Population stress, growth deficit, and degenerative joint ... · Acknowledgements...

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

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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

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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.

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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.4 Osteological measurements and joint modification variables of the full sample

(page 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211


(page 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212


(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



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


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


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-


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-


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).


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


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).


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


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


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-


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),


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


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


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


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


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


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


(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.,


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


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


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-


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


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-


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-


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,


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


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


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).


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


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).


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-


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.,


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


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


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


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


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


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


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.


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


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


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


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).


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


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.


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


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


“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


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


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


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).


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


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.


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


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


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


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


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


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


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).


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,


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


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.


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


(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).


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


(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


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.


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).


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


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).


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


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


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.


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


(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,


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


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


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


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;


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


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


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


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.


3 Strong polish with grooves or flat-tening of the surface.


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]


3 Deep/ coalesced [one or more poresgreater than 3mm in size]


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)


3 Severe deposition (stands veryproud of articular surface, similarin appearance to a deposit of can-dle wax)


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.


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


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


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:


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


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


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.


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



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




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.


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


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,


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


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).


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


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.


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.


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.


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


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.


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.


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


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


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


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.


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).


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.


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.


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).


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).


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.


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).


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.


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.


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.


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


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


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


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


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.


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.


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


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.


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.


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.


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

.


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


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.


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.


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.


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.


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.


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


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-


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


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).


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.


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 ).


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


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.


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,


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


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


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.


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


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


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).


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),


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).


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.


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




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 MA/EA 5.6 2.3—13.8

MLR T1AP.rank ns None-Mod Middle 0.3 0.1—1.2








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 MA/EA 9.5 3.1—28.4

MLR T6AP.rank ns None-Mