (Kim et al., 2014). Thus, the applicability of ethnographic
analogies is constrained by the historical and geographic specificity of living KhoeSan peoples
(Kusimba, 2005; Mitchell, 2005; Pfeiffer, 2009). Ethnographic models may provide an informa-
tive starting point for inferences about Later Stone Age life and society, but archaeological and
biological data are crucial to understanding the temporal and spatial variability in southern
African foraging life.
4.2 Ecogeographic context
The regional population of interest in this case are the Holocene foraging peoples who occupied
the coastal ecogeographic region known as the Cape Floristic Region in southern Africa’s winter
rainfall zone.
Chapter 4. Coastal foragers of the Southern African Later Stone Age 61
4.2.1 Holocene ecology of the Cape Floristic Region
The southern African Cape hosts a wide variety of ecosystems thanks to its diverse topography,
geology, and rainfall regimes. The greatest Holocene density of human occupation is thought
to have been concentrated close to the coasts (Mitchell, 2002). The south and southwestern
aspects of the Cape are dominated by an ecogeographically distinct mosaic of highly diverse
plant communities known as the Cape Floristic Region (Goldblatt, 1978, 1997; Born et al.,
2007). The Cape Floristic Region corresponds roughly to the southern African winter rainfall
zone, which encompasses both the relatively flat coastal forelands and the mountainous Cape
Fold Belt, and is characterized generally by fynbos vegetation, a Mediterranean-type biome rich
in sclerophyllous herbaceous plant species (4.1). The Cape Floristic Region is surrounded by
succulent-dominated scrub and desert at its northern end, by arid scrubland on its continental
side, and by mixed thicket and savannah at its eastern extent (Goldblatt, 1978).
Ecological variation within the Cape Floristic Region corresponds strongly to topographic
relief, soil composition, and a diminishing south-to-north gradient in rainfall (Goldblatt, 1978,
1997). The geology of the Cape Floristic Region is highly variable: its uplands largely consist
of quartzitic sandstone, its valleys and lowlands of richer, shaley soils and intermittent granite
exposures. The coastal plains are made up of aeolian sandy soils eroded from the mountains,
although with extensive Tertiary limestone beds along the south coast (Born et al., 2007).
Nutrient profiles, erosional behaviour, and water-retention characteristics differ widely among
these substrates, and as a result the ecotones between plant communities are often very abrupt,
leading to a mosaic landscape with highly localized and variable vegetation. The northern part
of the West Coast area is the most arid, its vegetation characterised by a dune scrub form of
fynbos (strandveld) on the sandy coastal plain, by geophyte-rich renosterbos on the nutrient-
dense shale-clay soils further inland, and by mountain fynbos on the nutrient-poor sandstone
soils in the uplands (Meadows and Sugden, 1993; Procheş et al., 2006). The southern part of
the West Coast, including the Cape Peninsula where Cape Town is now situated, features a
cooler, wetter climate, vegetation dominated by fynbos on sandstone soils, and rocky shorelines
(Smith, 1984; Chase and Meadows, 2007). The southern coast, which receives high year-round
precipitation from the humid, warm Agulhas current, hosts both fynbos along the coastal
Chapter 4. Coastal foragers of the Southern African Later Stone Age 62
forelands and a narrow region of afromontane forest (Goldblatt, 1978; Meadows and Sugden,
1993). The northwest aspect of the Cape Floristic Region has the greatest climatic constraint,
being circumscribed dominated by arid karoo inland and by the coastal Namaqualand desert
directly to the north; mean annual precipitation is greatest in the southwest region, which
receives rainfall year-round.
In general, the species profile typical of contemporary Cape ecoystems has been fairly con-
sistent since the beginning of the Holocene (Deacon, 1987), but climatic regimes have changed
subtly over that time with considerable variation at local and regional scales. The climatic
characteristics of the region over the course of the Holocene have been driven by the behaviour
of two major ocean currents: the cold Benguela current, which brings cold Antarctic water to
the arid Atlantic coast; and the warm Agulhas, which brings warm water and humid air to the
south-facing Indian coast (Carr et al., 2006; Chase and Meadows, 2007). Temporal dynamics
of these two currents have been responsible for temporogeographical fluctuations in rainfall
patterns across the southern African subcontinent (Chase and Meadows, 2007).
An array of climatic indicators from sites across the southern African continent record a
relatively warm, humid period in the early millennia of the Holocene followed by a transition
to intermittent and progressive aridification, beginning between approximately 5000 – 4000BP
depending on locality (Cartwright and Parkington, 1997; Chase et al., 2010; Meadows et al.,
2010; Valsecchi et al., 2013). On the South Coast, a similar Late Holocene pattern is observed:
pollen profiles indicate a mid-Holocene transition from a mesic vegetation profile to one domi-
nated by more xerophytic species, particularly around 2800 – 2600 BP, followed by a reassertion
of the mesic profile after 2000BP (Carr et al., 2006). Considerable local variation is also evident
within the subcontinental pattern: for example, pollen cores from the Verlorenvlei area, on the
West Coast north of the Cape Peninsula, indicate that a xeric, grass-dominated plant commu-
nity prevailed in that area from approximately 5000 – 3800BP and was later followed by the
establishment of a more mesic, lowland fynbos ecosystem with better availability of fresh water,
a period that corresponds with an increase in local hunter-gatherer activity (Meadows et al.,
1994). The latter observation is also consistent with a more general observation that hunter-
gatherers in the Lamberts Bay region focussed most of their activity around rock shelters like
Elands Bay Cave (Meadows et al., 1994). On the Agulhas Plain, on the forelands of the South
Chapter 4. Coastal foragers of the Southern African Later Stone Age 63
Figure 4.1: Map of the major plant communities of the Southern African Cape. The Cape Floristic Regioncorresponds roughly to the Fynbos biome, but the Succulent Karoo is sometimes included (Born et al., 2007;Marean, 2011). Reprinted from the Journal of Human Evolution, 59(3-4), C. Marean, Pinnacle Point Cave 13B(Western Cape Province, South Africa) in context: The Cape Floral kingdom, shellfish, and modern humanorigins, p.426, ©2010, with permission from Elsevier.
Coast, the same early-mid Holocene period is marked by relative aridity (Carr et al., 2006).
Both pollen and isotopic records indicate considerable episodic fluctuations in moisture and
temperature throughout the Holocene (Chase et al., 2010; Chase and Meadows, 2007; Meadows
et al., 2010; Scott and Woodborne, 2007). Overall, the picture is one general consistency of the
greater ecogeographic entity that is the Cape Floristic Region, but with considerable temporal
and regional variation in temperature and rainfall related to the complexity and diversity of
climatic agents.
4.2.2 Subsistence in the Cape Floristic Region
Deep archaeological sequences from coastal cave sites demonstrate that humans have inhab-
ited the Cape Floristic Region since the Late Pleistocene and have relied on a wide variety
of indigenous marine and terrestrial taxa for their food and material needs (Hubbard, 1989;
Henshilwood et al., 2001; Inskeep and Avery, 1987; Kyriacou et al., 2015; Langejans et al., 2012;
Marean, 2010).
Large terrestrial quadrupeds such as Cape buffalo, Cape horse, and giant hartebeest are
relatively common in the Pleistocene and early Holocene faunal record (Deacon, 1987; Mitchell,
2002). During that time much of what is now fynbos had a higher proportion of grassy species
that could support large grazers. During the Holocene, however, climate amelioration resulted
in higher precipitation and warmer temperatures, allowing grasses to be replaced by shrubby
Chapter 4. Coastal foragers of the Southern African Later Stone Age 64
species and large grazers to be replaced by browsing animals (Klein, 1974; Klein and Cruz-Uribe,
1987, 2000; Thackeray, 1979; Weaver et al., 2011). Holocene Cape Floristic Region fauna are
largely dominated by smaller species with comparatively few large game animals. Accordingly,
small territorial bovids and warthog, hyrax, bird species, hare, rodents, and tortoises increase
in frequency in LSA archaeological sites relative to those of MSA date (Avery, 1987; Halkett
et al., 2003; Henshilwood et al., 1994; Jerardino and Yates, 1996; Inskeep and Avery, 1987).
The Cape Floristic Region hosts a very high proportion of geophytic plants. Many geophytes
provide a rich source of carbohydrates that are preferred foods of contemporary KhoeSan peo-
ples (Buchanan, 1987; Lee, 1979; Silberbauer, 1981). Many geophytic species are well adapted
to dry conditions, and this may have made them attractive staple foods that were reliable even
during dry climate cycles (Marean, 2010). The frequency of geophytic remains, digging sticks,
and adzes in the archaeological record from the mid- and late Holocene speak to the impor-
tance of geophytes as a carbohydrate source (Marean, 2011; Liengme, 1987; Mitchell, 2002;
Sealy, 1986).
All along the coasts, shellfish, in-shore fish, seabirds, seals, and even occasional scavenging
of beached whale have been documented archaeologically throughout the Later and Middle
Stone Age periods according to direct isotopic evidence, zooarchaeological accumulations, and
material presence of fish gorges and other accoutrements of marine food collection (Avery, 1987;
Buchanan, 1987; Conard and Kandel, 2006; Dewar, 2010; Jerardino et al., 2009a,b; Jerardino,
2010; Klein, 1974; Kyriacou et al., 2015; Marean, 2010; Parkington et al., 2013, 2014; Sealy and
Pfeiffer, 2000; Sealy, 2006, 1986; Sealy and Van der Merwe, 1988). The degree of intensity in
marine resource use varied over time and space: for instance, the period between approximately
3000–2000BP on the West Coast is marked by the appearance of “megamiddens” composed
mostly of black mussel shell, indicating intensive exploitation of this highly productive species
(Jerardino, 2010). Dietary isotopes from the West Coast also indicate that overall intake of
marine protein increased during this time (Sealy and Van der Merwe, 1988; Sealy et al., 1992).
In later centuries, however, West Coast foragers seem to shift toward a less formal marine-
exploitation strategy based on expedient collection rather than intensive harvesting (Kyriacou
et al., 2015; Jerardino, 2003; Parkington et al., 2014). On the South Coast, a similar pattern
is observed, with people exploiting mixed terrestrial-marine diets in general, but with a more
Chapter 4. Coastal foragers of the Southern African Later Stone Age 65
intense focus on marine foods between approximately 4000–2000BP and a subsequent shift
toward more terrestrial sources in later centuries (Sealy and Pfeiffer, 2000; Sealy, 2006).
Subsistence patterns varied from locale to locale. People whose skeletons were found inland
often have isotopic values indicating a more terrestrial diet (Sealy et al., 1992; Sealy and Pfeif-
fer, 2000), while those found on the coast often have isotopic values consistent with reliance
on marine protein (Dewar, 2010; Sealy et al., 1992; Sealy and Pfeiffer, 2000; Sealy, 2006). In
general, variability in dietary isotopic signatures (Pfeiffer, 2013; Sealy et al., 1992; Sealy, 1986),
and evidence of broad-spectrum, year-round resource collection in the food remains from several
deep cave sequences suggests that settlement strategies were both variable and flexible (Hub-
bard, 1989; Inskeep and Avery, 1987; Sealy, 1986, 1987). People seem to have concentrated
resource-getting within, rather than across, major ecotones and made use of local resources
on a year-round basis unless it was necessary to do otherwise (Sealy and Pfeiffer, 2000; Sealy,
2006).
Domesticates – notably sheep and cattle – begin to appear in the zooarchaeological record
by approximately 2000 BP (Henshilwood, 1996; Orton, 2012; Sadr et al., 2008; Sealy, 2010;
Sealy and Yates, 1994). Sheep are thought to have been brought into the region by migrating
herders from Eastern Africa, although the genetic contiguity among contemporary herding and
foraging KhoeSan groups suggests that herding practices spread by diffusion rather than by
population replacement (Barbieri et al., 2013; Macholdt et al., 2014; Sadr et al., 2008). The
mode and time of introduction for cattle is less well characterized: dates have been reported
as early as 2070BP in Botswana (Sealy, 2010), although for the most part cattle-bearing sites
appear between 2000-1500BP, somewhat later than the earliest sheep-bearing sites (Marshall
and Hildebrand, 2002). By the late first millennium AD some groups had adapted entirely to
a herding-based economy (Jerardino and Maggs, 2007); however, many maintained a mixed
economy that at times incorporated domesticates alongside the typical Holocene complement
of wild foods (Sadr, 2003; Sadr et al., 2008). Foraging remained an important component
of subsistence on the Cape for at least a thousand years after the earliest introduction of
pastoralism (Sadr, 2003). Some bands formed client relationships with nearby pastoralists or
agriculturalists, particularly in the Eastern Cape (Forssman, 2013; Kusimba, 2005; Mosothwane,
2010; Silberbauer, 1981). Early historical accounts suggest that coastal KhoeKhoe pastoralists
Chapter 4. Coastal foragers of the Southern African Later Stone Age 66
sometimes characterised foragers as outcasts and thieves (Sealy, 1987), but the time depth and
nature of this relationship in earlier centuries is not clear from the archaelogical record.
4.3 Holocene dynamics of land use
From the Middle Holocene onwards, land use along the SSW Coasts shifted substantially relative
to Early Holocene and Pleistocene patterns. In the Early Holocene, land use appears to have
been relatively light. Faunal remains from Early Holocene sites indicate regular pursuit of
large bovids (Smith et al., 1992; Sealy, 2006). Over time, the number of contemporaneous
archaeological sites increases, concurrent with the appearance, on the West Coast, of large,
densely concentrated shell middens, followed by proliferation of small, less intensely used sites
after about 2000BP (Conard and Kandel, 2006; Jerardino et al., 2009a,b; Jerardino, 2010;
Mitchell, 2002; Sealy, 2006; Sealy and Van der Merwe, 1988).
Though an immediate-return foraging economy prevailed throughout the Holocene, foraging
strategies shifted subtly in later millennia toward intensive focus on diverse, small-package, but
more predictable food supplies (Barham and Mitchell, 2008), including tortoise, small mammals,
territorial small bovids, marine and riverine invertebrates, and geophytic plants (Buchanan,
1987; Jerardino et al., 2008; Kyriacou et al., 2015; Liengme, 1987; Sealy et al., 1992; Sealy
and Van der Merwe, 1988). Emphasis on a few highly productive shellfish species, notably
mussels, also characterized this time, particularly on the West Coast (Jerardino et al., 2009a,b;
Jerardino, 2010). Though explicit delayed-return technologies such as pit storage and fish traps
have been recorded, the former are largely found in the Eastern Cape (Sealy, 2006) and the
latter are likely to be recent innovations, potentially even restricted to historic centuries (Hine
et al., 2010).
After approximately 2000BP, zooarchaeological and isotopic records from the South-West
indicate a shift toward more generalized exploitation of terrestrial and low-trophic marine foods,
particularly in the diets of men, which may partly reflect the incorporation of domesticated
animals into the subsistence base, but may also be read as a response to resource pressure
imposed by higher or more concentrated populations (Hubbard, 1989; Jerardino, 1998; Sealy,
2010; Sealy et al., 1992; Sealy and Van der Merwe, 1988). Stable isotope signals from human
Chapter 4. Coastal foragers of the Southern African Later Stone Age 67
bone suggest that people’s dependence on different types of foods became more variable in
the Late Holocene (Sealy and Van der Merwe, 1988; Sealy, 1997; Smith et al., 1992). Lithic
raw materials show a concomitant trend toward local, lower-quality sources over high-quality,
distant sources. The widespread Wilton tradition of carefully sourced and curated microlithic
tools was replaced by an expedient industry of locally sourced, often unretouched quartzite
pieces (Deacon and Deacon, 1999; Mitchell, 2002). Overall the record indicates a general shift
toward subsistence strategies emphasizing short-term risk management (Jerardino and Yates,
1996; Sealy, 2006).
Marine and terrestrial prey species may be overharvested by humans at times. Average
shellfish sizes fluctuate over the course of the late Pleistocene and Holocene (Sealy and Galim-
berti, 2011). The most common observation is a steady decline in average sizes from the Middle
Stone Age into the Later Stone Age. This has been interpreted as evidence of increased foraging
intensity during the LSA (Jerardino et al., 2008; Jerardino, 2010); however, it has also been
noted that even short-term intensive foraging can affect population structure in slow-growing
prey taxa such as limpets, meaning that large residential camps may not be necessary to cause
fluctuations in shell size (Kyriacou et al., 2015). Parallel changes in average size among subtidal
species and those that were collected for non-food purposes suggest that other factors, notably
water temperatures, probably also influenced growth rates (Jerardino et al., 2008; Sealy and
Galimberti, 2011). Middle and Late Holocene variability in the size of tortoises, another com-
mon prey species (Halkett et al., 2003; Klein and Cruz-Uribe, 2000, 1983; Kyriacou et al., 2015)
suggest that human foraging intensity did play a role in the population dynamics of some prey
species.
Exactly how many foragers occupied Southern Africa at any one point during the Holocene
is not clear, but populations are thought to have been large. Genetic reconstructions indicate
that, at least as recently as 20,000 years ago, effective population sizes were in the tens to
hundreds of thousands (Kim et al., 2014). Furthermore, autosomal sequence variation has
been fit to a pattern of steady population growth in most sub-Saharan African populations; in
ancestral KhoeSan lineages, that expansion is estimated to have begun between 30 and 50,000
years ago from an ancestral effective size of 11,000, and resulted in an approximately 14-fold
increase (Cox et al., 2009).
Chapter 4. Coastal foragers of the Southern African Later Stone Age 68
Genetic studies, most of which derive their data from a small number of sampled individu-
als, some of whom belong to historically displaced KhoeSan groups (e.g. Cox et al., 2009; Kim
et al., 2014; Pickrell et al., 2012), may not yet provide sufficient resolution to make specific
inferences about the size and density of regional populations during the past 10,000 years (Cox
et al., 2009). However, the genetic narrative of demographic dynamism is roughly consistent
with archaeological evidence and the frequency of dated skeletons from more recent millennia
on the Cape. Both of the latter lines of evidence suggest that the number of people occu-
pying the coastal forelands began to increase after approximately 5000BP and peaked in the
Later Holocene, likely before the introduction of domesticates (Ginter, 2011; Hall, 2000; Jer-
ardino, 2010; Pfeiffer and Sealy, 2006; Pfeiffer, 2013; Sealy, 2006). The temporal distribution
of radiocarbon-dated skeletons from archaeological sites across the whole Cape, for example,
exhibits a pronounced peak between approximately 3000 and 2000BP representing 33% of all
dated skeletons in the database (Figure 4.2).
Territorial defence has not been substantively demonstrated in the LSA, but the regional
variability in skeletal stable isotopes and lithic raw materials from sites dating to this period has
been interpreted as evidence of a partitioning of the landscape, with foraging groups exploiting
smaller and more clearly demarcated territories (Mitchell, 2002; Pfeiffer and Sealy, 2006; Sealy,
2006; Sealy and Van der Merwe, 1988; Sealy et al., 1992; Smith et al., 1992). A few cases of burial
clusters that are closely related both temporally and spatially have also been interpreted as
evidence that cohesive social groups were using ceremonial placement of the dead to mark their
place on the landscape (Dewar, 2010; Hall, 2000; Pfeiffer, 2013; Sealy et al., 2000). Linguistic
observations also contribute to a narrative of nucleation in both landscape and group identity:
Humphreys (2007) has pointed out that the high degree of linguistic diversity observed among
living hunter gatherers, including KhoeSan groups, is not consistent with broad egalitarianism
and regional interaction, but rather rigid ethnic identity, and argues that such is likely to be
a general phenomenon among small scale hunting and gathering peoples . Access to resources
is controlled among some living Kalahari foragers by means of social boundary defence, in
which land rights are tied to group membership and access is mediated by a formal system
of reciprocal exchange (Cashdan et al., 1983). In the Kalahari, seasonality is pronounced
and both the density and predictability of many resources are low and territories quite large,
Chapter 4. Coastal foragers of the Southern African Later Stone Age 69
and many individuals have access rights to several territories via their participation in the
reciprocal system (Cashdan et al., 1983). The ecological context of the Kalahari contrasts with
the Holocene Cape Floristic Region where food and water sources might have been less widely
dispersed and more predictable from season to season and year to year. On such a landscape,
resource control by means of overt perimeter defence can be both efficient and effective (Cashdan
et al., 1983). If such was the case in the Later Stone Age, circumscription, defense, and more
rigid social partitioning may have been a reasonable response to resource limitation.
The period between approximately 3000–2000BP is marked by more variability and a change
in average body sizes (Ginter, 2008; Pfeiffer and Sealy, 2006; Pfeiffer, 2013; Sealy and Pfeiffer,
2000; Stynder et al., 2007a) and a few cases that bear evidence of deliberate interpersonal
violence (Dewar, 2010; Doyle, 2012; Morris et al., 1987; Morris, 2012; Pfeiffer et al., 1999;
Pfeiffer and van der Merwe, 2004; Pfeiffer, 2010, 2012a).
Average size in both cranial and postcranial measures decreases significantly across the en-
tire region, but with notably greater concentration on the more arid, sandy West Coast (Ginter,
2011; Wilson and Lundy, 1994; Pfeiffer and Sealy, 2006; Pfeiffer, 2013; Stynder et al., 2007a,b).
Average body sizes recovered by approximately 2000BP, leading Pfeiffer and colleagues to infer
that greater population size and intensity of land use may have led to a transient increase
in the prevalence of nutritional stress, which resolved well before herding and other forms of
food production became common in the region (Pfeiffer and Sealy, 2006; Pfeiffer, 2010). This
temporal pattern has not been corroborated by the juvenile record from the total LSA period,
which generally demonstrates a lack of growth failure in those who did not survive to adult-
hood (Harrington and Pfeiffer, 2008; Pfeiffer, 2011), although future diachronic comparisons
may reveal a temporal aggregation of slower than expected growth around this time. If nutri-
tional constraints were indeed the underlying cause of the change in body size, they may have
concerned limitations in availability of carbohydrate from starchy corms or of fat-rich marine
foods such as seal, whale, pelagic fish, and seabirds, many of which are thought to have been
collected from wash-ups, though some were hunted actively (Avery and Underhill, 1986; Avery,
1987; Jerardino, 2003; Jerardino et al., 2009a; Parkington et al., 2014). Shellfish are poor in
calories per unit of volume (Buchanan, 1987), so although mussels were abundantly available
and intensively harvested on the West Coast during the 3000–2000BP period, they alone may
Chapter 4. Coastal foragers of the Southern African Later Stone Age 70
not have been an adequate replacement for other plant and animal foods if those were impacted
by over-harvesting (Pfeiffer, 2013).
A few cases of clear or likely interpersonal violence have been documented and are distinctly
temporogeographically clustered between 3000 and 2000BP along the West Coast. Women and
juveniles feature prominently in these examples: one includes three juveniles found together
with extreme, unhealed cranial fractures (Pfeiffer and van der Merwe, 2004); another, a young
woman found with bone points embedded in her lower back who was buried with an infant
(Morris and Parkington, 1982); a third, a slightly older woman and juvenile found together
with substantial cranial injuries (Pfeiffer et al., 1999); and at least two other instances of young
and mature adult women with unhealed cranial fractures (Doyle, 2012; Morris, 2012). Although
a few instances of males with cranial injuries have been documented, nearly all exhibit some
healing (Doyle, 2012; Morris et al., 1987; Morris, 2012; Pfeiffer, 2012a).
Several cases involve localized, rounded depressed fractures that penetrate both the inner
and outer cranial tables and are located at the top or rear of the head. Some additional
cases have been documented with bone points either embedded in the skeleton (Morris and
Parkington, 1982) or found in situ in locations consistent with the individual having been shot
before burial (Dewar, 2010; Pfeiffer, 2013). Pfeiffer and colleagues have observed that one tool
that could produce the rounded depressed fractures is the digging stick — a tool with feminine
gendered associations — and have speculated that women may have been the killers in these
instances (Pfeiffer and van der Merwe, 2004). In the context of the material culture and land
use characteristics of that period, it seems possible that interpersonal conflicts escalated during
the time of greatest land use intensity, and may have been motivated by defence of important
food resources (Morris, 2012; Pfeiffer, 2013).
The general archaeological picture of coastal life in the Later Stone Age is one of flexibility
and mobility, with at least one notable period of intensification in which people in some regions
focussed their foraging efforts in more localized territories and made use of a wider array of
foodstuffs in general, but intensively harvested a few more dependable species when they were
available. More people seem to have been using the coastal areas of the Cape Floristic Region
at this time, and they may have been competing for space on the landscape using means both
overt and symbolic.
Chapter 4. Coastal foragers of the Southern African Later Stone Age 71
Figure 4.2: Frequency distribution of radiocarbon dates from 369 skeletons recovered from archaeological sitesacross the Cape of South Africa. The dashed reference line indicates a peak frequency of 33% (122 dates). Datesare from Morris and Pfeiffer (unpublished dataset).)
4.4 Coastal Later Stone Age people as a test case for develop-
mental stress effects in a prehistoric foraging population
4.4.1 Causes of mortality and morbidity
General causes
Most causes of mortality were probably acute and related to parasitosis, infection, or trauma
(Pfeiffer, 2007). Women ran the risk of dying as a result of obstetric causes: among young
adults, women generally outnumber men, a commonly observed pattern that is attributed to
obstetric death during the years of initial fertility (Pfeiffer et al., 2014; Wells et al., 2012). In-
stances of chronic pathology of the pubic symphysis have also been observed in several female
skeletons and have been attributed to birth trauma (Pfeiffer, 2011). Healed fractures and joint
disease are occasionally observed, consistent with an active life in a landscape that included
both geographical and animal hazards (Pfeiffer, 2007, 2012a). A few examples of adult cribra
orbitalia (Morris et al., 1987, 2005; Pfeiffer, 2012b), one instance of possible congenital rickets
Chapter 4. Coastal foragers of the Southern African Later Stone Age 72
(Pfeiffer and Crowder, 2004) and even one case of potential inflammatory disease (unpublished
data), provide evidence that endogenous and chronic health problems did occur in this pop-
ulation; however, most skeletons bear no evidence of disease apart from healed fractures and
osteoarthritis.
A study of LSA children’s growth provides further evidence that mortality tended to strike
swiftly: though growth-arrest lines are a fairly common observation among LSA non-adults
(Pfeiffer, 2012a, 2007), their long bones show little measurable delay of linear growth, in contrast
with the children of Iron-Age farming people, who showed significant growth delays among those
who died in childhood (Harrington and Pfeiffer, 2008). Although growth failure seems not to
have contributed significantly to childhood mortality, nutritional stress was likely an occasional
factor in the lives of LSA foragers, as it is for most small-scale peoples worldwide (Berbesque
et al., 2014; Pfeiffer, 2013; Ulijaszek and Huss-Ashmore, 1997).
Violence
While systematic conflict does not appear to have been a common phenomenon among LSA for-
agers, interpersonal violence did occur and occasionally resulted in death. The few documented
cases of clear, deliberate trauma (Dewar, 2010; Doyle, 2012; Hall and Binneman, 1987; Morris
and Parkington, 1982; Morris, 2012; Morris et al., 1987; Morris and Parkington, 1982; Pfeiffer
et al., 1999; Pfeiffer and van der Merwe, 2004; Pfeiffer, 2010, 2012a) suggest that interpersonal
killing could have been fairly common at this time, but made use of methods that leave no clear
evidence in the record. Despite the moniker “The Harmless People”, living KhoeSan do occa-
sionally participate in infanticide, domestic violence, and deliberate killing for various reasons
(Howell, 2000; Lee, 1979; Silberbauer, 1981). It is likely that many of the same motivations
for violence applied among coastal LSA people, although overt territoriality may have been an
additional motivation. Furthermore, although evidence of deliberate skeletal trauma is rare,
instances of contemporary Ju/’hoansi using poison arrows to kill remind us that LSA people,
too, had means of killing that would not leave skeletal evidence (D’Errico et al., 2012; Deacon
and Deacon, 1999; Lee, 1979; Pfeiffer and van der Merwe, 2004).
Chapter 4. Coastal foragers of the Southern African Later Stone Age 73
4.4.2 Social and environmental determinants of resource access and risk ex-
posure
For the most part, there is little evidence of socially structured deprivation in the coastal
Later Stone Age (Mitchell, 2002). Socioeconomic inequality being a well-recognised mediator
of morbidity and mortality risk among stratified populations (Braveman et al., 2011; Ziol-
Guest et al., 2012), this suggests that exposure to risk in LSA populations was structured by
ecogeographic and climatic factors and that systemic economic inequalities — and associated
differentiation of morbidity and mortality risks — were comparatively small.
In a coastal environment like that of the Cape Floristic Region, conditions may have been
amenable to the development of an intensive, stratified, and settled way of life (Borgerhoff
Mulder et al., 2010; Gurven et al., 2010; Waller, 2010). Though the evidence indicates that Later
Stone Age economies and social organization may have been more diverse and fluid than those
represented by ethnographies of contemporary Kalahari peoples (Humphreys, 2007; Kusimba,
2005), there is no evidence that they constructed permanent settlements or infrastructure, apart
from dry-stone structures like stock pens, game fences and fish traps, many of which date to
recent centuries (Hine et al., 2010; Jerardino and Maggs, 2007; Sadr, 2012). The basic structure
of LSA social organization is thought to have been that of relatively small, mobile groups of
people with occasional aggregation of larger groups (Barnard, 1992; Jerardino, 2010; Parkington
et al., 2014; Wadley, 1987). Relationships among groups may have been structured by kinship
and negotiated by means of gift exchange and reciprocal obligation rather than by formalized
political entities (Barnard, 1992; Silberbauer, 1981; Wadley, 1987). Patterning in the richness
of burial goods (e.g. (Morris, 1987)) and in dietary evidence of access to high-quality foods like
seal (Sealy and Pfeiffer, 2000; Pfeiffer and Sealy, 2006), suggest that social position and access
to resources, while not necessarily egalitarian, were structured by factors other than formal
hierarchies or accumulation of material wealth (Hall and Binneman, 1987; Wadley, 1997).
Gendered differences in diet and activity have been indicated by several studies of Later
Stone Age people. In general, men probably covered greater daily distances and, in the forest
biome along the South Coast, may have specialized in spear-hunting, while women appear to
have been less mobile and to perform work tasks that required bilateral upper-body strength
Chapter 4. Coastal foragers of the Southern African Later Stone Age 74
(Cameron and Pfeiffer, 2014; Churchill and Morris, 1998; Stock and Pfeiffer, 2004; Sealy and
Van der Merwe, 1988). There is little direct evidence of systematic deprivation or violence
directed at one gender or the other, although it is telling that, of the few unambiguous cases of
violent deaths dated to the Later Stone Age, nearly all are women and juveniles (Doyle, 2012;
Dewar, 2010; Morris, 2012; Morris and Parkington, 1982; Pfeiffer and van der Merwe, 2004;
Pfeiffer et al., 1999; Pfeiffer, 2010). Overall, accumulated evidence does not discount the model
of relative gender equality derived from ethnographic observations of pastoralist and foraging
KhoeSan societies (Barnard, 1992; Howell, 2000; Lee, 1979; Silberbauer, 1981), but does indicate
that men and women were doing different daily activities and may have experienced somewhat
different dietary, pathogenic, and traumatic exposures.
4.5 The coastal Later Stone Age collection as a palaeoepidemi-
ological sample
Collectively, the variety and intensity of risk factors for disease and death inferred for the
peoples of South Africa’s Later Stone Age speak to an epidemiologic and demographic profile
quite different from those of most settled human populations, past or present. Environmental,
technological, and behavioural factors restricted the sedentary lifestyle, excess adiposity, easy
dietary access to simple carbohydrates, fats and animal proteins, and paucity of complex car-
bohyrates and crucial micronutrients that are the major modifiable risk factors for the “diseases
of civilization”. The capacity to treat illness and support those incapacitated by age, disease,
or injury was also constrained by technology and infrastructure. Although cases exist of LSA
individuals who lived long lives, and of individuals who survived severe injury and debilitating
conditions, presumably with the care of their peers, they are relatively rare (Pfeiffer, 2007, 2011;
Pfeiffer and Crowder, 2004). Conversely, many sources of risk characteristic of agricultural and
urban societies are not common in a foraging context: the population centres and accumulation
of waste that facilitate epidemic infectious disease are rare among foragers, as are economic and
political causes of famine (Armelagos et al., 2009).
The foraging peoples of the LSA are not to be mistaken for utopian archetypes of Homo
sapiens: like all human groups, they sometimes experienced hardships that affected their growth
Chapter 4. Coastal foragers of the Southern African Later Stone Age 75
and survival (Pfeiffer and Sealy, 2006). On the coasts of the Cape Floristic Region, the centuries
between 3000 and 2000BP may have been marked by increased frequency of such times, and
may have prompted behavioural adaptations that sometimes affected individuals’ growth and
brought people into direct conflict. Whether because of factors that may have been ecological,
historical, or cultural, subsistence intensification on the southern Cape coasts did not catalyse
a widespread shift to either food production, or to the sedentary, stratified societies like those
observed in other resource-rich, densely populated hunting and gathering contexts.
4.5.1 Sample structure and provenience
An unbiased and representative sample is a crucial assumption in any palaeoepidemiological
study. It is particularly important that intra-population structure in fertility, mortality, and
stress exposure not inflate or obscure the biological effect that is in question, and that tapho-
nomic bias by mortuary practice and environmental variability not systematically exclude any
subset of the eligible population
Thanks to much prior research on this collection, the Later Stone Age people are well
characterized for population of mobile foragers with no permanent settlements. The collection
of documented LSA skeletons from the Cape is adequate for the questions that are posed here
for several reasons.
First, the mortality profile of this collection is broadly consistent with that observed ethno-
graphically in many hunting-gathering populations (Blurton Jones et al., 2002; Gage and Mode,
1993; Gage, 1990; Gurven and Kaplan, 2007; Howell, 2000; Milner et al., 1989): though many
individuals survived into their middle years, relatively few lived long enough to become truly
elderly (Pfeiffer, 2007; Pfeiffer et al., 2014). .
Second, variability in exposure to physiological stress and ability to recover from it is likely
mediated by environmental rather than socioeconomic processes. Social groups were likely
mobile and made use of a variety of resources within their habitual ranges, which would have
enabled them to adjust to local availability of animal and plant foods better than settled
populations could have (Mitchell, 2002; Pfeiffer and Sealy, 2006; Sealy, 2006). Cultural and
mortuary contexts suggest that, in terms of material wealth, the foraging population of this
region was fairly non-stratified.
Chapter 4. Coastal foragers of the Southern African Later Stone Age 76
Third, archaeological contexts and the distribution of radiocarbon dates from the Cape LSA
indicate that socioeconomic stratification was minimal and burial practices mostly expedient,
indicating that culturally mediated over- or under-representation of population subsets with dif-
ferent risks of growth and mortality is as minimal as could be expected from a bioarchaeological
collection. It has been observed that burials tend to be detected wherever archaeological exca-
vations include deposits deep enough to contain them (Sealy et al., 2000, p.32), suggesting that
the most part, burial location reflecta habitual use of territory rather than a formalized ceme-
tery practice that could overrepresent some people over others (Hall, 2000; Hall and Binneman,
1987; Pfeiffer, 2013; Inskeep, 1986; Morris, 1987).
Finally, while uneven preservation of remains and poorly documented provenance are a
concern here, as with many bioarchaeological collections, there does not seem to be a strong
likelihood of systematic bias toward or against the recovery of particular subsets of adults who
were buried over time on the Cape. Preservation varies and is sometimes quite good, even
in older burials. The demography of contemporary hunter-gatherers suggests that a relatively
small subset of the population would have survived to the stage at which precipitous bone
loss could make their skeletons particularly vulnerable to deterioration (Blurton Jones et al.,
2002; Howell, 2000). Though underrepresentation of juveniles and infants is observed here as
in many other samples (Harrington and Pfeiffer, 2008; Pfeiffer, 2011), this systematic bias does
not stand in the way of asking questions about adult individuals who had, by default, survived
childhood.
In sum, based on what we know about burial practices and socioeconomic dynamics, the
LSA burial collection represent a sparse but unbiased sample of the foraging population of this
region over the course of the Holocene. The most obvious risk is of oversampling the middle
period, which may be of concern if young adults are overrepresented in the 3000–2000BP time
period because of elevated fertility or very high early mortality. However, Pfeiffer et al. (2014)
have demonstrated that very young adults are distributed fairly uniformly over time in this
sample, which suggests that, in terms of age and social structure, this period is unlikely to
introduce demographic bias. The breadth of the time scale means that short-term demographic
fluctuations should average out across the total time range of the collection. Most small-
scale societies tend towards stable population structure in the long term (Milner et al., 2008).
Chapter 4. Coastal foragers of the Southern African Later Stone Age 77
Though variability and complexity in burial practices, quality of life, and community identity
are undoubtedly part of the picture, a broad-brush approach will likely be able to detect large-
scale patterns in the biological effects that are of interest here.
Chapter 5
Research Questions and Hypotheses
Three general research objectives were outlined in the introduction to this thesis:
1. To explore the diameter of the adult neural canal and appendicular osteoarthritis as
prospective indicators of developmental stress;
2. To test for developmental stress effects in a population with a mobile, immediate-return
foraging subsistence pattern and no evidence of socioeconomic stratification;
3. To explore temporal variation in neuroskeletal size and joint degeneration in the context
of that same foraging population.
In the context of a challenging environment with limited therapeutic options, differential
frailty may be a significant contributor to variability in both morbidity and mortality. The
“Developmental Origins of Health and Disease” model predicts that constrained growth and
development should influence susceptibility to early morbidity and mortality by directly compro-
mising immunological capacity, among other components of the organism’s biological capital.
Of those who survive long enough for physical degeneration to set in, compromised growth
should also associate with earlier, more severe, manifestation of degenerative disease. Over
time, episodes of resource pressure would be expected to prompt compromised growth and its
associated effects to be more common in populations.
However, in epidemiological and palaeoepidemiological settings, socioenvironmental factors
create a significant confound e.g. (DeWitte and Wood, 2008). Testing the predictions of
78
Chapter 5. Research Questions and Hypotheses 79
the Developmental Origins model in a mobile foraging sample will remove the issue from the
confounding matrices of both contemporary epidemiological populations with their high fre-
quencies of inactivity, overnutrition, and socioeconomic marginalization, and of more common
palaeoepidemiological samples, which are often drawn from relatively dense, settled, and often
socioeconomically stratified populations.
The DOHaD predictions and the research objectives outlined above give rise to the four
hypotheses that are tested here. The first two explore the associations between growth and
health outcomes, namely, (I) whether poorer skeletal growth outcomes predict age at death, and
(II) whether OA, the candidate marker of degeneration used here, correlates with poorer skeletal
growth outcomes when age at death is controlled. The second pair address whether temporal
variation in the two candidate stress indicators corresponds with the timing of archaeological
evidence for increased foraging intensity and smaller average body size (III and IV).
Null and alternative hypotheses are outlined below:
Hypothesis I: Does poorer growth outcome relate to age at death?
H0: Skeletal growth outcomes (body size and neuroskeletal size) do not associate with
probability of early death;
HA1: Measures of skeletal growth outcome are more likely to be small in adults who died
earlier than in those who survived longer;
HA2: Measures of skeletal growth outcome are larger in individuals who died at earlier ages
than in those who survived longer.
Hypothesis II: Do presence and severity of synovial joint degeneration in the
appendicular skeleton associate with skeletal growth outcome?
H0: Neither presence nor severity of joint degeneration associates with skeletal growth
outcome;
HA1: Individuals with smaller skeletal measurements are more likely to have skeletal degen-
eration when age-at-death is controlled;
HA2: Individuals with larger skeletal measurements are more likely to have skeletal degen-
eration when age-at-death is controlled.
Chapter 5. Research Questions and Hypotheses 80
Hypothesis III: Do growth outcomes vary over time?
H0: Temporal variation in skeletal growth outcomes is not significant;
HA1: Mean growth outcomes are largest during the Middle period (3000–2000BP);
HA2: Mean growth outcomes are smallest during the Middle period (3000–2000BP).
Hypothesis IV: Does joint degenerative disease vary over time, independent of
age at death?
H0: Temporal variation in joint degeneration is not significant;
HA1: Joint degeneration is highest during the Middle period (3000–2000BP);
HA2: Joint degeneration is lowest during the Middle period (3000–2000BP).
Chapter 6
Materials
6.1 Collections
A large number of archaeologically-derived skeletons ascribed to Holocene ancestors of today’s
KhoeSan-speaking peoples are curated by several major South African scientific institutions.
Many burials have been catalogued by Morris (Morris, 1992a), and their numbers continue to
grow as more burials are uncovered in the course of infrastructure development and construction
along the heavily populated coasts of the Cape (see Black, 2014). This study focusses on
the coastal Western Cape, where the evidence of mid-to-late Holocene population expansion
and contraction with associated changes in subsistence strategies is clearest (Jerardino, 2010;
Pfeiffer, 2013; Pfeiffer and Sealy, 2006; Sealy and Pfeiffer, 2000). The current research sample
is derived from the collections of the Iziko South African Museum (SAM) in Cape Town,
the Department of Human Biology at the University of Cape Town (UCT), and the National
Museum, Bloemfontein (NMB), in Bloemfontein, Free State. Between them, these institutions
curate most of the Holocene skeletons known from the West and South coasts of the Cape.
Cases inclued in the sample are detailed in Appendix Tables B.1 through B.6.
6.1.1 Geographical context
The sample consists of skeletons from sites distributed along the Cape coasts between Vredendal
(31°39’ 52” S, 18°30’ 22” E) on the West Coast, and the easternmost extent of Plettenberg Bay
(34°3’ 0” S, 23°22’ 0” E) on the South Coast. The West and South sub-regions regions are,
81
Chapter 6. Materials 82
Figure 6.1: Map of the study range, divided into West and South Coast regions. Reprinted from the Journalof Human Evolution, 59(3-4), C. Marean, Pinnacle Point Cave 13B (Western Cape Province, South Africa) incontext: The Cape Floral kingdom, shellfish, and modern human origins, p.426, ©2010, with permission fromElsevier.
for this study, roughly divided at the southwestern-most extent of the Cape Fold Belt, west of
the wide, low Agulhas Cape plain (Figure 6.1). The total range corresponds roughly to the
distribution of fynbos biome in the Cape Floristic Region (Goldblatt, 1978; Marean, 2010).
6.1.2 Temporal context
Direct radiocarbon dates are available for most skeletons in the research sample sample (N=135).
Radiometric dates have been compiled and generously shared by researchers at the University
of Toronto, University of Cape Town, and the Iziko South African Museum. Uncalibrated dates
are used in this research, as in other recent studies of these collections (Black, 2014; Ginter,
2011; Irish et al., 2014; Kurki et al., 2012; Pfeiffer and Sealy, 2006; Pfeiffer et al., 2014; Sealy,
2006; Stynder et al., 2007b,a).
6.1.3 Subsistence context
Extensive archaeological evidence indicates that Later Stone Age peoples relied exclusively on a
foraging subsistence base until recent millennia. The earliest evidence of domesticated animals
appears in the regional archaeological record at approximately 2000 years BP (reviewed in Sealy,
2010). Some groups developed fully pastoralist economies, but many maintained a foraging or
Chapter 6. Materials 83
mixed herding-foraging economy until well into the historic era (Kusimba, 2005; Sadr, 2003).
Genetic and dietary isotopic evidence suggests that wholesale adoption of pastoralism by some
groups did not take place until after 1500–1000 years BP (Breton et al., 2014; Macholdt et al.,
2014; Sealy, 2010). Although contact and gene flow with Bantu-speaking agricultural groups did
occur, particularly in the eastern aspect of the southern African Cape, these interactions were
relatively rare in the region under study until recent centuries (Barbieri et al., 2013; Barham
and Mitchell, 2008; Mitchell, 2002).
Marine dietary content and radiometric bias
Radiocarbon dates from coastal contexts worldwide are influenced by circulation of the radioac-
tive 14C isotope through marine environments. 14C is generally depleted in marine sources
because they carbon both from the atmosphere and from rocks and sediments at the sea-bed.
Globally, marine surface waters have a mean radiocarbon age of 405 radiocarbon years BP
(Dewar et al., 2012). Correspondingly, organisms in a marine food web tend to have depleted14C signatures, requiring consideration of potential bias in radiocarbon dates.
The adjustment of archaeological radiocarbon dates to accommodate the marine reservoir
effect is a focus of ongoing discussion in the field of radiometrics. Local estimates of the 14C
offset between marine and terrestrial sources are obtained by comparing radiocarbon dates
from paired marine and terrestrial samples, such as shell and wood or herbivore bone, that are
of equivalent or known age (Dewar et al., 2012). However, calibration of radiocarbon dates
from human skeletal material must take dietary variation into account in addition to correcting
for regional baseline marine reservoir effects because the bones of humans who derived a large
part of their dietary protein from marine sources will likely yield an older radiocarbon signature
than those of humans who consumed a largely terrestrial diet (Dewar and Pfeiffer, 2010; Yoneda
et al., 2006).
In the Southern African context, marine dynamics and ecogeographic variation make date
calibration a complex exercise. Along the Atlantic coast of the Cape, the cold Benguela current
brings 14C-depleted water up from the deep ocean, while warmer waters are brought to the
South Coast from the Indian Ocean by the Agulhas Current, making local measurements of
marine offsets essential for region-wide date calibration (Dewar et al., 2012).
Chapter 6. Materials 84
Though the isotope ecology of the coastal Cape is well-characterized thanks to the work of
many scholars (Lee-Thorp, 2008), standards for adjusting dates from human bone to correct
for dietary marine content are still under development, largely because determining exactly
how much marine food was present in each person’s diet is a complex process (Dewar and
Pfeiffer, 2010; Lee-Thorp, 2008). The presence of C4 photosynthesizers and succulent plants in
terrestrial ecosystems, especially the South Coast, also helps to complicate the picture because
as they are enriched in 13C. However, C4 contribution to the human diet along the Western
and Southern coasts is considered to be relatively minor, as grazers are not well represented in
the zooarchaeological record and most plant species directly exploited by human foragers are
C3 photosynthesizers (Lee-Thorp, 2008; Sealy, 2006, 2010, 1987; Inskeep and Avery, 1987).
Most osteological studies to date have not used adjusted radiocarbon dates because stan-
dards for adjustment have not yet been established (Dewar, 2010; Kurki et al., 2012; Pfeiffer,
2013; Pfeiffer and Sealy, 2006; Pfeiffer and van der Merwe, 2004; Sealy, 2006; Sealy et al., 2000;
Sealy and Pfeiffer, 2000). Uncalibrated dates are considered to be the best option available. As
most individuals along the coasts regularly consumed some marine foods (Conard and Kandel,
2006; Dewar and Pfeiffer, 2010; Sealy, 1986; Sealy and Van der Merwe, 1988; Sealy et al., 1992;
Sealy and Pfeiffer, 2000), most radiocarbon dates will be biased upwards, so inter-individual
variation in error may not be different from that expected after adjustment for marine effects
(Pfeiffer, 2013). Furthermore, the wide time intervals often employed are more robust to sources
of error, including bias associated with marine carbon intake (Sealy, 2006, 2010).
6.2 Sample composition
This section explains the criteria used in selecting the research sample from the study collections
and describes the temporal, geographical, and demographic composition of the research sample.
A summary of the research sample composition is presented in Table 8.2 and in Appendix Table
C.3. The research sample consists of 75 males, 64 females, and 4 indeterminate individuals
(N=143), who derive from archaeological sites distributed across the South and West Coasts.
Skeletons included in the sample were identified as members of the Later Stone Age ancestral
KhoeSan population on the basis of radiocarbon date and morphology . Additional contextual
Chapter 6. Materials 85
characteristics included mortuary features such as flexed burial, paucity of grave goods, and
absence of cairns or other notable grave furniture.
Most individuals in this sample are likely to have relied exclusively on a foraging-based
subsistence strategy; however, a pastoralist element in the diets of some people in this sample
cannot be completely excluded because the distinction between herders and foragers in the late
Holocene is often biologically and archaeologically subtle (e.g. Irish et al., 2014; Morris, 1992b,
2008; Morris et al., 2005; Sadr et al., 2008; Stynder et al., 2007a,b). While their radiocarbon
dates indicate that most people in this sample would not have been identified culturally as
KhoeKhoe, the historically known herding peoples of the Cape, the variation in isotopic sig-
natures described above suggests that some people with access to C4̂-based foods — possibly
including the meat and milk of domesticates — are included in the sample(Sealy, 2010).
6.2.1 Osteological inclusion criteria
The initial sample, comprising all cases examined in the field, consisted of 232 individuals
(M=102, F=99, Indeterminate=31). The research sample was selected from this collection.
Osteological selection criteria were as follows: at least one complete auricular surface or
pubic symphysis must be available for age estimation; age at death must be post-puberty,
although individuals with incomplete epiphyseal fusion at the iliac crest, humeral head, distal
radius, or distal femur were considered if pelvic sex indicators were discernible; co-mingling
was only tolerated when the individuals are sufficiently distinct in size and morphology to be
confidently separated. 160 individuals (M=81, F=75, Indeterminate=4) met the osteological
inclusion criteria.
The synovial articular surfaces, which are vulnerable to taphonomic degradation, were gen-
erally in good condition. Of the 160 individuals in the selected sample, 5 have no observable
articular surfaces and an additional 3 cases have no observable articular surfaces in the upper
limb.
6.2.2 Ecogeographic and temporal characteristics
Of those skeletons that met the osteological criteria, 48 (M=25, F=23) are from the South Coast,
which features rocky coasts and is dominated dually by fynbos vegetation and by a localized
Chapter 6. Materials 86
area of evergreen forest biome, which today is concentrated in the foothills and coastal forelands
of the southern Cape Fold range. Ninety-six individuals (M=51, F=41, Indeterminate=4) are
from the West Coast, where the ecosystem is characterized by lower precipitation, semiarid
fynbos and dune scrub (Goldblatt, 1997). A χ2 test of independence shows that there is no
non-random clustering of either sex according to ecogeographic region ( χ2 = 0.485, df=2,
p=0.787).
Six additional individuals from the arid Namaqualand coast north of Saldanha Bay (M=2,
F=4), and 10 individuals (M=3, F=7) from the continental Karoo ecosystem (see Morris, 1992a)
were examined because of their excellent state of preservation but were excluded because of late
dates, geographical isolation from the rest of the sample, and uncertainty regarding their status
as hunter-gatherers.
The uncalibrated radiocarbon dates from the research sample range from a maximum of
9100 ± 90BP to a minimum of 560 ± 50 BP but most fall between 3500 and 2000 BP as shown
in Figure 8.1. The temporal span was divided into three time intervals for categorical analysis
(Early:≥ 3000BP; Middle: 3000−−1900BP; Late: <1900BP). The dividing dates were selected
to separate the highest peak in the frequency of dates (Middle Period) from the foregoing and
subsequent periods.
6.3 Osteological Profiles
6.3.1 Methodological Preparation
Field procedures for age estimation and joint modification scoring were developed as part of
pilot work conducted prior to field data collection. The age estimation and joint scoring proce-
dure was applied to a sample of 50 individuals from the JCB Grant Collection, an anatomical
collection of 202 early-twentieth-century individuals (M=178; F=24) curated at the University
of Toronto Department of Anthropology. The results of the joint scoring procedure in partic-
ular are useful because they demonstrate that individual joint modification processes can be
assessed independently of one another.
Chapter 6. Materials 87
6.3.2 Sex estimation
Sex is assigned based on pelvic and craniofacial morphology depending on skeletal represen-
tation. Pelvic indicators, and particularly the pubis (Phenice, 1969), are preferred whenever
possible, as the os coxa is generally considered to be the most accurate osteological indicator
of sex in humans (Garvin, 2012; Meindl and Russell, 1998; Rogers and Dieppe, 1994; White
et al., 2012). Other indicators, including cranial morphology and femoral head size, were used
to corroborate pelvic estimates. Estimates made in the field were verified post hoc against inde-
pendent assessments by other researchers familiar with this population (S Pfeiffer; C Merritt).
In four cases sex estimates were indeterminate (2%); these individuals are excluded from any
tests that include sex as a variable.
6.3.3 Age at death estimation
Following a skeletal inventory, age at death was assessed using a non-invasive, non-radiological,
flexible protocol that was intended to maximize both accuracy and reliability of age estimates
and individual representation across a sample with uneven skeletal preservation. Supplementary
age indicators were also observed and were considered in the final summary age estimate (see
6.3.4).
Chronological age estimates
Chronological age estimates were made based on the morphology of the pubic symphysis (Hart-
nett, 2010a), auricular surface of the ilium (Buckberry and Chamberlain, 2002), and the sternal
ends of mid-thoracic ribs Işcan et al. (1984); Iscan et al. (1985) (Table 6.1).
The pubic symphysis is generally acknowledged to be a reliable indicator of age for adults
who died between terminal adolescence and the end of the fourth decade of life (Garvin and Pas-
salacqua, 2012; Garvin et al., 2012; Meindl and Russell, 1998). The symphysis was scored using
the system of Hartnett (2010a), a recent modification of the Suchey-Brooks system (Brooks
and Suchey, 1990), with phase descriptions intended to increase precision in older age groups
while preserving the key discriminating features of the Suchey-Brooks phases.
The auricular surface may distinguish older ages better than other non-destructive indicators
Chapter 6. Materials 88
(Bedford et al., 1993; San Millán et al., 2013; Saunders et al., 1992) and can survive burial
conditions more frequently than the pubic symphysis (Garvin and Passalacqua, 2012; Garvin
et al., 2012). This study used the auricular surface method developed by Buckberry and
Chamberlain (2002) as a revision to the method of Lovejoy et al. (1985). Rather than assign each
auricular surface to a fixed age interval based on simultaneous assessment of several unweighted
characters, the modified method places each individual within an age phase with a known
probability distribution. The probability-based approach provides an estimate of the likelihood
that an individual of a given age will fall into a certain phase, and where in that phase’s age
range it is likely to fall (Aykroyd et al., 1999; Boldsen et al., 2002; Chamberlain, 2000; Kimmerle
et al., 2008).
Finally, sternal rib ends were assessed where available using criteria modified by Hartnett
from those of Iscan and Loth (Hartnett, 2010b; Işcan et al., 1984; Iscan et al., 1985). In practice,
ribs were much less well-represented than other indicators in the study sample. The rib was
therefore considered to be a supplementary indicator at best.
Supplemental Indicators
Cases were seriated within the broad age intervals provided by the formal methods using ob-
servations of several supplementary features.
Closure and obliteration of the sutures of the late-fusing epiphyses (medial clavicle, spheno-
occipital synchondrosis, iliac crest and ischial tuberosity) were taken as evidence of transition
from early to full adulthood, as was eruption of the third molars. Persistence of billowing and
visible ring lines on the vertebrae ands of open cranial sutures and superior sacral segments
was considered to place an individual in early to middle adulthood (Albert and Maier, 2013;
Roksandic and Armstrong, 2011).
Occlusal dental wear was scored based on criteria presented in Buikstra and Ubelaker (1994,
pp.52-53). Maxillary and mandibular dentitions were assessed for cuspal rounding and flatten-
ing; visibility of occlusal dentine; proportion of occlusal surface occupied by secondary dentine;
and remaining crown height. All dentitions were photographed for re-examination when needed.
Dentitions with abnormal patterns of wear were not included in age seriation. Studies of dental
wear in this population indicate that the degree of wear does not vary significantly by sex,
Chapter 6. Materials 89
biome, distance from the coast, or the dietary proportion of marine foods (Sealy et al., 1992).
Dietary grit associated with edible geophytes and other terrestrial foods may have an effect
comparable to that of sand in shellfish and other marine foods (Henshilwood, 1995, p.61).
AnatomicalRegion
Method Citation
Pubic Symphysis Modified from Brooks andSuchey 1990.
Hartnett, K. M. (2010) Analysis of age-at-death estima-tion using data from a new, modern autopsy sample–partI: pubic bone. J. Forensic Sci. 55, 114
Auricular Surface(Illium)
Modified from Lovejoy et al.1985
Buckberry, J. L. & Chamberlain, A.T. (2002). Age esti-mation from the auricular surface of the ilium: a revisedmethod. Am. J. Phys. Anthropol. 119, 231–239.
Mid-ThoracicSternal Rib End
Modified from Iscan etal.,1984 and 1985
Hartnett, K. M. (2010). Analysis of age-at-death esti-mation using data from a new, modern autopsy sample–part II: sternal end of the fourth rib. J. Forensic Sci. 55,1152–6.
Epiphyseal union Roksandic and Armstrong Roksandic, M. & Armstrong, S. D. (2011) Using the lifehistory model to set the stage(s) of growth and senescencein bioarchaeology and paleodemography. Am. J. Phys.Anthropol. 145, 337–47.
Occlusal dental wear Buikstra and Ubelaker Buikstra, J. & Ubelaker, D. (1994) Standards for DataCollection from Human Skeletal Remains. ArkansasArcheological Survey.
Table 6.1: Chronological methods for estimating age for each anatomical region
Age estimation procedure
Both right and left sides of the skeleton were assessed when available. Minor differences were
commonly observed between sides, but were rarely great enough to place them in separate
phases. In the few cases where two sides with equal preservation and no evidence of pathology
were placed in separate age phases, both were recorded and were taken into account alongside
other indicators when making the final age estimate.
Joint degeneration is highly correlated with age and thus its presence or absence in a skeleton
conveys information that is relevant to age at death. In this study, where age-independent
influences on joint degeneration are under investigation, such an approach may seriously bias
results, making semi-independent age estimates necessary. To mitigate this source of bias, the
pubic symphysis, auricular surface, and dental wear were separated from the rest of the skeleton
for detailed examination prior to observing any other postcranial elements.
Two strategies were used to reduce observer error. First, the initial 36 cases assessed in the
Chapter 6. Materials 90
field were revisited after an interval of three weeks to ensure internal consistency. Secondly, my
estimates were calibrated post hoc against those of other researchers familiar with this collection
(S. Pfeiffer, C. Merritt). My field estimates were consistent with their independent assessments
in most cases; those that were inconsistent were revised.
6.3.4 Summary age phases
For each formal estimation method, each individual was given a best estimate interval, ranging
from 5 to 10 or more years in width, based on interpolation among the formal methods and
seriation with supplemental indicators.
The chronological estimates yielded by individual methods were sorted into four broad
age phases that roughly correspond to those proposed by Roksandic and Armstrong (2011).
Ultimately, because of uneven case distribution, two super-phases, Young Adults (YA) and
Mature-Elderly Adults (MA-EA) are used for most analyses. The latter variable is referred to
as AgeBinary.
The youngest phase consists of very young adults (VYA) between terminal adolescence and
approximately 25 years of age. This age phase is characterised by pubic symphysis and auricular
surface in the youngest phases of development; open or still-visible epiphyseal sutures in the
medial clavicle, iliac crest, sacrum, vertebrae, and spheno-occipital synchondrosis; incomplete
eruption of M3; minimal dental wear on all erupted teeth.
The second phase, Young Adult (YA), encompasses individuals assigned to age intervals
from approximately 25 through to 35 years, reflecting individuals who died in full adulthood
but before the onset of most age-related conditions. This phase is defined operationally by
any or all of the following: a pubic symphysis in phase 2, including some in early transition to
phase 3; an auricular surface in phase 1 through early phase 3; unobliterated epiphyseal lines
or vertebral rings and billows; open cranial sutures; erupted M3, but with minimal wear; light
dental wear with palpable cusps on M2. Of the classic indicators, the pubic symphysis is given
the greatest weight in assigning individuals to the Young Adult phase.
The Mature Adult (MA) phase consists of individuals with fully fused and obliterated
epiphyseal lines and vertebral rings, with PS morphology falling between a full phase 3 and
an early phase 5, an AS with neither evident billowing nor significant degeneration; moderate
Chapter 6. Materials 91
Summary Age Groups and Binary Phases
Binary Phase Group Estimated Range
Young Adults (YA) <35 years Very Young Adult <25 yearsYoung Adult 25–35 years
Mature-Elderly Adults (MA/EA) 35+ years Mature 35–55 yearsOlder 55+ years
Table 6.2: Summary age groups (Very Young, Young, Mature-Elderly) are folded into binary age phases (YoungAdult, Mature-Elderly Adult).
to heavy dental wear in which all cusps are flattened but most crowns are still extant; and no
evidence of osteoporosis.
The Elderly Adult (EA) phase, consisting of the clearly elderly, is distinguished by having a
pubic symphysis and auricular surface that have transitioned from mature equilibrium to active
degeneration; evidence of osteophytosis or osteoporosis, particularly in the pubis and vertebral
column; and dental wear that has reduced most teeth to the gingival line or below, combined
with premortem tooth loss.
The VYA and EA categories both have very small numbers of cases (NVYA=24; NEA
=12). For the purpose of analysis, the VYA-YA phases folded into a single group designated
Young Adults (YA, N=59), and the MA and EA phases were folded into a separate group
designated Mature-Elderly Adults (MA/EA, N=80).
6.3.5 Osteological measurements
Two aspects of growth outcome were quantified for this study: direct measures of neural canal
size and proxies of total body size.
Growth outcomes in the axial neuroskeleton are represented by neural canal measurements
from two thoracic vertebrae (T1 and T6) and two lumbar vertebrae (L1 and L5). These segments
were selected in order to sample variation along the thoracic and lumbar regions. Published
research indicates that there is consistent continuity in size within the thoracic and lumbar re-
gions, so representative sampling rather than exhaustive measurement was considered adequate
to capture size-related variation in the thoracolumbar spinal column (Clark et al., 1986, p.151).
Two dimensions of body size (stature and body mass) are represented by the maximum
length (FXL) and the maximum diameter of the head (FXH), respectively. Although FXH and
Chapter 6. Materials 92
FXL correlate with one another, each has been demonstrated to have an advantage over the
other in predicting stature (FXL) versus body mass (FXH) (Auerbach and Ruff, 2004; Kurki
et al., 2012). Including both measurements therefore allows assessment of potential differences
in associations between test variables and these distinct dimensions of size.
Measurements of FXL and FXH were used directly in the analysis rather than estimates
of body stature and mass. This is a common practice in analyses of skeletal size in this popu-
lation (Ginter, 2011; Kurki et al., 2012; Pfeiffer and Sealy, 2006; Pfeiffer, 2013; Pfeiffer et al.,
2014; Sealy and Pfeiffer, 2000). KhoeSan populations have been observed to have had very
small skeletal frames throughout the Holocene (Pfeiffer and Harrington, 2011), and have been
ethnographically observed to be typically lean, placing them close to the lowest extreme on the
continuum of human body size (Dewar and Pfeiffer, 2004; Kurki et al., 2010). Some equations
are available that incorporate KhoeSan data in their samples (Lundy and Feldesman, 1987;
McHenry, 1992), but none exist to date that are specific to KhoeSan populations. A test of
some commonly used regression equations for estimating body size from the dimensions of the
femur shows that their reliability suffers when they are applied in populations that lie close
to the extremes of body size (Kurki et al., 2010). Even the best regression estimates carry a
significant average error term. As this study’s research questions are focussed on the outcomes
of skeletal growth rather than on living body mass and stature, a direct measure of an aspect
of skeletal size is considered to be a better choice for analysis than a body size estimate that
may introduce unnecessary error.
Collection procedures
The neural canal diameter was measured at the cranial aperture, in the antero-posterior (AP)
and medio-lateral (ML) planes, following the standard outlined by White et al. (2012) (Figure
6.2). The cranial aperture is smaller than the caudal, so these measurements captured the
minimum diameters and thus the most likely site of stenosis in the neural canals in question.
Canal measurements are taken using Mitutoyo digital sliding calipers and measurements are
recorded to the nearest tenth (0.1) of a millimetre. Each measurement was repeated three
times and the mean diameter in each plane was computed. Analyses were based on these
mean diameters. Vertebrae with osteophytic intrusions into the neural canal were excluded;
Chapter 6. Materials 93
Figure 6.2: Cranial view of L1 illustrating anteroposterior (AP) and mediolateral (ML) dimensions of the neuralcanal. (Image modified from White et al. (2012).)
however, several cases with taphonomic breakage are represented by partial measurements
where appropriate.
Comparison of variance in this sample with published statistics from two other samples
(Holland, 2013; Watts, 2011) indicated that the variances observed here are consistent with
typical variation found among human populations (see Section 8.0.5).
Femoral osteometric data were generously contributed by two fellow researchers (SP, CM)
and were additionally collected from published sources (Pfeiffer and Sealy, 2006; Wilson and
Lundy, 1994). Although bilateral asymmetry is normally minimal in femoral length and head
diameter, the left femur was preferentially measured. Of the 143 individuals who met the
final criteria for inclusion in the research sample, 91 had FXL measurements and 93 had FXH
measureents available.
6.3.6 Joint Degeneration and Osteoarthritis
Several distinct degenerative processes, referred to hereafter as joint modifications, were recorded
in the seven major synovial joints of the appendicular skeleton (sternal, shoulder, elbow, wrist,
hip, knee, ankle). The field datasheet is reproduced in Appendix A Table A.1.
Osteoarthritis (OA) is diagnosed post hoc using the operational criteria of aWaldron (2009).
This diagnostic rubric conceptualizes osteoarthritis not as an essential but rather as an emergent
disease, a property of several distinct but interrelated pathological processes (Waldron, 2009,
p.34). Reflecting this, cases of full-blown osteoarthritis were identified by the presence of
Chapter 6. Materials 94
eburnation or at least two of the other three modification forms on the same articular surface.
OA was then quantified by calculating a separate summary value from the modification scores
for each surface in an affected joint. Formulae for calculating summary scores are given in
Section 6.3.6.
Inclusion Criteria
An articular surface was considered observable if more than 50% of the surface was present
and weathering was minimal. Joint surfaces that were obscured by adhered soil matrix or
weathering were excluded. Healed fractures and other skeletal abnormalities were noted, but
did not require exclusion, as post-traumatic OA may be induced by injuries whether or not they
cause identifiable bone trauma. To avoid cases of erosive, inflammatory, and infectious joint
disease, joint arthropathies with scooped lesions, exposed, sclerotic trabeculae, or periosteal
woven bone were excluded (Ortner, 2003; Waldron, 2009).
Taphonomic destruction is assumed to be independent of joint degeneration and was thus
treated as a random source of error. Given that most osteoarthritic processes produce exostoses
and densification of joint surfaces through superficial bone formation or eburnation, they are
unlikely to facilitate taphonomic destruction. Similarly, their restricted manifestation on all
but severely affected joints means that they are also unlikely to facilitate preservation of whole
articular surfaces.
Recording joint modification
Each skeleton was inventoried and laid out in anatomical position. Every available synovial ar-
ticular surface in the appendicular skeleton was examined separately. Although the extremities
were included in the initial survey, they were excluded from analysis because of high frequencies
of missing elements and co-mingling.
Five forms of joint modification were recorded separately: eburnation (EB), marginal os-
teophyte (OP), pitting on the joint surface (PIT), new bone on the joint surface (SNB), and
alteration in joint contour (JOINT) (Waldron, 2009). Separate assessment of each form of mod-
ification and each dimension of lesion severity allowed a formal test of the relationship between
them. The scoring system was modified from Buikstra and Ubelaker (1994) with additional
Chapter 6. Materials 95
descriptive criteria developed during pilot research to aid in distinguishing the stages. Although
observations were taken for all five forms, JOINT was excluded at the time of analysis, as it
was found to exclusively co-occur with other modification forms.
Joint modification was assessed for both the intensity of development (degree, Deg) and
the proportion of surface area occupied (extent, Ext). Scores of 0 through 3 were assigned
according to criteria given in Table 6.3. Scores for the first (36) individuals studied in the field
were revisited after an interval of three weeks to ensure consistency in observations and scoring.
Summary Score Calculation: Degree and Extent of Joint Modifications and Os-
teoarthritis
Summaries for lesion intensity (degree, Deg) and size (extent, Ext) were calculated for each
modification form (OP, EB, PIT or SNB). Care was taken to ensure that uneven preservation
did not bias summary scores.
Summary Degree was quantified by averaging scores for individual joint surfaces. The
whole-limb summary value consists of the bilateral average of all observable surfaces within a
given limb. The body-wide summary score is the bilateral average of all observable surfaces
within the skeleton. Quantitative expressions of bilateral asymmetry are generated for both
Deg and Ext, but these values are not analyzed here.
Body-wide Extent is recorded as the maximum Ext score in the entire skeleton. This method
does not scale the summary value to the size of a joint or the number of articular surfaces it
contains under the assumption that a score of 3 for extent reflects a lesion that would have
significant impact on the functionality of a joint, regardless of its absolute size or the number
of articular surfaces involved. Formulae are provided in Section 6.3.6.
OA-degree scores were calculated as the bilateral mean of all degree scores in individuals
diagnosed with osteoarthritis, plus an additional positive weight of 0.25 per unit EB score. As
eburnation is the only trait that is considered to be pathognomonic for osteoarthritis – and
indicates complete loss of cartilage at the site of eburnation (Ortner, 2003; Waldron, 2009) –
allocating additional weight to EB helps to ensure that all cases of severe disease stand out.
This OA variable is deliberately conservative in that it does not capture all individuals with
potential osteoarthritis (i.e., those who may have more than one modification form, distributed
Chapter 6. Materials 96
Score Degree (DEG) Extent (EXT)
Osteophyte (OP) 0 None. None.1 “Barely discernable.” From abnor-
mally sharp joint margin to veryminor lipping.
Very localised, from a single pointto an area less than 10% of the jointsurface.
2 A sharp, distinct ridge, standingproud of the original joint mar-gin. With or without independentspicules.
Moderate. Greater than 10%, lessthan 30% of joint surface.
3 A large, pronounced lip or distinctosteophyte.
Large. Upwards of 30% of the jointsurface is altered.
Eburnation (EB) 0 None. None.1 Very localised polish with no evi-
dence of topographic alteration.Very localised, from a single pointto an area less than 10% of the jointsurface.
2 A larger area of polish with no to-pographic alteration.
Moderate. Greater than 10%, lessthan 30% of joint surface.
3 Strong polish with grooves or flat-tening of the surface.
Large. Upwards of 30% of the jointsurface is altered.
Pitting (PIT) 0 None None.1 Barely discernible [nonperforated
or pores smaller than 1mm.]Very localised, from a single pointto an area less than 10% of the jointsurface.
2 Moderate pitting [open poresgreater than 1mm but smaller than3mm]
Moderate. Greater than 10%, lessthan 30% of joint surface.
3 Deep/ coalesced [one or more poresgreater than 3mm in size]
Large. Upwards of 30% of the jointsurface is altered.
Superficial NewBone (SNB)
0 None None.
1 Barely discernible (mild buildupslightly raised from articular sur-face)
Very localised, from a single pointto an area less than 10% of the jointsurface.
2 Moderate deposition (rugose, anddistinctly raised from articular sur-face)
Moderate. Greater than 10%, lessthan 30% of joint surface.
3 Severe deposition (stands veryproud of articular surface, similarin appearance to a deposit of can-dle wax)
Large. Upwards of 30% of the jointsurface is altered.
Table 6.3: Scoring criteria for the four forms of joint modification included in the study. Degree criteria modifiedfrom Buikstra and Ubelaker (1994) with descriptive details from Waldron (2009). Note that diagnostic OA wasidentified by the presence of eburnation (EB) or at least two of the other three modification forms on the samearticular surface. Where OP was counted as one of the two, a minimum score of 2 was required in order toeliminate cases with naturally rugose joint margins.
Chapter 6. Materials 97
either side of the body). However, individuals who do fit the diagnostic criteria for OA have their
contralateral scores included in the total OA score calculation. This is considered acceptable,
first, because it is applied to all OA cases, and because of the high likelihood that individuals
with “textbook” OA on one side will likely also have it on the other side, even if eburnation or
the 2+ criterion are not met; second, allowing bilateral combination would bias the frequency
of OA diagnoses towards individuals with bilateral representation.
Summary Score Calculation: OA Severity
Statistical testing with Spearman’s rank-order correlation (ρ) shows that small/intense and
large/mild lesions are uncommon, and lesion intensity and extent correlate strongly with an
average correlation of ρ=0.866 (p<0.01) for body-wide Deg versus Extent of OA. A composite
severity variable is generated to minimize problems of multicollinearity. “Severity” (Sev) was
calculated as the product of Deg and Ext for the focal form of modification and the given
level of specificity. As most analyses focussed at the level of the whole skeleton, body-wide
severity was used for most analyses. Using the maximum Ext score, rather than a mean, as
the summary value ensures that the local intensity of lesion is represented at the body-wide
scale, while using the mean to summarize Deg reflects the degree to which the entire body
is affected. By quantifying local intensity and systemic involvement separately, this strategy
allows differentiation of individuals with widespread, mild, small lesions from those with very
localized, but severe, disease.
OA Severity (see below) was calculated by multiplying OA.Deg by OA.Ext at each level
(whole-limb, whole-body)
Formulae
As the research questions concern the presence and intensity of joint degeneration at the pop-
ulation scale, summary scores of body-wide disease status are mostly used in the analysis.
Upper- and lower-limb summaries were also computed in order to allow detection of varia-
tion that could be linked to activity-related mechanical stress, for example between males and
females (Cameron and Pfeiffer, 2014; Churchill and Morris, 1998; Stock and Pfeiffer, 2004). The
upper limb includes all joints between the sternomanubrial and radiocarpal (wrist) joints; the
Chapter 6. Materials 98
lower limb includes all joints between the hip and talocalcaneal (ankle) joint. Hand and foot
scores were excluded from summary calculations because of very high levels of incompleteness
and co-mingling in the extremities. All summary levels, from single-joint through whole-limb
to body-wide, were calculated directly from individual joint surface scores.
Body-wide modification degree
DEG-Modbody =∑(DEG-Modsurface)
sbody
Body-wide osteoarthritis degree
DEG-OAbody =∑ (DEG-OAjoint)
J
Body-wide modification extent
EXT-Modbody = max (EXT-Modjoint)
Body-wide osteoarthritis extent
EXT-OAbody = max (EXT-OAjoint)
Body-wide modification severity
SEV-Modbody = (DEG-Modbody) (EXT-Modbody)
Body-wide osteoarthritis severity
SEV-OAbody = (DEG-OAbody) (EXT-OAbody)
Formulae Notes
Mod represents a score or summary for a particular modification form (OP, EB, PIT, or
SNB). OA represents the osteoarthritis value calculated from Mod scores. The subscript surface
Chapter 6. Materials 99
represents the score for a single articular surface (e.g. the medial femoral condyle); the subscript
joint represents the score for a joint with at least one observable side (e.g. the patellofemoral
joint); the subscript sbody represents a score calculated for a whole skeleton. The letter s repre-
sents the number of articular surfaces observable bilaterally per joint (sjoint) or per individual
(sbody). J represents the number of joints represented by at least one observable side in the
entire skeleton.
Degree of lesion intensity (DEG) is calculated as a bilateral average score for all observable
articular surfaces in a single joint (subscript joint) or individual skeleton (subscript body). Lesion
extent (EXT) is the maximum score observed across all articular surfaces in a single joint or
an individual skeleton. EXT scores are not averaged.
Chapter 7
Quantitative Methods
7.1 Preliminary diagnostic analyses and data management
The quantitative methodology for this project was designed to with a dataset that includes
discrete and continuous variables, several of which have a high frequency of missing values.
Osteoarthritis scores were non-normally distributed, as were several neural canal measurements
(Table 8.5). Data-cleaning and careful selection of robust analytical methods helped to mitigate
these potential problems (detailed in Section 7.4).
7.1.1 Missing Values
Uneven skeletal preservation is observed across the sample and does affect the representation
of osteological measurements, particularly of the mid-thoracic region. In T6, for example, 67%
of cases have missing values, giving a total N for T6 of 46. Because listwise deletion (removal
of incomplete datapoints) can reduce test sample size and thus affect statistical power, the
general best practice for a dataset with many missing values is to use imputation to fill in
missing values with estimates derived from other correlated variables (Harrell, 2001; Quinn and
Keough, 2002). Where data representation is patchy, multiple imputation is recognised as the
most robust imputation method. This process involves generating multiple filled-in datasets by
imputation. Separate analyses are then performed for each dataset, the resulting parameters
are pooled, and error estimates are adjusted to reflect the process (Harrell, 2001; van Buuren,
2007).
100
Chapter 7. Quantitative Methods 101
Missing neural canal values were replaced by linear regression using a fully conditional
specification method set to a maximum of ten iterations (van Buuren, 2007). The distribution
of missing values was examined beforehand and the sole identifiable influence on completeness
was radiocarbon date, with older skeletons being more likely to have missing values. AP and ML
values were imputed separately because they were found to exhibit slightly different patterns
of variation and slightly different sizes. Because T6 had more missing values than the other
vertebrae (N=46), T6AP.Z and T6ML.Z were not included as predictors in the imputation
models. After imputation, all neural canal Ns rose to n=105, meaning that approximately 30%
of values in T1, L1, and L5, and over 50% of T6 values in each new dataset were replaced.
Statistical comparisons of descriptive statistics indicated that the imputed dataset parameters
do not differ significantly from the original dataset’s (Appendix Table C.2).
All analyses were run first with the original datasets, and were then repeated with the
imputed datasets. Results from analyses of the five imputed datasets were pooled and confidence
intervals adjusted using SPSS’ native regression function (IBM Corporation, 2011).
7.1.2 Principal Components Analysis for Neural Canal Measurements
Separate tests were performed for each vertebral segment in order to characterize the variabil-
ity attributable to the cranio-caudal gradient in development timing. However, this repetitive
testing approach is accompanied by increased risk of Type I error (Harrell, 2001; Quinn and
Keough, 2002; Tabachnick and Fidell, 2007). Accordingly, linear principal components anal-
yses (PCA) were used to produce factor scores from the imputed datasets (Tabachnick and
Fidell, 2007). These factor scores were then included as summary variables in the supplemen-
tary analyses. Criteria for PCA include linear inter-variable relationships, matrix correlation
coefficients above 0.3, large N, and minimal outliers (Tabachnick and Fidell, 2007, pp.664–667),
all of which are adequately met by the NC variables (Appendix Table C.2). Sample sizes at or
below N=100 are acceptable if communalities, defined as the squared multiple correlation value
among variables, are greater than 0.60 (Tabachnick and Fidell, 2007).
PCA were applied separately to the AP and ML dimensions of each vertebra in each of
the five imputed datasets. Kaiser-Meyer-Olkin’s measure of sampling adequacy was calcu-
lated and Bartlett’s test of sphericity was performed for each individual PCA (Average KMO:
Chapter 7. Quantitative Methods 102
PCA-AP=0.70, PCA-ML= 0.78; Bartlett’s χ2: PCA-AP=70.16, PCA-ML=125.23). Average
communalities are PCA-AP=0.54 and PCA-ML=0.65. (Details presented in Table 8.10 and
Appendix Table C.1).
Components were extracted based on a correlation matrix, allowing a maximum convergence
time of 25 iterations. The minimum acceptable eigenvalue is set at 1.0. No rotation method was
used because only one dimension exceeded the set minimum eigenvalue in each PCA (Baxter,
2003; Tabachnick and Fidell, 2007). The resultant first dimensions represent approximately
52% (PCA-AP) and 65% (PCA-ML) of variation respectively.
Standardized factor scores, which represent the ranking of each case on the underlying factor
variable (Tabachnick and Fidell, 2007, p.703), for the first dimension were estimated with the
regression method using SPSS’ native estimation procedure. The regression method is the
most common means of producing standardized factor scores for PCA and factor analysis and
yields factor scores with the highest average correlation with the original factor (Tabachnick
and Fidell, 2007, p.703).
Regression scores were generated for each accepted dimension (PCA-AP and PCA-ML)
(IBM Corporation, 2011). Individual component scores were rendered as standardized regres-
sion residuals with means of 0.0 and standard deviations of 1.0. These standardized scores were
included in supplementary hypothesis testing with imputed datasets. Descriptive statistics of
PCA parameters are presented in Table 8.10 and in Appendix Table C.1. Descriptive statistics
for PCA-AP and PCA-ML by sex are presented alongside those for the imputed variables in
Table 8.6.
7.1.3 Categorization
Categorical analyses were required to address some of the questions posed in this study, notably
ordered logistic regression and categorical tests of independence (see Section 7.4).
Clinically and epidemiologically, complex continuous traits — notably disease states or other
outcomes that are multifaceted and difficult to measure directly — may be ranked by degree of
development (Harrell, 2001). In some cases, simple binaries (presence/absence) are sufficient,
but because meaningful distinctions are often made between mild and advanced outcomes, an
ordinal scale is also sometimes needed (Valenta et al., 2006). Epidemiological objectives often
Chapter 7. Quantitative Methods 103
consist of comparing stages of risk for a given condition across representative strata of a sample
population. Categorical analysis allows such comparisons while reducing random variation
within and among population subsets. Continuous distributions of body size, age, physical
activity level, and stages of disease progression are all routinely partitioned into ranked groups
for the purpose of analysis (e.g. Bernard et al., 2010; Dore et al., 2010a,b).
Both ratio-scale measurements and joint modification averages were converted to ordinal
scales for these tests. A 3-stage scale was used in all cases. Different strategies were used to
partition osteometric and joint modification variables into ordinal scales.
Categorization of Osteometric Variables
Each z-transformed osteometric variable was divided into three ranked strata representing,
respectively, the first quartile (Rank 1), second-third quartiles (Rank 2), and fourth quartile
(Rank 3), based on Harrell-Davis nonparamtric quantiles (Harrell, 1982) computed in R using
the hdquantiles command in the Hmisc package (Harrell, 2014). Though both femoral and
neural canal measurements are mostly normally distributed in their raw form, the Harrell-Davis
calculation was used to avoid potential bias introduced by those variables that do violate the
assumption of normality (see Table 8.5). Quantiles and the distribution of cases are presented
in Table 7.1.
This partitioning strategy designated the middle 50% of cases as the “normal” range, follow-
ing a typical epidemiologic strategy of comparing the uppermost and lowermost quartiles of a
normally distributed variable with the middle. Imputed values were not included in computing
quantile values, but were given ranks based on their position relative to the quantiles. Ranked
variables are denoted by the suffix .rank (as in FXH.rank, etc).
Categorization of Osteoarthritis
Unlike the ratio-scale osteometric variables, the “average” or “normal” condition for osteoarthri-
tis is to have none at all, and consequently all such variables are strongly skewed toward 0.
Rather than attempting to assign ranks based on quartiles, the median of all OA scores above
0 (i.e. all cases with identified modification) was calculated. This was done for analysis at the
body-wide and whole-limb levels. The first rank (1, Unaffected) consists of all cases with scores
Chapter 7. Quantitative Methods 104
RANK 1 (0–25%) RANK 2 (26–75%) RANK 3 (76–100%)Variable 0% 25% 50% 75% 100%
FXL.Z Quantiles -1.822 -0.662 -0.025 0.63 2.894N (orig) 21 48 23FXH.Z Quantiles -2.625 -0.668 -0.049 0.73 2.32N (orig) 25 47 22T1AP.Z Quantiles -1.817 -0.748 -0.099 0.562 2.833N (orig) 18 40 17N (imput) 24 54 24T1ML.Z Quantiles -2.175 -0.539 -0.021 0.504 2.42N (orig) 18 39 19N (imput) 25 52.2 27T6AP.Z Quantiles -2.627 -0.513 -0.123 0.59 3.02N (orig) 11 24 11N (imput) 31 49 25T6ML.Z Quantiles -1.868 -0.609 -0.178 0.445 3.071N (orig) 12 23 11N (imput) 25 49.6 30L1AP.Z Quantiles -1.963 -0.465 -0.007 0.644 2.417N (orig) 16 39 19N (imput) 28 52 25L1ML.Z Quantiles -2.159 -0.541 0.012 0.717 2.846N (orig) 18 38 18N (imput) 29 52 24L5AP.Z Quantiles -2.015 -0.532 -0.14 0.557 3.860N (orig) 18 36 19N (imput) 28 49.4 28L5ML.Z Quantiles -2.02 -0.565 0.0658 0.631 2.184N (orig) 19 37 17N (imput) 28 54.4 23PCA.AP Quantiles -2.22 -0.711 -0.0739 0.614 3.373N (orig) 6 16 8N (imput) 27 52.2 26PCA.ML Quantiles -2.571 -0.541 -0.029 0.558 2.86N (orig) 8 15 7N (imput) 26 52.4 27
Table 7.1: Distribution of cases according to ordinal size ranks. Ranks were assigned according to Harrell-Davisdistribution-free quantiles (Harrell, 1982). Quantiles were calculated using the hdquantile command in R fromthe original dataset.
Chapter 7. Quantitative Methods 105
of 0; the second (2, Moderate) contains all positive cases with scores lower than the median;
the third (3, Severe) contains all cases with scores greater than the median. Finally, binary
scores were assigned based on the presence (1) or absence (0) of osteoarthritis based on Wal-
dron’s diagnostic criteria (Waldron, 2009). Presence-absence data capture the coarsest scale of
variation in joint changes across the sample and were useful in binary logistic regression and
frequency matrices.
Summary of Variables
The dataset comprises an array of categorical and continuous variables ranging from binary
through to ratio scales of measurement. This includes several factors that reflect major strata
within the sample along demographic, ecogeographic, and temporal lines. Raw osteological
measurements include femoral length and head size and the vertebral canal diameters. Joint
modification and osteoarthritis averages were computed from the raw scores at whole joint,
whole limb, and body wide levels.
Most variables were transformed or modified prior to analysis. Osteoarthritis variables were
stratified into binary and ordinal categories. Raw osteological measurements were converted
to sex-standardized z scores for most tests, and were converted to ordinal ranks for ordered
logistic regression and independence tests. All variables used in hypothesis testing are presented
in Table 7.2.
Table 7.2: Summary description of variables used in descriptive statistics and hypothesis-testing. Note that zscores and PCA regression scores are standardized to a mean of 0 and a standard deviation of 1.
Name Type Unit Levels Abbrev
Sex Factor (Nominal) n/a Male, Female / Indetermi-
nate
M / F / I
Age Factor (Ordinal) n/a Very Young / Young /
Mature-Elderly
VYA / YA /
MA-EA
AgeBinary Factor (Ordinal) n/a Young / Mature-Elderly YA / MA-EA
Region Factor (Nominal) n/a West Coast / South Coast West / South
Period Factor (Ordinal) n/a >3000bp / 3000–1900bp /
1900bp<
Early / Mid-
dle / Late
Continued on next page
Chapter 7. Quantitative Methods 106
Name Type Unit Levels Abbrev
OA (Whole
Body)
Factor (Nominal) n/a Unaffected / Affected OA.Binary
OA (Upper
Limb)
Factor (Nominal) n/a Unaffected / Affected OA.Upper
OA (Upper
Limb)
Factor (Nominal) n/a Unaffected / Affected OA.Lower
OP Factor (Nominal) n/a Unaffected / Affected OP.Binary
EB Factor (Nominal) n/a Unaffected / Affected EB.Binary
PIT Factor (Nominal) n/a Unaffected / Affected PIT.Binary
SNB Factor (Nominal) n/a Unaffected / Affected SNB.Binary
OA Severity Factor (Ordinal) Whole Body Unaffected / Moderate / Se-
vere
OA.Sev
Femur Length Continuous (Ratio) mm, st.d n/a FXL
Femur Head
Diameter
Continuous (Ratio) mm, st.d n/a FXH
T1 (AP) Continuous (Ratio) mm, st.d n/a T1AP,
T1AP.Z
T1 (ML) Continuous (Ratio) mm, st.d n/a T1ML,
T1ML.Z
T6 (AP) Continuous (Ratio) mm, st.d n/a T6AP,
T6AP.Z
T6 (ML) Continuous (Ratio) mm, st.d n/a T6ML,
T6ML.Z
L1 (AP) Continuous (Ratio) mm, st.d n/a L1AP,
L1AP.Z
L1 (ML) Continuous (Ratio) mm, st.d n/a L1ML,
L1ML.Z
L5 (AP) Continuous (Ratio) mm, st.d n/a L5AP,
L5AP.Z
L5 (ML) Continuous (Ratio) mm, st.d n/a L5ML,
L5ML.Z
PCA (AP) Continuous (Ratio) st.regression
score
n/a PCA-AP
Continued on next page
Chapter 7. Quantitative Methods 107
Name Type Unit Levels Abbrev
PCA (ML) Continuous (Ratio) st.regression
score
n/a PCA-ML
Femur Length Factor (Ordinal) H.D. Quar-
tiles
0-25%; 26-75%, 76-100% FXL.rank
Femur Head
Diameter
Factor (Ordinal) H.D. Quar-
tiles
0-25%; 26-75%, 76-100% FXH.rank
T1 (AP) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% T1AP.rank
T1 (ML) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% T1ML.rank
T6 (AP) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% T6AP.rank
T6 (ML) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% T6ML.rank
L1 (AP) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% L1AP.rank
L1 (ML) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% L1ML.rank
L5 (AP) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% L5AP.rank
L5 (ML) Factor (Ordinal) Quartiles 0-25%; 26-75%, 76-100% L5ML.rank
7.2 Osteological Measurement Error and Reliability
7.2.1 Osteological Measurement Error
Instrument precision and measurement technique may introduce observer error, particularly in
complex forms such as the neural canal and femoral head. A post hoc validation study was
conducted for osteometric variables to ascertain the potential for measurement error to affect
results. Mean absolute and directional inter-observer differences were estimated. Total inter-
and intra-observer variation were summarized by the within-subject standard deviation ws and
95% repeatability statistics (Bland and Altman, 1996) (see Section 8.0.4).
Neural canal inter-observer variation was estimated from replicate measurements from 27
lumbar vertebrae (NL1=19; NL5=8) collected independently by SP. Femoral inter-observer
variation was estimated from 23 FXL and FXH measurements published by Pfeiffer and Sealy
(2006) and re-measured by Catherine Merritt (CM) in 2010.
Chapter 7. Quantitative Methods 108
The results showed that inter-and intra-observer reliability is high for most osteometric
variables, although measurement error in the anteroposterior (AP) dimension of the neural
canal warrants consideration during interpretation of results (see Section 8.0.4, Table 8.3 and
Figure 8.4).
7.2.2 Comparison of Neural Canal Variation
Alongside the reliability with which the neural canal can be measured, its range of variation
both within and across human populations has important implications for its utility as an
indicator of growth success. If it is relatively invariable, for example, then its utility as a
bioarchaeological indicator would be quite low.
Estimates of variation in neural canal size from this study were compared with those from
two published studies of adult neural canals from, respectively, Portugal and medieval England
(Holland, 2013; Watts, 2011). Both comparators used the same sliding caliper measurement
technique as this study. Holland included all thoracic and lumbar vertebrae in her study, while
Watts covered T10 through L5. Means and standard deviations for T1, T6, L1, and L5 in both
sexes are drawn from Holland, and for L1 and L5 from Watts. As Watts partitioned her adult
sample into individuals who had died before and after the approximate age of 26 years, and
the sample sizes for the former subsample are quite small (N<5), her published means were
averaged across age groups, but only the standard deviations for the larger subsamples were
used.
Standard errors of the mean and coefficients of variation were calculated from the published
descriptive statistics (Holland, 2013; Watts, 2011). The collected statistics were compared
using ANOVA and post hoc contrasts with Bonferroni corrections for multiple tests. Vertebral
measurement (T1AP, etc), Sex, and Collection were tested as fixed factors. The results are
explored in Section 8.0.5.
7.3 Descriptive Statistics and Preliminary Diagnostic Analyses
The demographic structure and sample-level parameters of the test variables were explored
through descriptive statistics and visualization. Coefficients of variation (CoV) were calculated
Chapter 7. Quantitative Methods 109
for both raw and transformed variables in order to provide an estimate of baseline population
variability, which is required to evaluate both the reliability and generalizability of results (see
Section 8.0.5). Possible confounding of the test variables by sample structure (ecogeographic,
temporal, sex, and age-based variation) was also explored.
7.3.1 Sex-based Differences
Sexual dimorphism is a well-known component of variation in biological size and both the fre-
quency and manifestation of osteoarthritis are known to vary by sex in contemporary urbanised
populations e.g. (Hanna et al., 2009). Raw (i.e. non-transformed) osteometric variables were
tested for sexual dimorphism with Welch’s robust t-tests, which do not assume equality of vari-
ances (Quinn and Keough, 2002). Ordinal and binary OA factors were tested for sex-related
variation with Fisher’s Exact tests of simple independece and Cochran-Mantel-Haenszel tests
of conditional independence (Tables 8.7, 8.8, and 8.9).
Measurement variables were adjusted to correct for sexual size dimorphism by calculating z
scores separately for males and females, thus removing the sexual size difference and allowing
males and females to be analyzed together. Z transformed variables are denoted by the suffix
.Z (FXL.Z, FXH.Z, T1AP.Z, etc). No sex bias was observed in OA scores, meaning that no sex
correction was necessary for those variables.
7.3.2 Central Tendency and Distribution
The Shapiro-Wilk W statistic was used to test for deviations from a normal distribution shape.
W is robust to a wide variety of non-normal distributions, and was originally developed for use
in small samples (Royston, 1982; Shapiro and Wilk, 1965). Homogeneity of variances among
the major sample strata (sex, age, region, period) was assessed with Levene tests as part of the
hypothesis-testing phase.
7.3.3 Correlations and Collinearity
Quantitative relationships among ratio-scale variables were assessed by fitting both bivariate
and polynomial ordinary least-squares (OLS) regressions, and with Pearson partial correlation
tests. This step helps to identify correlations that could confound hypothesis tests. In general,
Chapter 7. Quantitative Methods 110
these results indicate that body size should be examined in hypothesis tests as a potential
confounder, but correlations among neural canal dimensions do not appear to be affected when
FXH.Z and FXL.Z are controlled. Partial correlations for full and sex-specific imputed datasets
are presented in Tables 8.12 and 8.13. Zero-order and partial correlations from the original
datasets are presented in Appendix Tables C.6, C.5, C.7, and C.8.
7.4 Statistical Hypothesis Testing
Each hypothesis was tested separately using statistical methods selected according to dataset
characteristics and the assumptions and criteria of each method. The dataset consists of ratio-
scale osteometrics, ranked OA factors, and nominal demographic categories. A mix of para-
metric and nonparametric methods, based on both ordinary least squares (OLS) and maximum
likelihood (ML) techniques, was selected for hypothesis-testing. The criterion for two-tailed sig-
nificance was set at p values equal to or less than 0.05, but values approaching 0.05 (p <0.10)
were also noted. This significance standard was evaluated in the context of model parameters;
hypotheses themselves were accepted or rejected based on the weight of evidence across all tests
rather than strictly on the p<0.05 criterion (Cohen, 2011; Harvey and Lang, 2010; Harrell, 2001;
Quinn and Keough, 2002).
Analyses were performed chiefly in the R statistical environment for Mac OS X (R Founda-
tion, 2013) using the R Commander GUI (Fox, 2005; Fox et al., 2014a). Multiple imputation
and supplementary analyses were performed in SPSS/PASW 20 statistical software using its
imputation function and pooling procedures for imputed datasets (IBM Corporation, 2011).
Power analyses were performed in G*Power 3.1 for Mac (Faul et al., 2007).
Means Comparison
Means were compared with Welch’s t-tests for binary factors (Sex and AgeBinary), and with
one- and two-way fixed-effects analyses of variance (ANOVA) for non-binary factors. A factorial
ANOVA design was applied using either one factor or two depending on the hypothesis in
question. Main-effects models were generated for hypothesis-testing. As N is not uniform
across factor levels, the Type III Sum of Squares, which is robust to unbalanced models, was
Chapter 7. Quantitative Methods 111
selected as the measure of residual variance (Quinn and Keough, 2002, p.354).
The assumptions of conventional ANOVA are similar to those of linear regression (Quinn and
Keough, 2002, pp.339-358). Error terms are assumed to be independent, normally distributed,
and homogeneous across factor levels. Predictors must be independent, and the error variances
must be relatively homogeneous across factor levels. The design of the model should relatively
be balanced – that is, factor levels must hold approximately the same number of cases, although
ANOVA is robust to unbalanced designs as long as the assumption of homogeneity of error
variances holds (Quinn and Keough, 2002). These assumptions are considered in the analysis
and violations are mitigated where required.
Bivariate plots were assessed to ensure linearity of response-predictor associations and ab-
sence of multicollinearity. Error distributions were tested by plotting Pearson standardized
residuals in quantile-quantile plots and checking for departures from normality with the Shapiro-
Wilk test. Homogeneity of error variance across factor levels was tested formally with Levene’s
F -test. The assumption of parallel regression slopes was checked formally by including an in-
teraction term in cases where bivariate correlations suggested that the assumption might be
violated.
Tests of Independence
Contingency tables were generated with the CrossTables command in R’s gmodels package
(Warnes, 2012). The Cochran-Mantel-Haenszel test was used to assess conditional independence
of test variables across binary sample strata (Sex; AgeBinary). Fisher’s Exact χ2 statistic was
used to test the null hypothesis of independence for factors with three or more levels, such as
Period and OA.Sev. Although originally developed for contingency tables with fixed marginal
totals, Fisher’s Exact χ2 is considered to be the most accurate test of independence for small
samples and is commonly used in biological contexts where marginal totals are not fixed (Quinn
and Keough, 2002).
Chapter 7. Quantitative Methods 112
Probability Testing:
Simple and Adjusted Odds Ratios
The odds ratio (OR) is generally used in case-control study designs, in which cases of differing
outcomes are retrospectively compared in terms of exposure to hypothesized causal variables
(Bland and Altman, 2000; Harrell, 2001). An odds ratio describes the relative odds of a condi-
tion existing in a given member of one group versus another, but without asserting the baseline
odds of that condition. In a test sample, for instance, the odds of disease is simply the ratio
of cases with disease to the number of cases without. Although ORs have been found to over-
state effects in comparison to risk ratios, this is problematic only when effects are large (Davies
et al., 1998); however, published meta-analyses show that most developmental effects are small
(Victora et al., 2008). Finally, the OR facilitates comparison with logistic regression coefficients
(see below).
Because both age and sex are potential confounders for the effects that are of interest,
conventional odds ratios were calculated independently for sex- and age-controlled blocks, and
were then adjusted using the Mantel-Haenszel method, which enables comparison across strata.
Confidence intervals were calculated for both conventional and Mante-Haenszel odds ratios (Dos
Santos Silva, 1999). Odds ratios were considered to be significantly different from parity when
the confidence interval excluded 1.0.
Logistic Regression
Associations between factorial and continuous variables were explored with logistic regression.
Binary (BLR), ordered (OLR) and multinomial (MLR) methods were used.
Sample size was sufficient for both binary and ordered logistic regression because most
models include one or two predictors, and the maximum number is three; given a minimum
requirement of 10 datapoints per predictor, sample size is sufficient. The assumptions for logistic
models were checked with similar methods to those required by other regression methods. All
observations were made independently, meaning that dependence among error terms is minimal.
Multicollinearity was mitigated by correlation tests and by exercising parsimony in selecting
predictors for each model, a strategy that also mitigates overfitting (Harrell, 2001). Logistic
Chapter 7. Quantitative Methods 113
regression does not assume homogeneity of variances (Tabachnick and Fidell, 2007).
The threshold for model significance was set at an odds ratio different from 1.0 at 95%
confidence, or a model p smaller than 0.05. The p value was computed from a likelihood
ratio test (LRT) based on the -2x log likelihood. The LRT statistic measures the quantity
of unexplained variation in the outcome variable and was compared between the initial (null)
and final models to determine if the final form significantly reduces the amount of unexplained
variation (Harrell, 2001).
Predictive accuracy was assessed by calculating the area under the receiver operating char-
acteristic (ROC) curve for fitted probabilities. The area under the curve is identical to the
C -index of the probability of concordance between the predicted probability and an individual
response and can be interpreted as the proportion of cases in which the fitted probability will
correctly rank a case according to a dichotomous outcome. A C value of 0.5 indicates that
the classifier is successful 50% of the time — in other words, that the model is no better than
chance, while an area of 1.0 indicates a perfect model. In general, a C value of 0.80 is considered
the minimum threshold of model accuracy at the individual scale (Harrell, 2001).
Somers’ Dxy rank correlation is commonly used as an index of the independent variable’s
quality as a predictor of the outcome and is related to the area under an ROC curve as Dxy
= 2(C-0.5) (Harrell, 2001). Dxy has a value between 0 and 1 and can be interpreted like most
other correlation statistics.
Diagnostic techniques for logistic regression are analogous to those applied in least-squares
regression. For binary models, residual plots were examined and Cook’s Distance statistics (D)
and Studentized residuals were computed for each case. D is an index of both residual distance
and leverage (distance from the mean on the x-axis) and is commonly used as a barometer of case
influence, defined as the predicted change in model parameters when a given case is removed.
Conventionally, D>1 is considered as a threshold for re-examining a case or experimentally
removing it from the model (Quinn and Keough, 2002). Cases with D values that approach 1
were examined and experimentally removed to assess the strength and nature of their effect on
model parameters.
Chapter 7. Quantitative Methods 114
Multinomial and Ordered Logistic Regression
The proportional odds assumption of ordered logistic regression (OLR) was tested for each
model. Although R does not offer a direct statistical test of proportional odds, Harrell rec-
ommends a graphical method of testing this assumption by plotting the observed and ex-
pected conditional means of each predictor relative to each level of the outcome using the
plot.xmean.ordinaly function available in the rms package (Harrell, 2001). In cases where
the assumption of proportional odds was violated, multinomial logistic regression (MLR), which
does not make this assumption, was used to generate alternative models.
Case influence and residual statistics are not offered in ordered and multinomial logistic
regression procedures for SPSS or R, so the predictors and outcomes of those significant models
were dichotomized and re-analysed with a binary procedure in order to generate those diagnostic
statistics.
Power, Effect Size and Sensitivity
Power and sensitivity analyses were performed in G*Power 3.1 for Mac (Faul et al., 2007) to
assess confidence and reliability of results. Tests were selected based on the family of distribu-
tion, and on the specific method: F tests of equality of means, χ2, and binary logistic regression
are all supported by G*Power 3. The threshold of adequate power is set to 1-β=0.80 following
convention. In addition to assessing the reliability of the results, these post hoc estimates will
provide a benchmark for future data collection.
Sensitivity analyses were used to estimate the minimum effect size observable with attained
sample N. These estimates were then compared with observed effect sizes to determine whether
the observed effects could be reliably detected with the attained level of sample power, under
the assumption that they reflect those in the background population. Standard effect size
statistics and conventional thresholds for small, medium, and large effect size were used (Cohen,
1988). The most parsimonious hypothesis-testing models were analysed in order to provide a
minimum estimate of power and sensitivity. Diagnostic models, including those with exploratory
interaction terms, were not tested because of the decrease in power that comes with adding
degrees of freedom.
Chapter 7. Quantitative Methods 115
Supplementary Analysis with Imputed and Ordinated Datasets
The 5 imputed datasets were used to replicate all significant statistical hypothesis tests involv-
ing neural canal measurements. Each imputed dataset was analysed separately, and then all
model parameters were averaged and the confidence intervals were corrected using SPSS’ native
adjustment function. The summary NC variables PCA-AP and PCA-ML were also included in
these supplementary analyses.
7.5 Summary of Statistical Procedures
7.5.1 Hypothesis I: Skeletal growth outcome relative to age at death
This phase of the analysis addressed whether poorer skeletal growth outcomes correspond to
a younger age at death. The null hypothesis that skeletal growth is not related to age at
death was tested using direct comparison of means, tests of independence, and binary logistic
regression. Age at death was analysed as a binary category (AgeBinary) dividing Young Adults
(YA) in the VYA and YA age phases from Mature-Elderly (MA-EA) adults in the MA and EA
age phases.
Hypothesis I: Null and Alternative Hypotheses
H0: Skeletal growth outcomes (body size and neuroskeletal size) do not associate with
probability of early death;
HA1: Measures of skeletal growth outcome are more likely to be small in adults who
died earlier than in those who survived longer;
HA2: Measures of skeletal growth outcome are larger in individuals who died at earlier ages
than in those who survived longer.
Adjusted odds ratios were generated by binary logistic regression (BLR), with AgeBinary
entered as the response variable and growth outcome as the primary predictor. Cook’s D values
and Studentized residuals were examined in order to identify outliers and influential cases.
Somers’ Dxy and area under the ROC curve were used to assess the accuracy of AgeBinary
classification based on the predictors. Both pooled and sex-separated samples were tested.
Chapter 7. Quantitative Methods 116
Power analyses were performed in G*Power (3.1). Means comparisons were assessed with
a standard power test for t-statistics. Effect size d was computed from group means, standard
deviations, and the ratio of group N (NMA−EA/NY A) (Faul et al., 2007). Both observed effect
size and the minimum observable effect size N are reported.
Logistic regressions were tested for power using the Z family of tests with an enumeration
procedure for Likelihood Ratio (LRT) statistics (Lyles et al., 2007). Effect size was quantified as
the odds ratio. The probability of an individual falling into the MA-EA category under the null
hypothesis was estimated as the proportion of MA-EA cases in the test sample (81/139=0.59).
Target critical χ2 values and minimum detectable odds ratio are reported.
7.5.2 Hypothesis II: Presence and severity of joint degeneration relative to
skeletal growth outcome
The null hypothesis that skeletal growth is unrelated to joint degeneration was tested using
direct comparison of odds ratios, tests of independence, and logistic regression. As described
above, joint degeneration prevalence is represented both by binary factors and by joint severity
by ordinal factors (Table 7.2). As ordinal regression’s sensitivity to small and empty cells
makes continuous independent variables inadvisable, ratio-scale variables were represented by
ranked ordinal factors. Models were constructed mainly from the pooled sample because the
descriptive phase of analysis found no evidence of sexual dimorphism in joint degeneration;
however, sex-specific testing was also performed.
Hypothesis II: Null and Alternative Hypotheses
H0: Neither presence nor severity of joint degeneration associates with skeletal growth
outcome;
HA1: Individuals with smaller skeletal measurements are more likely to have skeletal
degeneration when age-at-death is controlled;
HA2: Individuals with larger skeletal measurements are more likely to have skeletal
degeneration when age-at-death is controlled.
Ordinal joint-degeneration factors were analysed with the polrfunction for ordered logistic
Chapter 7. Quantitative Methods 117
regression in R (Ripley et al., 2014). The three-stage OA Severity factor was entered as the
response variable, with ordered skeletal factors and AgeBinary as the predictors. Individual
modification variables (OP, EB, PIT, and SNB) were examined only when significant models
were identified. Sex was included as a main term and as an interaction term in diagnostic tests
of model reliability, but was not included as a predictor in most models because it was not
found to affect either presence or severity of joint disease.
Conditional means plots were generated to check the assumption of proportional odds (Har-
rell, 2001); where this assumption was violated, binary or multinomial regression models were
used instead. Model significance was tested by comparing -2 Log Likelihoods between intercept-
only and fitted models using the likelihood ratio test (LRT). Models with an an LRT p value
below 0.05 or an odds ratio confidence interval excluding 1.0 were considered statistically signif-
icant. Fit was assessed by Spearman’s ρ2 based on the correlation of predicted versus observed
outcome levels. Effect plots of mean fitted probabilities by predictor level are generated using
the allEffects function in the R effects package (Fox et al., 2014b).
As R packages currently do not provide comprehensive diagnostics for OLR and MLR
models, further diagnostics for significant models are obtained by dichotomizing predictors and
outcomes (for example, None vs Moderate, or Moderate vs Severe; YA vs MA/EA; MA vs EA)
and re-testing in binary logistic regression (BLR) (Ripley et al., 2014).
Power and sensitivity could not be directly assessed for multinomial logistic models in
G*Power, but tests were performed on statistics from BLR models following the procedure out-
lined in Hypothesis Test I (Section 7.5.1). For contingency tables, effect size w was calculated
by comparing cell proportions under the null hypothesis with those under the alternative (Faul
et al., 2007). Proportions under the null hypothesis were calculated from χ2 expected counts,
and those under the alternative were calculated from observed counts under the assumption
that the observed count represents a true effect.
7.5.3 Hypothesis III: Temporal variation in skeletal growth outcomes
The objectives of this stage of the analysis were to re-examine the pattern of femur size over
time, and to test the independent variation in neural canal size across time after controlling for
the effect of overall body size.
Chapter 7. Quantitative Methods 118
Hypothesis III: Null and Alternative Hypotheses
H0: Temporal variation in skeletal growth outcomes is not significant;
HA1: Mean growth outcomes are largest during the Middle period (3000–2000BP);
HA2: Mean growth outcomes are smallest during the Middle period (3000–2000BP).
Mean femur size and neural canal diameters were compared among temporal phases us-
ing fixed-effects multi-way ANOVA generated by the lm function in R’s stats package (R
Foundation, 2013). Period was entered as the main factorial predictor. Sex and AgeBinary
were each tested for correlations or interactions with Period; neither was found to interact sig-
nificantly with Period. Both linear and polynomial models were fitted based on Pfeiffer and
Sealy’s observation of a quadratic relationship between FXL and date (Pfeiffer, 2013; Pfeiffer
and Sealy, 2006). Standardized residuals were plotted against predicted values under a normal
distribution. Correspondingly, Fisher’s Exact tests of independence were applied to frequency
tables of osteometric ranks against period. Standardized residuals were computed based on χ2
2 expected values of each cell.
Linear and polynomial ordinary least-squares regression models were fit to bivariate plots
of skeletal measures against uncalibrated radiocarbon years BP. Sex-standardized Z values
from males and females were pooled. Standardized residuals were compared against a normal
distribution with Shapiro-Wilk tests.
Means comparisons were assessed for power and sensitivity with G*Power’s functions for
fixed-effects ANOVA. Effect size f, defined as the ratio of the standard deviation of group means
to the within-group standard deviation (Faul et al., 2007), was computed in G*Power from the
input group n, means, and common standard deviation. One-way ANOVA models from both
full and imputed datasets were tested. Multi-way models were not tested, as AgeBinary and
Sex were not found to be significant predictors.
G*Power provides test functions for t statistics of linear bivariate regressions, which assess
power and sensitivity to distinguish null and alternative slopes. G*Power functions for generic
F tests are used for OLS models because G*Power has no specific function for quadratic models;
critical F statistic was used in place of f as a measure of sensitivity for OLS models.
Chapter 7. Quantitative Methods 119
7.5.4 Hypothesis IV: Temporal variation in joint degeneration
This phase tested the null hypothesis that the frequency and severity of joint disease, once
adjusted for variation in age groups, does not vary significantly across time periods. The main
alternative hypothesis is that age-adjusted joint degeneration is most frequent and most severe
in the Middle Period (3000–2000BP). As with Hypothesis II, logistic regression and indepen-
dence tests were used to analyse ordinal severity (OA.Sev) and presence/absence (OA.Binary)
data. In the interest of avoiding overfitting, adjustment for sex and for body size (FXH.Z) was
applied only to models that yielded initally significant results.
Hypothesis IV: Null and Alternative Hypotheses
H0: Temporal variation in joint degeneration is not significant;
HA1: Joint degeneration is highest during the Middle period (3000–2000BP);
HA2: Joint degeneration is lowest during the Middle period (3000–2000BP).
Temporal variation in joint degeneration was explored with tests of simple and conditional
independence after the procedure followed in Hypothesis Test II. OLR was contra-indicated
by violation of the proportional-odds assumption, so MLR and contingency tables were used
instead. OA.binary and OA.sev frequencies were plotted against Period in two-way contin-
gency tables with and without stratification by age. The hypotheses of simple and conditional
independence (the latter adjusted for AgeBinary) were tested with Fisher’s Exact and Cochran-
Mantel-Haenszel Tests, respectively.
Sensitivity tests were used to establish minimum effect sizes and critical χ2 thresholds for
independence of OA.Sev from Period in the observed sample size at 2 and 4 degrees of freedom.
Chapter 8
Results: Descriptive Statistics and
Diagnostic Analyses
8.0.1 Sample Demographic Composition
Individuals are assigned to broad age categories of Very Young Adults (VYA, <25 years),
Young Adults (25-35 years), Mature Adults (MA, 35-55 years), and Elderly Adults (EA, 55+
years). Both VYA and EA groups have very low numbers, so the four categories are folded into
two: Young Adults (YA) and Mature-Elderly Adults (MA-EA) represent 42% and 58% of the
research sample, respectively (Figure 8.1).
Males are slightly overrepresented in the research sample: of those individuals with a con-
fident sex estimate (N=139), 54% are identified as male (M=75) and 46% as female (F=64).
This imbalance may be because of better preservation in males related to skeletal robustness
but does not appear to be an artefact of bias in mortuary or curatorial practice (Pfeiffer et al.,
2014). The slight overrepresentation of males is consistent and fairly uniform across age groups.
8.0.2 Sample Temporal and Ecogeographic Composition
The breakdown of the research sample along temporal, ecogreographic, and demographic lines
is presented in Table 8.2 and in Appendix Table C.3.
The sample encompasses a wide time range, with uncalibrated radiocarbon dates ranging
from 560 to 9100 BP. One-way ANOVA shows that the sample from the South Coast region
120
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 121
Age Groups, Stratified by SexPhase Group Est. Range Males Females Total
Young Adults (<35 years) Very Young Adult <25 years 12 11 23Young Adult 25–35 years 19 17 36YA total 31 28 59
Mature-Elderly Adults (35+ years) Mature 35–55 years 37 31 68Elderly 55+ years 7 5 12MA-EA total 44 36 80
Total Total 75 64 139
Table 8.1: Summary age groups (Very Young, Young, Mature-Elderly) are folded into binary age phases (YoungAdult, Mature-Elderly Adult). This table shows the distribution of males and females in each phase.
Young (<35 years) Mature–Elderly (35+ years)Male Female Male Female TOTALS
West Coast Early 2 2 5 4 13N=87 Middle 5 11 20 9 45
Late 11 4 5 9 29South Coast Early 4 6 8 6 24
N=47 Middle 6 2 3 4 15Late 0 2 3 3 8% 21% 20% 33% 26% 100%
Table 8.2: Distribution of cases across demographic, temporal, and ecogeographic strata.
has significantly older dates than that from the West Coast (F=11.99, df=2, p=0.000) (See
Appendix Table C.3). Skeletal size, however, does not vary significantly between regions in any
measurement. The difference in average age can be attributed to the smaller sample from the
South Coast and to the fact that both the Matjes River and Oakhurst Rockshelter sites were
used for multiple burial episodes over a long span of time. Preservation bias is also a possible
factor here: while rock-shelter burials are also known from the West Coast, many individuals
from the West Coast region were recovered from simple open-air dune burials (Jerardino et al.,
2000; Manhire, 1993; Morris, 1992a,c; Pfeiffer, 2013; Stynder, 2009). Erosion, exposure and
consequent weathering would undoubtedly have destroyed many early open-air burials.
The frequency distribution of 14C dates in the research sample (Figure 8.1) mimics the
frequency distribution of dates from the wider Cape database (Figure 4.2).
8.0.3 Marine Dietary Content
To determine whether cases with unusual dietary signatures can be identified, δ13C and δ15N
values were plotted against one another and against uncalibrated radiocarbon date. Reference
lines are included, approximating average tissue isotopic signatures for animals at primary
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 122
Figure 8.1: The frequency distribution of radiocarbon dates in the study sample
and secondary trophic levels (Sealy, 2006, p.574) (Figures 8.2 and 8.3). The bone collagen of
consumers is expected to be enriched relative to the isotopic value of their diet by approximately
5ppm on the δ13C axis and by approximately 3-4ppm on the δ15N axis (Sealy, 2006, p.574-575).
The distribution of isotopic values in this sample shows the same increased variability and
higher average marine signal during the Middle period, as that previously published (Pfeiffer and
Sealy, 2006; Sealy and Pfeiffer, 2000; Sealy, 2006). Most individuals have signatures consistent
with C3-based hunter-gatherer diets with some marine content. Several individuals previously
identified by Sealy (2010) do have unusually high δ13C values relative to their δ15N signatures
(M=UCT67, UCT583; F=UCT582, NMB1704), and so may have incorporated some C4-fixed
carbon into their diets, possibly via cattle or cultigens (Sealy, 2010).
The distribution of δ15N values corroborates that of δ13C, showing most individuals well
above the estimated average degree of δ15N enrichment even for terrestrial carnivores. An
ordinary least-squares regression model fit to the plot shows that the correlation between the
two isotope ratios explains 25% of the variation in the plot (R2=0.25, p<0.000). Nearly all
datapoints fall within the 95% confidence interval for the regression line; only 9 fall on or
outside the interval. All datapoints except for 6 of those 9 outliers lie above the δ13C and δ15N
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 123
Figure 8.2:Correlation of δ13C and δ15N stable isotopic signatures for N=105 individuals with carbon isotopic values andN=97 individuals with nitrogen isotopic values available the research sample. Reference lines approximateestimated average isotopic signatures for consumers at different trophic levels. All reference values from Sealy(2006, p.574-575).Carbon-13 reference values: δ13C = -21ppm: bone collagen of terrestrial C3 primary consumers; δ13C=-16ppm : Meat of filter-feeding shellfish and the approximate value of a mixed terrestrial-marine diet; δ13C=-12ppm: seal meat; δ13C=-6ppm: bone collagen of terrestrial C4 primary consumers.Nitrogen-15 reference values: δ15N = 4.7ppm: bone collagen of archaeological herbivores from Nelson BayCave; δ15N = 8ppm: bone collagen of a human with an entirely terrestrial diet; δ15N = 16.8ppm: mean value ofarchaeological seal bone from Nelson Bay Cave.
terrestrial carnivore thresholds. The four individuals who fall on or above the 95% confidence
interval on the δ13C axis (M=UCT67, UCT583; F=UCT582, NMB1704) are relatively recent
in date (<1000BP) and may well have had access to C4-fixed carbon through domesticates like
cattle (Sealy, 2010). Those individuals who fall close to or outside the line (N=8) on the δ15N
axis are less clearly patterned, aside from the fact that all derive from the West Coast. Their
dates vary from 1040 uncalBP to 3880 BP. Their δ15N values may reflect regular consumption
of drought-adapted terrestrial animals like tortoise or hyrax (Lee-Thorp, 2008), but only three
of the outliers are from the arid northern part of the West Coast and the rest are from the
southern half, where rainfall is relatively abundant.
With the exception of those few outliers, most individuals yield δ13C and δ15N signatures
that are consistent with consumption of marine foods, with a consistent spectrum of variation
from diets that derived protein from mixed marine and terrestrial sources, to those that ed
nearly all their protein from the ocean.
Examining the distribution of δ13C and δ15N values relative to uncalibrated radiocarbon
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 124
date does not suggest any significant association between diet and date (Figure 8.3). The great-
est variability in isotope signals appears to cluster around the middle period (3000–1900BP);
both the most and least-enriched datapoints are dated to this time. Comparing marginal
means among time periods (Period 1: 3100>BP, Period 2: 3000–1900BP, Period 3: <1900BP)
reveals no significant differences in δ values (F=1.86; p=0.160). Fitting ordinary least squares
(OLS) regression models (linear, quadratic, and cubic) to plots of uncalibrated radiocarbon
date against isotopic δ values reveals no bivariate relationship, linear or otherwise. The LOESS
regression line fit to 80% of datapoints using a biweight function illustrates a slight decrease in
both mean 13C and mean 15N between 0 and 2000uncalBP, but all datapoints remain within
the range expected for a marine-focussed diet with considerable scatter around the regression
line. Overall, average dietary values, particularly on the South coast, indicate a relatively con-
sistent dietary pattern across the span of time in question. If marine diet has introduced bias
to radiocarbon dates from this sample, it appears to be homogeneously distributed within the
sample, and is unlikely to influence conclusions about temporal population dynamic.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 125
Figure 8.3: LOESS regression of δ13C (L) and δ15N (R) stable isotopic signatures against radiocarbon date.
8.0.4 Osteological Measurement Error
Inter-observer difference is quantified as the mean absolute difference between Observer 1 and
Observer 2, and as the mean directional difference (bias) between the same obervers. Total
inter- and intra-observer variation is summarized using the within-subject standard deviation
ws and 95% repeatability statistics detailed by Bland and Altman (1996).
The results indicate that inter-observer and intra-observer reliability is generally good (Table
8.3). In the case of the femoral variables, correlation between observers is quite high, with FXL
R2=1.00 and FXH R2=0.98. The interval of 95% interobserver repeatability is 3.3mm for FXL
and 0.8mm for FXH (Bland and Altman, 1996).
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 126
In the case of the neural canals, measurement error is more complex. Measurements from
27 lumbar vertebrae (NL1=19; NL5=8) are compared to replicates collected independently by
SP. The mean intra-observer R2 across all vertebrae in the full study sample is R2 =0.97,
increasing slightly from thoracic (T1ML R2 =0.87) to lumbar (L5ML R2=1.00), with an aver-
age intra-observer repeatability of 0.53mm and no appreciable difference between AP and ML
measurements. In contrast, the inter-observer R2 is 0.785 across the replicated sub-sample (L1
and L5), with a mean repeatability of 1.43mm. Notably, antero-posterior (AP) measurements
exhibit a wider repeatability range than medio-lateral (ML): mean 95% repeatability for AP is
1.54mm, versus 0.70mm for ML. Similarly, inter-observer AP R2=0.59, while ML R2=0.99. The
AP to ML difference is almost certainly caused by the morphology of the posterior vertebral
body and neural arch, both of which are more variable in shape and have slightly less distinct
landmarks than the pedicles (Figure 8.4).
These results indicate that canals can be measured repeatably and reliably in the ML
dimension, but that differences in variation and technique may be relevant to interobserver
reliability in the AP dimension.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 127
Intraobserver Repeatability
ws
(mm)
Repeatability
(mm)
Intraobs R R2 N
T1 AP 0.14 0.40 0.98 0.96 90
ML 0.43 1.18 0.93 0.87 90
T6 AP 0.15 0.43 0.98 0.97 58
ML 0.18 0.49 0.99 0.98 58
L1 AP 0.17 0.46 0.99 0.97 90
ML 0.10 0.28 1.00 0.99 90
L5 AP 0.27 0.75 0.99 0.98 90
ML 0.09 0.25 1.00 1.00 90
Average AP 0.18 0.51 0.99 0.97
ML 0.20 0.55 0.98 0.96
Interobserver Repeatability
ws
(mm)
Repeatability
(mm)
Intraobs R R2 N
L1 AP 0.69 1.92 0.75 0.56 19
ML 0.23 0.63 0.98 0.96 19
L5 AP 0.42 1.16 0.86 0.74 7
ML 0.28 0.77 0.99 0.98 8
Average AP 0.56 1.54 0.76 0.58
ML 0.25 0.70 0.99 0.99
FXL 1.199 3.32 0.99 0.99 22
FXH 0.28 0.78 0.99 0.98 22
Interobserver Difference
Diff (mm) Mean Bias
(mm)
Sig
L1 AP 0.67 -0.15 ns
ML 0.26 -0.02 ns
L5 AP 0.54 -0.04 ns
ML 0.28 0.00 ns
FXL 1.39 -0.93 ns
FXH 0.33 -0.06 ns
Table 8.3: Results of a comparative study of inter versus intra-observer measurement variation in lumbar neuralcanal diameter and femur size. 95% repeatability is calculated following Bland and Altman (1996). Replicatemeasurements are contributed by SP and CM. Additional femoral length and head measurements are frompublished sources (Sealy and Pfeiffer 2006; Wilson and Lundy 1994.) Statistical tests of observer difference areone-way ANOVA with an α level of p<0.05.Notes: ws is calculated as the square of the within-subject variance and 95% Repeatability is calculated asws*2.77 (Bland and Altman, 1996). FXL and FXH measurements are taken preferentially from the left sidewhenever possible. Mean difference is the mean of absolute differences between Observer 1 (SP) and Observer 2(CM for femora and author for neural canals). Mean Bias is the mean of directional differences between Observer1 and Observer 2.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 128
8.0.5 Comparison of Neural Canal Variance
Standard errors of the mean and coefficients of variation (CoV) for two comparator samples
(recent Portuguese from Coimbra and Medieval Britons from Fishergate) are calculated from
descriptive statistics published by Holland (2013) and Watts (2011). The collected parameters
are compared using ANOVA and post hoc contrasts with Bonferroni corrections for multiple
tests (Table 8.4). Vertebral measurement (T1AP, etc), Sex, and Collection are tested as fixed
factors.
As might be expected, the means and CoVs are significantly different between vertebral
measurements, but they do not vary significantly between the sexes or collections. Means and
coefficients of variation do not differ significantly between the sexes (Figure 8.5). CoV does
approach significant difference between Watts (Fishergate) and Holland (Coimbra) (p<0.10),
but neither is significantly different from the LSA KhoeSan.
The results of this comparison indicate that coefficients of variation in the neural canal
are quite similar between the comparators, suggesting that observer error is not a significant
contributor to sample-level estimates of variation. The average coefficient of variation for LSA
neural canals is 10%, with a range from 8% to 16% (Table 8.4). NC variation in this sample is
greater than that observed in both femoral dimensions (FXL=6%, FXH=7%), suggesting that,
in terms of variability, the neural canal is no more canalized than femoral size and may record
developmental information additional to that of the femur (Table 8.5).
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 129
Figure 8.4: Scatterplots of neural canal measurements made by observer 1 (SP) and observer 2 (LED) on 27lumbar vertebrae (L1 and L5). Note that interobserver difference tends to be higher in the AP plane (left) thanin the ML plane (R).
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 130
Figure 8.5: Visual comparison of variability (Coefficient of variation) and mean size among three collections fromdiverse geographical populations (LSA = KhoeSan; Fishergate = Britons; Coimbra = Portuguese). Mean sizeis relatively stable between sexes and collections but does differ according to measurements. Average variabilityis also quite similar among the collections, although there is a range from approximately 5% to 16% amongmeasurements.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 131
8.1 Descriptive Statistics and Preliminary Diagnostic Analyses
Descriptive statistics and preliminary diagnostics for osteometric and joint-modification vari-
ables are presented in Tables 8.5, 8.7, 8.8, and 8.9. Descriptive statistics are generated and
analysed for both non-imputed and imputed datasets, including principal components scores
(PCA-AP and PCA-ML). The parameters of imputed models parallel those of non-imputed
models. Summary descriptive statistics of the latter datasets are presented in Tables 8.6 and
8.10. Full descriptions of imputed and PCA datasets are presented in Appendix Tables C.1
and C.2. Demographic distribution of severity by modification form is detailed in Appendix
Table C.4.
8.1.1 Sexual Dimorphism
All variables are tested for sex differences because sexual dimorphism is a well-known compo-
nent of variation in biological size and both the frequency and manifestation of osteoarthritis
are known to vary by sex in contemporary urbanised populations e.g. (Hanna et al., 2009).
Raw measurements are tested for sexual dimorphism with Welch’s robust t-tests (Quinn and
Keough, 2002). Ordinal and binary joint-modification categories are tested for sex bias with
Fisher’s Exact χ2 tests with and without stratification by age group (see below). Significant sex
differences are identified in nearly all raw osteological measurements (Table 8.5), but none are
detected in the frequency or severity of OA or modification forms (Table 8.7). Note that, while
non-imputed NC test results are discussed in the text, the tabulated results for NC-focussed
tests are from analyses of the imputed datasets unless otherwise mentioned.
Nonparametric comparisons of centrality and range fail to detect any significant sex-based
variation in average OA or modification values. Mantel-Haenszel odds ratios, adjusted for age
group, were calculated to compare the frequency of modification and OA between the sexes;
sex-adjusted Mantel-Haenszel odds ratios were then compared between age groups. There is
no significant sex difference in the frequency of OA, either at the level of the whole body (MH
OR=1.16, 95% CI=0.52–2.58 for males when age is controlled), or when stratified into upper
versus lower limbs (OR OAupper=0.95, 95% CI= 0.65–1.39; OAlower=1.18, 95% CI=0.81–1.71)
(Tables 8.7 and 8.8).
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 132
Males FemaleVertebra Plane Mean (mm) SEM CoV N Mean (mm) SEM CoV N
LSAT1 AP 13,70 0,16 0,08 44 13,54 0,15 0,07 44
ML 20,20 0,26 0,09 45 19,23 0,17 0,06 43T6 AP 14,46 0,23 0,09 30 14,16 0,21 0,08 26
ML 15,40 0,31 0,11 31 14,95 0,34 0,11 26L1 AP 15,67 0,21 0,09 47 16,39 0,22 0,08 40
ML 20,34 0,26 0,09 50 19,95 0,27 0,09 41L5 AP 15,34 0,36 0,16 45 15,79 0,40 0,16 40
ML 23.84 0,37 0,11 50 24,04 0,31 0,08 42Average AP 14,79 0,24 0,10 14,97 0,24 0,10
ML 19,94 0,30 0,10 19,54 0,27 0,09
CoimbraT1 AP 14.98 0.16 0,06 27 14,06 0,17 0,06 25
ML 20,87 0,29 0,07 27 19,57 0,25 0,06 25T6 AP 15,53 0,18 0,06 27 15,19 0.23 0,08 26
ML 16.1 0,28 0,09 27 15,07 0,26 0,09 26L1 AP 17,56 0.16 0,05 27 17.18 0.17 0,05 26
ML 22.37 0.35 0,08 27 20,19 0,28 0,07 26L5 AP 17.70 0.38 0,11 26 16,44 0.46 0,14 24
ML 25.31 0.37 0,08 26 24.9 0.51 0,10 24Average AP 16,44 0,22 0,07 15,71 0,26 0,08
ML 21,16 0,32 0,08 19,93 0,33 0,08
FishergateL1 AP 15.6 0,31 0,09 21 15.5 0,33 0,10 21
ML 21.7 0,23 0,05 22 20.15 0,33 0,08 23L5 AP 15.7 0,60 0,17 19 15.95 0.6 0,15 16
ML 24.1 0,50 0,10 21 23.15 0,59 0,11 18Average AP 15.65 0,45 0,13 15.73 0,46 0,12
ML 22.9 0,37 0,07 21.65 0,46 0,09One-way ANOVA
Factor N Dependent F Sig
Vertebra 40 Mean 104.23 p<0.0540 CoV 14.86 p<0.05
Sex 40 Mean 0.24 ns40 CoV 0 ns
Collection 40 Mean 0.667 ns40 CoV 3.04 p<0.10
Table 8.4: Comparative study of neural canal variability in LSA KhoeSan, Medieval Britons, and 19th-centuryPortuguese. Descriptive statistics (mean and coefficient of variation [CoV]) for the latter two collections arecalculated from summary statistics published by Holland (2013) and Watts (2011). They are compared usingone-way factorial ANOVA in which Collection, Sex, and Vertebra are entered as factorial predictors of Mean andCoV. N for each ANOVA consists of the number of values entered for each factor.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 133
Variable Sex N Mean (mm) StDev (CoV) SSD Shapiro-Wilk Var
FXL F 36 402.4 20.0 (5%) * 0.97 nsM 55 417.8 25.1 (6%) 0.98All 91 411.8 24.2 (6%) 0.98
FXH F 37 37.5 2.6 (7%) * 0.99 nsM 56 40.7 2.2 (5.4%) 0.98All 93 39.4 2.8 (7%) 0.99
T1.AP F 35 13.6 1.08 (8%) 0.96 nsM 41 13.6 1.01 (7.4%) 0.97All 76 13.6 1.03 (7.4%) 0.97*
T6.AP F 19 14.2 1.05 (7.4%) 0.88* nsM 27 14.3 1.20 (8.4%) 0.97All 46 14.3 1.10 (7.7%) 0.95*
L1.AP F 32 16.7 1.18 (7%) * 0.98 nsM 42 15.6 1.46 (9.3%) 0.96All 74 16.1 1.46 (9.1%) 0.99
L5.AP F 32 15.9 2.62 (16.5%) 0.94** nsM 41 15.3 2.22 (14.5%) 0.91**All 73 15.6 2.38 (15.3%) 0.94**
T1.ML F 35 19.2 1.11 (5.8%) * 0.99 nsM 41 20.1 1.73 (8.6%) 0.98All 76 19.7 1.53 (7.7%) 0.99
T6.ML F 19 14.7 1.61 (11%) 0.91*(-) nsM 27 15.4 1.60 (10%) 0.93All 46 15.1 1.61 (10.6%) 0.94**
L1.ML F 32 20.2 1.60 (7.9%) 0.98 nsM 42 20.2 1.75 (8.7%) 0.98All 74 20.2 1.68 (8.3%) 0.99
L5.ML F 32 24.4 2.00 (8.1%) 0.97 nsM 41 23.8 2.48 (10.0%) 0.99All 73 24 2.26 (9.4%) 0.99
Table 8.5: Descriptive statistics for all osteometric variables before transformation to z scores and multipleimputation.Notes: An asterisk (*) in the SSD column indicates significant sexual dimorphism based on t tests prior to ztransformation. All sexual size effects are resolved after z-transformation. An asterisk (*) in the Shapiro-Wilkcolumn indicates a significant W statistic when the variable is in raw form. A double asterisk (**) indicatesno improvement in W after multiple imputation and a dash (-) indicates amelioration. An asterisk (*) in theVar column indicates a significant difference (at p <0.05) in variance between males and females after multipleimputation.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 134
Measure Sex N Mean SEM
FXL.Z M 55 0,01 0,14F 36 0,03 0,16All 91 −0,04 0,22
FXH.Z M 56 0,03 0,13F 37 0,03 0,17All 93 −0,06 0,21
T1AP.Z M 56 −0,10 0,15F 49 −0,00 0,17All 105 −0,05 0,12
T1ML.Z M 56 −0,06 0,14F 49 −0,01 0,14All 105 −0,01 0,11
T6AP.Z M 56 −0,05 0,16F 49 0,03 0,17All 105 −0,01 0,11
T6ML.Z M 56 0,05 0,16F 49 0,03 0,20All 105 0,02 0,12
L1AP.Z M 56 −0,01 0,15F 49 −0,00 0,13All 105 0,01 0,12
L1ML.Z M 56 0,01 0,14F 49 −0,02 0,15All 105 0,02 0,12
L5AP.Z M 56 0,04 0,15F 49 −0,01 0,16All 105 0,04 0,12
L5ML.Z M 56 0,06 0,15F 49 0,04 0,14All 105 −0,03 0,11
PCA-AP M 56 −0,03 0,14F 49 0,03 0,15All 105 −0,01 0,12
PCA-ML M 56 0,00 0,14F 49 −0,00 0,14All 105 0,00 0,12
Table 8.6: Descriptive statistics of transformed femoral (FXL and FXH) and neural canal measures. Femoral andvertebra-specific NC variables are converted to z scores; PCA-AP and PCA-ML are converted to standardizedlinear regression scores. Standard errors of the mean (SEM) are generated by the pooling procedure instead ofstandard deviations. Note that all NC statistics are the pooled estimates from five imputed datasets. Full detailsof NC multiple imputations are presented in Appendix Table C.2. Results of normality tests are presented inTables 8.5 and 8.10.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 135
Case FrequencyOutcome Control Case
StatusAlternativeReference OR (95% CI) adjOR (95%
CI)sig
OA Age Male Female 1.07 (0.52–2.20) 1.16 (0.52–2.58) nsYoung(YA)
Affected 15 12
Unaffected 14 15Mature(MA–EA)
Affected 34 30
Unaffected 11 5
OP Age Male Female 0.80 (0.34–1.89) 0.86 (0.33–2.22) nsYoung(YA)
Affected 18 16
Unaffected 11 11Mature(MA–EA)
Affected 43 33
Unaffected 2 2
EB Age Male Female 0.38 (0.12–1.27) 0.39 (0.12–1.32) nsYoung(YA)
Affected 2 1
Unaffected 27 27Mature(MA–EA)
Affected 9 3
Unaffected 35 32
PIT Age Male Female 0.53 (0.23–1.17) 0.53 (0.22—1.31) nsYoung(YA)
Affected 21 12
Unaffected 8 15Mature(MA–EA)
Affected 39 31
Unaffected 6 4
SNB Age Male Female 0.74 (0.37–1.48) 0.74 (0.37–1.48) nsYoung(YA)
Affected 13 9
Unaffected 16 18Mature(MA–EA)
Affected 34 26
Unaffected 11 9
OA Sex YA MA–EA 0.23 (0.11–0.50) 0.23 (0.11–0.52) p<0.05Male Affected 15 34
Unaffected 14 11Female Affected 12 30
Unaffected 15 5N=136
Table 8.7: Osteoarthritis (OA) and joint modification case frequencies, odds ratios (OR), and two-tailed signifi-cance from Fisher’s exact tests.Notes: Odds ratios refer to the odds of the outcome occurring in the alternative stratum versus the referencestratum. Unadjusted and Mantel-Haenszel adjusted odds ratios are presented. Sample size includes all individualswith an identified sex.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 136
Upper LimbAge Phase Case
StatusMale Female OR (95%
CI)Total sig
Young (YA) Unaffected 17 16 33 1.07 (0.60–1.90) nsAffected 12 10 22Total 29 26 55
Mature(MA–EA)
Unaffected 15 9 24 0.81 (0.45–1.46) ns
Affected 29 25 54Total 44 34 78
TOTAL Unaffected 32 25 57 0.95 (0.65–1.39) nsAffected 41 35 76Total 73 60 133
Lower LimbAge Phase Case
StatusMale Female OR (95%
CI)Total sig
Young (YA) Unaffected 19 22 41 1.61 (0.75–3.48) nsAffected 10 5 15Total 29 27 56
Mature(MA–EA)
Unaffected 16 12 28 0.97 (0.57–1.64) ns
Affected 29 23 52Total 45 35 80
TOTAL Unaffected 35 34 69 1.18 (0.81–1.71) nsAffected 39 28 67Total 74 62 136
Table 8.8: Demographic distribution of case frequencies for upper versus lower limb osteoarthritis (OA). Oddsratios (OR) represent unadjusted odds of OA in Females vs Males.
Age Phase Severity Male Female Total sig
Young (YA) 1 (Unaffected) 16 18 34 ns2 (Mild-Moderate)
8 8 16
3 (Severe) 5 1 6Total 29 27 56
Mature(MA–EA)
1 (Unaffected) 10 5 15 ns
2 (Mild-Moderate)
14 14 28
3 (Severe) 21 16 37Total 45 35 80
TOTAL 1 (Unaffected) 26 23 49 ns2 (Mild-Moderate)
22 22 44
3 (Severe) 26 17 43Total 74 62 136
Table 8.9: Demographic distribution of severity (ordinal factor) for body-wide OA. Note: Severity levels areas follows: 1= Unaffected, 2=Moderate (OA.Sev score below median), 3=Severe (OA.Sev score above median).Demographic distribution of severity by modification form is detailed in Appendix Table C.4.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 137
Imput.No.
N Mean StDev KMO Bartlettχ2
Avg.Comm
Eigen % Var W W sig
PCA-AP
1 105 0.00 1.00 0.69 100.04 0.56 2.38 59.50 0.98 ns
2 105 0.00 1.00 0.71 69.02 0.52 2.23 55.64 0.97 *3 105 0.00 1.00 0.71 71.95 0.52 2.09 52.21 0.98 ns4 105 0.00 1.00 0.67 73.94 0.51 2.09 52.25 0.97 *5 105 0.00 1.00 0.70 76.39 0.53 2.05 51.35 0.97 *0 30 0.00 1.00 0.74 29.62 0.60 2.11 52.85 0.95 ns
PCA-ML
1 105 0.00 1.00 0.77 139.99 0.64 2.77 69.28 0.98 ns
2 105 0.00 1.00 0.78 134.17 0.63 2.57 64.25 0.99 ns3 105 0.00 1.00 0.78 125.20 0.62 2.53 63.15 0.99 ns4 105 0.00 1.00 0.75 155.92 0.65 2.50 62.49 0.98 *5 105 0.00 1.00 0.78 148.68 0.66 2.61 65.16 0.99 ns0 30 0.00 1.00 0.79 47.42 0.69 2.64 65.96 0.98 ns
Table 8.10: Descriptive statistics of the first dimension of Principal Components Analyses (PCA) for the neuralcanal in each of five separate imputed datasets and the original dataset. PCA is conducted separately for APand ML dimensions in each imputed dataset. Components are extracted based on a correlation matrix with 25iterations allowable. The minimum acceptable eigenvalue is set at 1.0.The percentage of variance represented bythe first dimension in each dataset is reported as “% Var”. The KMO (Kaiser-Meyer-Olkin) statistic is a measureof sampling adequacy and is considered satisfactory above KMO=0.600 (Tabachnick and Fidell, 2007, p.666).Average communality is calculated from the squared multiple correlation value for each of the variables includedin the PCA model. Individual communalities are presented in Appendix Table C.1. Standardized factor scoresare generated for each accepted dimension (PCA-AP and PCA-ML). Normality statistics (W ) are presented.
Sex-related variation in the three-stage ordinal factor OA.Sev is tested with Fisher’s Exact
tests of independence. Standardized residuals were computed for each cell. No differences are
observed between males and females, with or without correction for age (χ2= 1.03, p=0.59).
This holds true when each form of modification is tested separately (Table 8.9).
Measurement variables are adjusted to remove sexual size dimorphism by calculating z scores
separately for males and females, which remove the sex-based size effect and allows males and
females to be pooled for these variables. Joint modification variables exhibit no evidence of
sexual dimorphsim in frequency or severity (Tables 8.7 and 8.9).
8.1.2 Other Demographic Confounders
Significant differences of means between the two major age groups are identified in FXH.Z,
T1ML.Z, L1ML.Z, and L5ML.Z; in each, the YA group has a smaller mean size than the MA-
EA group. These differences hold when t-tests are repeated using imputed datasets, and on
PCA-ML. No significant differences are detected in FXL.Z, T6.Z, or any AP measurement,
including PCA-AP. This relationship is explored further in 9.1
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 138
Inter-regional comparison of means by one-way ANOVA and Levene tests of variance show
that none of the osteometric variables differ in mean size or variance between West and South
Coast subsamples, indicating that ecogeographic region is not a significant confounder (Ap-
pendix Table C.3).
There is a pronounced age effect in the prevalence of OA, which is not altered by correcting
for sex (OR OA=0.23, 95% CI=0.11–0.52) (See Section 9.6.2).
8.1.3 Central Tendency and Distribution
In their raw form all ratio-scale variables are normally distributed when the sexes are tested
separately; however, several neural canal dimensions are significantly non-normally distributed
when the sexes are pooled. When re-evaluated after replacing missing values by multiple im-
putation (N=105; M=56, F=49), T6AP.Z in females and L5AP.Z in males continue to show
non-normal distribution. First-dimension PCA scores from the AP neural canal diameters
(PCA-AP) are also non-normally distributed, but those from the ML diameters do not violate
the assumption of normality both when pooled and when separated by sex (Table 8.5, Table
8.10, Appendix Table C.2).
8.1.4 Homogeneity of Variance
Homogeneity of variances amongst the temporal and demographic strata is tested as part of
means comparison in hypotheses I and III (See Sections 9.1 and 9.9). Non-normal distri-
butions and inequality of variance, when they are detected, are most often found to occur in
the anteroposterior dimensions of neural canals (Table 8.5). This tendency may be related to
measurement error, which is greater in the AP dimension in general, and in the L5 segment in
particular (see Section 8.0.4). After missing values are replaced by multiple imputation the dif-
ferences between the sexes and between ages are largely eliminated. Although the distribution
of variance among the major demographic categories in this dataset is generally homogeneous,
uneven variance ratios in some variables (Table 8.5) support the use of robust methods such
as Welch’s t-tests and logistic regression.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 139
8.1.5 Correlations and Collinearity
Quantitative relationships among ratio-scale variables were assessed with both conventional
Pearson correlations (Table 8.11) and Pearson partial correlation (Tables 8.12 and 8.13).
In general AP and ML diameters correlate moderately within each vertebra, but only weakly
among vertebrae. ML diameters tend to have a stronger correlation with one another among
vertebrae than they do with AP diameters and vice versa.
Average correlations between femoral metrics and NC diameters are negligible in the an-
teroposterior dimension and weak to moderate in the mediolateral (e.g. when FXH is the
independent variable, PCA-AP R=0.23, p>0.05; PCA-ML R=0.477, p<0.05; Table 8.11). A
closer correlation between ML diameter and body size may be related to the fact that the ML
dimension follows a growth schedule that is closer to that of the femur than that of the AP
dimension (Figure 3.1). Correlations between NC-ML and femoral measures are observed to
be somewhat stronger in males than in females (Appendix Tables C.7 and C.8).
Partial correlations were used to compare the relative strength of correlations of NC.Z
variables on each of the two femoral dimensions (FXH.Z as independent variable, with FXL.Z
controlled; FXL.Z as independent with FXH.Z controlled). The results showed that, in general,
controlling FXH eliminates detectable variation in NC size that can be related to body size.
This pattern is affirmed when imputed PCA scores are tested against FXH.Z and FXL.Z.
Correlations among NC variables are moderate to strong (R>0.400, p<0.05) even when FXH.Z
is controlled (Table 8.12). Tabulated results are from pooled imputed datasets. Results from
original datasets are reported in Appendix Tables C.6, C.5, C.7, and C.8. Coefficients
generated from the original and imputed datasets are quite similar. The results suggest that
body size is a potential confounder that warrants examination in hypothesis-testing.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 140
Poo
ledIm
putedDatasets:
FullSa
mple
FXL.Z
FXH.Z
T1A
P.Z
T1M
L.Z
T6A
P.Z
T6M
L.Z
L1A
P.Z
L1M
L.Z
L5A
P.Z
L5M
L.Z
PCA.A
PPCA.M
L
FXH.Z
R0.67**
N92
T1A
P.Z
R0.04
0.29*
N55
56T1M
L.Z
R0.42**
0.25
.52**
N56
5576
T6A
P.Z
R0.14
0.19
0.65**
0.42*
N33
3437
37T6M
L.Z
R0.36*
0.60**
0.35*
0.69**
.53**
N34
3337
3746
L1A
P.Z
R0.30*
0.14
0.44**
0.44**
0.41**
0.60**
N48
5056
5641
41L1M
L.Z
R0.57**
0.48**
0.45**
0.64**
0.50**
0.63**
0.46**
N50
4856
5641
4174
L5A
P.Z
R0.26
0.28
0.40**
0.30*
0.42**
0.45**
0.27*
0.22
N49
4951
5138
3857
57L5M
L.Z
R0.39**
0.25
0.38**
0.50**
0.33*
0.49**
0.24
0.62**
0.38**
N49
4951
5138
3857
5773
PCA.A
PR
0.41
0.35
0.81**
0.58**
0.85**
0.64**
0.72**
0.60**
0.691**
0.476**
N23
2330
3030
3030
3030
PCA.M
LR
0.62**
0.42*
0.47**
0.83**
0.53**
0.86**
0.70**
0.86**
0.44*
0.78**
0.69**
N23
2330
3030
3030
3030
30
Table8.11:Pe
arsoncorrelationcoeffi
cients,p
ooledfrom
Pearsoncorrelationtestsin
thefiv
eim
putedda
tasets.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 141
Poo
ledIm
putedDatasets:
FullSa
mple
FXL.Z
T1A
P.Z
T1M
L.Z
T6A
P.Z
T6M
L.Z
L1A
P.Z
L1M
L.Z
L5A
P.Z
L5M
L.Z
PCA.A
PPCA.M
L
FXH.Z
R1.0
0.270
0.280
0.140
0.050
-0.120
0.290
0.080
0.250
0.140
0.280
T1A
P.Z
R-0.18
0.558
0.559
0.247
0.438
0.448
0.263
0.372
0.823
0.529
T1M
L.Z
R0.03
0.500
0.363
0.522
0.374
0.541
0.301
0.506
0.562
0.837
T6A
P.Z
R-0.03
0.546
0.330
0.273
0.381
0.361
0.247
0.206
0.788
0.390
T6M
L.Z
R0.24
0.184
0.512
0.247
0.334
0.375
0.259
0.350
0.378
0.710
L1A
P.Z
R0.23
0.416
0.412
0.378
0.382
0.344
0.212
0.166
0.704
0.393
L1M
L.Z
R0.06
0.381
0.499
0.335
0.376
0.401
0.140
0.523
0.463
0.793
L5A
P.Z
R0.08
0.237
0.290
0.233
0.264
0.230
0.128
0.333
0.540
0.330
L5M
L.Z
R-0.03
0.309
0.473
0.169
0.329
0.190
0.483
0.314
0.370
0.767
PCA.A
PR
0.02
0.807
0.541
0.782
0.365
0.708
0.446
0.537
0.337
0.574
PCA.M
LR
0.09
0.453
0.821
0.355
0.721
0.454
0.773
0.324
0.746
0.554
N65
105
105
105
105
105
105
105
105
105
105
Table8.12:Pa
rtialc
orrelatio
nsof
neural
cana
ldiameterswith
FXL.Zan
dFX
H.Z.P
artia
lcorrelatio
ncoeffi
cients
with
FXH.Z
controlle
darepresentedbe
low
the
diagon
al;c
oefficients
with
FXL.Zcontrolledarepresentedab
ovethediagon
al.Coefficients
arepo
oled
estim
ates
from
correlations
in5im
putedda
tasets.Pa
rtial
correlations
formales
andfemales
(impu
tedda
tasets)arepresentedin
Table
8.13.Con
ventiona
lPearson
coeffi
cients
andpa
rtialc
orrelatio
ncoeffi
cients
forthe
original
datasetarepresentedin
App
endixTa
bles
C.6
andC.5
.
Chapter 8. Results: Descriptive Statistics and Diagnostic Analyses 142
Poo
ledIm
putedDatasets:
Males
FXL.Z
T1A
P.Z
T1M
L.Z
T6A
P.Z
T6M
L.Z
L1A
P.Z
L1M
L.Z
L5A
P.Z
L5M
L.Z
PCA.A
PPCA.M
L
FXH.Z
R0.397
0.356
0.253
0.188
-0.011
0.462
0.001
0.446
0.243
0.475
T1A
P.Z
R-0.19
0.468
0.603
0.282
0.474
0.607
0.243
0.362
0.823
0.560
T1M
L.Z
R0.02
0.370
0.351
0.595
0.357
0.553
0.346
0.495
0.512
0.866
T6A
P.Z
R-0.07
0.571
0.286
0.313
0.369
0.419
0.284
0.159
0.806
0.404
T6M
L.Z
R0.18
0.187
0.563
0.260
0.283
0.297
0.385
0.309
0.414
0.704
L1A
P.Z
R0.32
0.423
0.367
0.339
0.329
0.344
0.374
0.180
0.731
0.380
L1M
L.Z
R0.04
0.505
0.469
0.349
0.243
0.383
0.206
0.475
0.552
0.760
L5A
P.Z
R0.10
0.238
0.370
0.284
0.402
0.388
0.233
0.402
0.574
0.432
L5M
L.Z
R-0.20
0.254
0.391
0.067
0.211
0.129
0.325
0.418
0.357
0.747
PCA.A
PR
0.04
0.793
0.468
0.788
0.386
0.729
0.512
0.593
0.271
0.598
PCA.M
LR
0.01
0.449
0.846
0.332
0.700
0.418
0.692
0.490
0.663
0.564
N65
105
105
105
105
105
105
105
105
105
105
Poo
ledIm
putedDatasets:
Females
FXL.Z
T1A
P.Z
T1M
L.Z
T6A
P.Z
T6M
L.Z
L1A
P.Z
L1M
L.Z
L5A
P.Z
L5M
L.Z
PCA.A
PPCA.M
L
FXH.Z
R0.258
0.274
-0.031
-0.105
-0.273
0.126
0.241
-0.045
0.096
0.095
T1A
P.Z
R-0.30
0.491
0.589
0.333
0.398
0.333
0.414
0.387
0.849
0.486
T1M
L.Z
R-0.020.437
0.246
0.495
0.396
0.545
0.133
0.513
0.442
0.813
T6A
P.Z
R0.01
0.579
0.262
0.330
0.423
0.305
0.307
0.346
0.807
0.382
T6M
L.Z
R0.33
0.226
0.5040.301
0.494
0.514
0.086
0.448
0.420
0.751
L1A
P.Z
R0.05
0.464
0.509
0.432
0.481
0.425
0.181
0.368
0.666
0.529
L1M
L.Z
R0.05
0.290
0.537
0.315
0.525
0.490
0.087
0.631
0.394
0.847
L5A
P.Z
R0.03
0.341
0.068
0.315
0.123
0.269
0.074
0.181
0.613
0.151
L5M
L.Z
R0.23
0.316
0.527
0.341
0.483
0.369
0.640
0.204
0.438
0.800
PCA.A
PR
-0.11
0.844
0.435
0.804
0.367
0.715
0.388
0.599
0.409
0.531
PCA.M
LR
0.17
0.396
0.806
0.380
0.771
0.585
0.842
0.143
0.817
0.500
N65
105
105
105
105
105
105
105
105
105
105
Table8.13:Pa
rtialc
orrelatio
nsof
neural
cana
ldiameterswith
FXL.Zan
dFX
H.Z
controlle
d.Pa
rtialc
orrelatio
ncoeffi
cients
with
FXH.Z
controlle
darepresented
below
thediagon
al;c
oefficients
with
FXL.Zcontrolle
darepresentedab
ovethediagon
al.Coefficients
arepo
oled
estim
ates
from
correlations
in5im
putedda
tasets.
Con
ventiona
lPearson
coeffi
cients
andpa
rtialc
orrelatio
ncoeffi
cients
fortheoriginal
datasetarepresentedin
App
endixTa
bles
C.6,C
.5,C
.7,a
ndC.8
.
Chapter 9
Results: Hypothesis Testing
The parameters of the dataset and its demographic substrata are described in Chapter 8.
Results of formal hypothesis tests are detailed here. Note that while non-imputed results are
discussed in the text, all tabulated results for neural-canal-focussed tests are from analyses of
the imputed datasets unless otherwise specified. The parameters of imputed models parallel
those of non-imputed models, but their diagnostic criteria are generally better, which indicates
that the imputed datasets yield more reliable test results.
9.1 Hypothesis I: Skeletal growth outcome relative to age at
death
9.2 Means comparison
The distribution of variance among the major demographic categories in this dataset is gen-
erally homogeneous; however, high variance ratios in some variables support the use of robust
comparative statistics 9.1. Welch’s t-tests, which assume no homogeneity of variance, are
therefore used to compare skeletal growth outcomes between the binary age groups.
T tests indicate no difference in mean AP neural canal size, but do indicate differences
between age phases in FXH.Z, T1ML.Z, L1ML.Z, and L5ML.Z. The difference in means reaches
p<0.05 in T1, L1, and L5, but not FXH.Z. In all cases, the mean of the YA phase is smaller
than that of the MA/EA phase (Table 9.1, Figure 9.1). Age differences are negligible in all
143
Chapter 9. Results: Hypothesis Testing 144
NC-AP measurements. When males and females are tested separately the effect is found to
have a significant sex bias: in females, the mean size is smaller in the YA phase in all femoral
and NC-ML measurements, although statistical significance criteria are not met in the original
dataset. Males shows no difference in either femoral measure, nor in NC-AP; male NC-ML
means are smaller in YA than in the MA/EA phase, but the difference is not strong enough to
yield p values below 0.05 (Table 9.1, Figure 9.2).
Supplementary replication using the imputed datasets reinforces the major patterns ob-
served in the original dataset. The YA and MA groups have significantly different means at
T1ML.Z and L1ML.Z and in the summary variable, PCA-ML, which is derived from analysis of
the four ML variables. There is no indication of an age difference in the anteroposterior dimen-
sion, including PCA-AP. Among females, age differences reach p<0.05 in T1ML.Z, L1ML.Z,
and in the summary variable PCA-ML. Age differences among males are found to be much less
pronounced with p>0.05 in all cases (Table 9.1).
9.3 Logistic regression
Generalized linear models (logistic regression) are computed in R 3.1.1 GUI 1.65 for Mac Snow
Leopard.Binomial probability distributions are assumed and logit link functions selected. Model
coefficients and confidence intervals are exponentiated to yield the outcome odds ratios and
their confidence intervals. Likelihood ratio tests (LRT) with p<0.05 are the criterion for model
selection. Significant models are further tested for sex and body-size effects by forward stepwise
selection based on LRT statistics.
Conventional diagnostic procedures are applied. Influential cases are addressed by inspect-
ing summary Cook’s D values and by Bonferroni’s outlier tests for extreme residual values.
Problematic cases, defined as those with D values approaching or larger than 1, or with sta-
tistically extreme residuals, are experimentally excluded. Model accuracy is assessed by the
area under the ROC curve (C index) and Somers’ Dxy rank correlation of predicted proba-
bilities against observed outcomes (computed as Dxy= 2(C − 0.5)). Values of C greater than
0.5 indicate a predictive value greater than random chance, although a C value of 0.80 is the
conventional minimum threshold for model accuracy (Harrell, 2001). Dxy values range from 0
Chapter 9. Results: Hypothesis Testing 145
Full Sample Males FemalesN Mean SEM sig N Mean SEM sig N Mean SEM sig
FXL.Z YA 39 -0.13 0.15 ns 22 0.04 0.20 ns 15 -0.37 0.21 nsMA-EA 53 0.04 0.14 33 -0.03 0.18 21 0.14 0.24
FXH.Z YA 39 -0.18 0.13 <0.10 23 -0.11 0.19 ns 16 -0.28 0.17 p<0.10MA-EA 53 0.16 0.15 33 0.08 0.19 21 0.27 0.24
T1AP.Z YA 45 -0.14 0.16 ns 22 -0.17 0.25 ns 23 -0.10 0.29 nsMA-EA 60 0.00 0.13 34 -0.06 0.18 26 0.09 0.22
T1ML.Z YA 45 -0.34 0.15 <0.05 22 -0.36 0.25 ns 23 -0.32 0.19 <0.05MA-EA 60 0.19 0.12 34 0.14 0.17 26 0.27 0.21
T6AP.Z YA 45 0.03 0.22 ns 22 0.04 0.25 ns 23 0.02 0.35 nsMA-EA 60 -0.05 0.13 34 -0.12 0.20 26 0.03 0.17
T6ML.Z YA 45 -0.08 0.19 ns 22 0.00 0.32 ns 23 -0.16 0.23 nsMA-EA 60 0.13 0.18 34 0.08 0.19 26 0.20 0.26
L1AP.Z YA 45 0.00 0.17 ns 22 0.14 0.24 ns 23 -0.13 0.23 nsMA-EA 60 -0.02 0.14 34 -0.11 0.20 26 0.11 0.17
L1ML.Z YA 45 -0.27 0.14 <0.05 22 -0.18 0.23 ns 23 -0.36 0.20 <0.05MA-EA 60 0.20 0.14 34 0.12 0.19 26 0.29 0.19
L5AP.Z YA 45 0.03 0.17 ns 22 0.06 0.26 ns 23 0.00 0.22 nsMA-EA 60 0.01 0.14 34 0.03 0.18 26 -0.01 0.23
L5ML.Z YA 45 -0.14 0.17 ns 22 -0.15 0.24 ns 23 -0.14 0.21 nsMA-EA 60 0.20 0.13 34 0.20 0.19 26 0.19 0.19
PCA.AP YA 45 -0.01 0.17 ns 22 0.04 0.27 ns 23 -0.05 0.27 nsMA-EA 60 0.00 0.12 34 -0.07 0.16 26 0.10 0.20
PCA.ML YA 45 -0.30 0.16 <0.05 22 -0.25 0.27 ns 23 -0.34 0.20 <0.05MA-EA 60 0.22 0.12 34 0.16 0.16 26 0.30 0.19
Table 9.1: Results of robust t tests, which do not assume equality of variance, comparing means between YoungAdults (<35 years) versus Mature-Elderly Adults (35+ years). Tests are conducted on the full sample, and onmales and females separately. Note that all NC tests are the pooled results of tests conducted on 5 imputeddatasets.
Chapter 9. Results: Hypothesis Testing 146
Figure 9.1: Comparison of mean size in FXL.Z, FXH.Z, and PCA summaries of anteroposterior and mediolateralneural canal diameter when both sexes are pooled. Error bars represent the 95% confidence interval for themean.
Chapter 9. Results: Hypothesis Testing 147
Figure 9.2: Comparison of mean size in FXL.Z, FXH.Z, and PCA summaries of anteroposterior and mediolateralneural canal diameter (PCA-AP and PCA-ML) when the sexes are separated. Error bars represent the 95%confidence interval for the mean.
Chapter 9. Results: Hypothesis Testing 148
to 1 and can be interpreted analogously to Spearman’s ρ. Full logistic regression results from
the imputed datasets are presented in Table 9.2.
9.3.1 Binary Age Group as an outcome of Body Size
FXH.Z exhibits a positive, although not significant, association with Age (OR=1.43, 95%
CI=0.93 – 2.22). Cook’s D values are very low (Dmax=0.05), indicating that case influence
is quite uniform. Predictive accuracy, however, is low (C=0.61; Dxy=0.22). When separated
by sex, the effect is detectable in females (OR=1.95, 95% CI=0.90–4.22; p<0.10; C=0.71;
Dxy=0.42) and not in males (OR= 1.36 [0.78–2.37]; p=0.27; C=0.55; Dxy=0.10). Maximum
Cook’s D values are quite low (Female Dmax=0.19; Male Dmax=0.07), indicating that the
model is reliable. FXL.Z models do not meet the criteria for significance in either sex, al-
though in females the model parameters are relatively close to those of FXH.Z (OR=1.74, 95%
CI=0.82–4.45, p>0.10;C=0.64, Dxy=0.29; Dmax=0.23). In males there is a null association.
Note that results from original and imputed datasets are discussed separately, but tabulated
results are from imputed datasets (Table 9.2).
9.3.2 Binary Age Group as an outcome of Neural Canal Size
No AP neural canal models fulfill the criteria for significance, but significant associations are
detected in NC-ML models representing both the upper thoracic and the upper lumbar. T6,
which represents the mid-thoracic and is under-represented because of preservation issues, and
L5, which has much greater variance than the other segments, both fail to exhibit consistent
directional age differences.
T1
T1ML.Z is associated with a significant increase in the probability of falling into the higher
age bracket (OR=1.98, 95% CI=1.15–3.42;p=0.01). Diagnostics indicate satisfactory fit, but
a relatively weak predictive performance (Dmax=0.23; C=0.67; Dxy=0.34). The maximum
Cook’s D value is low (Dmax=0.23); however, one case (SAM-AP6020) is identified as having a
significantly high residual value. After removing this case, the odds ratio increases to 2.87 (95%
CI=1.47–5.60) and C to 0.71 (Dxy=0.41), while no other significant residuals are identified.
Chapter 9. Results: Hypothesis Testing 149
Full Sample
Predictor n LRT p Coef SEE OR 95% CI C Dxy
FXL.Z 92 ns 0.16 0.22 1.18 0.76—1.82 0.53 0.06FXH.Z 94 ns 0.36 0.23 1.43 0.93 – 2.22 0.61 0.22T1AP.Z 105 ns 0.12 0.22 1.13 0.74 – 1.73 0.53 0.06T1ML.Z 105 p<0.05 0.63 0.25 1.87 1.14 – 3.06 0.66 0.31T6AP.Z 105 ns -0.08 0.25 0.92 0.55 – 1.54 0.54 0.08T6ML.Z 105 ns 0.26 0.30 1.29 0.68 – 2.47 0.58 0.16L1AP.Z 105 ns -0.03 0.24 0.97 0.60 – 1.56 0.51 0.02L1ML.Z 105 p<0.05 0.54 0.26 1.71 1.03 – 2.84 0.65 0.29L5AP.Z 105 ns -0.02 0.21 0.99 0.65 – 1.50 0.50 0.00L5ML.Z 105 p<0.10 0.40 0.24 1.50 0.93 – 2.41 0.62 0.23PCA.AP 105 ns 0.01 0.21 1.01 0.67 – 1.53 0.50 0.00PCA.ML 105 p<0.05 0.56 0.24 1.74 1.08 – 2.82 0.64 0.28FXH.Z+ PCA-ML 70 p<0.05
PCA.ML ns 0.54 0.33 1.72 0.90 – 3.30 0.64 0.28FXH.Z ns 0.24 0.3 1.27 0.71 – 2.26 0.61 0.22
Males
FXL.Z 55 ns -0.07 0.27 0.93 0.55 – 1.60 0.53 0.07FXH.Z 56 ns 0.19 0.27 1.21 0.71 – 2.07 0.55 0.01T1AP.Z 56 ns 0.07 0.31 1.08 0.58 – 1.99 0.53 0.06T1ML.Z 56 ns 0.56 0.39 1.75 0.79 – 3.86 0.63 0.26T6AP.Z 56 ns -0.16 0.29 0.85 0.48 – 1.52 0.54 0.08T6ML.Z 56 ns 0.11 0.37 1.12 0.52 – 2.38 0.55 0.11L1AP.Z 56 ns -0.27 0.32 0.76 0.40 – 1.46 0.51 0.02L1ML.Z 56 ns 0.31 0.32 1.36 0.72 – 2.60 0.58 0.16L5AP.Z 56 ns -0.02 0.34 0.99 0.50–1.93 0.50 0.00L5ML.Z 56 ns 0.38 0.30 1.46 0.81 – 2.66 0.61 0.23PCA.AP 56 ns -0.12 0.30 0.88 0.49 – 1.59 0.52 0.04PCA.ML 56 ns 0.40 0.31 1.50 0.82 – 2.73 0.59 0.19
Females
FXL.Z 36 ns 0.55 0.38 1.74 0.82 – 4.45 0.64 0.29FXH.Z 37 p<0.10 0.67 0.39 1.95 0.90 – 4.22 0.71 0.42T1AP.Z 49 ns 0.18 0.36 1.20 0.58 – 2.50 0.53 0.06T1ML.Z 49 p<0.10 0.71 0.37 2.04 0.98 – 4.25 0.68 0.36T6AP.Z 49 ns 0.04 0.50 1.04 0.36 – 3.04 0.58 0.16T6ML.Z 49 ns 0.45 0.40 1.57 0.71 – 3.49 0.62 0.23L1AP.Z 49 ns 0.29 0.37 1.34 0.65 – 2.77 0.55 0.10L1ML.Z 49 p<0.10 0.89 0.45 2.44 0.99 – 6.03 0.73 0.46L5AP.Z 49 ns -0.02 0.29 0.98 0.55 – 1.73 0.44 -0.12L5ML.Z 49 ns 0.45 0.40 1.56 0.70 – 3.51 0.62 0.25PCA.AP 49 ns 0.16 0.38 1.18 0.54 – 2.58 0.54 0.08PCA.ML 49 p<0.05 0.76 0.38 2.14 1.02 – 4.50 0.69 0.39
Table 9.2: Results of binary logistic regressions (BLR) that model the likelihood of age-at-death in the MatureAdult phase based on skeletal growth outcomes. Model significance is assessed with the likelihood ratio test(LRT) and the odds ratio (OR). Note that all NC results are the pooled results of separate analyses with the 5imputed datasets. FXL and FXH were not imputed. C and Dxy are calculated only for ML measurements inmales and females.
Chapter 9. Results: Hypothesis Testing 150
Including Sex and FXH.Z in the model does not add to its strength or fit, but does not detract
from the T1ML.Z effect (Table 9.2).
In females, the T1ML.Z model does not meet the criterion for significance, but exhibits
a positive association with AgeBinary (OR=1.84, 95% CI=0.81–4.18, p=0.13) with predictive
value comparable to the full T1ML.Z model (C=0.66; Dxy=0.31). Dmax is 0.33, suggesting
that influence is relatively high but does not meet the threshold to be considered problematic.
One case (UCT317) is identified as having a relatively high Studentized residual (Bonferroni test
p<0.05) and a very low T1ML.Z value of -2.09. After removal, the model likelihood ratio p=0.04,
and both fit and accuracy are slightly improved (C=0.70, Dxy=0.39). The highest Cook’s D at
this point is Dmax=0.23 and Bonferroni p>0.05, indicating that no cases are of concern. When
FXH.Z is included in the model, the T1ML.Z confidence interval increases in breadth (OR=3.86,
95% CI=0.77-19.12, p=0.02), while FXH.Z does not attain p<0.05 (OR=2.10, 95% CI=0.75–
5.88), p=0.13). Area under the ROC curve also increases (C=0.82, Dxy=0.64), suggesting
that FXH.Z and T1ML.Z have an additive effect. Cook’s D is relatively high (Dmax=0.44)
and SAM-AP1247a, the most influential case, also has a high Studentized residual (Res=2.34,
p=0.02). Including an interaction term (T1ML.Z*FXH.Z) does not alter or improve the model,
although SAM-AP1247a’s influence does increase (D=1.02). Given that the recommended
minimum n for logistic regression is at least 10 to 20 cases per predictor and female N=24 in
this case, it is not considered appropriate to further modify this model.
Among males, the association between T1ML.Z and Age mirrors that among females. The
model parameters are all similar to those in the female-only and full-sample models (OR=2.09,
95% CI=1.00–4.37, p=0.03; C=0.67; Dxy=0.33). Dmax is once again relatively high but still
below the threshold for removal (D=0.46). SAM-AP6020 is again identified as having a signifi-
cantly high Studentized residual (Res=-2.23, p=0.03). When SAM-AP6020 is excluded, model
parameters indicate a slight increase in strength and fit (C=0.70, Dxy=0.41), and a lower max-
imum Cook’s D value (Dmax=0.18). Including FXH.Z in the model attenuates the effect of
T1ML.Z (T1.ML.Z OR=1.8, 95% CI=0.74–4.47, p=0.11) and reduces the C index and Dxy
value (C=0.64; Dxy=0.29). The most influential case (SAM-AP6020) is once again identified
by a relatively high Cook’s D value (D=0.41). Repeating the T1ML.Z+FXH.Z model after
excluding this case results in a lower p value for T1ML.Z (p=0.02), but an inflated odds ratio
Chapter 9. Results: Hypothesis Testing 151
and confidence interval (OR=3.19, 95% CI=0.99–10.25) and a persistently high maximum D
(Dmax=0.47). As in the female-only model, sample size is affected by listwise deletion of cases
missing FXH values (N=31), meaning that model reliability suffers in the reduced and adjusted
model.
T6
T6ML.Z has no association with Age (OR=0.85, 95% CI=0.46–1.57, p=0.60; C=0.50; Dxy=0.00)
and no evidence of influential or outlying cases. It is not tested for sex-specific differences.
L1
L1ML.Z in the full sample exhibits a very similar relationship to age as in T1ML.Z (OR=2.2,
95% CI=1.25–3.90, p<0.01; C=0.70, Dxy=0.40). Although the maximum Cook’s D signals
no excessively influential cases (Dmax=0.23), UCT230 is found to have a significant residual
value of Res=-2.19 (p=0.03). After removing this case, odds ratio and predictive accuracy
increases modestly (OR=2.7, 95% CI=1.43–5.10; C=0.73, Dxy=0.45), and maximum D is
reduced (Dmax=0.16). SAM-AP6372a has a significantly high Studentized residual (Res=-
2.14), however, indicating that variance around the regression line is still considerable. Including
FXH.Z in the model does not alter the result.
After separation by sex, the relationship between L1ML.Z and Age appears inflated in
females, with very wide confidence intervals that warrant caution in interpreting the model
(OR=11.5, 95% CI=1.90–71.29; C==0.90; Dxy=0.80). One case (SAM-AP6372a) is identified
with a Cook’s D value of 1.31, indicating significant influence on the model. After removing
this case, however, the model parameters became extreme (OR=551.04, 95% CI=2.96–10 000
002) indicating that this model is unreliable. Adding FXH.Z to the model further exacerbated
this problem. Neither of the high-influence cases in these models has an extreme L1ML.Z value
(SAM-AP6372a=1.68; SAM-AP1131=-0.43).
In contrast, males exhibit a positive but weak predictive association between L1ML.Z and
Age (OR=1.37, 95% CI=0.73–2.56, p=0.32; C=0.57; Dxy=0.14). No evidence of excessive in-
fluence is identified, and the model does not meet the criteria for significance. Adding FXH.Z
to the model does not alter its predictive capacity, but does eliminate the positive slope be-
Chapter 9. Results: Hypothesis Testing 152
tween L1ML.Z and Age (OR=0.82, 95% CI=0.30–2.28), indicating that overall body size is a
confounder. The association between FXH.Z and Age is similar in this model to that in the
Age FXH.Z model described above (OR=1.40, 95% CI=0.53–.66).
L5
The positive association between ML neural canal diameter and Age is once again represented in
L5ML.Z (OR=1.70, 95% CI=1.0–2.88; p=0.04); predictive power is low, but comparable to that
of the T1ML.Z model (C=0.65; Dxy=0.30). Cook’s D values are low, indicating that influence is
homogeneous (maximum D=0.10). Including FXH.Z in the model does not significantly alter
the relationship between L5ML.Z and Age (OR=1.90, 95% CI=0.94–3.84, p=0.04) or affect
model fit (maximum D=0.15).
When females are tested alone, the p criterion is not met, but model parameters remain
relatively unchanged (OR=1.87, 95% CI=0.79–4.39; p=0.13; C=0.66; Dxy=0.33). No Cook’s
D values approach 1 (maximum D=0.29). Including FXH.Z in the model results in inflated
confidence intervals and C values for L5ML.Z (OR=5.63, 95% CI=1.01–31.41; p=0.01; C=0.82;
Dxy=0.64), indicating that overfitting is a possibility; however, the FXH.Z coefficient is non-
significant, suggesting that body size is, in this case, an underlying confounding factor in
the Age-L5ML.Z association. Cook’s maximum D=0.37 (SAM-AP6348b) and residual plots
indicate that the sparse data scatter (N=18 for this model) is a likely complication for model
reliability.
In males, the association between L5ML.Z and Age is positive but non-significant (OR=1.57,
95% CI=0.80–3.07; p=0.17; C=0.63; Dxy=0.26). No evidence of problematic influence is de-
tected (maximum D=0.17). Although FXH.Z does not have an independent association with
Age (p=0.27), adding it to the L5ML.Z model does depress the association between L5ML.Z
and Age (p=0.36); Cook’s D values are low (Dmax=0.18).
Replication with imputed datasets
Supplementary testing of the neural canal data after multiple imputation supports the patterns
indicated by the original dataset. The odds of falling into either age group are not related to AP
diameter in any one segment, nor in the summary variable PCA-AP. In contrast, an increase
Chapter 9. Results: Hypothesis Testing 153
in ML diameter equivalent to 1 standard deviation increases the odds of an individual being in
the older age group by an average of 1.6 in all segments, although T6 and L5 do not meet the
p value for model significance (T6 LRT p=0.32; L5 LRT p=0.10). Predictive performance is
weakest in T6 (C=0.580) and strongest in T1 (C=0.66). PCA-ML also exhibits a significant
positive association with age: the odds of membership in the older age group increase by 74%
for each standard deviation increase in size (Pooled OR=1.74, 95% CI=1.080–2.82; p=0.02;
C=0.64, Dxy=0.28) (Table 9.2). Including FXH.Z as a covariate with PCA-ML yields a model
with a significant likelihood ratio test (p<0.05), and does not change the β value associated
with PCA-ML, but does slightly weaken the predictive relationship between Age and PCA-ML
to the extent that the 95% confidence interval for the odds ratio no longer excludes 1.0 for that
predictor.
Diagnostic statistics support the imputed models. Cook’s D values and residuals are sub-
stantially smaller in imputed versus original datasets, indicating that model validity is improved
as a function of increases sample size.
Separating the imputed datasets by sex and reiterating the analyses affirms that the effect
is present in both sexes but is stronger in females than in males. In males, model parameters
remain relatively consistent, but p values exclude statistical significance for all pooled imputed
models and C index values range from 0.51 to 0.67 with an average of 0.59. The PCA-ML
model parameters are congruent with individual segments (p=0.19; OR=1.5, 95% CI=0.89–
2.73; pooled C=0.59). In females, T6ML and L5ML do not reach p=0.05, but T1ML, L1ML,
and PCA-ML all satisfied the criteria for statistical significance. The minimum pooled imputed
C index for females is 0.62 (T6.ML); the maximum value is 0.73 (L1ML); the mean of all
pooled models is 0.67. The PCA-ML model is statistically significant at p=0.04 (OR=2.1,
95% CI=1.0–4.5), and its pooled C=0.69. UCT 317 and SAM-AP6372a have the highest D
values for the various imputations with values ranging from 0.26 to 0.51. After removing these
cases, odds ratios increase, although area under the ROC curve does not increase markedly, and
pooled confidence intervals expand modestly, but model fit is still adequate (OR=3.22, 95%
CI=1.13–9.22; p=0.03; C=0.73; Dxy=0.47).
Chapter 9. Results: Hypothesis Testing 154
Full Sample Males FemalesComparators N Mean SEM sig N Mean SEM sig N Mean SEM sig
FXL.Z VYA 16 -0.16 0.25 ns 10 -0.09 0.40 ns 5 -0.31 0.10 nsYA 23 -0.04 0.17 13 0.10 0.20 10 -0.23 0.29
FXH.Z VYA 15 -0.16 0.25 ns 10 -0.24 0.34 ns 5 0.00 0.42 nsYA 25 -0.21 0.14 14 -0.06 0.22 11 -0.41 0.16
PCA-ML VYA 19 -0.04 0.25 ns 10 0.06 0.39 ns 9 -0.15 0.28 nsYA 27 -0.49 0.22 13 -0.52 0.32 14 -0.46 0.31
FXL.Z VYA 16 -0.16 0.25 ns 10 -0.09 0.40 ns 5 -0.31 0.10 nsMA 53 0.09 0.14 32 0.00 0.19 21 0.23 0.22
FXH.Z VYA 15 -0.16 0.25 ns 10 -0.24 0.34 ns 5 0.00 0.42 nsMA 52 0.17 0.15 32 0.10 0.19 21 0.27 0.24
PCA-ML VYA 19 -0.04 0.25 ns 10 0.06 0.39 ns 9 -0.15 0.28 nsMA 59 0.24 0.12 33 0.19 0.17 26 0.30 0.19
FXL.Z YA 23 -0.04 0.17 ns 13 0.10 0.20 ns 10 -0.23 0.29 nsMA 53 0.09 0.14 32 0.00 0.19 21 0.23 0.22
FXH.Z YA 25 -0.21 0.14 ns 14 -0.06 0.22 ns 11 -0.41 0.16 nsMA 52 0.17 0.15 32 0.10 0.19 21 0.27 0.24
PCA-ML YA 27 -0.49 0.22 <0.05 13 -0.52 0.32 ns 14 -0.46 0.31 nsMA 59 0.24 0.12 33 0.19 0.17 26 0.30 0.19
Table 9.3: Results of robust t tests, which do not assume equality of variance, comparing means between veryyoung (<25 years) and young adults (25–35 years) versus mature adults (35+ years). Tests are conducted on thefull sample and on males and females separately. Note that all NC tests are the pooled results of tests conductedon 5 imputed datasets, with adjusted confidence intervals.
9.3.3 Testing alternative age divisions: comparing Very Young Adults,
Young Adults, and Mature-Elderly Adults
Welch’s t tests are repeated on the imputed dataset after dividing the age distribution into
very young adults (VYA) under the age of 25 years, young adults (YA) between age 25 and 35
years, and mature adults (MA) over that age (NVYA=19, NYA=27, NMA/EA= 59). Logistic
regression is not repeated with these new divisions because its reliability would be affected by
the small subset sample N sizes. Tabulated results are presented for femoral measurements and
for PCA-ML (Table 9.3, Figure 9.3). VYA and YA cannot be distinguished on the basis of
ML canal size, but the YA group has a smaller mean than VYA in most dimensions. Similarly,
while both VYA and YA have consistently lower mean ML neural canal dimensions than the
MA group, VYA is not distinct from MA, while the YA group does tend to be smaller than
both VYA and MA. The same pattern is repeated in both sexes. Subset sample sizes are highly
reduced in these age-by-sex subsets, meaning that power is impaired relative to the full imputed
data set.
Chapter 9. Results: Hypothesis Testing 155
Figure 9.3: Comparison of mean size in FXL.Z, FXH.Z, and PCA-ML in between very young (<25 years) andyoung adults (25–35 years) versus mature adults (35+ years). Error bars represent the 95% confidence intervalfor the mean. Sex-specific values are not presented because the female VYA sample is very small (N)=5 ).
Chapter 9. Results: Hypothesis Testing 156
9.4 Effect size, power, and sensitivity
9.4.1 Power analysis of means contrasts
Observed effect sizes (d) from means contrasts in the un-imputed dataset are quite different
between femoral, NC-AP and NC-ML dimensions: effects are small (d≤ 0.2) in FXL.Z and all
NC-AP dimensions, and medium to large (d≥ 0.4) values in FXH.Z and all ML dimensions
(Cohen, 1988) (Table 9.4). Published meta-analyses indicate that the predictive effect of
developmental stress markers on biological outcomes is small when sample size is adequate (e.g.,
Risnes et al., 2011) so d values at or above 0.4 may over-estimate the real effect. Assuming that
the real effects are small, perhaps in the range of d=0.2, a minimum sample size of N=788 is
required to affirm such an effect with 80% power (β=0.8).
Multiple imputation helps to mitigate variation in effect size in the neural canal contrasts,
producing a clear bimodal distribution of small effects among AP contrasts (T1AP d=0.15 –
L5AP d=0.02), and medium-large effects among ML contrasts (T1ML d=0.59 – L5ML d=0.38).
The imputed n of 105 cases yields a minimum observable d value of 0.55, however, meaning that
1-β is less than 0.8 in most cases. T -tests of the PCA component scores PCA-AP and PCA-
ML yields effect sizes of d=0.01 and d=0.58 respectively. Although d estimates are likely to be
biased toward extreme values, they suggest strongly that average ML diameters are different in
the YA and MA/EA groups, while average AP are not.
9.4.2 Power analysis of binary logistic regression
Four models from the non-imputed dataset meet the significance criteria of p<0.05 or a 95% con-
fidence interval excluding 1.0 (AgeBinary+FXH.Z, AgeBinary+T1ML.Z, AgeBinary+L1ML.Z,
and AgeBinary+L5ML.Z). The average odds ratio observed is 1.88 with a pooled confidence
interval of 1.11–3.19 and a range from 1.65–2.20. The average minimum observable effect with
a working non-imputed sample size (excluding T6) of N=81 is an odds ratio of 2.11. The
average OR in significant or near-significant non-imputed models is approximately 1.96 (95%
CI=1.13–3.40). The results of this analysis are presented in Table 9.5.
The observed effect size is slightly lower in imputed models than in their non-imputed
counterparts (average OR=1.60, 95% CI=1.03–2.77). Although these results do raise the bar
Chapter 9. Results: Hypothesis Testing 157
Variable Groups N NY A/NMA−EA MEAN Observed d Sensitivity (min d)FXL.Z YA 37 1.46 -0.08 0.16 0.60
MA—EA 54 0.08total 91
FXH.Z YA 39 1.38 -0.25 0.47 0.60MA—EA 54 0.22total 93
T1AP.Z YA 45 1.33 -0.14 0.16 0.55MA—EA 60 0.00total 105
T1ML.Z YA 45 1.33 -0.34 0.59 0.55MA—EA 60 0.19total 105
T6AP.Z YA 45 1.33 0.03 0.09 0.55MA—EA 60 -0.05total 105
T6ML.Z YA 45 1.33 -0.08 0.23 0.55MA—EA 60 0.13total 105
L1AP.Z YA 45 1.33 0.00 0.02 0.55MA—EA 60 -0.02total 105
L1ML.Z YA 45 1.33 -0.27 0.52 0.55MA—EA 60 0.20total 105
L5AP.Z YA 45 1.33 0.03 0.02 0.55MA—EA 60 0.01total 105
L5ML.Z YA 45 1.33 -0.14 0.38 0.55MA—EA 60 0.20total 105
PCA.AP YA 45 1.33 -0.01 0.01 0.55MA—EA 60 0.00total 105
PCA.ML YA 45 1.33 -0.30 0.58 0.55MA—EA 60 0.22total 105
Table 9.4: Tests of power and sensitivity for t tests comparing young adults (YA) with mature-elderly adults(MA/EA). The observed effect size d (Cohen, 1988) is estimated and compared with an estimate of the minimumdetectable effect size d. Note that NC tests are from imputed datasets.
Chapter 9. Results: Hypothesis Testing 158
to achieve minimum power, they also increase confidence in the imputed, pooled results by
reducing the highest odds ratios to a more moderate value (L1ML.Z OR=1.71, 95% CI=1.03–
2.84). The imputed models also provide better evidence of no age effect in NC-AP, as all
observed odds ratios are very close to 1.0. If those effects are “real”, or representative of true
relationships between NC-AP and age in this population, an average sample size of 43,540
would be required to affirm them.
Sensitivity analyses show that the minimum effect that can be detected with 80% power in
the imputed sample (N=105) is OR=1.80, very close to the observed average of OR=1.60. If
the true effect is smaller, however, the future sample would need to be substantially larger. A
priori power modeling of a hypothetical test sample with an age distribution of 50% YA and
50% MA/EA cases produced minimum sample sizes needed to effects between OR=1.7 and
OR=1.10. Assuming that the real effect size is at or above 1.2, the minimum n required in
follow-up research is N= 960.
9.5 Hypothesis test I summary
This phase tests the null hypothesis that skeletal outcome measures are not related to age
at death. Results differ between the sexes, between femur head diameter (FXH.Z) and length
(FXL.Z), and between the anteroposterior (AP) and mediolateral (ML) dimensions of the neural
canals. In general there is a significant positive association between age at death after young
adulthood (35+) and larger size in FXH.Z and ML diameter of the neural canal. FXL.Z shows
a similar association that does not meet criteria for significance, and AP diameters consistently
fail to show a similar association. Although it appears that the null hypothesis can be rejected
when both sexes are pooled, it is found that males exhibit weaker age-related differences than
do females (Tables 9.1 and 9.2).
Partitioning the age distribution into very young adults (VYA, <25 years), young adults
(YA, 25–35 years), and mature adults (MA, 35+) also reveals more complex variation in the
ML dimension of NC size: means comparison in the imputed datasets shows that, while VYA
and YA means are similar, those of the YA group are slightly smaller than those of the VYA
group. VYA means are also not significantly different than MA means in any ML measurement,
Chapter 9. Results: Hypothesis Testing 159
Variable Groups N H0 Observed OR 95% CI Sensitivity (Min. OR)FXL.Z YA 37 0.59 1.18 0.76–1.82 1.9
MA-EA 54total 91
FXH.Z YA 39 0.58 1.65 1.06–2.57 1.9MA-EA 54total 93
T1AP.Z YA 45 0.57 1.13 0.74–1.73 1.8MA-EA 60total 105
T1ML.Z YA 45 0.57 1.868 1.14–3.06 1.8MA-EA 60total 105
T6AP.Z YA 45 0.57 0.923 0.55–1.54 1.8MA-EA 60total 105
T6ML.Z YA 45 0.57 1.293 0.68–2.47 1.8MA-EA 60total 105
L1AP.Z YA 45 0.57 0.966 0.60–1.56 1.8MA-EA 60total 105
L1ML.Z YA 45 0.57 1.709 1.03–2.84 1.8MA-EA 60total 105
L5AP.Z YA 45 0.57 0.985 0.65–1.50 1.8MA-EA 60total 105
L5ML.Z YA 45 0.57 1.495 0.93–2.41 1.8MA-EA 60total 105
PCA.AP YA 45 0.57 1.01 0.67–1.53 1.8MA-EA 60total 105
PCA.ML YA 45 0.57 1.743 1.08–2.82 1.8MA-EA 60total 105
Table 9.5: Tests of effect size and sensitivity for binary logistic regressions. Observed effect size (odds ratio) isequal to the exponentiated regression coefficient. Sensitivity is the estimated minimum effect size detectable withthe given sample size. The null hypothesis condition H0 (Y = 1|X = 1) is equal to the frequency of individualsin the MA/EA age phase (% MA/EA). Note that sex-specific contrasts are not tested
Chapter 9. Results: Hypothesis Testing 160
although they are consistently smaller. It is difficult to interpret the significance of this finding
with confidence, as sample power is affected by subdividing the original YA subsample into two
groups (NV Y A = 19, NY A = 27, NMA = 59), but it does suggest a relationship between neural
canal size and adulthood mortality that may be more complex than a simple linear one (Table
9.3, Figure 9.3).
Statistical power is an ongoing issue, particularly in the neural canals where preservation
is uneven along the spinal column. Supplementary testing after imputation does reduce this
problem and corroborates the interpretation that an age at death in the MA/EA phase is
associated with larger average size in the mediolateral aspect of the canal, and that the effect is
robust in females and negligible in males. Larger sample sizes are would be needed to further
clarify the size and sex-distribution of the effect.
9.6 Hypothesis II: Presence and severity of joint degeneration
relative to skeletal growth
Relationships between ordered OA severity factors (OA.Sev) and skeletal outcomes are tested
with simple contrasts and with generalized linear models. Because ordered logistic models
are sensitive to large numbers of empty cells the osteometric variables are ordinalized for this
analysis. Z -transformed values are allocated to ranked intervals that encompass the lowest
25%, middle 50%, and upper 75% of variables based on Harrell-Davis nonparametric quartiles
computed in R (Harrell, 1982) (Table 7.1). These variables are denoted by the suffix .rank
(as in FXH.rank, etc). Because significant age effects have been detected in skeletal measures,
AgeBinary is included as a predictor.
9.6.1 Tests of independence
Cochran-Mantel-Haenszel (CMH) tests of conditional independence are used to determine
whether frequency distributions of OA presence (OA.Bin) and OA severity (OA.Sev) across
ordered skeletal outcomes are non-random when age group is controlled (Table 9.6).
Of all the skeletal outcome variables, only FXL.rank is found to be non-independent from
OA.Sev when age is controlled (χ2=10.32, p=0.04). The effect is driven by a significantly
Chapter 9. Results: Hypothesis Testing 161
Figure 9.4: Bar graph showing the distribution of osteoarthritis severity among ranked FXL.Z size categories.Error bars represent the 95% confidence interval for the count in each rank category.
Chapter 9. Results: Hypothesis Testing 162
OA Frequency OA SeverityPresence Small Mid Large Total Severity Small Mid Large Total
FXL Unaffected 6 16 6 28 1 5 20 7 32Affected 14 31 17 62 2 4 18 5 27Total 20 47 23 90 3 11 9 11 31sig ns Total 20 47 23 90df 2 sig p<0.05
df 44
Presence Small Mid Large Total Sev Small Mid Large Total
FXH Unaffected 7 15 6 28 1 8 18 6 32Affected 16 32 16 64 2 9 14 6 29Total 23 47 22 92 3 6 15 10 31CMH sig ns Total 23 47 22 92df 2 sig ns
df 4Vert Plane N sig df Vert Plane N CMH
sigdf
4
NC T1 AP 104 ns 2 T1 AP 104 ns 4T1 ML 104 ns 2 T1 ML 104 ns 4T6 AP 104 ns 2 T6 AP 104 ns 4T6 ML 104 ns 2 T6 ML 104 ns 4L1 AP 104 ns 2 L1 AP 104 ns 4L1 ML 104 ns 2 L1 ML 104 ns 4L5 AP 104 ns 2 L5 AP 104 ns 4L5 ML 104 ns 2 L5 ML 104 ns 4
Table 9.6: Cochran-Mantel-Haenszel (CMH) tests of conditional independence for OA frequency and severityrelative to ranked size in the femur and neural canal. Age (Binary) is controlled in all tests.
high proportion of Middle FXL cases with no OA, and, correspondingly, a significantly low
proportion with Severe OA (Figure 9.4). Both Small and Large FXL groups also have a
higher-than-expected proportion of Severe cases. This pattern is still extant when the table is
stratified by AgeBinary. In age-stratified Exact tests of independence, however, T6AP is the
sole skeletal outcome with any evidence of an association with presence and severity of OA
among young adults (YA). No skeletal variable is associated, either positively or negatively,
with the presence of OA among mature-elderly adults (MA/EA).
9.6.2 Logistic regression
Ordinal joint-degeneration factors are analysed with ordered logistic regression in R. The polr
(proportional odds logistic regression) function is used here with a logit link function. Condi-
tional means plots are generated to check the assumption of proportional odds (Harrell, 2001,
p.332); where this assumption is violated, binary or multinomial regression models are generated
Chapter 9. Results: Hypothesis Testing 163
instead (Appendix Figure C.1). Model significance is tested by comparing -2 Log Likelihoods
between intercept-only and fitted models using the likelihood ratio test (LRT). Significance
criteria are set at an LRT p value below 0.05 or an odds ratio confidence interval excluding 1.0.
Fit is assessed by the Spearman correlation test of predicted versus observed outcome levels,
which yields the Spearman ρ2 (spearman.test (Harrell, 2014)). Effect plots are generated us-
ing the allEffects function in the R effects package (Fox et al., 2014b), which plots mean fitted
probabilities by predictor level (Fox, 2003, 2005). Further diagnostics for significant models
are generated by dichotomizing predictors and outcomes (for example, None vs Moderate, or
Moderate vs Severe; YA vs MA/EA; MA vs EA) and re-testing in binary logistic regression
(BLR). Results are presented in Table 9.7.
OA as an outcome of Age
AgeBinary is the strongest single predictor of OA.Sev. Individuals in the MA/EA phase have,
overall, almost four times greater odds of having a high OA score than individuals in the YA
group (p<0.00; OR=3.90, 95% CI=2.36–6.44) (Figure 9.5). The intermediate OA.sev category
(Moderate), however, is not clearly distinguished by the ordered model: individuals in both age
groups have very similar probabilities of moderate OA (YA= 0.28; MA/EA=0.35) and no cases
have sufficiently high probability of Moderate score to be placed in that category; as a result,
the predicted categories are binary (None, Severe). This is reflected in the Spearman test, which
yields ρ2=0.22, p<0.00, exactly the same as that derived from a Spearman test of Age against
OA.sev (ρ2=0.22, p<0.00). A second OLR, modeling OA.Sev as an outcome of the three-
stage age factor (YA, MA, EA) returned a similar result: while there is a positive relationship
between age phase and probability of a higher OA score (MA OR=5.26, 95% CI=2.58–10.75;
EA OR= 22.11, 95% CI=5.24–93.32) there is no particularly strong distinction between YA
and MA in terms of probability of Moderate OA (YA=0.29, 95% CI=0.20–0.39; MA=0.37,
95% CI=0.28–0.46; EA=0.19, 95% CI=0.07–0.42). Predicted outcomes are again binary, with
approximately two-thirds of observed Moderate cases (28 out of 44, or 64%) being allocated to
the Severe predicted category. LRT comparison fails to show that the second model improved
significantly on the first. (p=0.15). Effect plots show a relatively consistent, linear distribution
of probabilities by age for Unaffected and Severe scores, but relatively uniform distribution of
Chapter 9. Results: Hypothesis Testing 164
probabilities for Moderate scores(Figure 9.5). Plots of conditional means indicate that the
assumption of proportional odds is sound in this case (Appendix Figure C.1).
BLR models are used to test associations between various Age and OA binaries. The
first model tests the association between presence and absence of OA (OA.binary) and YA–
MA/EA age categories (AgeBinary) and is moderately effective at identifying cases with a
positive OA.sev score (C=0.67, p<0.00; Dxy=0.35). YA cases have a 0.48 (95% CI=0.36–0.61)
probability of affectedness, versus 0.80 (95% CI=0.70–0.87) for MA/EA cases (OR=2.8, 95%
CI=1.64–4.79).
A second BLR models Unaffected-Moderate OA as an outcome of AgeBinary. Although
predictive accuracy is similar (C=0.67, Dxy=0.33), the odds ratio of MA/EA cases having
Moderate OA is much greater and the confidence interval much wider (OR=3.97, 95% CI=1.67–
9.41), suggesting that there is indeed an age gradient in Moderate OA, but that reliability may
be affected by n of the sample subset. Restricting the sample to YA (25–35 yrs) and MA (35–
50yrs) cases (YA N=59; MA N=68) reduces the odds ratio and narrows the confidence interval
(OR=2.64, 95% CI=1.41–5.91), but maintains predictive accuracy (C=0.66, Dxy=0.33).
A weaker, positive effect is found when moderate-severe OA is modelled as a binary out-
come of AgeBinary to test the hypothesis that the age effect is driven largely by unaffected
cases (OR=2.4, 95% CI=1.15–5.15, p=0.015; C=0.612; Dxy=0.22). Restricting the sample to
moderate-severe OA.Binary and MA/EA cases yields a positive association, with EA individu-
als having a probability of 0.80 (CI=0.49–0.95) of Severe OA, versus 0.50 (95% CI=0.38–0.64)
for MA individuals; however, model accuracy is poor, undoubtedly because of the small number
of cases in the EA phase (N=12) (OR=2.8, 95% CI=0.89–8.89, p=0.05, C=0.589, Dxy=0.18).
Ultimately, these results indicate that there is a roughly linear relationship between age
group and OA severity, but that, as might be expected, the relative distinction between un-
affected and severe OA cases is greater than that between either and moderate cases. Never-
theless, the binary models show that the likelihood of YA cases having even moderate OA is
substantially lower than that for MA cases, counter to the OLR model (Figure 9.5).
Chapter 9. Results: Hypothesis Testing 165
Figure 9.5: Effect plots from ordered and multinomial logistic regressions of OA Severity as an outcome of ageat death. These plots illustrate the probability an outcome condition given a one-unit increase in the value ofthe predictor (Y = 1|X = 1). AgeBinary is the strongest single predictor of OA.sev. The intermediate OA.sevcategory (Moderate), however, is not clearly distinguished by the ordered model: effect plots showed a relativelyconsistent, linear distribution of probabilities by age for Unaffected and Severe scores, but relatively uniformdistribution of probabilities for Moderate scores.
OA as an outcome of Body Size
The most robust interpretation of models comparing body size with OA is that body size is not
a significant predictor of joint disease presence or severity in this sample.
Modelling OA.Sev as an outcome of FXL.rank and AgeBinary shows that the relationship
between OA.Sev and FXL.rank is nonlinear. Although FXL.rank is identified as a significant
predictor (p=0.027), a nonlinear relationship means that the assumption of proportional odds
has probably been violated, and that a single coefficient by an OLR model does not adequately
describe the relationship between OA.sev and each level of FXL.rank. The violation of propor-
tional odds is demonstrated by the distribution of observed relative to predicted conditional
means, which shows that the relationship between OA.sev and the Middle FXL level is differ-
ent than that between OA.sev and each of the other two FXL levels (Appendix Figure C.1).
Multinomial logistic regression (MLR) procedure, which does not assume proportional odds, is
used in place of OLR (Table 9.7).
The relationship of FXL.rank to OA.sev is driven by the discrepancy in predicted probabil-
ities between Middle FXL cases and those at either end, in which Small and Large FXL cases
have much lower probabilities of having OA Severity scores of 1 or 2 (Unaffected or Moderate),
Chapter 9. Results: Hypothesis Testing 166
and much higher probability of Severe OA than cases in the Middle FXL category (Table 9.6).
Exponentiated confidence intervals of the coefficients at each level of OA.Sev exclude 1.0 only at
the Severe level (OR=3.67, 95% CI=1.37–9.83), which indicates that the probability difference
reaches statistical significance only among Severe OA cases.
Although FXL.rank has a detectable relationship with OA.Sev, AgeBinary remains the
dominant predictive factor: Spearman’s test indicates that a model of OA.Sev as an outcome
of AgeBinary+FXL.rank explains approximately 26% of variance in OA.sev (ρ2=0.26, p<0.00),
compared with 22% by AgeBinary alone (ρ2=0.22, p<0.00). The increase in ρ2 is relatively
small and does not exceed that expected from simply adding an extra term to the model.
Removing AgeBinary from the equation does not alter the FXL.rank coefficient, but model
comparison by LRT shows that the inclusion of AgeBinary markedly improves the model’s
descriptive ability (LRT χ2 = 19.67, p<0.000).
As partitioning a ratio-scale variable into ordinal levels reduces power and may increase the
risk of error, particularly in a subset sample of N=90, the relationship between OA and FXL
is re-examined with a multinomial logistic model in which the FXL.Z term is described as a
natural spline with two degrees of freedom. The number of degrees of freedom is constrained to
2 to reduce the risk of overfitting. Although the distribution of predicted probabilities records a
slight increase in the probability of unaffected (OA.Sev=None) cases in the range of -0.5 through
+0.5 FXL.z, and a corresponding increase in the probability of Severe OA, particularly among
FXL.z values close to +2, the confidence intervals are very wide (at FXL.z ≥ 2, OR=3.1, 95%
CI=0.08–60.09), and the model does not fulfill any significance criterion (p=0.803) (Figures
9.5 and 9.6). The implication is that, despite the apparent nonlinear relationship between OA
and femur length indicates by the previous two models, the effect is likely to be spurious.
As with FXL.rank, the ordinal model of OA.Sev as an outcome of FXH.rank and AgeBinary
appears to violate the assumption of proportional odds; however, in this model no independent
association between OA.sev and any level of FXH.rank is detected. A multinomial model yields
the same result. The distribution of predicted probabilities shows that there is a slight positive
slope in the Severe OA level, but a corresponding negative slope in the Moderate level. Neither
is significant enough to warrant further investigation (Figure 9.6).
Chapter 9. Results: Hypothesis Testing 167
Figure 9.6: Effect plots from ordered and multinomial logistic regressions of OA Severity as an outcome of bodysize. A prospective FXL.rank effect is driven by the discrepancy between Middle FXL cases and those at eitherend, in which Small and Large FXL cases have much lower probabilities of having no OA or Moderate OA thanMiddle cases, and much higher probability of Severe OA.
9.6.3 OA as an outcome of Neural Canal Size
The association of OA.Sev with NC.rank violates the assumption of proportional odds in most
dimensions and segments, so multinomial rather than ordered logistic models are computed.
LRT comparison demonstrates that they performed no better than age-only models at all verte-
bral levels and in both AP and ML axes; directionality and magnitude of regression coefficients
are also highly inconsistent. Two models (T1AP.Z, T1ML.Z) are fitted to natural splines with
two equal knots (k=2). They affirm that neural canal size has no association with OA.sev
(Table 9.7). Imputed datasets are not used to these tests because no coherent pattern of
significance is detected in the original dataset.
Chapter 9. Results: Hypothesis Testing 168
Table 9.7: Results of logistic regressions of OA frequency (OA.Binary) and severity (OA.Sev) against rankedbody and and neural canal size. Model types are as follows: Ordinal (OLR), Multinomial (MLR), Binary (BLR).Notes: In OLR and BLR, the odds ratio at each predictor level is assumed to be linear and is therefore uniformat each outcome level. MLR coefficients approximate the odds ratio for each transition from one outcome level tothe next at each predictor level. An asterisk (*) indicates that a covariate (Age) significantly improves a reducedmodel (LRT p<0.05). ns*2 indicates a natural spline model with two knots.
Model Type Predictor sig Severity Level Predictor Level OR 95% CI
BLR Age (Binary) <0.05 n/s YA–MA/EA 2.8 1.6–4.8
OLR Age (Binary) <0.05 all YA–MA/EA 3.9 2.4—6.4
OLR Age (3 phase) <0.05 all YA-MA 5.3 2.6—10.7
MA/EA 22.1 5.2—93.3
MLR FXL.rank <0.05 None-Mod Middle 0.9 0.3—3.2
Mod-Sev Middle 0.8 0.3—2.2
None-Mod Large 0.9 0.4—2.1
Mod-Sev Large 3.2 1.3—7.6
MLR FXL.rank <0.05 None-Mod Middle 0.9 0.3—3.4
Mod-Sev Middle 0.8 0.3—2.5
None-Mod Large 1.0 0.4—2.4
Mod-Sev Large 3.7 1.4—9.8
Age (Binary)* <0.05 None-Mod MA/EA 2.8 1.3—6.2
Mod-Sev MA/EA 6.1 2.4—15.1
MLR FXL.Z ns None-Mod Knot 1 0.9 0.0—32.5
Mod-Sev Knot 1 0.2 0.0—7.0
None-Mod Knot 2 0.3 0.1–60.1
Mod-Sev Knot 2 3.1 0.1—70.4
MLR FXH.rank ns None-Mod Middle 0.7 0.2—2.2
Mod-Sev Middle 1.2 0.4—3.8
None-Mod Large 1.0 0.4—2.4
Mod-Sev Large 0.9 0.3—2.2
Age (Binary)* <0.05 None-Mod MA/EA 2.6 1.2—5.6
Mod-Sev MA/EA 5.6 2.3—13.8
MLR T1AP.rank ns None-Mod Middle 0.3 0.1—1.2
Mod-Sev Middle 0.4 0.11—1.8
None-Mod Large 1.0 0.4—2.7
Continued on next page
Chapter 9. Results: Hypothesis Testing 169
Model Type Predictor sig Severity Level Predictor Level OR 95% CI
Mod-Sev Large 1.5 0.5—4.5
Age (Binary)* <0.05 None-Mod MA/EA 2.1 0.8—5.0
Mod-Sev MA/EA 10.0 3.3—30.5
MLR T1ML.rank ns None-Mod Middle 0.7 0.2—2.2
Mod-Sev Middle 0.8 0.2—3.2
None-Mod Large 1.4 0.5—3.5
Mod-Sev Large 1.3 0.4—3.7
Age (Binary)* <0.05 None-Mod MA/EA 2.1 0.9—5.2
Mod-Sev MA/EA 9.5 3.1—28.4
MLR T6AP.rank ns None-Mod Middle 3.4 0.6—19.5
Mod-Sev Middle 0.8 0.1—4.8
None-Mod Large 0.2 0.0—0.8
Mod-Sev Large 0.5 0.1—2.1
Age (Binary)* <0.05 None-Mod MA/EA 0.9 0.1—5.3
Mod-Sev MA/EA 22.5 2.0—243.0
MLR T6ML.rank ns None-Mod Middle 1.3 0.3—4.8
Mod-Sev Middle 0.6 0.1—3.7
None-Mod Large 1.2 0.4—3.9
Mod-Sev Large 0.6 0.1—2.2
Age (Binary)* <0.05 None-Mod MA/EA 1.9 0.4—8.4
Mod-Sev MA/EA 30.6 3.0—310.3
MLR L1AP.rank ns None-Mod Middle 1.0 0.3—3.2
Mod-Sev Middle 0.5 0.1—1.9
None-Mod Large 1.1 0.5—2.9
Mod-Sev Large 0.8 0.3—2.4
Age (Binary)* <0.05 None-Mod MA/EA 3.8 1.2—12.0
Mod-Sev MA/EA 24.7 4.5—134.4
MLR L1ML.rank ns None-Mod Middle 0.8 0.2—2.6
Mod-Sev Middle 0.4 0.1—1.8
None-Mod Large 1.1 0.5—2.9
Mod-Sev Large 1.0 0.3—3.0
Age (Binary)* <0.05 None-Mod MA/EA 4.1 1.2—14.0
Continued on next page
Chapter 9. Results: Hypothesis Testing 170
Model Type Predictor sig Severity Level Predictor Level OR 95% CI
Mod-Sev MA/EA 31.5 5.3—187.4
MLR L5AP.rank ns None-Mod Middle 1.0 0.3—2.9
Mod-Sev Middle 0.5 0.1—1.9
None-Mod Large 1.4 0.6—3.8
Mod-Sev Large 1.0 0.3—2.9
Age (Binary)* <0.05 None-Mod MA/EA 3.3 1.0 10.8
Mod-Sev MA/EA 25.8 4.7—142.8
MLR L5ML.rank ns None-Mod Middle 1.3 0.4—3.9
Mod-Sev Middle 1.7 0.4—8.1
None-Mod Large 1.3 0.5—3.6
Mod-Sev Large 0.6 0.2—1.9
Age (Binary)* <0.05 None-Mod MA/EA 3.2 0.9—11.1
Mod-Sev MA/EA 18.3 3.3—102.2
9.7 Effect size, power, and sensitivity
Effect sizes are computed for BLRmodels of OA.Binary Age and independence tests of FXL.rank.
Full sample models are tested; models from sample subsets are not tested because of the effect
on power. Sensitivity tests are used to establish minimum effect sizes and critical χ2 thresholds
for sample sizes of N=90, N=70, and N=45 at 2 and 4 degrees of freedom. In the sole signifi-
cant test of independence (FXL.Rank OA.Sev), w=0.34 with 4 degrees of freedom and N=90;
however, the minimum w detectable with 1 − β = 0.80 at df=4 and N=90 is 0.36. In contrast,
the BLR model of OA.binary as an outcome of AgeBinary has excellent power to detect the
observed odds ratio of 2.8 (1.63–4.79) (Table 9.8).
9.8 Hypothesis test II summary
Although graphical methods and simple tests of independence suggest initially that stature
(FXL.Z) has a non-linear relationship with osteoarthritis severity (OA.Sev), with individuals
Chapter 9. Results: Hypothesis Testing 171
Predictor Outcome Method N df Effect Observed Effect Sensitivity
Age (Binary) OA (Binary) BLR 136 1 exp(coef) 2.8 (1.63–4.79) 1.7FXL.rank OA (Binary) CMH 89 2 w 0.08 0.37FXL.rank OA Severity CMH 89 4 w 0.34 0.36
Table 9.8: Effect size estimates and sensitivity tests for Hypothesis II contingency tables and binary logisticregression models. Logistic regression effect size is quantified as the exponentiated regression coefficient (oddsratio) and for contingency tables as w (Cohen, 1988; Faul et al., 2007). Sensitivity is quantified as the minimumeffect size detectable with the observed sample N. Note that a probability of the outcome state under the nullhypothesis (Y = 1|X = 1 under H0) is required for effect size estimates in logistic regression. The null hypothesiscondition used here is equal to the frequency of individuals in the MA/EA age phase (% MA/EA). Note thatsex-specific contrasts are not tested because power for those subsamples will be significantly lower than for thefull sample. Sensitivity tests for FXH.rank and NC.rank models are not performed because their results aresimilar and much weaker than that observed in FXL.rank.
in the middle 50% of FXL.Z values having greater odds of being unaffected by OA, and indi-
viduals in the bottom and upper quartiles having greater odds of severe OA, logistic regression
diagnostics indicate that this result is unreliable and is likely a consequence of sampling error.
No other osteometric variable is found to associate with OA.Sev in either a linear or nonlinear
manner and age is found to be the only consistent, significant predictor. The null hypothesis
that skeletal growth outcome does not associate with presence or severity of joint degeneration
cannot be rejected. Estimates of applied power and sensitivity reinforce the balance of evidence
from both independence and regression models, which collectively support the null hypothesis.
Sensitivity analysis demonstrates that power is adequate to capture medium- and large-sized
effects at N=90 with 2 and 4 degrees of freedom. The only strong, consistent pattern in the
data with regard to this hypothesis is an absence of detectable relationship between skeletal
size and OA, indicating very strongly that a biological effect, if it exists, is too small to be
captured with this sample size.
9.9 Hypothesis III: Temporal variation in skeletal growth out-
comes
The null hypothesis that variation in skeletal growth outcomes is randomly distributed over
time is tested with both categorical and continuous methods: tests of independence are ap-
plied to ranked osteometric factors (Rank.FXL, etc), regression models are fit to uncalibrated
radiocarbon dates (14C) and osteometric z-scores (FXL.Z, etc), and analyses of variance are
Chapter 9. Results: Hypothesis Testing 172
used to compare mean z-scores among temporal phases (Period). Periods are defined as Early
(≥3100BP), Middle (3000–1900BP) and Late (<1900BP).
9.10 Means comparison
Mean z scores are compared among temporal phases using fixed-effects multi-way ANOVA
generated by the lm function in R’s stats package (R Foundation, 2013). Period is entered
as the main factorial predictor. Sex and Age are each tested for correlations or interactions
with Period; neither is found to interact significantly with Period. Both linear and polynomial
models are fit, based on Pfeiffer and Sealy (2006)’s finding of a quadratic relationship between
body size and date. Standardized residuals are plotted against their predicted counterparts
under a normal distribution.
ANOVA results are presented in Table 9.9. Levene tests of variance indicate that the
assumption of homogeneity is not violated. Mean z scores differ significantly by Period in
both femoral measures and in most mediolateral NC diameters, but not in the anteroposterior
diameter of any vertebral segment. In most cases, a linear model gives the best fit based
on comparison of F statistics, although in the case of T1ML.Z a quadratic model fits better
than a linear one. Direct comparison of mean z-scores shows that means in the Late period
are significantly greater than those in the Middle period (p<0.05 for Middle-Late contrasts in
FXL.Z, FXH.Z, T1ML.Z, T6ML.Z) but that the Early and Middle periods have very similar
values; most often, the mean of the Middle period is smaller than, but not significantly different
from, the Early period mean (Figure 9.7, Table 9.9). Sex is not a significant confounder in
most models, but a weak sex-by-period interaction is detected in T6AP and L1AP in that
males exhibit a greater mean in the Late period, while females do not. In both of the latter
models, however, time period is not a significant overall predictor of skeletal size. Standardized
residuals are normally distributed in all cases.
9.11 OLS regression
Linear and quadratic polynomial Ordinary Least Squares regression models are fit to osteo-
metric z scores and uncalibrated radiocarbon years BP (14C). As sexual size dimorphism is
Chapter 9. Results: Hypothesis Testing 173
Variable Period N Mean (Z) SEM Contrasts p<0.05 Var sig
FXL.Z Early 22 −0,15 0,19 Late nsMiddle 41 −0,27 0,14 LateLate 26 0,67 0,17 Early, Middle
FXH.Z Early 22 −0,31 0,25 Late nsMiddle 43 −0,20 0,13 LateLate 26 0,69 0,17 Early, Middle
T1.AP.Z Early 24 0,05 0,27 ns nsMiddle 48 −0,20 0,14 nsLate 28 0,00 0,23 ns
T6.AP.Z Early 24 0,10 0,21 ns nsMiddle 48 −0,13 0,15 nsLate 28 0,10 0,22 ns
L1.AP.Z Early 24 −0,12 0,18 ns nsMiddle 48 −0,16 0,15 nsLate 28 0,24 0,24 ns
L5.AP.Z Early 24 −0,00 0,24 ns nsMiddle 48 −0,01 0,20 nsLate 28 0,09 0,23 ns
PCA-AP Early 24 0,05 0,19 ns nsMiddle 48 −0,16 0,15 nsLate 28 0,17 0,23 ns
T1.ML.Z Early 24 0,08 0,22 ns nsMiddle 48 −0,37 0,14 LateLate 28 0,38 0,21 Middle
T6.ML.Z Early 24 −0,11 0,23 Late nsMiddle 48 −0,15 0,16 LateLate 28 0,49 0,26 Early, Middle
L1.ML.Z Early 24 −0,16 0,19 Late nsMiddle 48 −0,21 0,15 LateLate 28 0,48 0,22 Early, Middle
L5.ML.Z Early 24 −0,05 0,21 ns nsMiddle 48 −0,05 0,16 nsLate 28 0,34 0,18 ns
PCA-ML Early 24 −0,09 0,21 Late nsMiddle 48 −0,28 0,13 LateLate 28 0,53 0,21 Early, Middle
Table 9.9: Comparison of mean skeletal size among time periods based on the most parsimonious, best-fittingunweighted ANOVA model. The “Sig contrast” column indicates significant inter-period contrasts. NC resultsare from imputed datasets. Levene tests (“Var sig”) indicate that the assumption of homogeneity of variance isupheld in all contrasts.
Chapter 9. Results: Hypothesis Testing 174
controlled by z-score standardization, and sex is not found to be a significant confound in
analyses of variance (above), the whole sample is analysed without separating the sexes. Stan-
dardized residuals are compared against a normal distribution with Shapiro-Wilk tests and
are found to be normally distributed in most models, but they violate normality in two cases
(T1AP.Z, T6AP.Z). Standardized coefficients and adjusted R2 values are reported (Table 9.10).
Distributions of femoral z-scores on (14C values are best fit to quadratic regression equations
(FXL.Z adjR2=0.20, p<0.00; FXH.Z adjR2=0.21, p<0.00); T6ML, L1AP, and L1ML are weakly
described by linear equations (T6ML adjR2=0.19, p<0.00; L1AP adjR2= 0.04, p<0.05; L1ML
adjR2=0.11, p<0.05), and T1 (AP and ML), T6AP, and L5 (AP and ML) do not meet any
significance criteria. In general, the largest values fall into the Late period and the smallest into
the Middle, but there is considerable scatter around the regression lines and little distinction
between Middle and Early periods (Table 9.10, Figures 9.8 and 9.9).
9.12 Supplementary replication with MI datasets
Means contrasts and OLS regression models are replicated iteratively using the five imputed
datasets of neural canal dimensions, including summary variables PCA-ML and PCA-AP. Av-
eraged parameters and adjusted confidence intervals are reported (Tables 9.9 and 9.10).
Pooled contrast models generally support a difference of means between the Late and Early-
Middle periods in T1ML.Z, T6ML.Z, and L1ML.Z, although several individual datasets yields
estimates that do not reach p<0.05. In most cases, linear models provided better fit than
quadratic. Contrasts with the summary variable PCA-ML demonstrate significant differences
between the Late and Middle periods and a consistent absence of difference between Middle
and Early period means (Early = -0.09, SEM=0.21; Middle = -0.28, SEM=0.13; Late = 0.53,
SEM=0.21). No significant differences are found in the AP diameter of any segment, nor in the
summary variable PCA-AP (Table 9.9).
The OLS regression models results reveal that temporal trends are obscured by high vari-
ability around the regression lines. Adjusted R2 values are much smaller in regression models
fitted to imputed datasets, which indicates that those fitted to the original dataset are likely
overfitted. The most consistent pattern is a mild increase in average ML size between approxi-
Chapter 9. Results: Hypothesis Testing 175
Variable Model sig Adj.R2 SEE Level Slope β
FXL.Z Quadratic p<0.05 0,20 0,88 B1 −0,00 −1,60B2 0,00 1,43Constant 1,72
FXH.Z Quadratic p<0.05 0,20 0,91 B1 −0,00 −1,64B2 0,00 1,49Constant 1,84
T1.AP.Z Linear ns 0,00 0,97 B1 0,00 0,08Constant −0,21
T6.AP.Z Linear ns −0,01 0,96 B1 0,00 0,03Constant −0,01
L1.AP.Z Linear ns 0,00 0,94 B1 −0,00 −0,09Constant 0,09
L5.AP.Z Linear ns 0,01 1,00 B1 −0,00 −0,13Constant 0,24
PCA-AP Linear ns 0,00 1,02 B1 −0,00 −0,03Constant 0,04
T1.ML.Z Quadratic p<0.10 0,03 0,94 B1 −0,00 −0,65B2 0,00 0,68Constant 0,61
T6.ML.Z Linear p<0.05 0,04 0,95 B1 −0,00 −0,22Constant 0,31
L1.ML.Z Linear p<0.05 0,03 0,96 B1 −0,00 −0,23Constant 0,41
L5.ML.Z Linear ns 0,02 1,01 B1 −0,00 −0,16Constant 0,36
PCA-ML Quadratic p<0.05 0,06 0,99 B1 −0,00 −0,83B2 0,00 0,68Constant 0,96
Table 9.10: Parameters of ordinary least squares (OLS) regressions of skeletal size on date (14 years BP). Param-eters of the best-fitting model with the fewest degrees of freedom are tabulated. The adjusted R2, standard errorof the estimate (SEE) and standardized coefficient (β) are presented along with the unstandardized regressionparameters.
Chapter 9. Results: Hypothesis Testing 176
Figure 9.7: Box plots of skeletal sizes in the Early, Middle, and Late periods. Whiskers and box bordersrepresent the minimum and maximum values and quartiles. The middle line of each box represents the medianvalue. Outlying cases are identified by catalogue number.
mately 4000BP and 500BP (see Figures 9.8 and 9.9 and 9.7). The few cases predating 4000BP
exhibit wide variation around the regression line, so conclusions regarding early Holocene size
variability cannot be drawn.
9.12.1 Effect size, power, and sensitivity
Means comparison
In most ANOVA, including several in which p>0.05, observed f values fall between conventional
thresholds for medium (f=0.25) and large (f=0.40) effects. Although in several cases the
observed f exceeds the minimum detectable effect at the test sample size (FXL.Z, FXH.Z,
T1ML.Z, L1ML.Z), inter-group differences may be exaggerated by means comparison when the
Chapter 9. Results: Hypothesis Testing 177
Figure 9.8: Scatter plots of body sizes (FXL.Z, FXH.Z, and PCA scores from imputed ML and AP datasets)against uncalibrated radiocarbon date.
Chapter 9. Results: Hypothesis Testing 178
Figure 9.9: Scatter plots of neural canal sizes (PCA scores from imputed ML and AP datasets) against uncali-brated radiocarbon date.
Chapter 9. Results: Hypothesis Testing 179
sample is small (Table 9.11). At N=70 (the largest sample size for neural canals), the models
are capable of detecting only large effects of (f=0.37); at N=90, the smallest detectable effect
is f=0.33. Contrasts using the imputed datasets yields smaller, more homogeneous effect sizes
than the original dataset: at N=100 the minimum detectable effect size at β=0.80 is f=0.315;
consequently, the only contrasts to achieve β=0.80 are those for T1ML.Z and PCA-ML.
OLS regression
Linear bivariate regression shows that, in most cases, sample sizes are not sufficient to distin-
guish the observed slopes from b=0 (power β<0.8). The same held true for imputed datasets,
although, in the latter, average R values are somewhat lower than in models generated from
the original dataset and are therefore likely more accurate.
9.12.2 Hypothesis test III summary
Pfeiffer and colleagues have observed pronounced temporal changes in average body size, char-
acterised by a dip in the mid- to late Holocene, followed by an increase after approximately
2000BP (Ginter, 2011; Kurki et al., 2012; Pfeiffer and Sealy, 2006; Pfeiffer, 2013; Stynder et al.,
2007a; Wilson and Lundy, 1994). The period of smallest average size is also characterised by
increased variability in body size, consistent with the increase in the number of individuals
represented in the skeletal record from that time.
Of the two posited alternative hypotheses HA2 is most accurate with regard to mean body
size and its correlates, which are smallest during the Middle period between 3000 and 2000BP;
however, the difference in means between the Early and Middle periods is negligible and tempo-
ral effects are driven mostly by a marked increase in means between the Middle and Late periods.
Temporal variability is, however, unequivocally less clearly patterned in the neural canals than
in body size. Supplementary testing with an imputed dataset suggests that, although mean
ML neural canal size does covary with femur size between time periods, nonrandom temporal
variability is much weaker than implied in the initial analysis and can be largely attributed to
correlation between ML diameter and overall body size.
The first alternative hypothesis (HA1), that mean skeletal sizes are largest during the Middle
period (3000–2000BP), can be confidently rejected. Nevertheless, while HA2 cannot be rejected
Chapter 9. Results: Hypothesis Testing 180
ANOVA (omnibus one-way) with Means ContrastsPredictor N df Residual df No. Groups Observed f Minimum f
FXL.Z 89 2 86 3 0.42 0.34FXH.Z 91 2 88 3 0.41 0.33T1AP.Z 100 2 97 3 0.11 0.32T1ML.Z 100 2 97 3 0.32 0.32T6AP.Z 100 2 97 3 0.22 0.32T6ML.Z 100 2 97 3 0.27 0.32L1AP.Z 100 2 97 3 0.18 0.32L1ML.Z 100 2 97 3 0.30 0.32L5AP.Z 100 2 97 3 0.04 0.32L5ML.Z 100 2 97 3 0.18 0.32PCA-AP 100 2 97 3 0.14 0.32PCA-ML 100 2 97 3 0.34 0.32
Ordinary Least-Squares RegressionsPredictor N df Residual df Observed F Critical F
FXL.Z 89 2 86 11.84 3.10FXH.Z 91 2 88 12.77 3.09T1AP.Z 100 1 99 0.58 3.08T1ML.Z 100 1 99 2.86 3.08T6AP.Z 100 1 99 0.10 3.08T6ML.Z 100 1 99 3.35 3.08L1AP.Z 100 1 99 1.19 3.08L1ML.Z 100 1 99 4.36 3.08L5AP.Z 100 1 99 1.52 3.08L5ML.Z 100 1 99 3.02 3.08PCA-AP 100 1 99 0.23 3.08PCA-ML 100 1 99 4.32 3.08
Table 9.11: Power and sensitivity analysis for means contrasts and ordinary least squares (OLS) regressions.Sensitivity represents the minimum observable effect with the available sample size. Effect for ANOVA modelsis quantified as f (Cohen, 1988; Faul et al., 2007) and is computed from group descriptive statistics. G*Powerfunctions for generic F tests are used for OLS models because G*Power has no specific function for quadraticmodels (Faul et al., 2007); the critical F statistic is used in place of Cohen’s standardized effect measure f (Cohen,1988) as a measure of sensitivity for OLS models. NC results are pooled from imputed datasets.
Chapter 9. Results: Hypothesis Testing 181
wholesale, it also cannot be accepted without qualification. Rather than any distinction between
the Early and Middle periods, the largest average sizes are found in the Late period, when not
only the overall number of skeletons is smaller, but some important changes occur, including
the arrival of stock-herding and possible genetic admixture (Breton et al., 2014; Ginter, 2008;
Macholdt et al., 2014; Sealy, 2010). Notably, the largest FXL.Z and PCA-ML values in the
Late period belong to individuals identified by Sealy (2010) as possible herders based on their
dietary isotopic signatures (UCT582, UCT067). This suggests that dynamic population may
not be the primary determinant of temporal variability in skeletal size.
9.13 Hypothesis IV: Temporal variation in joint degeneration
Temporal variation in joint degeneration is explored with tests of simple and conditional in-
dependence after the procedure followed in Hypothesis Test II. Ordered logistic regression is
contraindicates by violation of the proportional-odds assumption (Appendix Figure C.1).
9.13.1 Tests of independence
OA.binary and OA.sev frequencies are plotted against Period in two-way contingency tables
with and without stratification by age. The hypotheses of simple and conditional independence
(the latter adjusted for AgeBinary) are tested with Fisher’s Exact and Cochran-Mantel-Haenszel
Tests, respectively. Results are presented in Table 9.12.
The frequency of Unaffected cases appears somewhat higher in the Early and Late periods.
This is notable in the YA age phase (76% and 68%, respectively) than in the Middle period
(45%), but the difference is not significant, and no parallel pattern is detected in the MA/EA
age phase. Ultimately, the presence and severity of OA are found to be independent of time
period even when age is controlled.
9.13.2 Logistic regression
Conditional means plots of OA.Sev against Period strongly indicate that the proportional odds
assumption does not hold (Appendix Figure C.1), meaning that ordered logistic regression is
not appropriate for this analysis; multinomial logistic regression (MLR) is applied alternatively.
Chapter 9. Results: Hypothesis Testing 182
OA PresenceEarly Middle Late Total CMH sig
Unaffected 11 18 14 43 nsAffected 24 42 22 88Total 35 60 36 131
OA SeverityEarly Middle Late Total CMH sig
Unaffected 13 18 16 47 nsModerate 9 23 11 43Severe 13 19 9 41Total 35 60 36 131
OA Severity by time period, stratified by Age PhaseYoung AdultsSeverity Early Middle Late Total
1 10 11 11 322 1 10 4 153 2 3 1 6Total 13 24 16 53
Mature Adults
1 3 7 5 152 8 13 7 283 11 16 8 35Total 35 60 36 78
Table 9.12: Results of Cochran-Mantel-Haenszel tests of conditional independence for OA severity relative totime period (Early, Middle and Late). Age (Binary) is controlled. The bottom panel shows the distribution ofseverity ranks according to time period in the two binary age phases (YA and MA/EA).
Chapter 9. Results: Hypothesis Testing 183
Figure 9.10: Effects plots from multinomial logistic regression of OA Severity as an outcome of Time Period.These plots illustrate the probability an outcome condition given a one-unit increase in the value of the predictor(Y = 1|X = 1).
OA.Sev is entered as the outcome, and Period and AgeBinary are entered as predictors. MLR
modeling confirms that Period is not a significant predictor of OA.Sev and that AgeBinary
is the only significant predictor (Table 9.13). The probability of Unaffected status appears
somewhat higher in the Early and Late periods than in the Middle, with the probability of
Severe disease being lowest in the Late period; however, the confidence intervals at all levels
overlap heavily (Figure 9.10).
9.13.3 Effect size, power, and sensitivity
Sensitivity tests are used to establish minimum effect sizes and critical χ2 thresholds for in-
dependence of OA.Sev from Period in the observed sample size N=131 at 2 and 4 degrees of
freedom. The smallest observable effects at df=2 and df=4 are w=0.27 and w=0.30, respec-
Chapter 9. Results: Hypothesis Testing 184
OA Severity as an outcome of Time Period and Age PhaseModel Type Predictor sig Outcome Level Predictor Level OR 95% CI
MLR Period ns None-Mod Middle 1.0 0.4–2.4Mod-Sev Middle 0.7 0.3–1.7None-Mod Late 0.6 0.3–1.2Mod-Sev Late 0.7 0.3–1.5
Age (Binary)* <0.05 None-Mod MA/EA 2.7 1.4–5.1Mod-Sev MA/EA 6.0 2.8–12.7
Table 9.13: Results of multinomial logistic regressions of OA frequency (OA Binary) and severity (OA.Sev)against Time Period. Odds ratios are computed for the transition from one outcome level to another at eachpredictor level. An asterisk indicates that a covariate (Age) significantly improves a reduced model (LRT p<0.05).
tively. Comparison with the estimated actual effects of w=0.08 (OA.Binary Period, df=2) and
w=0.18 (OA.Sev Period, df=4) confirms that, if a smaller effect does exist in the population,
this sample does not meet thresholds for confident detection (Table 9.14).
Predictor Outcome N df Effect Size Sensitivity
Period OA (Binary) 131 2 0.08 0.27
Period OA Severity 131 4 0.18 0.36
Table 9.14: Power and sensitivity analysis for tests of conditional independence for Hypothesis IV. Effect size (w)is estimated from the observed results; sensitivity represents the minimum observable effect w with the availablesample size.
9.13.4 Hypothesis test IV summary
The null hypothesis cannot be rejected based on the frequency distribution of OA and of OA.Sev
stages among time periods. At N=131, statistical power is sufficient to detect a medium-sized
effect with 80% certainty. Ultimately, this suggests that, among the various predictors examined
in Hypothesis Tests II and IV, Age is the sole significant, reliable predictor of OA presence and
severity in this sample.
Chapter 10
Discussion and Conclusion
10.1 Summary of Results
10.1.1 Sample demographics
A study sample of 143 individuals (M=75, F=64, I=4) was selected according to osteological,
chronological, and ecogeographic criteria. Uncalibrated radiocarbon dates ranged from 560–
9100BP
10.1.2 Measurement error and reliability
Measurement error was estimated for osteometric variables by examining inter-observer differ-
ences in 23 femora and 27 lumbar vertebrae. Inter-observer error was found to be negligible and
unbiased for FXL, FXH, and NC-ML measurements, but significant interobserver differences
were observed in NC-AP measurements. Pairwise comparison of mean sizes, coefficients of vari-
ation, and standard errors of the mean with two published studies of vertebrae from Portugal
and England found no significant differences in size or variance among samples. The results
of these analyses suggest that measurement error levels are acceptable, ane that population
variance in the neural canal is comparable to that in the femur, and is not likely to have been
affected by observer measurement technique.
185
Chapter 10. Discussion and Conclusion 186
10.1.3 Distribution, homogeneity of variance, and collinearity testing
Osteometric variables were separated by sex and converted to z-scores in order to eliminate
sexual dimorphism. Most z-scores are normally distributed after z-transformation and multiple
imputation with the exception of several NC-AP measurements. As the principal analytical
methods are robust to slight deviations from normality, this was not cause for concern. Between-
groups variance was found to be homogeneous between sex and age categories, but uneven
among Region and Period categories. Ecogeographic region was not found to be a significant
influence on skeletal size or variance. Regional and temporal differences in variance meant that
robust comparative methods were warranted.
Joint-modification severity scores exhibit significant non-normal distributions and were
therefore converted to ordinal levels for analysis. Fisher’s Exact tests demonstrate that, while
age is a significant factor in presence and severity of OA, sex is not. Sex was therefore not
controlled in testing osteoarthritis-related hypotheses
Significant correlations were identified among osteological measurement variablesles, partic-
ularly between femoral and NC-ML, so that body size was considered as a possible confounder
in hypothesis-testing.
10.1.4 Hypothesis testing
Of the four research questions under investigation, the null hypotheses were rejected for Hy-
potheses I and III and retained for Hypotheses II and IV. In brief, while significant demographic
and temporal variation was identified in skeletal growth outcomes, joint degeneration (OA) was
found to associate with no predictor besides age at death.
In Hypothesis I, the first alternative position (age at death correlates positively with skeletal
size) was provisionally accepted. A 1-standard deviation increase in size translates to an average
of 60% greater odds of membership in the MA/EA age phase (OR=1.60, 95% CI=1.03–2.77).
The effect was found to be extant in both sexes but stronger in females than in males. Sup-
plementary analysis of NC measurements in multiple imputed datasets affirms the presence of
significant differences between YA and MA/EA in NC-ML.Z and FXH.Z. Multiple imputation
also allowed preliminary exploration of variation within the YA age group: sub-dividing this
Chapter 10. Discussion and Conclusion 187
group into VYA (those under 25 years) and YA (between 25–35 years) reveals nuance in the
relationship between NC size and age at death. Although both younger groups have smaller
means than the MA/EA group, the YA (25–35 years) group has the smallest mean. The reduc-
tion in power and concerns about balance and homogeneity of variance resulting from sample
subdivision precluded exploring this difference further at this time, but testing with a larger,
independent sample could further clarify the relationship between age and skeletal size.
In Hypothesis II, initial exploration of conditional independence suggests that individuals
who are in the first and last quartiles of body size (FXL.Z) might have greater chance of
having severe OA than those in the middle 50% of cases. However, age-controlled models
demonstrated that the only significant predictor for both presence and severity of OA is age
at death (OR=3.97, 95% CI=1.67–9.41 for binary age). Including FXL.Z in the model did not
improve its accuracy. The null hypothesis was upheld in this case.
In Hypothesis III, the null hypothesis that skeletal size variability is random and unbiased
throughout the middle and late Holocene was rejected; however, neither of the alternative
hypotheses could be unconditionally accepted. ANOVA with a polynomial model demonstrated
that the lowest mean values of both femoral size and NC-ML size are in the Middle period
(2000–3000BP) the time in which the number of skeletons represented in the record is also
greatest. OLS regression revealed considerably more complexity in the relationship, with wide
scatter and very low predictive accuracy in all vertebral segments. The most consistent pattern
of variation is a roughly linear increase in mean body size and NC-ML between the Middle
and Late periods (e.g. FXL.Z means: Early= -0.15, SEM=0.19; Middle = -0.28, SEM=0.14;
Late= 0.67, SEM=0.17. PCA-ML means: Early= -0.09, SEM=0.21; Middle= -0.28, SEM=0.13;
Late= 0.53, SEM=0.21). The Middle and Early periods cannot be distinguished in any size
variable. This result affirms prior findings of temporal changes in average body size over time,
particularly within the last two thousand years, although with a background of long-term
stability in body size that extends back at least as far as the Early Holocene (Kurki et al.,
2012; Pfeiffer and Sealy, 2006; Pfeiffer and Harrington, 2011; Sealy and Pfeiffer, 2000; Wilson
and Lundy, 1994). NC-ML diameters exhibit a similar but much weaker pattern of variability.
In contrast, variability in the anteroposterior (AP) diameter has no relationship to 14C and
unequivocally contradicts both alternative hypotheses.
Chapter 10. Discussion and Conclusion 188
Finally, the null position of Hypothesis IV was upheld. No meaningful temporal variation
could be detected in the prevalence or severity of OA cases, leading to the conclusion that age
at death is the sole reliable predictor of joint degeneration in this sample.
10.1.5 Power and sensitivity
Low power and sensitivity were a concern throughout this analysis. Even among models that
met the criteria for significance, and with the artificial assumption that the observed effect size
is representative of the effect in the greater population, average power was below β=0.8. At
minimum, follow-up research would require a test sample above N=200, with optimum power
being achieved at approximately N=1000.
10.2 Pathways between growth deficits and early death in the
Later Stone Age context
The positive relationship observed between age at death and growth outcome in this sample of
LSA KhoeSan men and women is broadly consistent with that reported by other bioarchaeo-
logical studies on widely differing samples for the neural canal (Clark et al., 1986, 1988, 1989;
Porter et al., 1987a; Watts, 2011, 2013a) and for body size (Dewitte and Hughes-Morey, 2012;
DeWitte and Wood, 2008; Gunnell et al., 2001; Kemkes-Grottenthaler, 2005; Steckel, 2005;
Watts, 2011) and more generally for enamel hypoplastic defects and other lesions (DeWitte,
2014; DeWitte and Bekvalac, 2010; DeWitte and Wood, 2008; Goodman and Armelagos, 1988,
1989; Klaus and Tam, 2009; Temple and Goodman, 2014; Usher, 2000; Wilson, 2014). These
results also parallel those of many studies that have linked growth deficit to various risk markers
and poorer life outcomes in a wide diversity of living populations (Adair et al., 2013; Barker
et al., 1989; Kuzawa et al., 2011; Jeffrey et al., 2003; Moore et al., 1997; Moura-Dos-Santos
et al., 2012; Norris et al., 2012; Stein et al., 2010; Van IJzendoorn et al., 2007; Victora et al.,
2008; Worthman and Kuzara, 2005; Ziol-Guest et al., 2012). However, reported results that
have shown inconsistent or null associations between age-at-death and growth outcomes in some
contexts (cf. Dewitte and Hughes-Morey, 2012; Goodman and Armelagos, 1989; Holland, 2013;
Klaus and Tam, 2009; Watts, 2013a) warrant cautious interpretation and examination of the
Chapter 10. Discussion and Conclusion 189
limitations and possible alternative explanations.
The observed pattern may reflect the influence of stress exposures beyond those experienced
only between gestation and early childhood, the time frame normally identified as being most
salient to developmental programming (Ice and James, 2012; Drake and Liu, 2010; Godfrey
et al., 2010; Monaghan, 2008; Moore et al., 1999; Stein et al., 2010). The null hypothesis is
contradicted only in the femoral head and the mediolateral neural canal, both measures that
reach their adult size at a later stage of ontogeny than the end of growth in the anteroposterior
neural canal (Eisenstein, 1977; Hinck et al., 1966; Papp et al., 1994; Ursu et al., 1996; Scheuer
and Black, 2000; Watts, 2013a). If interpreted through the lens of growth scheduling, these
results suggest that adult mortality risk in this population is associated with prolonged stunting
in childhood and adolescence rather than the gestational and infancy exposures cited by most
literature on developmental stress effects (Barker et al., 1989; Bateson et al., 2004; Benyshek,
2013; Drake and Liu, 2010; Hales and Barker, 2001; Gluckman and Hanson, 2006a; Godfrey
et al., 2010; Monaghan, 2008; Victora et al., 2008).
Though the correlation between growth deficit and early mortality is upheld, the pathway
that would connect the two conditions remains to be clarified. The rarity of growth faltering in
individuals who died prior to adulthood (Harrington and Pfeiffer, 2008; Pfeiffer and Harrington,
2011), suggests that most non-adults died relatively quickly, presumably of acute causes, and did
not often suffer from prolonged deprivation, though children did experience periods of arrested
growth (Pfeiffer and van der Merwe, 2004; Pfeiffer, 2012a; Sealy et al., 2000) and sick children
were sustained and cared for (Pfeiffer, 2011). Prenatal and postnatal exposure to nutritional
and pathogenic stresses may have been buffered by prolonged supplementary breastfeeding
(Clayton et al., 2006). People who grew to be unusually small adults may have been among a
relatively small number who survived repeated or chronic stresses in childhood — likely thanks
to such buffering practices — but for reasons that may have been either social or environmental
did not achieve their full potential growth.
Those very small adults who died earlier than their cohorts may have experienced deficits
in aspects of their physical capacity — immune defence, physical endurance and strength –
that influenced their ability to survive in later life. Both juveniles and adults are occasionally
found with evidence of cribra orbitalia and other skeletal lesions (e.g. Morris et al., 1987, 2005;
Chapter 10. Discussion and Conclusion 190
Pfeiffer, 2007, 2012b), though such lesions are still rare compared with more settled small-scale
societies (Pfeiffer, 2007). DeWitte and colleagues have shown that such indicators of proximate
physical stress are strong predictors of age-specific mortality hazard (DeWitte, 2014; DeWitte
and Wood, 2008). Questions of whether such indicators correlate with smaller skeletal size, are
more frequent between 3000–2000BP, or are independently associated with higher mortality
risk, could be answered by a systematic study of nonspecific stress indicators across the Later
Stone Age collection (Stynder et al., 2007a).
The principle of hidden heterogeneity may also contribute to the correlation between size
and age at death (Wood et al., 1992b; Wood, 1998): these individuals may have been exposed
to life-long stresses that influenced both childhood and adolescent growth and adulthood death
risks, such as undernutrition or infection, that occurred periodically throughout the lifespan.
Even in a non-stratified foraging society, individuals with prolonged exposure to deprivation
during ontogeny may continue to experience harsh conditions in adulthood that might influ-
ence their risk of early death. Though overall Holocene climate has been relatively stable
and non-challenging in the Cape Floristic Region, occasional periods of aridity (Cartwright
and Parkington, 1997; Chase et al., 2010; Meadows et al., 2010; Scott and Woodborne, 2007;
Valsecchi et al., 2013), alongside endemically high forager populations (Cox et al., 2009; Kim
et al., 2014) and possible territoriality (Cashdan et al., 1983; Dewar, 2010; Pfeiffer and Sealy,
2006; Sealy, 2006), could have caused times of stress for coastal foragers. Under such conditions,
developmentally-caused deficits in physical capacities such as strength and endurance may also
have a stronger influence on later survival than in less challenging contexts.
Both sexes exhibit a positive association between size and age at death, but the effect
appears to be stronger in females. The model of innate, development-induced frailty assumes
that causes of growth constraint and mortality are equivalent in both sexes, and that both
sexes suffer roughly equal susceptibility and exposure. Evidence of a stronger effect in women
may be evidence that, though men and women may have experienced exposure to growth
constraints, men may have been less exposed to direct mortality risks mediated by skeletal size.
Candidate causes might include gendered bias in access to nutrition or exposure to pathogens
in childhood, adulthood, or both, at sufficient intensity to stunt some women and enhance
their risk of early death; however, cultural data, ethnographic or archaeological, provide little
Chapter 10. Discussion and Conclusion 191
support for this scenario. A more parsimonious explanation is that small body size increased
the risk of early death through factors that uniquely affect reproductive-aged women. Short
stature, for example, is a noted risk factor for obstetric and postpartum complications, as is
maternal underweight (Brabin et al., 2002; Kemkes-Grottenthaler, 2005; Lewis, 2007; McCarthy
and Maine, 2013; Rush, 2000; Sokal et al., 1991; van Roosmalen and Brand, 1992; Wells et al.,
2012). Unusually small stature is associated with increased risk of obstructed labour, but also
with a higher risk of preterm birth, which itself comes with numerous potential complications
(Han et al., 2011).
Over-representation of reproductive-aged women in palaeodemographic assemblages is a
common finding and is althought to reflect deaths during or after pregnancy and childbirth
(Arriaza et al., 1988; Eshed et al., 2010; Lieverse et al., 2015; MacDonell, 1913; Wells et al.,
2012; Wilson, 2014). Recent findings show that young women in general outnumber young
men in the wider LSA death assemblage (Pfeiffer et al., 2014), although that sample, which
focussed on very young adults (late adolescents and early post-adolescents) was not large enough
to directly test for body size-related effects. Recent findings show that very young women in
general outnumber young men in the LSA death assemblage from all regions of the Cape (Pfeiffer
et al., 2014), a common observation in ancient populations, as first births are associated with
the highest proportionate risk of complications (Arriaza et al., 1988; MacDonell, 1913; Wells
et al., 2012; Wilcocks and Lancaster, 1951).
Pfeiffer’s recent study (2014), which compared the very young adults (including late adoles-
cents) to adults older than 23 years of age, found no significant difference in femur size (FXL
and FXH). In the present study, as well, VYA women were not found to be significantly smaller
than women who died in later life (MA, 35+), although the N for the VYA age group in this
sample is quite small (F = 9, M =10). This result seems to contradict any interpretation that
small size in women who died young is tied to obstetric mortality. One would intuitively ex-
pect the probabilistic relationship between small body size and age-at-death to be linear, with
the smallest individuals being most likely to die in the youngest age range, those who were
not so small being likely to survive longer, and so on. One might expect that women who
were at the greatest risk of obstetric complications – primiparae – would be more vulnerable
to complications related to small body size, and therefore exhibit smaller sizes in those who
Chapter 10. Discussion and Conclusion 192
did not survive their first births. The resolution to the contradiction may lie in the fact that
the relative influence of individual risk factors changes over the life span, so that very young
women may die for slightly different reasons than women in their peak reproductive years (25 –
35 years). Small body size, both as an indicator of maternal physical capacity and of maternal
health status, is an independent risk factor for first births, but does not necessarily alleviate
if the first birth is successful (Brabin et al., 2002; Gudmundsson et al., 2005; Harrison, 1983;
McCarthy and Maine, 2013; Rush, 2000; Sokal et al., 1991). Possibly the other common risks
associated with first births overwhelm the effect of small body size on mortality among very
young women – that they may have entered the death assemblage for a number of other com-
plications related to primiparturition, notably haemorrhage or obstruction (Becker et al., 2010;
Megafu and Ozumba, 1988; Ronsmans and Graham, 2006; Rush, 2000; van Roosmalen and
Brand, 1992; Wilcocks and Lancaster, 1951). If the relative magnitude of other obstetric risks
declines after the first birth, then size-related complications could make up a relatively greater
component of obstetric risk for multiparae. Thus, these results may not mean that VYA women
were less susceptible to size-related obstetric risks, but that they were more susceptible to a
range of other risks that come with first birth, which wiped out the effect of body size. This
question can be immediately and directly addressed by replicating the present analysis in the
larger sample studied by Pfeiffer and colleagues (N=155 femora, F=79, M=76), and in future
by incorporating other foraging populations to yield a larger, multiregional foraging sample.
In the LSA context, the concurrent processes of greater foraging intensity and reduced
mobility may have prompted a rise in fertility (Gage and DeWitte, 2009; Little, 1997; Sattenspiel
and Harpending, 1983). As no major economic or technological transformation took place over
this time, it is likely that mortality rates stayed relatively constant (Wood, 1998). Given an
upward trend in fertility, more frequent obstetric deaths could occur as a corollary of more
frequent exposure to pregnancy and parturition (Cohen and Armelagos, 1984; Eshed et al.,
2010; Gage and DeWitte, 2009; Larsen, 1997; Wells et al., 2012). Declining average stature
over the same time period is also a possible contributing factor, as small maternal birth-size
and small adult stature are both known risk factors for obstetric complications and mortality
(Brabin et al., 2002; Megafu and Ozumba, 1988; van Roosmalen and Brand, 1992; Rush, 2000;
Wilcocks and Lancaster, 1951). Findings from divergent, although overlapping, LSA KhoeSan
Chapter 10. Discussion and Conclusion 193
samples, including this study, indicate that both men and women exhibit a decline in size of the
postcranium, cranium, and dentition approximately 3000–2000 years ago (Ginter, 2011; Kurki
et al., 2012; Pfeiffer and Sealy, 2006; Stynder et al., 2007a).
The hypothesis that small body size influenced women’s risk of death from peri- and post-
partum complications cannot be tested directly in the present sample. There are a few recorded
instances of foetal and neonatal deaths (e.g. Harrington and Pfeiffer, 2008; Harrington, 2010)
and even of women buried with infant skeletons (unpublished data – NMB Skeleton 1; UCT317),
but cause of death cannot be identified in most cases. A maximum-likelihood-based hazard
modelling approach may be an effective way to indirectly test this hypothesis (Wood et al.,
2002). Hazard models permit the construction of population mortality schedules that are
relatively robust to small samples, explicitly account for population nonstationarity, and can
be used to estimate the relative mortality risk associated with specific factors, such as small
body size (Boldsen, 2007; Boldsen and Milner, 2012; DeWitte, 2014; DiGangi and Moore, 2012;
Usher, 2000; DeWitte and Wood, 2008). If the observed relationship between age-at-death
and skeletal size in LSA women is a consequence of selective obstetric mortality stemming
from higher fertility compounded by small body size, then the greatest sexual bias in mortality
hazard is should be observed during the Middle period (3000–2000BP). Small size would be
expected to yield an enhanced relative mortality risk for women in their reproductive years.
In sum, early growth conditions do seem to have been relevant to survival in Later Stone
Age people, especially among women. Although systemic inequality is not thought to be a
significant characteristic of Later Stone Age social organization, other contributing contextual
variables such as a decrease in nutritional resources — and therefore growth conditions —
concurrent with an increase in fertility, may have disproportionately influenced the risk of early
mortality among women.
10.3 Temporal variation in skeletal growth outcomes: the neu-
ral canal versus body size
The second half of the Holocene is characterized by intensified land use and by increasing
population sizes in many regions across the African continent and, indeed, the rest of the world
Chapter 10. Discussion and Conclusion 194
(Barham and Mitchell, 2008; Mitchell, 2002; Stock and Pinhasi, 2011). In coastal Southern
Africa, unlike in other regions, this process is associated with more intense foraging of small-
package foodstuffs, especially on the West Coast (Jerardino, 1998, 2010; Klein and Cruz-Uribe,
1983), with social partitioning of the landscape through direct and symbolic means (Dewar,
2010; Hall and Binneman, 1987; Hall, 2000; Sealy, 1986; Sealy and Pfeiffer, 2000; Wadley,
1987), but with only some groups taking up full-time herding (Sadr, 2003; Sealy, 2010; Kusimba,
2005). Reasons for this regional historical distinction are widely discussed and include ecological
constraints on the spread of domesticated species and the simple absence of a vacant niche in
local subsistence strategies (Barham and Mitchell, 2008; Gifford-Gonzalez, 2000; Marshall and
Hildebrand, 2002; Sadr, 2003; Sadr et al., 2008). Hunter-gatherers occupying the coastal and
near-coastal zone on the Cape were able to sustain relatively high populations based largely on
a foraging strategy that capitalized on the availability of reliable marine and terrestrial food
sources (Kim et al., 2014; Pfeiffer and Sealy, 2006).
The period of apparent peak activity is associated not only with greater visible variability in
body size, but also with decreased average body size (Ginter, 2011; Kurki et al., 2012; Pfeiffer,
2013; Pfeiffer and Sealy, 2006; Sealy and Pfeiffer, 2000; Wilson and Lundy, 1994). While the
former observation would be expected as a simple corollary of increased sample size, the latter
would not, and has been interpreted as evidence of occasional nutritional insufficiency during
that time (Ginter, 2011; Pfeiffer, 2013; Pfeiffer and Sealy, 2006).
Several researchers have documented this pattern in various aspects of skeletal size during
the later Holocene LSA: Wilson and Lundy (1994), who estimated the living statures of LSA
KhoeSan people (N=45) from approximately the same geographic study as in this study, are
credited with first noting a distinction in average statures between those who died between 2000–
3000BP and those who died in earlier or later time periods. Sealy and Pfeiffer (2000) report
similar observations in a sample from the South Coast. A number of those same skeletons
are included in the sample studied by Pfeiffer and Sealy (2006), who first fitted quadratic
regression models to the temporal distribution of femur lengths and head sizes (N=127) of
dated LSA skeletons recovered from the fynbos biome of the West Coast and the small subregion
of afromontane forest biome on the South Coast, a sample that includes many of the same
individuals as the current study sample. Their analysis shows that average body sizes (stature
Chapter 10. Discussion and Conclusion 195
and mass) based on FXL and FXH, are more similar prior to 4000BP and after 2000BP than
in the intervening period, in which variability is greater and average values smaller 2006, p.3.
This temporal pattern is evident in both biomes, though Pfeiffer (2013), in comparing temporal
variability in femoral size between North and South subregions of the West Coast, has shown
that the decline-rebound pattern is most pronounced in the arid northern sub-region of the
West Coast.
Pfeiffer et al. (2014) plotted regression curves for a sample of 155 femoral lengths derived
from the wider Southern African Cape (including the Eastern Cape province in addition to
the area represented in the current sample), and representing a time span from approximately
200 years to 9000 years BP. Among females at least, the quadratic curve in femoral lengths is
strongly influenced by a small number of very small, very young women around 3000BP; as a
result, the decline-rebound temporal pattern among women is attenuated when the youngest
are removed from the sample. Notably, Pfeiffer and Sealy, in their earlier analysis, also observe
this problem in the FXL sample, but note that femoral head diameters, which preserve a
larger number of relatively small individuals in the same time range, affirm the nadir in body
sizes between 3000–2000BP (Pfeiffer and Sealy, 2006, pp.3-7). Although these authors did not
estimate a quadratic OLS regression model for the entire sample of 155, their scatter plot of
FXL values against radiocarbon date indicates an upward trend in average statures during the
past 4500 years.
Bi-iliac breadth, a morphometric index of body mass (Auerbach and Ruff, 2004), has also
been compared across time periods on the Cape (Kurki et al., 2012). Kurki and colleagues
analysed temporal variation in bi-iliac breadth and the two femoral measures since 5000BP.
Preservation issues severely reduces the number of complete bi-iliac measurements (N=27),
but the extant measurements show a clear temporal pattern: neither linear, nor higher-order
polynomial regression curves are able to explain variation in body breadth across time (Kurki
et al., 2012, p.465), suggesting that truncal breadth is more stable than femoral size over time
(Auerbach and Ruff, 2004; Kurki et al., 2012). Kurki’s study also tested temporal variation in
cranial size, and in the allometry of head and body size (N=62). Their data show no evidence
of a significant change in the relationship between craniofacial and body size over time; nor do
they show significant change in absolute craniofacial size (Kurki et al., 2012, p.467). Ginter
Chapter 10. Discussion and Conclusion 196
(2011), too, studied cranial, dental, body, and limb size and shape across time in 73 KhoeSan
skeletons from the Eastern Cape, a distinct sample from that analysed here and by most of
the other studied described above. PCA scores of size in each of these skeletal regions exhibit
a generally linear increase in size from approximately 4000 BP to protohistoric times in both
males and females. Though significant quadratic models were reported for both cranial and
postcranial variables, sample sizes are quite small for individual variables and representation
through time is patchy see (Ginter, 2011). Finally, Stynder et al. (2007a) analysed a much larger
sample of 153 crania encompassing the Eastern, Southern, and Western geographical regions.
They report a mild, linear increase in cranial centroid side from 4000BP to the protohistoric
period with no evidence of a decline around 2000BP.
My results echo those of the studies described above in the case of both body size proxies and
neural canals. Both FXL and FXH z-scores are best described by a quadratic regression model,
although they exhibit considerable variation on either side of the regression line. However,
temporal patterns are much less clear in the neural canals: anteroposterior dimensions exhibit no
temporal patterning at all, while mediolateral dimensions exhibit a general increase in average
size between the Middle (3000–2000BP) and Late (post-2000BP) periods, but also a wide range
of variation, meaning that the descriptive power of the regression models is very weak 9.10. The
upward trend from the Middle to Late periods parallels that of cranial and odontometric size
as reported by Ginter (2011) and Stynder et al. (2007a). The distinction between ML and AP
dimesions may be a consequence of intersecting factors, including greater measurement error in
the AP dimension, potential developmental buffering of AP size, and more prolonged growth in
the mediolateral dimension, which results in greater correlation with overall body size (Clark
et al., 1986; Papp et al., 1997; Porter et al., 1987a; Ursu et al., 1996). The contrast between the
regression parameters from both femoral dimensions and the much lower adjusted R2 values
and wider standard errors observed in the NC-ML regressions (e.g.: PCA-ML adjR2=0.06,
SEE=0.99; FXL.Z adjR2=0.20, SEE=0.88) may be partly a product of sampling differences
between vertebral columns and femora: the higher adjusted R2 values may reflect the influence
of a few individuals with unusually short femora dating to between 2000 and 4000 BP and an
equally small number of medium to large-bodied individuals dating to between 4000 and 8000
BP (See Figures 9.8 and 9.9). The imputed NC datasets have a slightly higher sample size
Chapter 10. Discussion and Conclusion 197
than the two femoral variables (NC=105, FXL.Z=91, FXH.Z=93), which may also affect the
representation of variability.
In general, my results are consistent with the narrative of a period in which some people
experienced restricted linear growth. While average ML neural canal diameters follow a similar
temporal pattern as average femoral dimensions, not all individuals with linear growth restric-
tion suffered neuroskeletal restriction, and vice versa. Differences in the ontogenetic timing of
stress episodes may be implicated, although it is also likely that sampling error plays a role.
While the increase in average size between the Middle and Late Holocene does appear to
coincide with evidence of declining intensity of foraging activity, the influence of other rele-
vant factors such as the gradual arrival of stock-herding, cannot be ruled out without further
investigation. Historical records from early European visitors to the Cape of Good Hope, for
example, note visible differences in height between KhoeKhoe herders and the Sonqua, the
historical name given to Cape foragers (Stynder, 2009; Wilson and Lundy, 1994). It is possible
that the difference in average body size between the Middle and Late periods observed in this
study and by each of the published studies above is driven by herders or hunter-herders who
not only had access to the products of domesticated stock, but may also have had genuinely
higher status than full-time foragers at the time. This interpretation rises from historical ac-
counts that depicted the foraging Sonqua as subservient to or marginalized by neighbouring
pastoralists (Hall, 1986; Jerardino, 2003; Parkington, 1986), although, as Kusimba points out,
the relationships between Cape foragers and herders have been both variable across space and
fluid across time (Kusimba, 2005, p.340). This scenario could be tested by removing all those
individuals identified as possible herders by their isotopic signatures and replicating the analy-
sis. This strategy would affect statistical power because the sample from more recent centuries
tends to be small. It would also not be guaranteed to remove all herders from the Late period
sample because sheep were also part of the pastoral complex throughout the Cape (Jerardino
and Maggs, 2007; Sadr et al., 2008; Webley, 2007) and are broad-spectrum browsers that would
not necessarily yield isotopic signatures distinguishable from a mixed terrestrial-marine foraging
diet, particularly given the complex mixed C3 and C4 plant communities of the South Coast
(Sealy, 2010).
Chapter 10. Discussion and Conclusion 198
10.4 Joint degeneration as an osteological indicator of early
stress and allostatic disease
Even although preliminary exploration of simple independence did suggest that individuals in
the first and last quartiles of body size (FXL.Z) might have had greater chance of having severe
OA than those in the middle 50% of cases 9.4, controlled model comparisons demonstrate that
the only significant predictor for both presence and severity of OA is age at death; models
were not improved by incorporating any dimension of skeletal growth. Similarly, degenerative
disease does not vary in severity or frequency across time, indicating that neither physical
workloads nor systemic biological risk factors changed enough to affect the prevalence or severity
of degenerative joint disease during this time.
These findings are inconsistent with those of studies in archaeological (Weiss, 2005, 2006)
and living cohorts (Clynes et al., 2014; Jordan et al., 2005; Peterson, 1988; Peterson et al., 2010;
Sayer et al., 2003; Ziol-Guest et al., 2012) that link OA to reduced growth and derangement of
metabolic allostasis (Conaghan et al., 2005; Katz et al., 2010). The absence of a skeletal size
effect related may be a consequence of very low cardiometabolic risk factors in this population,
which would mean that the prospective cardiometabolic sequelae of early growth constraints
cited in living cohorts simply did not here. Though inflammatory and metabolic processes may
be significant contributors to OA in contemporary epidemiological populations, in which social
and ecological conditions promote high life expectancies and tendency to allostatic overload,
the very different conditions experienced by immediate-return foragers (high physical activity;
low adiposity; relatively low life expectancy) may well mean that the contributing factors here
are much more consistent with the classical wear-and-tear model of OA aetiology (Abramson
and Attur, 2009; Weiss and Jurmain, 2007).
Exploring age-controlled prevalence and intraskeletal patterning of OA as an indicator of
physical workload and general work behaviours (Jurmain et al., 2012) is a possible alternative
for interpreting OA patterns in this LSA collection. Though temporal variation in absolute
workloads may not have been sufficient to alter age-specific rates of joint degeneration, the
current study did not address the possibility of change in the types of physical activities that
made up those workloads. Weiss and Jurmain (2007) assert that, although the lifetime risk
Chapter 10. Discussion and Conclusion 199
of osteoarthritis is strongly determined by age and other non-volitional factors, the general
location of lesions may more accurately reflect the general distribution of mechanical stresses,
notably those heavy enough to cause injury, particularly when compared at the population
level.
Biomechanical studies of limb bone robusticity have uncovered differences among ecolog-
ically distinctive subregions like the inland Karoo, the flatter coastal forelands of the West
Coast, and the afromontane forest biome of the South Coast that seem to reflect ecologically
mediated differences in everyday physical practice such as hunting technique and terrestrial
mobility (Cameron and Pfeiffer, 2014; Churchill and Morris, 1998; Stock and Pfeiffer, 2004). A
study of regional and temporal patterns in the intra-skeletal distribution and bilateral asym-
metry of osteoarthritic lesions may reinforce these observations or uncover additional variation
in subsistence techniques among these groups.
10.5 Conclusions
This thesis sought to answer questions about the role played by developmental stress in the
adulthood morbidity and survival of a Later Stone Age population, via skeletal size as a proxy
indicator of growth quality. The people in focus pursued a mobile, immediate-return foraging
strategy in coastal and near-coastal habitats and maintained a small-scale, non-stratified social
structure until the centuries prior to European colonization. The study sought to address the
problem of confounding by heteroscedasticity of social and environmental conditions, a common
confound in bioarchaeological stress research . It also sought to provide a robust test of efficacy
for the neural canal, and to explore the prospective link between skeletal osteoarthritis and
growth, with an eye to its utility as an osteological indicator of allostatic degeneration.
10.5.1 Exploring the neural canal and degenerative joint disease as candidate
indicators of developmental stress
Prior bioarchaeological examination of early stress and mortality in foraging-specific contexts
has indicated mild associations between stress indicators and later outcomes (e.g. Lieverse et al.,
2007a; Temple and Goodman, 2014). This study uncovered evidence of a positive association
Chapter 10. Discussion and Conclusion 200
between indices of neuroskeletal and body size and the odds of surviving into later adulthood.
Both males and females have a positive association between NC-ML and age at death; how-
ever, counter to the prediction of a simple “developmental stress” model, the effect seems to
be strongest in women. This finding may be an artefact of sample size. Alternatively, it may
indicate that early stress affected skeletal growth but the association with mortality outcome
was mediated by intervening social or environmental factors that affected the sexes unequally.
Differential risk of peripartum complications among reproductive-aged women with small skele-
tal sizes is one possible intervenor that could be explored. Temporal variation in demographic
processes such as fertility may have contributed to the pattern.
The neural canal does appear to be a promising addition to the toolkit of stress indicators
that may be considered by bioarchaeological investigation in addition to discrete indicators like
enamel hypoplastic defects and continuous ones like body size. Transverse diameters appear to
compliment appendicular measures of body size. Though they mature later than the midsagittal
diameter (Hinck et al., 1966) and thus have a growth schedule somewhat closer to the general
somatic schedule (Bogin et al., 2012; Scheuer and Black, 2000), the weak correlation with femur
size also suggests that NC size encodes considerable variation that does not relate directly to
body size (Kurki et al., 2012). This suggests that the two growth trajectories are sufficiently
independent that they can reflect complimentary, if overlapping, aspects of information about
growth. Having constrained early growth may actually compound the effect of constrained later
growth in women, but the current sample precludes a direct test of this hypothesis.
Methodologically, NC-ML diameters are easy to measure reliably and a few can often be
measured in cases where the femora are not complete. Variability in size is sufficiently consistent
along the vertebral column, especially within the individual regions, that multiple imputation
and ordination methods may be used to generate a column-wide summary NC variable and a
filled-in dataset. Thus, they can also provide an important supplement to appendicular length
measurements in bioarchaeological settings.
The absence of effect in the AP dimension may indicate effective prenatal and postnatal
buffering in this population, but issues of measurement reliability in the AP dimension must
be resolved before drawing conclusions. The quantitative strategy of using multiple imputation
and ordination to extract a single representative measure of size is also a promising way to deal
Chapter 10. Discussion and Conclusion 201
with uneven preservation and the risk of multiple-testing error. This dimension may be better
characterized by a simple technical advance, such as using a long-jawed calipers to measure
the internal canal diameters, rather than the diameters of the cranial aperture. Explicitly
measuring the internal minimum and maximum dimensions of the canal, rather than diameters
at the cranial aperture, is also worth testing to see whether this would enhance its inter-observer
reliability, and its utility as a stress indicator.
Productive future avenues for research may include an explicit test for a correlation between
NC diameter and other nonspecific stress indicators, such as linear enamel hypoplastic defects,
which are known to form in infancy and early childhood. Hypoplastic defects were not initially
assessed in this study because of the frequency of heavy dental wear (Sealy et al., 1992); however,
their association with NC size may be investigated both among the younger people in this
population, and in other populations with less prevalent dental wear.
10.5.2 Applying the Developmental Origins of Health and Disease Hypoth-
esis to Later Stone Age foragers
Developmental stress would have been a relatively common phenomenon in the Later Stone
Age of southern Africa, as with most past populations and, indeed, most peoples living today.
Episodes of stress likely influenced patterns of growth and may also have programmed other
aspects of phenotype in ways that affected adult risk of early death. Though mortality rates
may indeed have varied over time and space during the LSA, and this may have influenced the
magnitude of the effect, the sample-wide signal detected here indicates that, on average, smaller
growth outcomes are associated with shorter adulthood survival, particularly for women.
The expectation that an enhanced association with age-at-death would be detected in struc-
tures that attain adult size in infancy (NC-AP) was not met, though childhood and adolescent
growth are implicated (FXH and NC-ML). The LSA AP measurements exhibit no significant
relationship to age at death. Though this result may be influenced by measurement error, it
does imply that, in the absence of systemic inequalities and the pathogenic and nutritional risks
of sedentary agricultural life, prenatal and postnatal conditions in this population were robust
to environmental insults, at least in those who survived to adulthood.
Although hopeful, these results imply that a DOHaD model does not wholly explain the
Chapter 10. Discussion and Conclusion 202
dynamics of growth and survival in past small-scale populations. Adulthood survival and growth
in childhood and adolescence may be mediated by external factors even among foragers whose
archaeological signature reveals no evidence of systemic inequalities in exposure to stressors.
The candidate causes of death may be more proximate and more direct than intrinsic frailty
precipitated by developmental programming: alternative mechanisms may include hidden intra-
sample heterogeneity in living conditions throughout the life course, and, for women, enhanced
risk of gestational and parturitional complications. Other, more proximate stress indicators,
like active evidence of infection, may more directly reflect greater susceptibility to early death
(e.g. DeWitte and Wood, 2008; DeWitte, 2014).
Appendix A
Appendix
203
Appendix A. Appendix 204
Figure A.1: Field Datasheet Sample, page 1
Appendix A. Appendix 205
Figure A.2: Field Datasheet Sample, page 2
Appendix A. Appendix 206
Figure A.3: Field Datasheet Sample, page 3
Appendix B
Appendix
207
Appendix B. Appendix 208
Table B.1: Demographic, geographic, and temporal variables of the full sample (page 1)
Appendix B. Appendix 209
Table B.2: Demographic, geographic, and temporal variables of the full sample (page 2)
Appendix B. Appendix 210
Table B.3: Demographic, geographic, and temporal variables of the full sample.Notes: F and M are assigned to cases with robust estimates of sex based on pelvic morphology (White et al.,2012). F (I) and M (I) are assigned to probable females and probable males based on body size and cranialmorphology. I is assigned to juveniles and cases that are morphologically intermediate or otherwise unidentifiable.Contextual information is derived from published sources and from field notes collected by Susan Pfeiffer (unpub-lished data). “No data” designations under "Site" are usually given because the skeleton was recovered withoutarchaeological investigation, typically by members of the public or police services, or because the archaeologicalreports were not available for this study. “Salvage” designations are given when the case was recovered in salvageoperations.Updated radiocarbon dates and stable isotope data are derived from a catalogue compiled by Alan Morrisand Susan Pfeiffer (unpublished data). Radiocarbon dates were produced by the following laboratories: OxfordUniversity (OxA), CSIR Pretoria (Pta), Beta Analytic Radiocarbon Services (Beta), the University of Groenigen(Gx), and University of Georgia (UGAMS).
Appendix B. Appendix 211
TableB.4:Osteologicalm
easurements
andjointmod
ificatio
nvaria
bles
ofthefullsample(page1)
Appendix B. Appendix 212
TableB.5:Osteologicalm
easurements
andjointmod
ificatio
nvaria
bles
ofthefullsample(page2)
Appendix B. Appendix 213
TableB.6:
Osteologicalm
easurements
andjointmod
ificatio
nvaria
bles
ofthefullsample.
Neu
ralc
analsan
djointmod
ificatio
nda
taarecolle
cted
forthis
stud
y.Fe
moral
leng
thsan
dhe
addiam
etersarecontrib
uted
bySu
sanPfeiffe
ran
dCathe
rineMerrit
t.Pu
blish
edFX
Han
dFX
Lmeasurements
areavailablein
Pfeiffe
r(201
3),P
feiffer
andSe
aly(200
6),S
ealy
andPfeiffe
r(200
0),a
ndW
ilson
andLu
ndy(199
4).FX
Lan
dFX
Hvalues
markedwith
anasteris
kareestim
ated
from
theirc
orrespon
ding
FXLor
FXH
measurementu
singthefollo
wingregressio
nequa
tions:FX
L=
7.27
07(F
XH)+
123.11
;FXH
=0.07
31(F
XL)
+9.38
49.
Appendix C
Appendix
214
Appendix C. Appendix 215
Table C.1: Parameters of Principal Components Analyses (PCA) for neural canal measurements. Ordinationis conducted separately for AP and ML dimensions at each imputation level. Components are extracted basedon a correlation matrix with 25 iterations allowable. The minimum acceptable eigenvalue is set at 1.0. Norotation method is used because only one dimension is generated in both cases. The minimum acceptableeigenvalue is set at 1.0. The KMO (Kaiser-Meyer-Olkin) statistic is a measure of sampling adequacy and isconsidered satisfactory above KMO=0.600 (Tabachnick and Fidell, 2007). Communalities are calculated for thefull correlation matrix and represent the squared multiple correlation value for each of the variables included inthe PCA model. Standardized factor scores are generated for each accepted dimension (PCA-AP and PCA-ML).
Appendix C. Appendix 216
Table C.2: Descriptive statistics of five imputed datasets generated by linear regression using the fully conditionalspecification method set to a maximum of 10 iterations (SPSS 20, IBM Corporation 2011). Imputation 0 is theoriginal dataset. F statistics are generated from comparison of means by one-way analysis of variance.
Appendix C. Appendix 217
Table C.3: Regional comparison of means and variance for z-transformed skeletal size variables. NC statisticsare from imputed datasets. An asterisk (*) indicates significant regional difference in means or variance at an αlevel of p<0.05.
Appendix C. Appendix 218
Table C.4: Demographic distribution of severity (ordinal factor) for OA and joint modification. Note: Severitylevels are as follows: 1= Unaffected, 2=Moderate (OA.Sev score below median), 3=Severe (OA.Sev score abovemedian).
Appendix C. Appendix 219
Table C.5: Zero-order correlation coefficients for all osteological measurements.
Appendix C. Appendix 220
Table C.6: Zero-order correlation coefficients for all osteological measurements. Coefficients for the originaldataset are presented above; those for the imputed dataset are presented below.
Appendix C. Appendix 221
Table C.7: Zero-order correlation coefficients for all osteological measurements among males. Coefficients for theoriginal dataset are presented above; those for the imputed dataset are presented below.
Appendix C. Appendix 222
Table C.8: Zero-order correlation coefficients for all osteological measurements among females. Coefficients forthe original dataset are presented above; those for the imputed dataset are presented below.
Appendix C. Appendix 223
Figure C.1: Conditional means plots for hypotheses II and IV. These plots help to test the assumption ofproportional odds, which is central to ordered logistic regression (Harrell, 2001). The violation of proportionalodds in the relationship between OA Severity and body size rank (FXL.rank), and between OA Severity and TimePeriod is demonstrated by the distribution of observed (solid line) relative to predicted (dashed line) conditionalmeans. The proportional odds assumption holds in the case of OA Age(Binary).
Bibliography
Abramson, S. B. and Attur, M. (2009). Developments in the scientific understanding of osteoarthritis. Arthritis
Res. Ther., 11(3):227.
Adair, L. S., Fall, C. H. D., Osmond, C., Stein, A. D., Martorell, R., Ramirez-Zea, M., Sachdev, H. S., Dahly,
D. L., Bas, I., Norris, S. a., Micklesfield, L., Hallal, P., and Victora, C. G. (2013). Associations of linear
growth and relative weight gain during early life with adult health and human capital in countries of low and
middle income: Findings from five birth cohort studies. The Lancet, 382(9891):525–534.
Adams, M. a. (2006). The mechanical environment of chondrocytes in articular cartilage. Biorheology, 43(3-
4):537–545.
Albert, M. A. and Maier, C. A. (2013). Epiphyseal union of the cervical vertebral centra: its relationship to
skeletal age and maturation of thoracic vertebral centra. J. Forensic Sci., 58(6):1568–1574.
Alwasel, S. H., Abotalib, Z., Aljarallah, J. S., Osmond, C., Alkharaz, S. M., Alhazza, I. M., Harrath, a.,
Thornburg, K., and Barker, D. J. P. (2011). Sex differences in birth size and intergenerational effects of
intrauterine exposure to Ramadan in Saudi Arabia. American Journal of Human Biology, 23(5):651–654.
Alwasel, S. H., Harrath, a., Aljarallah, J. S., Abotalib, Z., Osmond, C., Al Omar, S. Y., Khaled, I., and Barker,
D. J. P. (2013). Intergenerational effects of in utero exposure to Ramadan in Tunisia. American Journal of
Human Biology, 000(October 2012):2–4.
Araújo de França, G. V., Restrepo-Méndez, M. C., Loret de Mola, C., and Victora, C. G. (2014). Size at birth
and abdominal adiposity in adults: a systematic review and meta-analysis. Obes. Rev., 15(2):77–91.
Archer, C. W., Caterson, B., Benjamin, M., and Ralphs, J. R., editors (1999). The development of joints and
articular cartilage. Harwood Academic Publishers, Amsterdam, 1 edition.
Armelagos, G. J., Goodman, A. H., Harper, K. N., and Blakey, M. L. (2009). Enamel hypoplasia and early
mortality: Bioarcheological support for the Barker hypothesis. Evolutionary Anthropology, 18(6):261–271.
Arriaza, B., Allison, M., and Gerszten, E. (1988). Maternal mortality in pre-Columbian Indians of Arica, Chile.
American Journal of Physical Anthropology, 77(1):35–41.
224
BIBLIOGRAPHY 225
Auerbach, B. M. and Ruff, C. B. (2004). Human Body Mass Estimation : A Comparison of “ Morphometric ”
and “ Mechanical ” Methods. American Journal of Physical Anthropology, 125(December 2003):331–342.
Avery, G. (1987). Coastal birds and prehistory in the Western Cape. In Parkington, J. and Hall, M., editors, Pap.
Prehistory West. Cape, South Africa, chapter 09, pages 164–191. BAR International Reports 332, Oxford, UK.
Avery, G. and Underhill, L. G. (1986). Seasonal Exploitation of Seabirds by Late Holocene Coastal Foragers :
Analysis of Modern and Archaeological Data from the Western Cape , South Africa. Journal of Archaeological
Science.
Aykroyd, R. G., Lucy, D., Pollard, A. M., and Roberts, C. A. (1999). Nasty, Brutish, but Not Necessarily Short:
A Reconsideration of the Statistical Methods Used to Calculate Age at Death from Adult Human Skeletal
and Dental Age Indicators. American Antiquity, 64(1):55–70.
Azcorra, H., Dickinson, F., Bogin, B., Rodríguez, L., and Varela-Silva, M. I. (2015). Intergenerational influ-
ences on the growth of Maya children: The effect of living conditions experienced by mothers and maternal
grandmothers during their childhood. American Journal of Human Biology, 00(November 2014).
Baker, J., Hurtado, A. M., Pearson, O. M., Hill, K. R., Jones, T., and Frey, M. A. (2009). Developmental
plasticity in fat patterning of Ache children in response to variation in interbirth intervals: a preliminary
test of the roles of external environment and maternal reproductive strategies. American Journal of Human
Biology, 21(1):77–83.
Barbieri, C., Vicente, M., Rocha, J., Mpoloka, S. W., Stoneking, M., and Pakendorf, B. (2013). Ancient
substructure in early mtDNA lineages of Southern Africa. Am. J. Hum. Genet., 92(2):285–292.
Barham, L. and Mitchell, P. (2008). The first Africans: African archaeology from the earliest tool makers to most
recent foragers. Cambridge University Press, Cambridge, UK.
Barker, D. J. P. (2007). The origins of the developmental origins theory. J. Intern. Med., 261(5):412–417.
Barker, D. J. P. and Bagby, S. P. (2005). Developmental antecedents of cardiovascular disease: a historical
perspective. J. Am. Soc. Nephrol., 16(9):2537–2544.
Barker, D. J. P., Eriksson, J. G., Forsén, T., and Osmond, C. (2002). Fetal origins of adult disease: Strength of
effects and biological basis. Int. J. Epidemiol., 31(6):1235–1239.
Barker, D. J. P., Winter, P. D., Osmond, C., Margetts, B., and Simmonds, S. J. (1989). Weight in infancy and
death from ischaemic heart disease. Lancet, 2(8663):577–580.
Barnard, A. (1992). Hunters and herders of southern Africa: a comparative ethnography of the Khoisan peoples.
Cambridge University Press, Cambridge, UK, 1 edition.
BIBLIOGRAPHY 226
Bassan, H., Trejo, L. L., Kariv, N., Bassan, M., Berger, E., Fattal, A., Gozes, I., and Harel, S. (2000). Experi-
mental intrauterine growth retardation alters renal development. Pediatr. Nephrol., 15(3-4):192–195.
Bateson, P., Barker, D., Clutton-Brock, T., Deb, D., D’Udine, B., Foley, R., Gluckman, P., Godfrey, K., Kirk-
wood, T., Lahr, M., McNamara, J., Metcalfe, N. B., Monaghan, P., Spencer, H. G., and Sultan, S. E. (2004).
Developmental plasticity and human health. Nature, 430(6998):419–421.
Baxter, M. J. (2003). Statistics in archaeology. Oxford University Press, Oxford, UK.
Becker, N. S. A., Verdu, P., and Hewlett, B. (2010). Can Life History Trade-Offs Explain the Evolution of Short
Stature in Human Pygmies ? A Response to Migliano et al . () Can Life History Trade-Offs Explain the
Evolution of Short Stature in Human Pygmies ? A Response to Migliano et al . ( 2007 ). Human Biology,
82(1):17–27.
Bedford, M. E., Russell, K. F., Lovejoy, C. O., Meindl, R. S., Simpson, S. W., and Stuart-Macadam, P. L. (1993).
Test of the multifactorial aging method using skeletons with known ages-at-death from the Grant Collection.
American Journal of Physical Anthropology, 91(3):287–297.
Bellanti, J. A., Zeligs, B. J., and Kulszycki, L. L. (1997). Nutrition and development of pulmonary defense
mechanisms. Pediatr. Pulmonol. Suppl., 16:170–171.
Benyshek, D. C. (2013). The “early life” origins of obesity-related health disorders: New discoveries regarding
the intergenerational transmission of developmentally programmed traits in the global cardiometabolic health
crisis. American Journal of Physical Anthropology, 00:n/a—-n/a.
Berbesque, J. C., Marlowe, F. W., Shaw, P., and Thompson, P. (2014). Hunter – gatherers have less famine than
agriculturalists. Biology Letters, 10(January).
Bernard, T. E., Wilder, F. V., Aluoch, M., and Leaverton, P. E. (2010). Job-related osteoarthritis of the knee,
foot, hand, and cervical spine. J. Occup. Environ. Med., 52(1):33–38.
Binder, D. K., Ph, D., Schmidt, M. H., and Weinstein, P. R. (2002). Lumbar Spinal Stenosis. Semin. Neurol.,
22(2):157–165.
Black, W. (2014). Dental morphology and variation across Holocene Khoesan people of southern Africa. Phd
dissertation, University of Cape Town.
Blackwell, A. D., Snodgrass, J. J., Madimenos, F. C., and Sugiyama, L. S. (2010). Life history, immune func-
tion, and intestinal helminths: Trade-offs among immunoglobulin E, C-reactive protein, and growth in an
Amazonian population. American Journal of Human Biology, 22(6):836–848.
Bland, J. M. and Altman, D. G. (1996). Statistics Notes: Measurement error and correlation coefficients. Br.
Med. J., 313(7):41.
BIBLIOGRAPHY 227
Bland, J. M. and Altman, D. G. (2000). The odds ratio. Br. Med. J., 320(5):2000.
Blurton Jones, N. G., Hawkes, K., and O’Connell, J. F. (2002). Antiquity of postreproductive life: are there mod-
ern impacts on hunter-gatherer postreproductive life spans? American Journal of Human Biology, 14(2):184–
205.
Blurton Jones, N. G., Smith, L. C., O’Connell, J. F., Hawkes, K., and Kamuzora, C. L. (1992). Demography
of the Hadza, an increasing and high density population of Savanna foragers. American Journal of Physical
Anthropology, 89(2):159–181.
Bogin, B. and Baker, J. (2012). Low birth weight does not predict the ontogeny of relative leg length of infants
and children: an allometric analysis of the NHANES III sample. American Journal of Physical Anthropology,
148(4):487–494.
Bogin, B. and Rios, L. (2003). Rapid morphological change in living humans: implications for modern human
origins. Comp. Biochem. Physiol. Part A, 136(1):71–84.
Bogin, B., Smith, B. H., and Smithl, M. D. H. (2012). Evolution of the Human Life Cycle. In Stinson, S., Bogin,
B., and O’Rourke, D., editors, Human Biology: An Evolutionary and Biocultural Perspective, chapter 11,
pages 703–716. Wiley-Blackwell, Hoboken, 2 edition.
Bogin, B. and Varela-Silva, M. I. (2010). Leg length, body proportion, and health: a review with a note on
beauty. Int. J. Environ. Res. Public Health, 7(3):1047–1075.
Bogin, B., Varela-Silva, M. I., and Rios, L. (2007). Life History Trade-Offs in Human Growth: Adaptation or
Pathology ? American Journal of Human Biology, 19:631–642.
Boldsen, J. L. (1998). Body proportions in a medieval village population: effects of early childhood episodes of
ill health. Annals of Human Biology, 25(4):309–317.
Boldsen, J. L. (2005). Analysis of dental attrition and mortality in the Medieval village of Tirup, Denmark.
American Journal of Physical Anthropology, 126(2):169–176.
Boldsen, J. L. (2007). Early Childhood Stress and Adult Age Mortality — A Study of Dental Enamel Hypoplasia
in the Medieval Danish Village of Tirup. American Journal of Physical Anthropology, 66(April 2006):59–66.
Boldsen, J. L. and Milner, G. R. (2012). An Epidemiological Approach to Paleopathology. In Grauer, A. L.,
editor, A Companion to Palaeopathology, chapter 7, pages 114–132. Blackwell Publishing, Ltd, 1 edition.
Boldsen, J. L., Milner, G. R., Konigsberg, L. W., and Wood, J. W. (2002). Transition analysis: a new method for
estimating age from skeletons. In Hoppa, R. D. and Vaupel, J. W., editors, Paleodemography: Age Distributions
from Skeletal Samples, chapter 5, pages 73–106. Cambridge University Press, Cambridge, UK, 1 edition.
BIBLIOGRAPHY 228
Boocock, P., Roberts, C. A., and Manchester, K. (1995). Maxillary sinusitis in Medieval Chichester, England.
American Journal of Physical Anthropology, 98(4):483–495.
Borgerhoff Mulder, M., Fazzio, I., Irons, W., McElreath, R. L., Bowles, S., Bell, A., Hertz, T., and Hazzah, L.
(2010). Pastoralism and Wealth Inequality. Current Anthropology, 51(1):35–48.
Born, J., Linder, H. P., and Desmet, P. (2007). The Greater Cape Floristic Region. J. Biogeogr., 34(1):147–162.
Brabin, L., Verhoeff, F., and Brabin, B. J. (2002). Maternal height, birthweight and cephalo pelvic disproportion
in urban Nigeria and rural Malawi. Acta Obstet. Gynecol. Scand., 81(6):502–507.
Brandt, I., Sticker, E. J., and Lentze, M. J. (2003). Catch-up growth of head circumference of very low
birth weight, small for gestational age preterm infants and mental development to adulthood. J. Pediatr.,
142(5):463–470.
Braveman, P., Egerter, S., and Williams, D. R. (2011). The social determinants of health: coming of age. Annu.
Rev. Public Health, 32:381–398.
Breton, G., Schlebusch, C. M., and Soodyall, H. (2014). Report: Lactase Persistence Alleles Reveal Partial East
African Ancestry of Southern African Khoe Pastoralists. Curr. Biol., 24(8):852–858.
Bridges, P. S. (1991). Degenerative joint disease in hunter-gatherers and agriculturalists from the Southeastern
United States. American Journal of Physical Anthropology, 85(4):379–391.
Brooks, S. and Suchey, J. M. (1990). Skeletal age determination based on the os pubis: A comparison of the
Acsádi-Nemeskéri and Suchey-Brooks methods. Journal of Human Evolution, 5(3):227–238.
Buchanan, W. F. (1987). Calories in prehistory. In Parkington, J. and Hall, M., editors, Pap. Prehistory West.
Cape, South Africa, chapter 10, pages 192–211. BAR International Reports 332, Oxford, UK.
Buckberry, J. L. and Chamberlain, A. T. (2002). Age estimation from the auricular surface of the ilium: a
revised method. American Journal of Physical Anthropology, 119(3):231–239.
Buckwalter, J. a. and Brown, T. D. (2004). Joint Injury, Repair, and Remodeling. Clin. Orthop. Relat. Res.,
423(423):7–16.
Buckwalter, J. a., Saltzman, C., and Brown, T. (2004). The Impact of Osteoarthritis. Clin. Orthop. Relat. Res.,
427(427):S6—-S15.
Buckwalter JA and Hunziker, E. B. (1999). Articular cartilage morphology and biology. Harwood Academic
Publishers, Amsterdam, 1 edition.
Buikstra, J. and Ubelaker, D. (1994). Standards for Data Collection from Human Skeletal Remains. Arkansas
Archeological Survey, Fayetteville.
BIBLIOGRAPHY 229
Calder, P. C., Krauss-etschmann, S., Jong, E. C. D., Dupont, C., Frick, J.-S., Frokiaer, H., Heinrich, J., Garn,
H., Koletzko, S., and Lack, G. (2006). Early nutrition and immunity – progress and perspectives. Br. J. Nutr.,
44:774–790.
Cameron, L. (2007). Growth Patterns in Adverse Environments. American Journal of Human Biology,
19(February):615–621.
Cameron, L., Preece, M. A., and Cole, T. I. M. J. (2005). Catch-up Growth or Regression to the Mean ?
Recovery from Stunting Revisited. American Journal of Human Biology, 417:412–417.
Cameron, M. E. and Pfeiffer, S. (2014). Long bone cross-sectional geometric properties of Later Stone Age
foragers and herder–foragers. S. Afr. J. Sci., 110(9/10):1–11.
Cameron, N. (1991). Human Growth, Nutrition , and Health Status in Su b-Saharan Africa. Yearbook of Physical
Anthropology, 34:211–250.
Cameron, N. and Demerath, E. W. (2002). Critical periods in human growth and their relationship to diseases
of aging. American Journal of Physical Anthropology, 119(S35):159–184.
Cardoso, H. F. V. (2007). Environmental Effects on Skeletal Versus Dental Development : Using a Documented
Subadult Skeletal Sample to Test a Basic Assumption in Human Osteological Research. American Journal of
Physical Anthropology, 233(October 2006):223–233.
Carr, A. S., Thomas, D. S. G., Bateman, M. D., Meadows, M. E., and Chase, B. (2006). Late Quaternary
palaeoenvironments of the winter-rainfall zone of southern Africa: Palynological and sedimentological evidence
from the Agulhas Plain. Palaeogeogr. Palaeoclimatol. Palaeoecol., 239(1-2):147–165.
Cartwright, C. and Parkington, J. (1997). The wood charcoal assemblages from Elands Bay Cave, South-
western Cape: principles, procedures and preliminary interpretation. South African Archaeological Bulletin,
52(165):59–72.
Cashdan, E., Barnard, A., Bicchieri, M. C., Bishop, C. A., Blundell, V., Ehrenreich, J., Guenther, M., Hamilton,
A., Harpending, H. C., Howell, N., and Others (1983). Territoriality among human foragers: ecological models
and an application to four Bushman groups [and Comments and Reply]. Current Anthropology, 24(1):47–66.
Catalano, R., Yorifuji, T., and Kawachi, I. (2013). Natural selection In Utero: Evidence from the great east
japan earthquake. American Journal of Human Biology, 00(May):1–5.
Caulfield, L. E., Onis, M. D., Blössner, M., and Black, R. E. (2004). Undernutrition as an underlying cause
of child deaths associated with diarrhea , pneumonia , malaria , and measles. American Journal of Clinical
Nutrition, 80:193–198.
BIBLIOGRAPHY 230
Chamberlain, A. (2000). Problems and prospects in palaeodemography. Hum. Osteol. Archaeol. forensic Sci.,
pages 101–115.
Chase, B. M. and Meadows, M. E. (2007). Late Quaternary dynamics of southern Africa’s winter rainfall zone.
Earth-Science Rev., 84(3-4):103–138.
Chase, B. M., Meadows, M. E., Carr, A. S., and Reimer, P. J. (2010). Evidence for progressive Holocene
aridification in southern Africa recorded in Namibian hyrax middens: Implications for African Monsoon
dynamics and the ”African Humid Period”. Quat. Res., 74(1):36–45.
Chen, X., Zhang, Z.-X., George, L. K., Wang, Z.-S., Fan, Z.-J., Xu, T., Zhou, X.-L., Han, S.-M., Wen, H.-B.,
and Zeng, Y. (2012). Birth measurements, family history, and environmental factors associated with later-life
hypertensive status. Am. J. Hypertens., 25(4):464–471.
Chen, Y. and Zhou, L.-A. (2007). The long-term health and economic consequences of the 1959–1961 famine in
China. J. Health Econ., 26(4):659–681.
Christian, P., Bunjun Srihari, S., Thorne-Lyman, A., Khatry, S. K., LeClerq, S. C., and Ram Shrestha, S. (2006).
Eating Down in Pregnancy: Exploring Food-Related Beliefs and Practices of Pregnancy in Rural Nepal.
Chung, G. C. and Kuzawa, C. W. (2014). Intergenerational Effects of Early Life Nutrition : Maternal Leg Length
Predicts Offspring Placental Weight and Birth Weight Among Women in Rural Luzon ,. American Journal of
Human Biology, 659(November 2013):652–659.
Churchill, S. E. and Morris, A. G. (1998). Muscle marking morphology and labour intensity in prehistoric
Khoisan foragers. International Journal of Osteoarchaeology, 8(5):390–411.
Clark, G., Panjabi, M., and Wetzel, F. T. (1985). Can Infant Malnutrition Cause Adult Vertebral Stenosis?
Spine (Phila. Pa. 1976)., 10(2):165–170.
Clark, G. A. (1988). New method for assessing changes in growth and sexual dimorphism in paleoepidemiology.
American Journal of Physical Anthropology, 77(1):105–116.
Clark, G. A., Hall, N. R., Adlwin, C. M., Harris, J. M., Borkan, G. A., and Srinivasan, M. (1988). Measures of
Poor Early Growth Are Correlated with Lower Adult Levels of Thymosin-alpha1: Results from the Normative
Aging Study. Human Biology, 60(3):435–451.
Clark, G. a., Hall, N. R., Armelagos, G. J., Borkan, G. a., Panjabi, M. M., Wetzel, F. T., Armelagos Gand
Borkan, G., Panjabi, M. M., Wetzel, F. T., Armelagos, G. J., Borkan, G. a., Panjabi, M. M., and Wetzel,
F. T. (1986). Poor Growth Prior to Early Childhood: Decreased Health and Life-Span in the Adult. American
Journal of Physical Anthropology, 70(2):145–160.
BIBLIOGRAPHY 231
Clark, G. A., Hall, N. R., and Spirosz, A. (1989). Is Poor Early Growth Related to Adult Immune Aging ? A
Follow-Up Study. American Journal of Human Biology, 1:331–337.
Clarke, E. M., Thompson, R. C., Allam, A. H., Wann, L. S., Lombardi, G. P., Sutherland, M. L., Sutherland,
J. D., Cox, S. L., Soliman, M. A. T., Abd el Maksoud, G., Badr, I., Miyamoto, M. I., Frohlich, B., Nur el din,
A. H., Stewart, A. F. R., Narula, J., Zink, A. R., Finch, C. E., Michalik, D. E., and Thomas, G. S. (2014). Is
atherosclerosis fundamental to human aging? Lessons from ancient mummies. J. Cardiol., 63(5):329–334.
Clarkin, P. F. (2008). Adiposity and Height of Adult Hmong Refugees : Relationship with War-Related Early
Malnutrition and Later Migration. American Journal of Human Biology, 184:174–184.
Clarkin, P. F. (2012). War, forced displacement and growth in Laotian adults. Annals of Human Biology,
39(1):36–45.
Clayton, F., Sealy, J., and Pfeiffer, S. (2006). Weaning age among foragers at Matjes River Rock Shelter,
South Africa, from stable nitrogen and carbon isotope analyses. American Journal of Physical Anthropology,
129(2):311–317.
Clements, K. M., Bee, Z. C., Crossingham, G. V., Adams, M. a., and Sharif, M. (2001). How severe must
repetitive loading be to kill chondrocytes in articular cartilage? Osteoarthritis Cartilage, 9(5):499–507.
Clynes, M., Parsons, C., Edwards, M., Jameson, K., Harvey, N., Aihie Sayer, A., Cooper, C., and Dennison, E.
(2014). Further evidence of the developmental origins of osteoarthritis: results from the Hertfordshire Cohort
Study. J. Dev. Orig. Health Dis., 5(6):453–458.
Coale, A. J. (1972). The growth and structure of human populations: a mathematical investigation. Princeton
University Press, Princeton, NJ.
Coale, A. J. and P, D. (1966). Regional model life tables and stable populations. Princeton University Press,
Princeton, NJ.
Cohen, H. W. (2011). P values: use and misuse in medical literature. Am. J. Hypertens., 24(1):18–23.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, New
York, NY, 2 edition.
Cohen, M. N. and Armelagos, G. J. (1984). Paleopathology at the Origins of Agriculture. Academic Press,
Orlando, FL.
Cohen, M. N. and Crane-Kramer, G. M. M. (2007). Ancient Health: Skeletal Indicators of Agricultural and
Economic Intensification. University Press of Florida, Gainesville, FL.
Cohen, M. N., Wood, J. W., and Milner, G. R. (1994). The Osteological Paradox Reconsidered. Current
Anthropology, 35(5):629–637.
BIBLIOGRAPHY 232
Cohen, S., Doyle, W. J., Turner, R. B., Alper, C. M., and Skoner, D. P. (2004). Childhood socioeconomic status
and host resistance to infectious illness in adulthood. Psychosom. Med., 66:553–558.
Collinson, A. C., Moore, S. E., Cole, T. J., and Prentice, A. M. (2003). Birth season and environmental influences
on patterns of thymic growth in rural Gambian infants. Acta Paediatr., 92(10):1014–1020.
Conaghan, P. G., Vanharanta, H., and Dieppe, P. a. (2005). Is progressive osteoarthritis an atheromatous vascular
disease? Ann. Rheum. Dis., 64(11):1539–1541.
Conard, N. J. and Kandel, A. W. (2006). The economics and settlement dynamics of the later Holocene inhabi-
tants of near coastal environments in the West Coast National Park (South Africa). In Wotzka, H.-P., editor,
Grundlegungen. Beiträge zur europäischen und afrikanischen Archäeologie für Manfred Manfred KH Eggert,
pages 329–355. Franke, Tübingen.
Cook, D. C. and Buikstra, J. E. (1979). Health and differential survival in prehistoric populations: prenatal
dental defects. American Journal of Physical Anthropology, 51:649–664.
Cordain, L., Eaton, S. B., Sebastian, A., Mann, N., Lindeberg, S., Watkins, B. A., and Keefe, J. H. O. (2005).
Origins and evolution of the Western diet : health implications for the 21st century. American Journal of
Clinical Nutrition.
Cordain, L., Miller, J. B., Eaton, S. B., Mann, N., Holt, S. H., and Speth, J. D. (2000). Plant-animal subsistence
ratios and macronutrient energy estimations in worldwide hunter-gatherer diets. American Journal of Clinical
Nutrition, 71(3):682–692.
Cottrell, E. C., Holmes, M. C., Livingstone, D. E., Kenyon, C. J., and Seckl, J. R. (2012). Reconciling the
nutritional and glucocorticoid hypotheses of fetal programming. FASEB J., 26(5):1866–1874.
Cottrell, E. C. and Seckl, J. R. (2009). Prenatal stress, glucocorticoids and the programming of adult disease.
Front. Behav. Neurosci., 3(September):19.
Cox, M. P., Morales, D. a., Woerner, A. E., Sozanski, J., Wall, J. D., and Hammer, M. F. (2009). Autosomal
resequence data reveal Late Stone Age signals of population expansion in sub-Saharan African foraging and
farming populations. PLoS One, 4(7):e6366.
Crubézy, É., Goulet, J., Bruzek, J., Jelinek, J., Rougé, D., and Ludes, B. (2002). Épidémiologie De L’Arthrose
Et Des Enthésopathies Dans Une Population Européenne D’Il Y a 7 700 Ans. Rev. Rhum., 69(12):1217–1225.
Dahaghin, S., Bierma-Zeinstra, S. M. a., Koes, B. W., Hazes, J. M. W., and Pols, H. a. P. (2007). Do metabolic
factors add to the effect of overweight on hand osteoarthritis? The Rotterdam Study. Ann. Rheum. Dis.,
66(7):916–920.
BIBLIOGRAPHY 233
Dancause, K. N., Cao, X. J., Veru, F., Xu, S., Long, H., Yu, C., Laplante, D. P., Walker, C. D., and King,
S. (2012). Brief communication: prenatal and early postnatal stress exposure influences long bone length in
adult rat offspring. American Journal of Physical Anthropology, 149(2):307–311.
Dancause, K. N., Laplante, D. P., Oremus, C., Fraser, S., Brunet, A., and King, S. (2011). Disaster-related
prenatal maternal stress influences birth outcomes: project Ice Storm. Early Human Development, 87(12):813–
820.
Davies, H., Crombie, I. K., and Tavakoli, M. (1998). When can odds ratios mislead? Br. Med. J.,
316(February):1–12.
Dayal, M. R., Kegley, A. D. T., Strkalj, G., Bidmos, M. a., and Kuykendall, K. L. (2009). The history and
composition of the Raymond A. Dart Collection of Human Skeletons at the University of the Witwatersrand,
Johannesburg, South Africa. American Journal of Physical Anthropology, 140(2):324–335.
Deacon, H. J. and Deacon, J. (1999). Human beginnings in South Africa: uncovering the secrets of the Stone
Age. Altamira Press, Walnut Creek, CA.
Deacon, J. (1987). Holocene and Pleistocene palaeoclimates in the Western Cape. In Parkington, J. and Hall,
M., editors, Pap. Prehistory West. Cape, South Africa, chapter 2, pages 24–34. BAR International Reports
332, Oxford, UK.
DeBoer, M. D., Lima, A., Oría, R. B., Scharf, R. J., Moore, S. R., Luna, M., and Guerrant, R. L. (2012). Early
childhood growth failure and the developmental origins of adult disease: do enteric infections and malnutrition
increase risk for the metabolic syndrome? Nutr. Rev., 70(11):642–653.
Demas, G. E. (2004). The energetics of immunity: A neuroendocrine link between energy balance and immune
function.
Dequeker, J., Aerssens, J., Luyten, F. P., and Discovery, D. (2003). Osteoarthritis and osteoporosis: clinical and
research evidence of inverse relationship. Aging Clin. Exp. Res., 15(5):426–439.
D’Errico, F., Backwell, L., Villa, P., Degano, I., Lucejko, J. J., Bamford, M. K., Higham, T. F. G., Colombini,
M. P., and Beaumont, P. B. (2012). Early evidence of San material culture represented by organic artifacts
from Border Cave, South Africa. Proceedings of the National Academy of Sciences, 109(33):13214–13219.
Dettwyler, K. A. (1991). Can paleopathology provide evidence for compassion? American Journal of Physical
Anthropology, 84:375–384.
Dewar, G. (2010). Late Holocene burial cluster at Diaz Street Midden, Saldanha Bay, Western Cape, South
Africa. South African Archaeological Bulletin, 65(191):26–34.
BIBLIOGRAPHY 234
Dewar, G. and Pfeiffer, S. (2004). Postural Behaviour of Later Stone Age People in South Africa. South African
Archaeological Bulletin, pages 52–58.
Dewar, G. and Pfeiffer, S. (2010). Approaches to estimating marine protein in human collagen for radiocarbon
date calibration. Radiocarbon, 52(4):1611–1625.
Dewar, G., Reimer, P. J., Sealy, J., and Woodborne, S. (2012). Late-Holocene marine radiocarbon reservoir
correction ( R) for the west coast of South Africa. The Holocene, 22(12):1481–1489.
DeWitte, S. N. (2014). Differential survival among individuals with active and healed periosteal new bone
formation. International Journal of Paleopathology, 7:38–44.
DeWitte, S. N. and Bekvalac, J. (2010). Oral health and frailty in the medieval English cemetery of St Mary
Graces. American Journal of Physical Anthropology, 142(3):341–354.
Dewitte, S. N. and Hughes-Morey, G. (2012). Stature and frailty during the Black Death: the effect of stature on
risks of epidemic mortality in London, A.D. 1348-1350. Journal of Archaeological Science, 39(5):1412–1419.
DeWitte, S. N. and Wood, J. W. (2008). Selectivity of black death mortality with respect to preexisting health.
Proceedings of the National Academy of Sciences, 105(5):1436–1441.
DiGangi, E. A. and Moore, M. K. (2012). Research methods in human skeletal biology. Academic Press.
Doak, C. M., Adair, L. S., Bentley, M., Monteiro, C., and Popkin, B. M. (2005). The dual burden household and
the nutrition transition paradox. Int. J. Obes., 29(1):129–136.
Dore, D., Martens, A., Quinn, S., Ding, C., Winzenberg, T., Zhai, G., Pelletier, J.-P., Martel-Pelletier, J., Abram,
F., Cicuttini, F., and Jones, G. (2010a). Bone marrow lesions predict site-specific cartilage defect development
and volume loss: a prospective study in older adults. Arthritis Res. Ther., 12(6):R222.
Dore, D., Quinn, S., Ding, C., Winzenberg, T., Zhai, G., Cicuttini, F., and Jones, G. (2010b). Natural history
and clinical significance of MRI-detected bone marrow lesions at the knee: a prospective study in community
dwelling older adults. Arthritis Res. Ther., 12(6):R223.
Dos Santos Silva, I. (1999). Cancer Epidemiology: Principles and Methods.
Dow, W. H., Schoeni, R. F., Adler, N. E., and Stewart, J. (2010). Evaluating the evidence base: policies and
interventions to address socioeconomic status gradients in health. Ann. N. Y. Acad. Sci., 1186:240–251.
Doyle, L. E. (2012). The vertebral neural canal: exploring the effect of body size on demographic patterns in a
potential indicator of nonspecific childhood stress.
Doyle, L. E. (2015). Early-life growth deficits and adulthood mortality: developmental stress effects in KhoeSan
foragers from southern Africa’s Later Stone Age.
BIBLIOGRAPHY 235
Drake, A. J. and Liu, L. (2010). Intergenerational transmission of programmed effects: Public health conse-
quences. Trends Endocrinol. Metab., 21(December):206–213.
Drake, A. J., Walker, B. R., Seckl, J. R., Amanda, J., Walker, B. R., and Seckl, J. R. (2005). Intergenerational
consequences of fetal programming by in utero exposure to glucocorticoids in rats. Am. J. Physiol. Regul.
Integr. Comp. Physiol., 288(6):34–38.
Drawer, S. (2001). Propensity for osteoarthritis and lower limb joint pain in retired professional soccer players.
Br. J. Sports Med., 35(6):402–408.
Early, J. D. and Headland, T. N. (1998). Population Dynamics of a Philippine Rain Forest People: The San
Ildefonso Agta. University Press of Florida1, Gainesville, 1 edition.
Eaton, S. B., Konner, M., and Shostak, M. (1988). Stone agers in the fast lane: chronic degenerative diseases in
evolutionary perspective. American Journal of Medicine, 84(4):739–749.
Eisenstein, S. (1977). The morphometry and pathological anatomy of the lumbar spine in South African Negroes
and Caucasoids with specific reference to spinal stenosis. J. Bone Jt. Surg., 59-B(2):173–180.
Eisenstein, S. (1983). Lumbar Vertebral Canal Morphometry for Computerised Tomography in Spinal Stenosis.
Spine (Phila. Pa. 1976)., 8(2):187–191.
Englund, M. and Lohmander, L. S. (2004). Risk factors for symptomatic knee osteoarthritis fifteen to twenty-two
years after meniscectomy. Arthritis Rheum., 50(9):2811–2819.
Englund, M., Paradowski, P. T., and Lohmander, L. S. (2004a). Association of radiographic hand osteoarthritis
with radiographic knee osteoarthritis after meniscectomy. Arthritis Rheum., 50(2):469–475.
Englund, M., Roos, H., and Lohmander, L. S. (2004b). High Prevalence of Knee Osteoarthritis , Pain , and
Functional Limitations in Female Soccer Players Twelve Years After Anterior Cruciate Ligament Injury.
Arthritis Rheum., 50(10):3145–3152.
Engström, G., Gerhardsson de Verdier, M., Rollof, J., Nilsson, P. M., and Lohmander, L. S. (2009). C-reactive
protein, metabolic syndrome and incidence of severe hip and knee osteoarthritis. A population-based cohort
study. Osteoarthr. Cartil., 17(2):168–173.
Epstein, H. T. (1986). Stages in human brain development. Brain Res., 395:114–119.
Eriksson, J. G. (2006). Patterns of growth : relevance to developmental origins of health and disease. In
Gluckman, P. D., editor, Dev. Orig. Heal. Dis., chapter 15, pages 223–232. Cambridge University Press,
Cambridge, UK, 1 edition.
Eriksson, M., Räikkönen, K., and Eriksson, J. G. (2014). Early life stress and later health outcomes-findings
from the Helsinki Birth Cohort Study. American Journal of Human Biology, 26(2):111–116.
BIBLIOGRAPHY 236
Eshed, V., Gopher, A., Gage, T. B., and Hershkovitz, I. (2004). Has the transition to agriculture reshaped
the demographic structure of prehistoric populations? New evidence from the Levant. American Journal of
Physical Anthropology, 124(4):315–329.
Eshed, V., Gopher, A., Pinhasi, R., and Hershkovitz, I. (2010). Paleopathology and the origin of agriculture in
the Levant. American Journal of Physical Anthropology, 143(1):121–133.
Fall, C. H. D., Dennison, E., Cooper, C., Pringle, J., Kellingray, S. D., and Hindmarsh, P. (2002). Does Birth
weight Predict Adult Serum Cortisol Concentrations? Twenty-Four-Hour Profiles in the United Kingdom 1920
– 1930 Hertfordshire Birth Cohort. J. Clin. Endocrinol. Metab., 87(5):2001–2007.
Faul, F., Erdfelder, E., Buchner, A., and Lang, A.-G. (2007). G * Power 3 : A flexible statistical power analysis
program for the social , behavioral , and biomedical sciences. Behav. Res. Methods, 39(2):175–191.
Forsdahl, A. (1977). "Are poor living conditions in childhood and adolescence an important risk factor for
arteriosclerotic heart disease?". Br. J. Prev. Soc. Med., 31:91–95.
Forssman, T. (2013). Missing pieces : Later Stone Age surface assemblages on the greater Mapungubwe landscape
, South Africa. South African Humanities, 25(September):65–85.
Foster, Z., Byron, E., Reyes-García, V., Huanca, T., Vadez, V., Apaza, L., Pérez, E., Tanner, S., Gutierrez,
Y., Sandstrom, B., Yakhedts, a., Osborn, C., Godoy, R. a., and Leonard, W. R. (2005). Physical growth
and nutritional status of Tsimane’ Amerindian children of lowland Bolivia. American Journal of Physical
Anthropology, 126(3):343–351.
Fox, J. (2003). Effect Displays in R for Generalised Linear Models. J. Stat. Softw.
Fox, J. (2005). Getting started with the R commander: a basic-statistics graphical user interface to R. J. Stat.
Softw., 14(9):1–42.
Fox, J., Bouchet-Valat, M., Andronic, L., Ash, M., Boye, T., Calza, S., Chang, A., Grosjean, P., Heiberger, R.,
Jarimi Pour, K., Kerns, G. J., Lancelot, R., Lesnoff, M., Ligges, U., Messad, S., Maechler, M., Muenchen,
R., Murdoch, D., Neuwirth, E., Putler, D., Ripley, B., Ristic, M., Wolf, P., and Wright, K. (2014a). R
Commander.
Fox, J., Weisberg, S., Friendly, M., Hong, J., Andersen, R., Firth, D., and Taylor, S. (2014b). Effect Displays for
Linear, Generalized Linear, Multinomial-Logit, Proportional-Odds Logit Models and Mixed-Effects Models.
Frankish, C. J., Hwang, S. W., and Quantz, D. (2005). Homelessness and health in Canada: research lessons
and priorities. Can. J. public Heal. Rev. Can. santé publique, 96 Suppl 2:S23—-9.
Fukui, N., Yamane, S., Ishida, S., Tanaka, K., Masuda, R., Tanaka, N., Katsuragawa, Y., and Fukui, S. (2010).
Relationship between radiographic changes and symptoms or physical examination findings in subjects with
BIBLIOGRAPHY 237
symptomatic medial knee osteoarthritis : a three-year prospective study. BMC Musculoskeletal Disorders,
11(1):269.
Gage, T. B. (1988). Mathematical Hazard Models of Mortality : An Alternative to Model Life Tables. American
Journal of Physical Anthropology, 76:429–441.
Gage, T. B. (1990). Variation and classification of human age patterns of mortality: analysis using competing
hazards models. Human Biology, 62(5):589–617.
Gage, T. B. and DeWitte, S. (2009). What Do We Know About the Agricultural Demographic Transition?
Current Anthropology, 50(5):649–655.
Gage, T. B. and Mode, C. J. (1993). Some laws of mortality: how well do they fit? Human Biology, 65(3):445–461.
Gandhi, R., Razak, F., Davey, J. R., and Mahomed, N. N. (2010). Metabolic syndrome and the functional
outcomes of hip and knee arthroplasty. J. Rheumatol., 37(9):1917–1922.
Garvin, H. M. (2012). Adult Sex Determination: Methods and Application. In Dirkmaat, D. C., editor, A
Companion to Forensic Anthropol., chapter 12, pages 239–247. Blackwell Publishing, Ltd, Hoboken, NJ.
Garvin, H. M., Nicholas, V., Uhl, N. M., Gipson, D. R., Overbury, R. S., and Cabo, L. L. (2012). Developments
in Forensic Anthropology: Age- at-Death Estimation. In Dirkmaat, D. C., editor, A Companion to Forensic
Anthropol., chapter 10, pages 202–223. Blackwell Publishing, Ltd, Hoboken, NJ.
Garvin, H. M. and Passalacqua, N. V. (2012). Current practices by forensic anthropologists in adult skeletal age
estimation. J. Forensic Sci., 57(2):427–433.
Ghattas, H., Wallace, D. L., Solon, J. A., Henson, S. M., Zhang, Y., Ngom, P. T., Aspinall, R., Morgan, G.,
Griffin, G. E., Prentice, A. M., and Macallan, D. C. (2007). Long-term effects of perinatal nutrition on T
lymphocyte kinetics in. American Journal of Clinical Nutrition, 85(4):480–487.
Ghosh, S., Chowdhury, S. D., Chandra, A. M., and Ghosh, T. (2015). Grades of undernutrition and socioe-
conomic status influence cognitive development in school children of Kolkata. American Journal of Physical
Anthropology, 156(2):274–285.
Gifford-Gonzalez, D. (2000). Animal Disease Challenges to the Emergence of Pastoralism in Sub-Saharan Africa.
African Archaeol. Rev., 17(3):95–139.
Ginter, J. K. (2008). A bioarchaeological study of mid-Holocene communities in the Eastern Cape, South Africa:
the interface between foraging and pastoralism. PhD thesis, University of Toronto.
Ginter, J. K. (2011). Using a Bioarchaeological Approach to Explore Subsistence Transitions in the Eastern
Cape , South Africa During the Mid- to Late Holocene. In Pinhasi, R. and Stock, J. T., editors, Human
BIBLIOGRAPHY 238
Bioarchaeology of the Transition to Agriculture, chapter 6, pages 107–146. John Wiley & Sons, Hoboken, NJ,
1 edition.
Gluckman, P. D., Hanson, M., Bateson, P., Beedle, A. S., Law, C. M., Bhutta, Z., Anokhin, K. V., Bougnères,
P., Chandak, G. R., Dasgupta, P., Smith, G. D., Ellison, P. T., Forrester, T. E., Gilbert, S. F., Jablonka, E.,
Kaplan, H., Prentice, A. M., Simpson, S. J., Uauy, R., and West-Eberhard, M. J. (2009a). Towards a new
developmental synthesis: adaptive developmental plasticity and human disease. Lancet, 373(9675):1654–1657.
Gluckman, P. D. and Hanson, M. a. (2006a). Adult disease: echoes of the past. Eur. J. Endocrinol.,
155(suppl_1):S47—-S50.
Gluckman, P. D. and Hanson, M. A. (2006b). The conceptual basis for the developmental origins of health and
disease. In Gluckman, P. D. and Hanson, editors, Development, chapter 3, pages 33–50. Cambridge University
Press, Cambridge, UK, 1 edition.
Gluckman, P. D. and Hanson, M. A. (2006c). The developmental origins of health and disease : an overview.
In Gluckman, P. D. and Hanson, M. A., editors, Dev. Orig. Heal. Dis., chapter 1, pages 1–5. Cambridge
University Press, Cambridge, UK.
Gluckman, P. D., Hanson, M. a., Buklijas, T., Low, F. M., and Beedle, A. S. (2009b). Epigenetic mechanisms
that underpin metabolic and cardiovascular diseases. Nat. Rev. Endocrinol., 5(7):401–408.
Godfrey, K. M., Gluckman, P. D., and Hanson, M. (2010). Developmental origins of metabolic disease: Life
course and intergenerational perspectives. Trends Endocrinol. Metab., 21(4):199–205.
Godoy, R., Leonard, W. R., Reyes-García, V., Goodman, E., McDade, T., Huanca, T., Tanner, S., and Vadez,
V. (2006). Physical stature of adult Tsimane’ Amerindians, Bolivian Amazon in the 20th century. Economics
and Human Biology, 4(2):184–205.
Godoy, R., Magvanjav, O., Nyberg, C., Eisenberg, D. T. a., McDade, T. W., Leonard, W. R., Reyes-García,
V., Huanca, T., Tanner, S., and Gravlee, C. (2010a). Why no adult stunting penalty or height premium?
Estimates from native Amazonians in Bolivia. Economics and Human Biology, 8(1):88–99.
Godoy, R., Nyberg, C., Eisenberg, D. T. a., Magvanjav, O., Shinnar, E., Leonard, W. R., Gravlee, C., Reyes-
García, V., McDade, T. W., Huanca, T., and Tanner, S. (2010b). Short but catching up: statural growth
among native Amazonian Bolivian children. American Journal of Human Biology, 22(3):336–347.
Goldblatt, P. (1978). An Analysis of the Flora of Southern Africa: Its Characteristics , Relationships, and Orgins.
Ann. Missouri Bot. Gard., 65(2):369–436.
Goldblatt, P. (1997). Floristic diversity in the Cape Flora of South Africa. Biodivers. Conserv., 6:359–377.
BIBLIOGRAPHY 239
Goldring, M. B. and Goldring, S. R. (2010). Articular cartilage and subchondral bone in the pathogenesis of
osteoarthritis. Ann. N. Y. Acad. Sci., 1192:230–237.
Goodman, A. H. and Armelagos, G. J. (1988). Childhood Stress and Decreased Longevity in a Prehistoric
Population. Am. Anthropol., 90(4):936–944.
Goodman, A. H. and Armelagos, G. J. (1989). Infant and Childhood Morbidity and Mortality Risks in Archae-
ological Populations. World Archaeol., 21(2):225–243.
Gudmundsson, S., Henningsson, A.-C., and Lindqvist, P. (2005). Correlation of birth injury with maternal height
and birthweight. BJOG An Int. J. Obstet. Gynaecol., 112(6):764–767.
Gunnell, D., Rogers, J., and Dieppe, P. (2001). Height and health: predicting longevity from bone length in
archaeological remains. J. Epidemiol. Community Health, 55:505–507.
Gunnell, D. J., Smith, G. D., Frankel, S., Nanchahal, K., Braddon, F. E. M., Pemberton, J., Peters, T. J., Smith,
G. D., and Fields, C. (1998). Childhood leg length and adult mortality : follow up of the Carnegie ( Boyd
Orr ) Survey of Diet and Health in Pre-war Britain. J. Epidemiol. Community Heal., 52:142–152.
Gurven, M. and Hill, K. (2009). Why do men hunt? A reevaluation of "man the hunter" and the sexual division
of labor. Current Anthropology, 50(1):51–74.
Gurven, M. and Kaplan, H. (2007). Longevity among hunter-gatherers: A cross-cultural examination. Population
and Development Review, 33(2):321–365.
Gurven, M., Mulder, M. B., Hooper, P. L., Kaplan, H., Quinlan, R., Sear, R., Schniter, E., Rueden, C. V.,
Bowles, S., Hertz, T., and Bell, A. (2010). Domestication alone does not lead to inequality : intergenerational
wealth transmission among horticulturalists. Current Anthropology, 51(1):49–64.
Hales, C. N. and Barker, D. J. P. (1992). Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype
hypothesis. Int. J. Epidemiol., 42(5):1215–1222.
Hales, C. N. and Barker, D. J. P. (2001). The thrifty phenotype hypothesis. Br. Med. Bull., 60(1):5–20.
Halkett, D., Hart, T., Yates, R., Volman, T. P., Parkington, J. E., Orton, J., Klein, R. G., Cruz-Uribe, K., and
Avery, G. (2003). First excavation of intact Middle Stone Age layers at Ysterfontein, Western Cape Province,
South Africa: implications for Middle Stone Age ecology. Journal of Archaeological Science, 30(8):955–971.
Hall, S. (2000). Burial and sequence in the Later Stone Age of the Eastern Cape Province, South Africa. South
African Archaeological Bulletin, 55(172):137–146.
Hall, S. and Binneman, J. (1987). Later Stone Age Burial Variability in the Cape: A Social Interpretation. South
African Archaeological Bulletin, 42(146):140–152.
BIBLIOGRAPHY 240
Hall, S. L. (1986). Pastoral Adaptations and Forager Reactions in the Eastern Cape. Goodwin Ser. Prehist.
Pastor. South. Africa, 5(June):42–49.
Hallgrimsson, B. (1999). Ontogenetic Patterning of Skeletal Fluctuating Asymmetry in Rhesus Macaques and
Humans : Evolutionary and Developmental Implications. Int. J. Primatol., 20(1):121–151.
Han, Z., Mulla, S., Beyene, J., Liao, G., and McDonald, S. D. (2011). Maternal underweight and the risk of
preterm birth and low birth weight: a systematic review and meta-analyses. Int. J. Epidemiol., 40(1):65–101.
Hanna, F. S., Teichtahl, A. J., Wluka, A. E., Wang, Y., Urquhart, D. M., English, D. R., Giles, G. G., and
Cicuttini, F. M. (2009). Women have increased rates of cartilage loss and progression of cartilage defects at
the knee than men. Menopause, 16(4):666–670.
Harding, R., Cock, M. L., and Maritz, G. S. (2006). The developmental environment : effects on lung structure
and function. In Gluckman, P. D., editor, Dev. Orig. Heal. Dis., chapter 25, pages 336–348. Cambridge
University Press, Cambridge, UK, 1 edition.
Harper, K. N. and Armelagos, G. J. (2013). Genomics, the origins of agriculture, and our changing microbe-
scape: Time to revisit some old tales and tell some new ones. American Journal of Physical Anthropology,
152:135–152.
Harrell, B. Y. F. E. (1982). A new distribution-free quantile estimator. Biometrika69, 69(3):635–640.
Harrell, F. (2014). Package ‘ Hmisc ’.
Harrell, F. E. (2001). Regression Modeling Strategies With Applications to Linear Models, Logistic Regression,
and Survival Analysis. Springer New York, New York, NY, 1 edition.
Harrington, L. (2010). Ontogeny of Postcranial Robusticity among Holocene Hunter-Gatherers of Southernmost
Africa. PhD thesis, University of Toronto.
Harrington, L. and Pfeiffer, S. (2008). Juvenile mortality in Southern African archaeological contexts. South
African Archaeological Bulletin, 63(188):95–101.
Harrison, K. (1983). Predicting trends in operative delivery for cephalopelvic disproportion in Africa. Lancet,
335(4):861–862.
Hartnett, K. M. (2010a). Analysis of age-at-death estimation using data from a new, modern autopsy sample–part
I: pubic bone. J. Forensic Sci., 55(5):1145–1151.
Hartnett, K. M. (2010b). Analysis of age-at-death estimation using data from a new, modern autopsy sample–part
II: sternal end of the fourth rib. Journal of Forensic Sciences, 55(5):1152–1156.
Harvey, B. J. and Lang, T. A. (2010). Hypothesis testing, study power, and sample size. Chest, 138(3):734–737.
BIBLIOGRAPHY 241
Hausman, A. J. and Wilmsen, E. N. (1985). Economic Change and Secular Trends in the Growth of San Children.
Human Biology, 57(4):563–571.
Hawkey, D. E. (1998). Disability, compassion and the skeletal record: using musculoskeletal stress markers
(MSM) to construct an osteobiography from early New Mexico. Int. J. Osteoarchaeol., 8(May):326–340.
Hayward, A. D. and Lummaa, V. (2013). Testing the evolutionary basis of the predictive adaptive response
hypothesis in a preindustrial human population. Evol. Med. public Heal., 2013(1):106–117.
Headland, T. N. (1989). Population Decline in a Philippine Negrito Hunter-Gatherer Society. American Journal
of Human Biology, 72:59–72.
Heinke, D. and Kuzawa, C. W. (2008). Self-reported illness and birth weight in the Philippines: implications for
hypotheses of adaptive fetal plasticity. American Journal of Human Biology, 20(5):538–544.
Henn, B. M., Cavalli-Sforza, L. L., and Feldman, M. W. (2012). The great human expansion. Proceedings of the
National Academy of Sciences, 109(44):17758–17764.
Henry, C. J. K. and Ulijaszek, S. J. (1996). Long-term consequences of early environment : growth, development,
and the lifespan developmental perspective. Cambridge University Press, Cambridge, UK, 1 edition.
Henshilwood, C. (1996). A revised chronology for pastoralism in southernmost Africa: new evidence of sheep at
c. 2000 bp from Blombos Cave, South Africa. Antiquity, 70(270):945–949.
Henshilwood, C., Nilssen, P., and Parkington, J. (1994). Mussel Drying and Food Storage in the Late Holocene,
SW Cape, South Africa. J. F. Archaeol., 21:103–109.
Henshilwood, C. S. (1995). Holocene Archaeology of the Coastal Garcia State Forest, Southern Cape, South
Africa. Phd dissertation, University of Cambridge.
Henshilwood, C. S., Sealy, J. C., Yates, R., Cruz-Uribe, K., Goldberg, P., Grine, F. E., Klein, R. G., Poggenpoel,
C., van Niekerk, K., and Watts, I. (2001). Blombos Cave, Southern Cape, South Africa: Preliminary Report
on the 1992–1999 Excavations of the Middle Stone Age Levels. Journal of Archaeological Science, 28(June
2000):421–448.
Hill, K. and Hurtado, a. M. (2009). Cooperative breeding in South American hunter-gatherers. Proceedings of
the Royal Society B: Biological Sciences, 276(1674):3863–3870.
Hill, K., Hurtado, A. M., and Walker, R. S. (2007). High adult mortality among Hiwi hunter-gatherers: Impli-
cations for human evolution. Journal of Human Evolution, 52(4):443–454.
Hill, K. R. and Hurtado, A. M. (1996). Ache Life History: The Ecology and Demography of a Foraging People.
Transaction Publishers, Piscatawny, NJ.
BIBLIOGRAPHY 242
Hillson, S. (2014). Tooth development in human evolution and bioarchaeology. In Tooth Development in Human
Evolution and Bioarchaeology. Cambridge University Press, Cambridge, UK.
Hinchliffe, S. A., Lynch, M. R., Sargent, P. H., Howard, C. V., and Van Velzen, D. (1992). The effect of
intrauterine growth retardation on the development of renal nephrons. Br. J. Obstet. Gynaecol., 99(4):296–
301.
Hinck, V., Clark, W. M., and Hopkins, C. E. (1966). Normal interpediculate distances (minimum and maximum)
in children and adults. Am. J. Roentgenol., 97(1):141–153.
Hine, P., Sealy, J., Halkett, D., and Hart, T. (2010). Antiquity of stone-walled tidal fish traps on the Cape coast,
South Africa. South African Archaeological Bulletin, 65(191):35–44.
Hogan, M. C., Foreman, K. J., Naghavi, M., Ahn, S. Y., Wang, M., Makela, S. M., Lopez, A. D., Lozano, R., and
Murray, C. J. L. (2010). Maternal mortality for 181 countries, 1980-2008: a systematic analysis of progress
towards Millennium Development Goal 5. Lancet, 375(9726):1609–1623.
Holland, E. J. (2013). Bringing Childhood Health Into Focus : Incorporating Survivors Into Standard Methods of
Investigation. Phd, University of Toronto.
Hoppa, R. and Vaupel, J. W. (2002). The Rostock Manifesto for paleodemography: the way from stage to
age. In Hoppa, R. D. and Vaupel, J. W., editors, Paleodemography: Age Distributions from Skeletal Samples.
Cambridge University Press, Cambridge, UK.
Howell, N. (2000). Demography of the Dobe !Kung. Aldine de Gruyter, New York, NY, 2 edition.
Howell, N. (2010). Life Histories of the Dobe! Kung: Food, Fatness, and Well-Being Over the Life Span, volume 4.
Univ of California Press, Berkeley, CA.
Hubbard, N. (1989). Holocene settlement in the Western Cape, South Africa.pdf. British Archaeological Reports,
Oxford, UK.
Humphrey, L. T. (1998). Growth patterns in the modern human skeleton. American Journal of Physical An-
thropology, 105(1):57–72.
Humphreys, J. B. (2007). Behavioural Ecology and Hunter-Gatherers: from the Kalahari to the Later Stone
Age. South African Archaeological Bulletin, 62(186):98–103.
Huss-Ashmore, R. (1997). Human adaptability research among African agriculturalists. In Ulijaszek, S. J. and
Huss-Ashmore, R., editors, Hum. Adapt. Past, Present. Futur., chapter 5, pages 61–81. Oxford University
Press, Oxford, UK.
BIBLIOGRAPHY 243
Huss-Ashmore, R. and Ulijaszek, S. J. (1997). The contributions of field research in the Gambia to the study of
human adaptability. In Ulijaszek, S. J. and Huss-Ashmore, R., editors, Hum. Adapt. Past, Present. Futur.,
chapter 6, pages 82–101. Oxford University Press, Oxford, UK.
IBM Corporation (2011). IBM SPSS Missing Values 20.
Ice, G. H. and James, G. D. (2012). Stress and Human Biology. In Stinson, S., Bogin, B., and O’Rourke, D.,
editors, Human Biology: An Evolutionary and Biocultural Perspective, pages 459–512. John Wiley & Sons,
Inc., Hoboken, NJ, 2 edition.
Inskeep, R. R. (1986). A preliminary survey of burial practices in the Later Stone Age, from the Orange River
to the Cape coast. Var. Cult. Evol. African Popul., 221:239.
Inskeep, R. R. and Avery, G. (1987). Nelson Bay Cave, Cape Province, South Africa: the Holocene levels, volume
357. BAR International Reports.
Irish, J. D., Black, W., Sealy, J., and Ackermann, R. R. (2014). Questions of Khoesan continuity: dental
affinities among the indigenous Holocene peoples of South Africa. American Journal of Physical Anthropology,
155(1):33–44.
Iscan, M. Y., Loth, S. R., and Wright, R. K. (1985). Age estimation from the rib by phase analysis: white
females. J. Forensic Sci., 30(3):853–863.
Işcan, M. Y., Loth, S. R., and Wright, R. K. (1984). Metamorphosis at the sternal rib end: a new method to
estimate age at death in white males. American Journal of Physical Anthropology, 65(2):147–156.
Jackes, M. (2011). Representativeness and Bias in Archaeological Skeletal Samples. Social Bioarchaeology, pages
107–146.
Jeffrey, J. E., Campbell, D. M., Golden, M. H. N., Smith, F. W., and Porter, R. W. (2003). Antenatal factors
in the development of the lumbar vertebral canal: a magnetic resonance imaging study. Spine (Phila. Pa.
1976)., 28(13):1418–1423.
Jerardino, A. (1998). Excavations at Pancho’s Kitchen Midden, Western Cape coast, South Africa: further
observations into the megamidden period. South African Archaeological Bulletin, 53(167):16–25.
Jerardino, A. (2003). Pre-colonial settlement and subsistence along sandy shores south of Elands Bay, West
Coast, South Africa. South African Archaeological Bulletin, 58(178):53–62.
Jerardino, A. (2010). Large shell middens in Lamberts Bay, South Africa: A case of hunter-gatherer resource
intensification. Journal of Archaeological Science, 37(9):2291–2302.
BIBLIOGRAPHY 244
Jerardino, A., Branch, G. M., and Navarro, R. (2008). Human Impact on Precolonial West Coast Marine
Environments of South Africa. In Rock, T. and Erlandson, J. M., editors, Human Impacts on Ancient Marine
Ecosystems: A Global Perspective, chapter 12, pages 279–296. University of California Press, Berkeley, 1
edition.
Jerardino, A., Dewar, G., and Navarro, R. (2009a). Opportunistic Subsistence Strategies among Late Holocene
Coastal Hunter-Gatherers, Elands Bay, South Africa. Journal of Island and Coastal Archaeology, 4(1):37–60.
Jerardino, A., Horwitz, L. K., Mazel, A., and Navarro, R. (2009b). Just before van Riebeeck: Glimpses into
terminal LSA lifestyle at Connies Limpet Bar, west coast of South Africa. South African Archaeological
Bulletin, 64(189):75–86.
Jerardino, A. and Maggs, T. (2007). Simon se Klip at Steenbokfontein: the settlement pattern of a built
pastoralist encampment on the west coast of South Africa. South African Archaeological Bulletin, 62(186):104–
114.
Jerardino, A., Sealy, J., and Pfeiffer, S. (2000). An Infant Burial from Steenbokfontein Cave, West Coast, South
Africa: Its Archaeological, Nutritional and Anatomical Context. South African Archaeological Bulletin.
Jerardino, A. and Yates, R. (1996). Preliminary results from excavations at Steenbokfontein Cave: implications
for past and future research. South African Archaeological Bulletin, 51(163):7–16.
Jones, G., Ding, C., Glisson, M., Hynes, K., Ma, D., and Cicuttini, F. (2003). Knee articular cartilage develop-
ment in children: a longitudinal study of the effect of sex, growth, body composition, and physical activity.
Pediatr. Res., 54(2):230–236.
Jordan, K. M., Syddall, H., Dennison, E. M., Cooper, C., and Arden, N. K. (2005). Birthweight, vitamin D
receptor gene polymorphism, and risk of lumbar spine osteoarthritis. J. Rheumatol., 32(4):678–683.
Jurmain, R. (1999). Stories from the skeleton: behavioral reconstruction in human osteology. Gordon and Breach,
Amsterdam, 1 edition.
Jurmain, R., Cardoso, F., Henderson, C., and Villote, S. (2012). Bioarchaeology’s Holy Grail : The Recon-
struction of Activity. In Grauer, A. L., editor, A Companion to Palaeopathology, chapter 29, pages 531–552.
Blackwell Publishing, Ltd, Hoboken, NJ.
Jurmain, R. D. (1977a). Part one: Paleoepidemiology of degenerative knee disease. Med. Anthropol., 1(1):1–23.
Jurmain, R. D. (1977b). Stress and the etiology of osteoarthritis. American Journal of Physical Anthropology,
46(2):353–365.
Jurmain, R. D. (1991). Degenerative Changes in Peripheral Joints as Indicators of Mechanical Stress : Oppor-
tunities and Limitations. Int. J. Osteoarchaeol., I(March):247–252.
BIBLIOGRAPHY 245
Kaplan, H., Hill, K. I. M., Lancaster, J., and Hurtado, A. M. (2000). A Theory of Human Life History Evolution:
Diet, Intelligence, and Longevity. Evol. Anthropol., 9(4):156–183.
Kapoor, M., Martel-Pelletier, J., Lajeunesse, D., Pelletier, J.-P., and Fahmi, H. (2011). Role of proinflammatory
cytokines in the pathophysiology of osteoarthritis. Nat. Rev. Rheumatol., 7(1):33–42.
Katz, J. D., Agrawal, S., and Velasquez, M. (2010). Getting to the heart of the matter: osteoarthritis takes its
place as part of the metabolic syndrome. Curr. Opin. Rheumatol., 22(5):512–519.
Kemkes-Grottenthaler, A. (2005). The short die young: the interrelationship between stature and longevity-
evidence from skeletal remains. American Journal of Physical Anthropology, 128(2):340–347.
Kerkhof, H. J. M., Meulenbelt, I., Carr, A., Gonzalez, A., Hart, D., Hofman, A., Kloppenburg, M., Lane, N. E.,
Loughlin, J., Nevitt, M. C., Pols, H. a. P., Rivadeneira, F., Slagboom, E. P., Spector, T. D., Stolk, L.,
Tsezou, A., Uitterlinden, A. G., Valdes, A. M., and van Meurs, J. B. J. (2010). Common genetic variation in
the Estrogen Receptor Beta (ESR2) gene and osteoarthritis: results of a meta-analysis. BMC Med. Genet.,
11(1):164.
Kim, H. L., Ratan, A., Perry, G. H., Montenegro, A., Miller, W., and Schuster, S. C. (2014). Khoisan hunter-
gatherers have been the largest population throughout most of modern-human demographic history. Nature
Communications, 5:1–8.
Kimmerle, E. H., Konigsberg, L. W., Jantz, R. L., and Baraybar, J. P. (2008). Analysis of age-at-death estimation
through the use of pubic symphyseal data. American Journal of Physical Anthropology, 53(3):558–568.
Klaus, H. D. (2014). Frontiers in the Bioarchaeology of Stress and Disease : Cross-Disciplinary Perspectives From
Pathophysiology, Human Biology, and Epidemiology. American Journal of Physical Anthropology, 00(July):0.
Klaus, H. D., Spencer Larsen, C., and Tam, M. E. (2009). Economic intensification and degenerative joint
disease: life and labor on the postcontact north coast of Peru. American Journal of Physical Anthropology,
139(2):204–221.
Klaus, H. D. and Tam, M. E. (2009). Contact in the Andes : Bioarchaeology of Systemic Stress in Colonial
Morrope, Peru. American Journal of Physical Anthropology, 368(October 2008):356–368.
Klein, R. G. (1974). Environment and subsistence of prehistoric man in the southern Cape Province, South
Africa. World Archaeol., 5(3):249–284.
Klein, R. G. and Cruz-Uribe, K. (1983). Stone Age Population Numbers and Average Tortoise Size at Byneskran-
skop Cave 1 and Die Kelders Cave 1, Southern Cape Province, South Africa. South African Archaeological
Bulletin, 38(137):26–30.
BIBLIOGRAPHY 246
Klein, R. G. and Cruz-Uribe, K. (1987). Large mammal and tortoise bones from Elands Bay Cave and nearby
sites, western Cape Province, South Africa. Pap. Prehistory West. Cape, South Africarehistory West. Cape,
South Africa, 1:132–163.
Klein, R. G. and Cruz-Uribe, K. (2000). Middle and Later Stone Age large mammal and tortoise remains from
Die Kelders Cave 1, Western Cape Province, South Africa. Journal of Human Evolution, 38(1):169–195.
Koletzko, B. and Brands, B. (2011). The Early Nutrition Programming Project ( EARNEST ): 5 y of successful.
American Journal of Clinical Nutrition, 94:1749–1753.
Konigsberg, L. W. and Frankenberg, S. R. (2002). Deconstructing death in paleodemography. American Journal
of Physical Anthropology, 117(4):297–309.
Konner, M. and Eaton, S. B. (2010). Paleolithic Nutrition: Twenty-Five Years Later. Nutrition in Clinical
Practice, 25(6):594–602.
Kornaat, P. R., Sharma, R., van der Geest, R. J., Lamb, H. J., Kloppenburg, M., Hellio le Graverand, M.-P.,
Bloem, J. L., and Watt, I. (2009). Positive association between increased popliteal artery vessel wall thickness
and generalized osteoarthritis: is OA also part of the metabolic syndrome? Skeletal Radiol., 38(12):1147–1151.
Kramer, K. L., Greaves, R. D., and Ellison, P. T. (2009). Early reproductive maturity among Pumé foragers:
Implications of a pooled energy model to fast life histories. American Journal of Human Biology, 21(4):430–
437.
Kurki, H. K., Ginter, J. K., Stock, J. T., and Pfeiffer, S. (2010). Body Size Estimation of Small-Bodied Humans
: Applicability of Current Methods. American Journal of Physical Anthropology, 180(May 2009):169–180.
Kurki, H. K., Pfeiffer, S., and Stynder, D. D. (2012). Allometry of head and body size in Holocene foragers of
the South African Cape. American Journal of Physical Anthropology, 147(3):462–471.
Kusimba, S. B. (2005). What Is a Hunter-Gatherer? Variation in the Archaeological Record of Eastern and
Southern Africa. Journal of Archaeological Research, 13(4):337–366.
Kuzawa, C. W. (1998). Adipose Tissue in Human Infancy and Childhood : An Evolutionary Perspective.
Yearbook of Physical Anthropology, 209(1998):177–209.
Kuzawa, C. W. (2005). Fetal origins of developmental plasticity: Are fetal cues reliable predictors of future
nutritional environments? American Journal of Human Biology, 17(October 2004):5–21.
Kuzawa, C. W. and Bragg, J. M. (2012). Plasticity in Human Life History Strategy. Current Anthropology,
53(S6):S369—-S382.
BIBLIOGRAPHY 247
Kuzawa, C. W., Hallal, P. C., Adair, L., Bhargava, S. K., Fall, C. H. D., Lee, N., Norris, S. a., Osmond, C.,
Ramirez-Zea, M., Sachdev, H. S., Stein, A. D., and Victora, C. G. (2011). Birth weight, postnatal weight gain,
and adult body composition in five low and middle income countries. American Journal of Human Biology,
24(1):5–13.
Kuzawa, C. W. and Quinn, E. a. (2009). Developmental Origins of Adult Function and Health: Evolutionary
Hypotheses. Annual Review of Anthropology, 38(1):131–147.
Kyle, U. G. and Pichard, C. (2006). The Dutch Famine of 1944 – 1945 : a pathophysiological model of long-term
consequences of wasting disease. Curr. Opin. Clin. Nutr. Metab. Care.
Kyriacou, K., Parkington, J. E., Will, M., Kandel, A. W., and Conard, N. J. (2015). Middle and Later Stone Age
shellfish exploitation strategies and coastal foraging at Hoedjiespunt and Lynch Point, Saldanha Bay, South
Africa. Journal of Archaeological Science, 57(0):197–206.
Lampl, M., Gotsch, F., Kusanovic, J. P., Espinoza, J., Gonçalves, L., Gomez, R., Nien, J. K., Frongillo, E. a.,
and Romero, R. (2008). Downward percentile crossing as an indicator of an adverse prenatal environment.
Annals of Human Biology, 35(5):462–474.
Lampl, M., Kuzawa, C. W., and Jeanty, P. (2002). Infants thinner at birth exhibit smaller kidneys for their
size in late gestation in a sample of fetuses with appropriate growth. American Journal of Human Biology,
14(3):398–406.
Langejans, G. H. J., van Niekerk, K. L., Dusseldorp, G. L., and Thackeray, J. F. (2012). Middle Stone Age
shellfish exploitation: Potential indications for mass collecting and resource intensification at Blombos Cave
and Klasies River, South Africa. Quat. Int., 270:80–94.
Larsen, C. S. (1997). Bioarchaeology: Interpreting Behavior From the Human Skeleton. Cambridge University
Press, Cambridge, UK.
Larsen, C. S. and Crosby, A. W. (2002). A biohistory of health and behaviour in the Georgia Bight: the
agricultural transition and the impact of European contact. In Steckel, R. H. and Rose, J. C., editors, The
Backbone of History: Health and Nutrition in the Western Hemisphere, chapter 14, pages 406–439. Cambridge
University Press, Cambridge, UK, 1 edition.
Larsen, C. S., Hutchinson, D. L., Stojanowski, C. M., Williamson, M. A., Griffin, M. C., Simpson, S. W., Ruff,
C. B., Schoeninger, M. J., Norr, L., Teaford, M. F., and Others (2007). Health and lifestyle in Georgia and
Florida: agricultural origins and intensification in regional perspective. In Cohen, M. N. and Crane-Kramer,
G. M., editors, Ancient Health: Skeletal Indicators of Agricultural Economic Intensification, pages 20–34.
University Press of Florida, Gainesville, FL.
BIBLIOGRAPHY 248
Lawlor, D. a., Ebrahim, S., and Davey Smith, G. (2005). Association of birth weight with adult lung function:
findings from the British Women’s Heart and Health Study and a meta-analysis. Thorax, 60(10):851–858.
Lee, R. B. (1979). The !Kung San: men, women, and work in a foraging society. Cambridge University Press,
Cambridge, UK.
Lee-Thorp, J. a. (2008). On isotopes and old bones. Archaeometry, 50(6):925–950.
Levitt, N. S., Lambert, E. V., Woods, D., Hales, C. N., Andrew, R., Seckl, J. R., and Bioenergetics, T. (2000).
Impaired Glucose Tolerance and Elevated Blood Pressure in Low Birth Weight , Nonobese , Young South
African Adults : Early Programming of Cortisol Axis *. J. Clin. Endocrinol. Metab., 85(12).
Lewis, M. E. (2007). The Bioarchaeology of Children: Perspectives from Biological and Forensic Anthropology.
Cambridge University Press, Cambridge, UK.
Lewitus, G. M., Schwartz-Stav, O., and Schwartz, M. (2010). Immunity to Self Maintains Resistance to Mental
Stress: Boosting Immunity as a Complement to Psychological Therapy. In Stress - From Mol. to Behav.,
pages 229–242. Wiley-VCH Verlag GmbH & Co. KGaA.
Liengme, C. (1987). Botanical remains from archaeological sites in the Western Cape. In Parkington, J. and
Hall, M., editors, Pap. Prehistory West. Cape, South Africa, chapter 12, pages 237–261. BAR International
Reports 332, Oxford, UK.
Lieverse, A. R., Bazaliiskii, V. I., and Weber, A. W. (2015). Death by twins: a remarkable case of dystocic
childbirth in Early Neolithic Siberia. Antiquity, 89(343):23–38.
Lieverse, A. R., Link, D. W., Bazaliiskiy, V. I., Goriunova, O. I., and Weber, A. W. (2007a). Dental Health
Indicators of Hunter–Gatherer Adaptation and Cultural Change in Siberia’s Cis-Baikal. American Journal of
Physical Anthropology, 134(2007):323–339.
Lieverse, A. R., Weber, A. W., Bazaliiskiy, V. I., Goriunova, O. I., and Aleksandrovich, N. (2007b). Osteoarthritis
in Siberia ’ s Cis-Baikal : Skeletal Indicators of Hunter-Gatherer Adaptation and Cultural Change. American
Journal of Physical Anthropology, 16(May 2006):1–16.
Liston, W. A. (2003). Rising caesarean section rates: can evolution and ecology explain some of the difficulties
of modern childbirth? J. R. Soc. Med., 96(11):559–561.
Little, M. A. (1997). Adaptability of African pastoralists. In Ulijaszek, S. J. and Huss-Ashmore, R., editors,
Hum. Adapt. Past, Present. Futur., chapter 4, pages 29–60. Oxford University Press, Oxford, UK.
Liu, W., Li, Y., Learn, G. H., Rudicell, R. S., Robertson, J. D., Keele, B. F., Ndjango, J.-B. N., Sanz, C. M.,
Morgan, D. B., Locatelli, S., Gonder, M. K., Kranzusch, P. J., Walsh, P. D., Delaporte, E., Mpoudi-Ngole,
BIBLIOGRAPHY 249
E., Georgiev, A. V., Muller, M. N., Shaw, G. M., Peeters, M., Sharp, P. M., Rayner, J. C., and Hahn, B. H.
(2010). Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature, 467(7314):420–425.
Lohmander, L. S., Englund, P. M., Dahl, L. L., and Roos, E. M. (2007). The long-term consequence of anterior
cruciate ligament and meniscus injuries: osteoarthritis. American Journal of Sports Medicine, 35(10):1756–
1769.
Lombard, M. (2007). The gripping nature of ochre: The association of ochre with Howiesons Poort adhesives
and Later Stone Age mastics from South Africa. Journal of Human Evolution, 53(4):406–419.
Lombard, M., Wadley, L., Deacon, J., Wurz, S., Parsons, I., Mohapi, M., Swart, J., and Mitchell, P. (2012). South
African and Lesotho Stone Age sequence updated. South African Archaeological Bulletin, 67(195):123–144.
Lotz, M. K. (2010). Posttraumatic osteoarthritis: pathogenesis and pharmacological treatment options. Arthritis
Res. Ther., 12(3):1–9.
Love, B. and Muller, H.-G. (2002). A solution to the problem of obtaining a mortality schedule for paleodemo-
graphic data. In Hoppa, R. D. and Vaupel, J. W., editors, Paleodemography: Age Distributions from Skeletal
Samples, chapter 9, pages 181–192. Cambridge University Press, Cambridge, UK.
Lovejoy, C. O., Meindl, R. S., Pryzbeck, T. R., and Mensforth, R. P. (1985). Chronological metamorphosis of the
auricular surface of the ilium: a new method for the determination of adult skeletal age at death. American
Journal of Physical Anthropology, 68(1):15–28.
Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R.,
Ahn, S. Y., Alvarado, M., Anderson, H. R., Anderson, L. M., Andrews, K. G., Atkinson, C., Baddour, L. M.,
Barker-Collo, S., Bartels, D. H., Bell, M. L., Benjamin, E. J., Bennett, D., Bhalla, K., Bikbov, B., Abdulhak,
A. B., Birbeck, G., Blyth, F., Bolliger, I., Boufous, S., Bucello, C., Burch, M., Burney, P., Carapetis, J.,
Chen, H., Chou, D., Chugh, S. S., Coffeng, L. E., Colan, S. D., Colquhoun, S., Colson, K. E., Condon, J.,
Connor, M. D., Cooper, L. T., Corriere, M., Cortinovis, M., De Vaccaro, K. C., Couser, W., Cowie, B. C.,
Criqui, M. H., Cross, M., Dabhadkar, K. C., Dahodwala, N., De Leo, D., Degenhardt, L., Delossantos, A.,
Denenberg, J., Des Jarlais, D. C., Dharmaratne, S. D., Dorsey, E. R., Driscoll, T., Duber, H., Ebel, B., Erwin,
P. J., Espindola, P., Ezzati, M., Feigin, V., Flaxman, A. D., Forouzanfar, M. H., Fowkes, F. G. R., Franklin,
R., Fransen, M., Freeman, M. K., Gabriel, S. E., Gakidou, E., Gaspari, F., Gillum, R. F., Gonzalez-Medina,
D., Halasa, Y. A., Haring, D., Harrison, J. E., Havmoeller, R., Hay, R. J., Hoen, B., Hotez, P. J., Hoy, D.,
Jacobsen, K. H., James, S. L., Jasrasaria, R., Jayaraman, S., Johns, N., Karthikeyan, G., Kassebaum, N.,
Keren, A., Khoo, J. P., Knowlton, L. M., Kobusingye, O., Koranteng, A., Krishnamurthi, R., Lipnick, M.,
Lipshultz, S. E., Ohno, S. L., Mabweijano, J., MacIntyre, M. F., Mallinger, L., March, L., Marks, G. B.,
Marks, R., Matsumori, A., Matzopoulos, R., Mayosi, B. M., McAnulty, J. H., McDermott, M. M., McGrath,
J., Mensah, G. A., Merriman, T. R., Michaud, C., Miller, M., Miller, T. R., Mock, C., Mocumbi, A. O.,
BIBLIOGRAPHY 250
Mokdad, A. A., Moran, A., Mulholland, K., Nair, M. N., Naldi, L., Narayan, K. M. V., Nasseri, K., Norman,
P., O’Donnell, M., Omer, S. B., Ortblad, K., Osborne, R., Ozgediz, D., Pahari, B., Pandian, J. D., Rivero,
A. P., Padilla, R. P., Perez-Ruiz, F., Perico, N., Phillips, D., Pierce, K., Pope, C. A., Porrini, E., Pourmalek,
F., Raju, M., Ranganathan, D., Rehm, J. T., Rein, D. B., Remuzzi, G., Rivara, F. P., Roberts, T., De León,
F. R., Rosenfeld, L. C., Rushton, L., Sacco, R. L., Salomon, J. A., Sampson, U., Sanman, E., Schwebel,
D. C., Segui-Gomez, M., Shepard, D. S., Singh, D., Singleton, J., Sliwa, K., Smith, E., Steer, A., Taylor,
J. A., Thomas, B., Tleyjeh, I. M., Towbin, J. A., Truelsen, T., Undurraga, E. A., Venketasubramanian, N.,
Vijayakumar, L., Vos, T., Wagner, G. R., Wang, M., Wang, W., Watt, K., Weinstock, M. A., Weintraub, R.,
Wilkinson, J. D., Woolf, A. D., Wulf, S., Yeh, P. H., Yip, P., Zabetian, A., Zheng, Z. J., Lopez, A. D., and
Murray, C. J. L. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990
and 2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380(9859):2095–2128.
Lumey, L. H., Stein, A. D., and Susser, E. (2011). Prenatal famine and adult health. Annu. Rev. Public Health,
32:237–262.
Lundy, J. K. and Feldesman, M. R. (1987). Revised equations for estimating living stature from the long bones
of the South-African Negro.
Lyles, R. H., Lin, H.-M., and Williamson, J. M. (2007). A practical approach to computing power for generalized
linear models with nominal, count, or ordinal responses. Stat. Med., 26(7):1632–1648.
MacDonell, W. R. (1913). On the expectation of life in ancient Rome, and in the provinces of Hispania and
Lusitania, and Africa. Biometrika, 9(3-4):366–380.
Macholdt, E., Lede, V., Barbieri, C., Mpoloka, S. W., Chen, H., Slatkin, M., and Lumie, L. (2014). Tracing
Pastoralist Migrations to Southern Africa with Lactase Persistence Alleles. Current Biology, 24:875–879.
Manalich, R., Reyes, L., Herrera, M., Melendi, C., and Fundora, I. (2000). Relationship between weight at birth
and the number and size of renal glomeruli in humans: A histomorphometric study. Kidney Int., 58:770–773.
Manhire, A. (1993). A report on the excavations at Faraoskop Rock Shelter in the Graafwater district of the
south-western Cape. South. African F. Archaeol., 2:3–23.
Mapp, P. I. and Walsh, D. a. (2012). Mechanisms and targets of angiogenesis and nerve growth in osteoarthritis.
Nat. Rev. Rheumatol., 8(7):390–398.
Marean, C. W. (2010). Pinnacle Point Cave 13B (Western Cape Province, South Africa) in context: The Cape
Floral kingdom, shellfish, and modern human origins. Journal of Human Evolution, 59(3-4):425–443.
Marean, C. W. (2011). Coastal South Africa and the co-evolution of the modern human lineage and the coastal
adaption. In Trekking Shore. Chang. coastlines Antiq. Coast. Settl., pages 421–440. Springer.
BIBLIOGRAPHY 251
Marshall, F. and Hildebrand, E. (2002). Cattle before crops: The beginnings of food production in Africa. J.
World Prehistory, 16(2):99–143.
Martorell, R. and Zongrone, A. (2012). Intergenerational influences on child growth and undernutrition. Paediatr.
Perinat. Epidemiol., 26 Suppl 1:302–314.
McCarthy, J. and Maine, D. (2013). A framework for analyzing the determinants of maternal mortality. Stud.
Fam. Plann., 23(1):23–33.
McDade, T. W. (2002). Status Incongruity in Samoan Youth: A Biocultural Analysis of Culture Change, Stress,
and Immune Function. Med. Anthropol. Q., 16(2):123–150.
McDade, T. W., Beck, M. A., Kuzawa, C., and Adair, L. S. (2001a). Prenatal undernutrition , postnatal
environments , and antibody response to vaccination in adolescence. American Journal of Clinical Nutrition,
74(4):543–548.
McDade, T. W., Beck, M. A., Kuzawa, C. W., and Adair, L. S. (2001b). Prenatal Undernutrition and Postnatal
Growth Are Associated with Adolescent Thymic Function 1. J. Nutr., 131(4):1225–1231.
McDade, T. W., Reyes-García, V., Tanner, S., Huanca, T., and Leonard, W. R. (2008). Maintenance versus
growth: investigating the costs of immune activation among children in lowland Bolivia. American Journal of
Physical Anthropology, 136(4):478–484.
Mcdade, T. W., Tallman, P. S., Adair, L. S., Borja, J., and Kuzawa, C. W. (2011). Comparative Insights Into
the Regulation of Inflammation : Levels and Predictors of Interleukin 6 and Interleukin 10 in Young Adults
in the Philippines. American Journal of Physical Anthropology, 384(March):373–384.
Mcewen, B. S. and Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeco-
nomic status, health, and disease. Annals of the New York Academy of Sciences, 1186:190–222.
McGowan, P. O., Sasaki, A., D’Alessio, A. C., Dymov, S., Labonté, B., Szyf, M., Turecki, G., and Meaney, M. J.
(2009). Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse.
Nat. Neurosci., 12(3):342–348.
McHenry, H. M. (1992). Body size and proportions in early hominids. American Journal of Physical Anthropology,
87(4):407–431.
Meadows, M. E., Baxter, A. J., and Adams, T. (1994). The late Holocene vegetation history of lowland fynbos,
Verlorenvlei, southwestern Cape Province, South Africa. Hist. Biol., 9(1-2):47–59.
Meadows, M. E., Chase, B. M., and Seliane, M. (2010). Holocene palaeoenvironments of the Cederberg and
Swartruggens mountains, Western Cape, South Africa: Pollen and stable isotope evidence from hyrax dung
middens. J. Arid Environ., 74(7):786–793.
BIBLIOGRAPHY 252
Meadows, M. E. and Sugden, J. M. (1993). The late Quaternary palaeoecology of a floristic kingdom : the
southwestern Cape of South Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol., 101:271–281.
Megafu, U. and Ozumba, B. C. (1988). Obstetric complications of macrosomic babies in African women. Int. J.
Gynaecol. Obstet., 26(2):197–202.
Meindl, R. S. and Russell, K. F. (1998). Recent Advances in Method and Theory in Paleodemography. Annu.
Rev. Anthropol., 27(May):375–399.
Merrett, D. C. (2003). Maxillary sinusitis among the Moatfield people. Bones Ancestors Archaeol. Asteobiography
Moatf. Site, pages 242–261.
Migliano, A. B. and Guillon, M. (2012). The Effects of Mortality, Subsistence, and Ecology on Human Adult
Height and Implications for Homo Evolution. Current Anthropology, 53(S6):S359—-S368.
Migliano, A. B., Romero, I. G., Metspalu, M., Leavesley, M., Pagani, L., Antao, T., Huang, D.-w., Sherman,
B. T., Siddle, K., Hudjashov, G., Kaitokai, E., Babalu, A., Belatti, M., Cagan, A., Biology, H., June, F.,
Hopkinshaw, B., Shaw, C., Nelis, M., Metspalu, E., Mägi, R., Lempicki, A., Villems, R., Lahr, M. M.,
Kivisild, T., and Scholes, C. (2013). Evolution of the Pygmy Phenotype : Evidence of Positive Selection
from Genome-wide Scans in African , Asian , and Melanesian Pygmies Evolution of the Pygmy Phenotype :
Evidence of Positive Selection from Genome-wide Scans in African , Asian , and Melanesi. Human Biology,
85(1-3):251–284.
Migliano, A. B., Vinicius, L., and Lahr, M. M. (2007). Life history trade-offs explain the evolution of human
pygmies. Proceedings of the National Academy of Sciences, 104(51):20216–20219.
Miller, G. E., Chen, E., Fok, A. K., Walker, H., Lim, A., Nicholls, E. F., Cole, S., and Kobor, M. S. (2009).
Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased
proinflammatory signaling. Proceedings of the National Academy of Sciences, 106(34):14716–14721.
Milner, G. R., Humpf, D. A., and Harpending, H. C. (1989). Pattern Matching of Age-at-Death Distributions
in Paleodemographic Analysis. American Journal of Physical Anthropology, 80:49–58.
Milner, G. R., Wood, J. M., and Boldsen, J. L. (2008). Advances in Paleodemography. In Katzenberg, M. A.
and Saunders, S. R., editors, Biological Anthropology of the Human Skeleton, chapter 18, pages 561–600. John
Wiley & Sons, Hoboken, NJ, 2 edition.
Milton, K. (2000). Hunter-gatherer diets — a different perspective. American Journal of Clinical Nutrition,
71:665–667.
Mitchell, P. (2002). The Archaeology of Southern Africa. Cambridge University Press, Cambridge, UK, 1 edition.
BIBLIOGRAPHY 253
Mitchell, P. (2005). Why Hunter-Gatherer Archaeology Matters: A Personal Perspective on Renaissance and
Renewal in Southern African Later Stone Age Research. South African Archaeological Bulletin, 60(182):64–71.
Monaghan, P. (2008). Early growth conditions, phenotypic development and environmental change. Philos.
Trans. R. Soc. Lond. B. Biol. Sci., 363(November 2007):1635–1645.
Moore, S., Cole, T., Poskitt, E., Sonko, B., Whitehead, R., McGregor, I., and Prentice, A. (1997). Season of
birth predicts mortality in rural Gambia. Nature, 388(July):1393–1394.
Moore, S. E., Cole, T. J., Collinson, a. C., Poskitt, E. M., McGregor, I. a., and Prentice, a. M. (1999). Prenatal
or early postnatal events predict infectious deaths in young adulthood in rural Africa. Int. J. Epidemiol.,
28:1088–1095.
Moore, S. E., Collinson, A. C., Tamba N’Gom, P., Aspinall, R., and Prentice, A. M. (2007). Early immunological
development and mortality from infectious disease in later life. Proc. Nutr. Soc., 65(03):311–318.
Moore, S. E., Jalil, F., Ashraf, R., Szu, S. C., Prentice, A. M., and Hanson, L. Å. (2004). Birth weight predicts
response to vaccination in adults born in an urban slum in Lahore, Pakistan. American Journal of Clinical
Nutrition, 80:453–459.
Morris, A. (1992a). A master catalogue: Holocene human skeletons from South Africa. Witwatersrand University
Press., Johannesburg, 1 edition.
Morris, A. (1992b). The Skeletons of Contact: a Study of Protohistoric Burials from the Lower Orange River
Valley, South Africa. Witwatersrand University Press, Johannesburg, 1 edition.
Morris, A., Dlamini, N., Joseph, J., Parker, A., Powrie, C., Ribot, I., and Stynder, D. (2005). Later Stone Age
Burials from the Western Cape Province, South Africa Part 1 : Voëlvlei. South African Field Archaeology, 13
& 14:19–26.
Morris, A. and Parkington, J. (1982). Prehistoric homicide: a case of violent death on the Cape South Coast,
South Africa. South African Journal of Science.
Morris, A. G. (1987). Nelson Bay Cave: The Burials. In Inskeep, R. R., editor, Nelson Bay Cave, Cape Prov.
South Africa Holocene Levels., chapter 8. BAR International Reports.
Morris, A. G. (1992c). Guide to Holocene human skeletons from Saldanha and Elands Bay regions of the western
Cape Province: with notes on certain specimens. In Smith, A. B. and Mutti, B., editors, Guid. to Archaeol.
Sites South West. Cape. South African Association of Archaeologists, Cape Town.
Morris, A. G. (2002). Isolation and the Origin of the Khoisan : Late Pleistocene and Early Holocene Human
Evolution at the Southern End of Africa. Hum. Evol., 17(3-4):231–240.
BIBLIOGRAPHY 254
Morris, A. G. (2008). Searching for ‘real’ Hottentots: the Khoekhoe in the history of South African physical
anthropology. South African Humanities, 20(12):221–233.
Morris, A. G. (2012). Trauma and violence in the Later Stone Age of southern Africa. South African Medical
Journal, 102(6):1–4.
Morris, A. G. (2014). Controversies About the Study of Human Remains in Post-Apartheid South Africa.
In O’Donnabhain, B. and Lozada, M. C., editors, Archaeol. Hum. Remain., SpringerBriefs in Archaeology,
chapter 14, pages 189–198. Springer International Publishing, Cham.
Morris, A. G., Heinze, A., Chan, E. K. F., Smith, A. B., and Hayes, V. M. (2014). First Ancient Mitochondrial
Human Genome from a Prepastoralist Southern African. Genome Biology and Evolution, 6(10):2647–2653.
Morris, A. G., Thackeray, A. I., and Thackeray, J. F. (1987). Late Holocene Human Skeletal Remains from
Snuifklip, near Vleesbaai, Southern Cape. South African Archaeological Bulletin, 42(146):153–160.
Mosothwane, M. (2010). Foragers among farmers in the Early Iron Age of Botswana? Dietary evidence from
stable isotopes. Phd dissertation, University of the Witwatersrand.
Moura-Dos-Santos, M., Wellington-Barros, J., Brito-Almeida, M., Manhães-de Castro, R., Maia, J., and Góis
Leandro, C. (2012). Permanent deficits in handgrip strength and running speed performance in low birth
weight children. American Journal of Human Biology, 25(1):58–62.
Mulligan, C. J., D’Errico, N. C., Stees, J., and Hughes, D. A. (2012). Methylation changes at NR3C1 in newborns
associate with maternal prenatal stress exposure and newborn birth weight. Epigenetics, 7(8):853–857.
Mummert, A., Esche, E., Robinson, J., and Armelagos, G. J. (2011). Stature and robusticity during the agricul-
tural transition : Evidence from the bioarchaeological record. Economics and Human Biology, 9(3):284–301.
Murata, M. M. D., Yudoh, K. M. D., Masuko, K. M. D., Ph, D., Yudoh, K. M. D., Ph, D., Masuko, K. M. D.,
and Ph, D. (2008). The potential role of vascular endothelial growth factor (VEGF) in cartilage: how the
angiogenic factor could be involved in the pathogenesis of osteoarthritis? Osteoarthritis Cartilage, 16(3):279–
286.
Nasanen-Gilmore, S. P. K., Saha, S., Rasul, I., and Rousham, E. K. (2015). Household environment and
behavioral determinants of respiratory tract infection in infants and young children in northern bangladesh.
American Journal of Human Biology, pages n/a—-n/a.
Ngom, P. T., Collinson, A. C., Pido-Lopez, J., Henson, S. M., Prentice, A. M., and Aspinall, R. (2004). Improved
thymic function in exclusively breastfed infants is associated with higher interleukin 7 concentrations in their
mothers’ breast milk. American Journal of Clinical Nutrition, 80:722–728.
BIBLIOGRAPHY 255
Nikita, E., Mattingly, D., and Lahr, M. M. (2013). Methodological considerations in the statistical analysis of
degenerative joint and disc disease. International Journal of Paleopathology, 3(2):105–112.
Nikitovic, D. and Bogin, B. (2013). Ontogeny of sexual size dimorphism and environmental quality in Guatemalan
children. American Journal of Human Biology, 00(April):1–7.
Norris, S. a., Osmond, C., Gigante, D., Kuzawa, C. W., Ramakrishnan, L., Lee, N. R., Ramirez-Zea, M., Richter,
L. M., Stein, A. D., Tandon, N., and Fall, C. H. D. (2012). Size at birth, weight gain in infancy and childhood,
and adult diabetes risk in five low- or middle-income country birth cohorts. Diabetes Care, 35(1):72–79.
Ortner, D. J. (2003). Identification of Pathological Conditions in Human Skeletal Remains. Academic Press,
New York, NY.
Orton, J. (2012). Tortoise burials in Namaqualand : uncovering ritual behaviour on South Africa ’ s west coast.
Azania Archaeol. Res. Africa, 47(1):99–114.
Orton, J., Mitchell, P., Klein, R., Steele, T., and Horsburgh, K. A. (2013). An early date for cattle from
Namaqualand, South Africa: implications for the origins of herding in southern Africa. Antiquity, 87(335):108–
120.
Paine, R. R. and Boldsen, J. L. (2002). Linking age-at-death distributions and ancient population dynamics: a
case study. In Hoppa, R. D. and Vaupel, J. W., editors, Paleodemography: Age Distributions from Skeletal
Samples, chapter 8, pages 169–180. Cambridge University Press, Cambridge, UK, 1 edition.
Papp, T., Porter, R., Craig, C., Aspden, R., and Campbell, D. (1997). Significant Antenatal Factors in the
Development of Lumbar Spinal Stenosis. Spine (Phila. Pa. 1976)., 22(16):1805–1810.
Papp, T., Porter, R. W., and Aspden, R. M. (1994). The growth of the lumbar vertebral canal. Spine (Phila.
Pa. 1976)., 19(24):2770–2773.
Papp, T., Porter, R. W., and Aspden, R. M. (1995). Trefoil configuration and developmental stenosis of the
lumbar vertebral canal. J. Bone Jt. Surg., 77(3).
Parkington, J., Fisher, J. W., and Kyriacou, K. (2013). Limpet Gathering Strategies in the Later Stone Age
Along the Cape West Coast, South Africa. Journal of Island and Coastal Archaeology, 8(January 2015):91–107.
Parkington, J., Fisher, J. W., Poggenpoel, C., and Kyriacou, K. (2014). Strandloping as a Resource-Gathering
Strategy in the Cape, South African Holocene Later Stone Age: The Verloren Vlei Record. Journal of Island
and Coastal Archaeology, 9(2):219–237.
Parkington, J. E. (1986). Landscape and subsistence changes since the Last Glacial Maximum along the western
Cape coast. End Palaeolithic Old World, pages 201–227.
BIBLIOGRAPHY 256
Perry, G. H. and Dominy, N. J. (2009). Evolution of the human pygmy phenotype. Trends Ecol. Evol., 24(4):218–
225.
Peterson, R. O. (1988). Increased osteoarthritis in moose from Isle Royale. J. Wildl. Dis., 24(3):461–466.
Peterson, R. O., Vucetich, J. a., Fenton, G., Drummer, T. D., and Larsen, C. S. (2010). Ecology of arthritis.
Ecol. Lett., 13(9):1124–1128.
Pfeiffer, S. (1984). Paleopathology in an Iroquoian ossuary, with special reference to tuberculosis. American
Journal of Physical Anthropology, 65:181–189.
Pfeiffer, S. (2007). The Health of Foragers: People of the Later Stone Age, Southern Africa. In Cohen, M. N. and
Crane-Kramer, G. M., editors, Ancient Health: Skeletal Indicators of Agricultural Economic Intensification,
chapter 15, pages 223–236. University Press of Florida, Gainesville, FL, 1 edition.
Pfeiffer, S. (2009). The incorporation of bioarchaeology into KhoeSan studies. South African Archaeological
Bulletin, 64(190):193–195.
Pfeiffer, S. (2010). Cranial trauma as evidence of a stressful period among southern African foragers. In
The "Compleat Archaeologist: Papers in Honour of Michael W Spence, volume 9, pages 227–237. Ontario
Archaeological Society, London, ON.
Pfeiffer, S. (2011). Pelvic stress injuries in a small-bodied forager. Int. J. Osteoarchaeol., 21(6):694–703.
Pfeiffer, S. (2012a). Conditions for Evolution of Small Adult Body Size in Southern Africa. Current Anthropology,
53(December 2012):S000—-S000.
Pfeiffer, S. (2012b). Two disparate instances of healed cranial trauma from the Later Stone Age of South Africa.
South African Archaeological Bulletin, 67(196):pp. 256—-261.
Pfeiffer, S. (2013). Population dynamics in the Southern African Holocene: Human burials from the West Coast.
In Jerardino, A. M., Malan, A., and Braun, D., editors, Archaeol. West Coast South Africa, chapter 8, pages
143–154. Archaeopress, Cambridge, UK.
Pfeiffer, S. and Crowder, C. (2004). An ill child among mid-Holocene foragers of Southern Africa. American
Journal of Physical Anthropology, 123(1):23–29.
Pfeiffer, S., Doyle, L. E., Kurki, H. K., Harrington, L., Ginter, J. K., and Merritt, C. E. (2014). Discernment of
mortality risk associated with childbirth in archaeologically derived forager skeletons. International Journal
of Paleopathology, 1(7):15–24.
Pfeiffer, S. and Harrington, L. (2011). Bioarchaeological Evidence for the Basis of Small Adult Stature in Southern
Africa. Current Anthropology, 52(3):449–461.
BIBLIOGRAPHY 257
Pfeiffer, S. and Sealy, J. (2006). Body size among Holocene foragers of the Cape Ecozone, southern Africa.
American Journal of Physical Anthropology, 129(1):1–11.
Pfeiffer, S. and van der Merwe, N. J. (2004). Cranial Injuries to Later Stone Age Children from the Modder
River Mouth, Western Cape Province, South Africa. South African Archaeological Bulletin, 59(180):59–65.
Pfeiffer, S., van der Merwe, N. J., Parkington, J. E., and Yates, R. J. (1999). Violent human death in the past:
a case from the western Cape. S. Afr. J. Sci., 95(3):137–141.
Phenice, T. W. (1969). A newly developed visual method of sexing the os pubis. American Journal of Physical
Anthropology, 30(2):297–301.
Pickrell, J. K., Patterson, N., Barbieri, C., Berthold, F., Gerlach, L., Güldemann, T., Kure, B., Mpoloka, S. W.,
Nakagawa, H., Naumann, C., Lipson, M., Loh, P.-R., Lachance, J., Mountain, J., Bustamante, C. D., Berger,
B., Tishkoff, S. a., Henn, B. M., Stoneking, M., Reich, D., and Pakendorf, B. (2012). The genetic prehistory
of southern Africa. Nature Communications, 3:1143.
Pike, I. L. (2005). Maternal stress and fetal responses: evolutionary perspectives on preterm delivery. American
Journal of Human Biology, 17(1):55–65.
Pinhasi, R. (2008). Growth in Archaeological Populations. In Adv. Hum. Palaeopathology, pages 363–380. John
Wiley & Sons, Ltd.
Pinhasi, R. and Bourbou, C. (2008). How Representative Are Human Skeletal Assemblages for Population
Analysis? In Adv. Hum. Palaeopathology, pages 31–44. John Wiley & Sons, Ltd.
Pinhasi, R. and Turner, K. (2008). Epidemiological Approaches in Palaeopathology. In Adv. Hum. Palaeopathol-
ogy, pages 45–56. John Wiley & Sons, Ltd.
Pomeroy, E., Stock, J. T., Stanojevic, S., Miranda, J. J., Cole, T. J., and Wells, J. C. K. (2012). Trade-offs in
relative limb length among Peruvian children: extending the thrifty phenotype hypothesis to limb proportions.
PLoS One, 7(12):e51795.
Pomeroy, E., Stock, J. T., Stanojevic, S., Miranda, J. J., Cole, T. J., and Wells, J. C. K. (2014). Stunting,
adiposity, and the individual-level "dual burden" among urban lowland and rural highland peruvian children.
American Journal of Human Biology, 00(November 2013).
Porter, Porter, R. W., and Pavitt, D. (1987a). The Vertebral Canal: I. Nutrition and Development, an Archae-
ological Study. Spine (Phila. Pa. 1976)., 12(9):901–906.
Porter, R. W., Drinkall, J. N., Porter, D. E., and Thorp, L. (1987b). The Vertebral Canal: II. Health and
Academic Status, a Clinical Study. Spine, 12(9):907–911.
BIBLIOGRAPHY 258
Porter, R. W., Hibbert, C., and Wellman, P. (1980). Backache and the lumbar spinal canal. Spine (Phila. Pa.
1976)., 5(2):99–105.
Porter, R. W. and Oakshot, G. (1994). Spinal stenosis and health status. Spine (Phila. Pa. 1976)., 19(8):901–903.
Procheş, , Cowling, R. M., Goldblatt, P., Manning, J. C., and Snijman, D. a. (2006). An overview of the Cape
geophytes. Biol. J. Linn. Soc., 87(1):27–43.
Puenpatom, R. A. and Victor, T. W. (2009). Increased Prevalence of Metabolic Syndrome in Individuals with
Osteoarthritis. Postgrad. Med., 121(6).
Quinn, G. P. and Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge
University Press.
R Foundation (2013). The R Stats Package.
Raqib, R., Alam, D. S., Sarker, P., Ahmad, S. M., Ara, G., Yunus, M., Moore, S. E., and Fuchs, G. (2007). Low
birth weight is associated with altered immune function in rural Bangladeshi children : a birth cohort study.
American Journal of Clinical Nutrition, 85(4):845–852.
Raxter, M. H., Auerbach, B. M., and Ruff, C. B. (2006). Revision of the Fully technique for estimating statures.
American Journal of Physical Anthropology, 130(3):374–384.
Redfern, R. C. and Dewitte, S. N. (2011). A new approach to the study of Romanization in Britain: a regional
perspective of cultural change in late iron age and roman dorset using the siler and gompertz-makeham models
of mortality. American Journal of Physical Anthropology, 144(2):269–285.
Redfern, R. C., Dewitte, S. N., Pearce, J., Hamlin, C., and Dinwiddy, K. E. (2015). Urban – Rural Differences
in Roman Dorset , England : A Bioarchaeological Perspective on Roman Settlements. American Journal of
Physical Anthropology, 00(October 2014).
Ribot, I. and Roberts, C. (1996). A Study of Non-specific Stress Indicators and Skeletal Growth in Two Mediaeval
Subadult Populations. Journal of Archaeological Science, 23(1):67–79.
Rickard, I. J. and Lummaa, V. (2007). The predictive adaptive response and metabolic syndrome: challenges
for the hypothesis. Trends Endocrinol. Metab., 18(3):94–99.
Ripley, B., Venables, B., Bates, D., Hornik, K., Gebhardt, A., and Firth, D. (2014). Support Functions and
Datasets for Venables and Ripley’s MASS.
Risnes, K. R., Vatten, L. J., Baker, J. L., Jameson, K., Sovio, U., Kajantie, E., Osler, M., Morley, R., Jokela,
M., Painter, R. C., Sundh, V., Jacobsen, G. W., Eriksson, J. G., Sørensen, T. I. a., and Bracken, M. B.
(2011). Birthweight and mortality in adulthood: a systematic review and meta-analysis. Int. J. Epidemiol.,
40(3):647–661.
BIBLIOGRAPHY 259
Roberts, C. A. and Buikstra, J. E. (2003). The Bioarchaeology of Tuberculosis: A Global View on a Reemerging
Disease. University Press of Florida, Gainesville, FL.
Rodney, N. C. and Mulligan, C. J. (2014). A biocultural study of the effects of maternal stress on mother and
newborn health in the Democratic Republic of Congo. American Journal of Physical Anthropology, 155(2):200–
209.
Rogers, J. and Dieppe, P. (1994). Is tibiofemoral osteoarthritis in the knee joint a new disease? Ann. Rheum.
Dis., 53(9):612–613.
Rogers, J., Shepstone, L., and Dieppe, P. (2004). Is osteoarthritis a systemic disorder of bone? Arthritis Rheum.,
50(2):452–457.
Roksandic, M. and Armstrong, S. D. (2011). Using the life history model to set the stage(s) of growth and
senescence in bioarchaeology and paleodemography. American Journal of Physical Anthropology, 145(3):337–
347.
Ronsmans, C. and Graham, W. J. (2006). Maternal mortality: who, when, where, and why. Lancet,
368(9542):1189–1200.
Roos, E. M. (2005). Joint injury causes knee osteoarthritis in young adults. Curr. Opin. Rheumatol., 17(2):195–
200.
Roseboom, T. J., Meulen, J. H. P. V. D., Osmond, C., Barker, D. J. P., Ravelli, A. C. J., Montfrans, G. A. V.,
Michels, R. P. J., and Bleker, O. P. (2000). Coronary heart disease after prenatal exposure to the Dutch
famine , 1944 – 45. Heart, 84:595–598.
Roth, M., Krkoska, J., and Toman, I. (1976). Morphogenesis of the Spinal Canal, Normal and Stenotic. Neuro-
radiology, 10:277–286.
Royston, J. P. (1982). An extension of Shapiro and Wilk’s W test for normality to large samples. Appl. Stat.,
31(2):115–124.
Ruff, C. (2002). Variation in Human Body Size and Shape. Annual Review of Anthropology, 31(1):211–232.
Ruff, C. B., Scott, W. W., and Liu, a. Y. (1991). Articular and diaphyseal remodeling of the proximal femur
with changes in body mass in adults. American Journal of Physical Anthropology, 86(3):397–413.
Rush, D. (2000). Nutrition and maternal mortality in the developing world. American Journal of Clinical
Nutrition, 72(1):212–240.
Rutherford, J. N. (2009). Fetal signaling through placental structure and endocrine function: illustrations and
implications from a nonhuman primate model. American Journal of Human Biology, 21(6):745–753.
BIBLIOGRAPHY 260
Sabban, E. L. (2009). Catecholamines and Stress. In Stress - From Mol. to Behav., pages 19–35. Wiley-VCH
Verlag GmbH & Co. KGaA.
Sadr, K. (2003). The Neolithic of Southern Africa. J. Afr. Hist., 44(2):195–209.
Sadr, K. (2012). The Origins and Spread of Dry Laid , Stone-Walled Architecture in Pre-colonial Southern
Africa. J. South. Afr. Stud., 38(2):257–263.
Sadr, K., Sampson, C. G., South, T., Archaeological, A., and Jun, N. (2008). KhoeKhoe ceramics of the Upper
Seacow Valley. South African Archaeological Bulletin, 54(169):3–15.
Saifuddin, A., Royal, T., Orthopaedic, N., Trust, H., and Hill, B. (2000). The Imaging of Lumbar Spinal Stenosis.
Clin. Radiol., 55:581–594.
Salonen, M. K., Kajantie, E., Osmond, C., Forsén, T., Ylihärsilä, H., Paile-Hyvärinen, M., Barker, D. J. P., and
Eriksson, J. G. (2011). Developmental Origins of Physical Fitness: The Helsinki Birth Cohort Study. PLoS
One, 6(7):7.
San Millán, M., Rissech, C., and Turbón, D. (2013). A test of Suchey-Brooks (pubic symphysis) and Buckberry-
Chamberlain (auricular surface) methods on an identified Spanish sample: Paleodemographic implications.
Journal of Archaeological Science, 40(4):1743–1751.
Sandell, L. J. (2012). Etiology of osteoarthritis: genetics and synovial joint development. Nat. Rev. Rheumatol.,
8(2):77–89.
Sattenspiel, L. and Harpending, H. (1983). Stable Populations and Skeletal Age. American Antiquity, 48(3):489–
498.
Saunders, S. R., Fitzgerald, C., Rogers, T., Dudar, C., and McKillop, H. (1992). A test of several methods of
skeletal age estimation using a documented archaeological sample. Can. Soc. Forensic Sci. J., 25(2):97–118.
Sayer, A. A., Poole, J., Cox, V., Kuh, D., Hardy, R., Wadsworth, M., and Cooper, C. (2003). Weight from
birth to 53 years: a longitudinal study of the influence on clinical hand osteoarthritis. Arthritis Rheum.,
48(4):1030–1033.
Scheinfeldt, L. B., Soi, S., and Tishkoff, S. a. (2010). Colloquium paper: working toward a synthesis of ar-
chaeological, linguistic, and genetic data for inferring African population history. Proceedings of the National
Academy of Sciences, 107 Suppl:8931–8938.
Scheuer, L. and Black, S. (2000). Developmental Juvenile Osteology. Elsevier Academic Press, San Diego, CA,
1 edition.
BIBLIOGRAPHY 261
Schlebusch, C. M., Skoglund, P., Sjödin, P., Gattepaille, L. M., Hernandez, D., Jay, F., Li, S., De Jongh, M.,
Singleton, A., Blum, M. G. B., Soodyall, H., and Jakobsson, M. (2012). Genomic variation in seven Khoe-San
groups reveals adaptation and complex African history. Science, 338(6105):374–379.
Schlotz, W., Jones, A., Godfrey, K. M., and Phillips, D. I. W. (2008). Effortful control mediates associations
of fetal growth with hyperactivity and behavioural problems in 7- to 9-year-old children. J. Child Psychol.
Psychiatry, 11:1228–1236.
Schlotz, W., Phillips, D. I. W., Cohort, H., and Group, S. (2013). Birth Weight and Perceived Stress Reactivity
in Older Age. Stress Heal., 29(1):56–63.
Schuster, S. C., Miller, W., Ratan, A., Tomsho, L. P., Giardine, B., Kasson, L. R., Harris, R. S., Petersen, D. C.,
Zhao, F., Qi, J., Alkan, C., Kidd, J. M., Sun, Y., Drautz, D. I., Bouffard, P., Muzny, D. M., Reid, J. G.,
Nazareth, L. V., Wang, Q., Burhans, R., Riemer, C., Wittekindt, N. E., Moorjani, P., Tindall, E. a., Danko,
C. G., Teo, W. S., Buboltz, A. M., Zhang, Z., Ma, Q., Oosthuysen, A., Steenkamp, A. W., Oostuisen, H.,
Venter, P., Gajewski, J., Zhang, Y., Pugh, B. F., Makova, K. D., Nekrutenko, A., Mardis, E. R., Patterson, N.,
Pringle, T. H., Chiaromonte, F., Mullikin, J. C., Eichler, E. E., Hardison, R. C., Gibbs, R., Harkins, T. T., and
Hayes, V. M. (2010). Complete Khoisan and Bantu genomes from southern Africa. Nature, 463(7283):943–947.
Scott, L. and Woodborne, S. (2007). Pollen analysis and dating of Late Quaternary faecal deposits (hyraceum)
in the Cederberg, Western Cape, South Africa. Rev. Palaeobot. Palynol., 144(3-4):123–134.
Sealy, J. (1986). Stable Carbon Isotopes and Prehistoric Diets in the South-Western Cape Province, South Africa.
British Archaeological Reports 293, Cambridge, UK.
Sealy, J. (1987). Stable carbon isotopes, Later Stone Age diets and seasonal mobility in the south-western Cape.
In Parkington, J. and Hall, M., editors, Pap. Prehistory West. Cape, South Africa, chapter 13, pages 262–268.
BAR International Reports 332, Oxford, UK.
Sealy, J. (2006). Diet, Mobility, and Settlement Pattern among Holocene HunterGatherers in Southernmost
Africa. Current Anthropology, 47(4):569–595.
Sealy, J. (2010). Isotopic evidence for the antiquity of cattle-based pastoralism in Southernmost Africa. Journal
of African Archaeology, 8(1):65–81.
Sealy, J. and Galimberti, M. (2011). Shellfishing and the interpretation of shellfish sizes in the Middle and Later
Stone Ages of South Africa. In Trekking the Shore, pages 405–419. Springer.
Sealy, J. and Pfeiffer, S. (2000). Diet, Body Size, and Landscape Use among Holocene People in the Southern
Cape, South Africa. Current Anthropology, 41(4):629–690.
BIBLIOGRAPHY 262
Sealy, J., Pfeiffer, S., Yates, R., Willmore, K., Manhire, A., Maggs, T., Lanham, J., and Wilmore, K. (2000).
Hunter-gatherer child burials from the Pakhuis Mountains, Western Cape: growth, diet and burial practices
in the Late Holocene. South African Archaeological Bulletin, 55(171):32–43.
Sealy, J. and Yates, R. (1994). The chronology of the introduction of pastoralism to the Cape, South Africa.
Antiquity, 68(258):58–67.
Sealy, J. C. (1997). Stable carbon and nitrogen isotope ratios and coastal diets in the Later Stone Age of South
Africa: a comparison and critical analysis of two data sets. Anc. Biomol., 1(2):131–147.
Sealy, J. C., Patrick, M. K., Morris, A. G., and Alder, D. (1992). Diet and dental caries among later stone age
inhabitants of the Cape Province, South Africa. American Journal of Physical Anthropology, 88(2):123–134.
Sealy, J. C. and Van der Merwe, N. J. (1988). Social, spatial and chronological patterning in marine food use
as determined by delta13C measurements of Holocene human skeletons from the south-western Cape, South
Africa. World Archaeol., 20(1):87–102.
Sear, R. and Mace, R. (2008). Who keeps children alive? A review of the effects of kin on child survival. Journal
of Human Evolution, 29(1):1–18.
Sear, R., Mace, R., and Mcgregor, I. A. (2003). The effects of kin on female fertility in rural Gambia. Evol.
Hum. Behav., 24:25–42.
Seckl, J. R. and Holmes, M. C. (2007). Mechanisms of disease: glucocorticoids, their placental metabolism and
fetal ’programming’ of adult pathophysiology. Nat. Clin. Pract. Endocrinol. Metab., 3(6):479–488.
Segerstrom, S. C. (2010). Resources, stress, and immunity: an ecological perspective on human psychoneuroim-
munology. Ann. Behav. Med., 40(1):114–125.
Segerstrom, S. C. and Miller, G. E. (2004). Psychological stress and the human immune system: a meta-analytic
study of 30 years of inquiry. Psychol. Bull., 130(4):601–630.
Séguy, I. and Buchet, L. (2013). Finding the Right Models for Pre-industrial Populations. In Handb. Palaeode-
mography, volume 2 of INED Population Studies, chapter 7, pages 113–121. Springer International Publishing,
Cham.
Sellen, D. W. (2007). Evolution of infant and young child feeding: implications for contemporary public health.
Annual Review of Nutrition, 27:123–148.
Shapira, M. (2010). Stress Effects on Immunity in Vertebrates and Invertebrates. In Stress - From Mol. to
Behav., pages 207–227. Wiley-VCH Verlag GmbH & Co. KGaA.
Shapiro, A. S. S. and Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples ).
Biometrika, 52(3/4):591–611.
BIBLIOGRAPHY 263
Sharp, D. S., Andrew, M. E., Fekedulegn, D. B., Burchfiel, C. M., Violanti, J. M., Wactawski-Wende, J., and
Miller, D. B. (2013). The cortisol response in policemen: Intraindividual variation, not concentration level,
predicts truncal obesity. American Journal of Human Biology, 000(February).
Silberbauer, G. B. (1981). Hunter and habitat in the central Kalahari Desert. Cambridge University Press, New
York, NY.
Smith, A. B. (1984). Environmental limitations on prehistoric pastoralism in Africa. African Archaeol. Rev.,
2(1):99–111.
Smith, P., Horwitz, L. K., and Kaplan, E. (1992). Skeletal Evidence for Population Change in the Late Holocene
of the South-Western Cape: A Radiological Study. South African Archaeological Bulletin, 47(156):82–88.
Snodgrass, J. J. (2012). Human Energetics. In Stinson, S., Bogin, B., and O’Rourke, D., editors, Human
Biology: An Evolutionary and Biocultural Perspective, chapter 8, pages 325–384. Wiley-Blackwell, Hoboken,
NJ, 2 edition.
Sofaer Derevenski, J. R. (2000). Sex differences in activity-related osseous change in the spine and the gendered
division of labor at Ensay and Wharram Percy, UK. American Journal of Physical Anthropology, 111(3):333–
354.
Sokal, D., Sawadogo, L., and Adjibade, a. (1991). Short stature and cephalopelvic disproportion in Burkina
Faso, West Africa. Int. J. Gynecol. Obstet., 35(4):347–350.
Song, S. (2013). Identifying the intergenerational effects of the 1959 – 1961 Chinese Great Leap Forward Famine
on infant mortality. Economics and Human Biology, 11:474–487.
Sorensen, M. V., Snodgrass, J. J., Leonard, W. R., McDade, T. W., Tarskaya, L. a., Ivanov, K. I., Krivoshapkin,
V. G., and Alekseev, V. P. (2009). Lifestyle incongruity, stress and immune function in indigenous Siberians:
the health impacts of rapid social and economic change. American Journal of Physical Anthropology, 138(1):62–
69.
Soreq, H., Friedman, A., and Kaufer, D., editors (2010). Stress - From Molecules to Behavior: A Comprehensive
Analysis of the Neurobiology of Stress Responses. John Wiley & Sons, Hoboken, NJ, 1 edition.
Sotomayor, O. (2012). Fetal and infant origins of diabetes and ill health: Evidence from Puerto Rico’s 1928 and
1932 hurricanes. Economics and Human Biology, 11(3):281–293.
Sowers, M. R. and Karvonen-Gutierrez, C. a. (2010). The evolving role of obesity in knee osteoarthritis. Curr.
Opin. Rheumatol., 22(5):533–537.
Starling, A. P. and Stock, J. T. (2007). Dental Indicators of Health and Stress in Early Egyptian and Nubian
Agriculturalists : A Difficult Transition and Gradual Recovery. American Journal of Physical Anthropology,
134(9):520–528.
BIBLIOGRAPHY 264
Steckel, R. H. (1979). Slave height profiles from coastwise manifests. Explor. Econ. Hist., 16(4):363–380.
Steckel, R. H. (1995). Stature and the Standard of Living. J. Econ. Lit., 33(4):1903–1940.
Steckel, R. H. (2005). Young adult mortality following severe physiological stress in childhood: skeletal evidence.
Economics and Human Biology, 3(2):314–328.
Steckel, R. H. and Rose, J. C. (2002). The Backbone of History: Health and Nutrition in the Western Hemisphere.
In Steckel, R. H. and Rose, J. C., editors, The Backbone of History: Health and Nutrition in the Western
Hemisphere. Cambridge University Press, Cambridge, UK, 1 edition.
Steckel, R. H., Sciulli, P. W., and Rose, J. C. (2002). A Health Index from Human Remains. In Steckel, R. H.
and Rose, J. C., editors, Backbone Hist. Heal. Hutrition West. Hemisphere. Vol. 2., chapter 3, pages 61–93.
Cambridge University Press, Cambridge, UK, 1 edition.
Stein, A. D., Kahn, H. S., and Lumey, L. H. (2010). The 2D:4D digit ratio is not a useful marker for prenatal
famine exposure: Evidence from the Dutch hunger winter families study. American Journal of Human Biology,
22(6):801–806.
Stein, C. E., Kumaran, K., Fall, C. H. D., Shaheen, S. O., Osmond, C., and Barker, D. J. P. (1997). Relation of
fetal growth to adult lung function in South India. Thorax, 52:895–899.
Stinson, S. (2012). Growth Variation : Biological and Cultural Factors. In Stinson, S., Bogin, B., and O’Rourke,
D., editors, Human Biology: An Evolutionary and Biocultural Perspective, chapter 12, pages 587–635. Wiley-
Blackwell, Hoboken, NJ, 2 edition.
Stoch, M. B., Smythe, P. M., and Ivanovic, D. M. (1963). Does undernutrition during infancy inhibit brain
growth and subsequent intellectual development? Nutrition, 38(7-8):546–552.
Stock, J. T. and Pfeiffer, S. K. (2004). Long bone robusticity and subsistence behaviour among Later Stone Age
foragers of the forest and fynbos biomes of South Africa. Journal of Archaeological Science, 31(7):999–1013.
Stock, J. T. and Pinhasi, R. (2011). Changing Paradigms in Our Understanding of the Transition to Agriculture
: Human. In Pinhasi, R. and Stock, J. T., editors, Human Bioarchaeology of the Transition to Agriculture,
chapter 1, pages 1–13. John Wiley & Sons, Hoboken, NJ, 1 edition.
Stürmer, T., Brenner, H., Brenner, R. E., and Günther, K. P. (2001). Non-insulin dependent diabetes mellitus
(NIDDM) and patterns of osteoarthritis. The Ulm osteoarthritis study. Scand. J. Rheumatol., 30(3):169–171.
Stynder, D. D. (2009). Craniometric evidence for South African Later Stone Age herders and hunter–gatherers
being a single biological population. Journal of Archaeological Science, 36(3):798–806.
Stynder, D. D., Ackermann, R. R., and Sealy, J. C. (2007a). Craniofacial Variation and Population Continuity
During the South African Holocene. American Journal of Physical Anthropology, 500(September):489–500.
BIBLIOGRAPHY 265
Stynder, D. D., Ackermann, R. R., and Sealy, J. C. (2007b). Early to mid-Holocene South African Later Stone
Age human crania exhibit a distinctly Khoesan morphological pattern. South African Journal of Science,
1342(August):349–353.
Suri, P., Katz, J. N., Rainville, J., Kalichman, L., Guermazi, a., and Hunter, D. J. (2010). Vascular disease is
associated with facet joint osteoarthritis. Osteoarthritis Cartilage, 18(9):1127–1132.
Syddall, H. E., Sayer, A. A., Simmonds, S. J., Osmond, C., Cox, V., Dennison, E. M., Barker, D. J. P., and
Cooper, C. (2005). Birth Weight, Infant Weight Gain, and Cause-specific Mortality: The Hertfordshire Cohort
Study. Am. J. Epidemiol., 161(11):1074–1080.
Szczodry, M., Coyle, C. H., Kramer, S. J., Smolinski, P., and Chu, C. R. (2009). Progressive chondrocyte death
after impact injury indicates a need for chondroprotective therapy. American Journal of Sports Medicine,
37(12):2318–2322.
Tabachnick, B. G. and Fidell, L. S. (2007). Using Multivariate Statistics, volume 3. Pearson, Boston, MA, 5
edition.
Tanner, S., Leonard, W. R., McDade, T. W., Reyes-Garcia, V., Godoy, R., and Huanca, T. (2009). Influence of
helminth infections on childhood nutritional status in lowland Bolivia. American Journal of Human Biology,
21(5):651–656.
Tanner, S., Leonard, W. R., and Reyes-García, V. (2014). The consequences of linear growth stunting: Influence
on body composition among youth in the bolivian amazon. American Journal of Physical Anthropology,
153(1):92–102.
Temple, D. H. (2008). What can variation in stature reveal about environmental differences between prehistoric
Jomon foragers? Understanding the impact of systemic stress on developmental stability. American Journal
of Human Biology, 20(4):431–439.
Temple, D. H. (2010). Patterns of systemic stress during the agricultural transition in prehistoric Japan. American
Journal of Physical Anthropology, 142(1):112–124.
Temple, D. H., Bazaliiskii, V. I., Goriunova, O. I., and Weber, A. W. (2014). Skeletal Growth in Early and Late
Neolithic Foragers from the Cis-Baikal Region of Eastern Siberia. American Journal of Physical Anthropology,
386(April 2013):377–386.
Temple, D. H. and Goodman, A. H. (2014). Bioarcheology Has a “Health” Problem: Conceptualizing “Stress”
and “Health” in Bioarcheological Research. American Journal of Physical Anthropology, 00(August).
Temple, D. H., McGroarty, J. N., Guatelli-steinberg, D., and Nakatsukasa, M. (2013). A Comparative Study of
Stress Episode Prevalence and Duration Among Jomon Period Foragers from Hokkaido. American Journal of
Physical Anthropology, 238(August):230–238.
BIBLIOGRAPHY 266
Thackeray, J. F. (1979). An analysis of faunal remains from archaeological sites in Southern South West Africa
(Namibia). South African Arcaheological Bull., 34(129):18–33.
Thayer, Z. M., Feranil, A. B., and Kuzawa, C. W. (2011). Maternal cortisol disproportionately impacts fetal
growth in male offspring: evidence from the Philippines. American Journal of Human Biology, 24(1):1–4.
Thayer, Z. M. and Kuzawa, C. W. (2014). Early origins of health disparities: Material deprivation predicts
maternal evening cortisol in pregnancy and offspring cortisol reactivity in the first few weeks of life. American
Journal of Human Biology, 730(November 2013):723–730.
Thomas, N., Grunnet, L. G., Poulsen, P., Christopher, S., Spurgeon, R., Inbakumari, M., Livingstone, R., Alex,
R., Mohan, V. R., Antonisamy, B., Geethanjali, F. S., Karol, R., Vaag, A., and Bygbjerg, I. C. (2012). Born
with low birth weight in rural Southern India: what are the metabolic consequences 20 years later? Eur. J.
Endocrinol., 166(4):647–655.
Thomas, R. B. (1997). Wandering toward the edge of adaptability: adjustments of Andean people to change. In
Ulijaszek, S. J. and Huss-Ashmore, R., editors, Hum. Adapt. Past, Present. Futur., chapter 10, pages 183–232.
Oxford University Press, Oxford, UK.
Thompson, R. C., Allam, A. H., Lombardi, G. P., Wann, L. S., Sutherland, M. L., Sutherland, J. D., Soliman,
M. A. T., Frohlich, B., Mininberg, D. T., Monge, J. M., Vallodolid, C. M., Cox, S. L., Abd El-Maksoud, G.,
Badr, I., Miyamoto, M. I., El-Halim Nur El-Din, A., Narula, J., Finch, C. E., and Thomas, G. S. (2013).
Atherosclerosis across 4000 years of human history: The Horus study of four ancient populations. Lancet,
381(9873):1211–1222.
Tishkoff, S. a., Gonder, M. K., Henn, B. M., Mortensen, H., Knight, A., Gignoux, C., Fernandopulle, N.,
Lema, G., Nyambo, T. B., Ramakrishnan, U., Reed, F. a., and Mountain, J. L. (2007). History of click-
speaking populations of Africa inferred from mtDNA and Y chromosome genetic variation. Mol. Biol. Evol.,
24(10):2180–2195.
Trippel, S. B. (2004). Growth Factor Inhibition. Clin. Orthop. Relat. Res., 427(427):S47—-S52.
Uauy, R., Kain, J., Mericq, V., Rojas, J., and Corvalán, C. (2008). Nutrition, child growth, and chronic disease
prevention. Ann. Med., 40(1):11–20.
Ulijaszek, S. (1997). Human adaptability research methodology. In Ulijaszek, S. J. and Huss-Ashmore, R.,
editors, Hum. Adapt. Past, Present. Futur., chapter 12, pages 261–280. Oxford University Press, Oxford, UK.
Ulijaszek, S. J. and Huss-Ashmore, R., editors (1997). Human Adaptability Past, Present, and Future: The First
Parkes Foundation Workshop, Oxford, January 1994. Oxford University Press, Oxford, UK.
Ulijaszek, S. J., Johnston, F. E., and Preece, M. A., editors (1998). The Cambridge Encyclopaedia of Human
Growth and Development. Cambridge University Press, Cambridge, UK, 1 edition.
BIBLIOGRAPHY 267
Ursu, T., Porter, R., and Navaratnam, V. (1996). Development of the Lumbar and Sacral Vertebral Canal in
Utero. Spine (Phila. Pa. 1976)., 21(December):2705–2708.
Usher, B. M. (2000). A Multistate Model of Health and Mortality for Paleodemography: The Tirup Cemetery.
Ph.d. dissertation, Pennsylvania State University.
Valderrabano, V., Horisberger, M., Russell, I., Dougall, H., and Hintermann, B. (2009). Etiology of ankle
osteoarthritis. Clin. Orthop. Relat. Res., 467(7):1800–1806.
Valdes, A. M. and Spector, T. D. (2008). The contribution of genes to osteoarthritis. Rheum. Dis. Clin. North
Am., 34(3):581–603.
Valenta, Z., Pitha, J., and Poledne, R. (2006). Proportional odds logistic regression — effective means of dealing
with limited uncertainty in dichotomizing clinical outcomes. Statistics (Ber)., 25(August):4227–4234.
Valsecchi, V., Chase, B. M., Slingsby, J., Carr, A. S., Quick, L. J., Meadows, M. E., Cheddadi, R., and Reimer,
P. J. (2013). A high resolution 15,600-year pollen and microcharcoal record from the Cederberg Mountains,
South Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol., 387:6–16.
van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification.
Statistical Methods in Medical Research, 16(3):219–242.
Van IJzendoorn, M. H., Bakermans-Kranenburg, M. J., and Juffer, F. (2007). Plasticity of Growth in Height ,
Weight , and Head Circumference: Meta-analytic Evidence of Massive Catch-up After International Adoption.
J. Dev. Behav. Pediatr., 28(4):334–343.
van Roosmalen, J. and Brand, R. (1992). Maternal height and the outcome of labor in rural Tanzania. Int. J.
Gynaecol. Obstet., 37(3):169–177.
Veile, A., Winking, J., Gurven, M., Greaves, R. D., and Kramer, K. L. (2012). Infant Growth and the Thymus
: Data from Two South American Native Societies. American Journal of Human Biology, 000:1–8.
Velasquez, M. T. and Katz, J. D. (2010). Osteoarthritis: another component of metabolic syndrome? Metab.
Syndr. Relat. Disord., 8(4):295–305.
Verbiest, H. (1955). Further Experiences on the Pathological Influence of a Developmental Narrowness Bony
Lumbar Vertebral Canal. The Journal of bone and joint surgery, 37B(4).
Victora, C. G., Adair, L., Fall, C., Hallal, P. C., Martorell, R., Richter, L., and Sachdev, H. S. (2008). Maternal
and child undernutrition: consequences for adult health and human capital. Lancet, 371(9609):340–357.
Victora, C. G., Menezes, A. M. B., and Barros, F. C. (2005). Respiratory Function in Adolescence in Relation
to Low Birth Weight , Preterm Delivery, and Intrauterine Growth Restriction. Chest, 27(128):2400–2407.
BIBLIOGRAPHY 268
Wadley, L. (1987). Later Stone Age Hunters and Gatherers of the Southern Transvaal: Social and ecological
interpretation. BAR International Reports 380, Oxford, UK.
Wadley, L. e. (1997). Our Gendered Past: Archaeological Studies of Gender in Southern Africa. University of
Witwatersrand Press, Johannesburg.
Waldron, T. (2009). Palaeopathology. Cambridge University Press, Cambridge, UK.
Walker, R., Gurven, M., Hill, K. I. M., Migliano, A., Chagnon, N., Souza, R. D. E., Djurovic, G., Hames, R.,
Hurtado, A. M., Kaplan, H., Kramer, K., Oliver, W. J., Valeggia, C., and Yamauchi, T. (2006). Growth Rates
and Life Histories in Twenty-Two Small-Scale Societies. American Journal of Human Biology, 18:295–311.
Walker, R. S. and Hamilton, M. J. (2008). LifeHistory Consequences of Density Dependence and the Evolution
of Human Body Size. Current Anthropology, 49(1):115–122.
Waller, R. (2010). The Emergence and Persistence of Inequality in Premodern Societies. Current Anthropology,
51(1):117–118.
Ward, A. (2004). Fetal Programming of the Hypothalamic-Pituitary-Adrenal (HPA) Axis: Low Birth Weight
and Central HPA Regulation. J. Clin. Endocrinol. Metab., 89(3):1227–1233.
Warnes, G. (2012). Various R programming tools for model fitting {gmodels 2.15.4.1}.
Watkins, R. (2012). Variation in health and socioeconomic status within the W. Montague Cobb skeletal collec-
tion: Degenerative joint disease, trauma and cause of death. Int. J. Osteoarchaeol., 22(1):22–44.
Watts, R. (2011). Non-specific indicators of stress and their association with age at death in Medieval York:
Using stature and vertebral neural canal size to examine the effects of stress occurring during different periods
of development. Int. J. Osteoarchaeol., 21(5):568–576.
Watts, R. (2013a). Childhood development and adult longevity in an archaeological population from Barton-
upon-Humber, Lincolnshire, England. Int. J. Paleo, 3:95–104.
Watts, R. (2013b). Lumbar vertebral canal size in adults and children: Observations from a skeletal sample from
London, England. HOMO - J. Comp. Hum. Biol., 64:120–128.
Weaver, T. D., Steele, T. E., and Klein, R. G. (2011). The abundance of eland , buffalo , and wild pigs in Middle
and Later Stone Age sites. Journal of Human Evolution, 60:309–314.
Webb, S. (1995). Palaeopathology of Aboriginal Australians: health and disease across a hunter-gatherer continent.
Cambridge University Press.
Webley, L. Ã. (2007). Archaeological evidence for pastoralist land-use and settlement in Namaqualand over the
last 2000 years. J. Arid Environ., 27(70):629–640.
BIBLIOGRAPHY 269
Weiss, E. (2005). Understanding osteoarthritis patterns: an examination of aggregate osteoarthritis. J. Pale-
opathol., 16(1):1–12.
Weiss, E. (2006). Osteoarthritis and body mass. Journal of Archaeological Science, 33(5):690–695.
Weiss, E. and Jurmain, R. (2007). Osteoarthritis Revisited : A Contemporary Review of Aetiology. Int. J.
Osteoarchaeol., 450(August 2005):437–450.
Wells, J. C. K. (2007). Flaws in the theory of predictive adaptive responses. Trends Endocrinol. Metab.,
18(9):331–337.
Wells, J. C. K. (2009). Historical cohort studies and the early origins of disease hypothesis: making sense of the
evidence. Proc. Nutr. Soc., 68(2):179–188.
Wells, J. C. K. (2010). Maternal capital and the metabolic ghetto: An evolutionary perspective on the trans-
generational basis of health inequalities. American Journal of Human Biology, 22(1):1–17.
Wells, J. C. K. (2011). The thrifty phenotype: An adaptation in growth or metabolism? American Journal of
Human Biology, 23(1):65–75.
Wells, J. C. K. (2012). A critical appraisal of the predictive adaptive response hypothesis. Int. J. Epidemiol.,
41(1):229–235.
Wells, J. C. K., DeSilva, J. M., and Stock, J. T. (2012). The obstetric dilemma: an ancient game of Rus-
sian roulette, or a variable dilemma sensitive to ecology? American Journal of Physical Anthropology, 149
Suppl(November):40–71.
Wells, J. C. K. and Stock, J. T. (2007). The Biology of the Colonizing Ape. Yearbook of Physical Anthropology,
50:191–222.
Westwood, M., Kramer, M. S., Munz, D., Lovett, J. M., and Watters, G. V. (1983). Growth and development
of full-term nonasphyxiated small-for-gestational-age newborns: follow-up through adolescence. Pediatrics,
71(3):376–382.
White, T. D., Black, M. T., and Folkens, P. A. (2012). Human Osteology. Academic Press, Waltham, 3 edition.
Wilcocks, W. J. and Lancaster, H. (1951). Maternal mortality in New South Wales with Special Reference to
Age and Parity. Br. J. Obstet. Gynaecol., 58(6):945–960.
Wilson, J. J. (2014). Paradox and promise: Research on the role of recent advances in paleodemography
and paleoepidemiology to the study of “Health” in precolumbian societies. American Journal of Physical
Anthropology, 155:268–280.
BIBLIOGRAPHY 270
Wilson, M. L. and Lundy, J. K. (1994). Estimated living statures of dated Khoisan skeletobs from the South-
Western coastal region of South Africa. South African Archaeological Bulletin, 49(159):2–8.
Winick, M. and Rosso, P. (1969). Head circumference and cellular growth of the brain in normal and marasmic
children. J. Pediatr., 74(357):774–778.
Woo, J., Leung, J. C. S., and Wong, S. Y. S. (2010). Impact of childhood experience of famine on late life health.
J. Nutr. Health Aging, 14(2):91–95.
Wood, E. T., Stover, D. a., Ehret, C., Destro-Bisol, G., Spedini, G., McLeod, H., Louie, L., Bamshad, M.,
Strassmann, B. I., Soodyall, H., and Hammer, M. F. (2005). Contrasting patterns of Y chromosome and
mtDNA variation in Africa: evidence for sex-biased demographic processes. European Journal of Human
Genetics, 13(7):867–876.
Wood, J. W. (1998). A Theory of Preindustrial Population Dynamics. Current Anthropology.
Wood, J. W., Holman, D. J., O’Connor, K. a., and Ferrell, R. J. (2002). Mortality models for paleodemography.
In Hoppa, R. D. and Vaupel, J. W., editors, Paleodemography: Age Distributions from Skeletal Samples,
chapter 7, pages 129–168. Cambridge University Press, Cambridge, UK, 1 edition.
Wood, J. W., Holman, D. J., Weiss, K. M., Buchanan, A. V., and Lefor, B. (1992a). Hazards Models for Human
Population Biology. Yearbook of Physical Anthropology, 87:43–87.
Wood, J. W., Milner, G. R., Harpending, H. C., Weiss, K. M., Cohen, N., Eisenberg, L. E., Hutchinson, D. L.,
Jankauskas, R., Česnys, G., Katzenberg, A., Lukacs, J. R., Mcgrath, J. W., Roth, E. A., Ubelaker, D. H.,
and Wilkinson, R. G. (1992b). The Osteological Paradox. Current Anthropology, 33(4):343–370.
Worthman, C. M. and Kuzara, J. (2005). Life history and the early origins of health differentials. American
Journal of Human Biology, 17(1):95–112.
Wrangham, R. W., Wilson, M. L., and Muller, M. N. (2006). Comparative rates of violence in chimpanzees and
humans. Primates., 47(1):14–26.
Wright, L. E. and Yoder, C. J. (2003). Recent Progress in Bioarchaeology: Approaches to the Osteological
Paradox. Journal of Archaeological Research, 11(1):43–70.
Yoneda, M., Hirota, M., Uchida, M., Uzawa, K., Tanaka, A., Shibata, Y., and Morita, M. (2006). Marine
radiocarbon reservoir effect in the western North Pacific observed in archaeological fauna.
Yoshimura, N., Muraki, S., Oka, H., Kawaguchi, H., Nakamura, K., and Akune, T. (2011). Association of knee
osteoarthritis with the accumulation of metabolic risk factors such as overweight, hypertension, dyslipidemia,
and impaired glucose tolerance in Japanese men and women: the ROAD study. J. Rheumatol., 38(5):921–930.
BIBLIOGRAPHY 271
Ziol-Guest, K. M., Duncan, G. J., Kalil, A., and Boyce, W. T. (2012). Early childhood poverty, immune-
mediated disease processes, and adult productivity. Proceedings of the National Academy of Sciences, 109
Suppl:17289–17293.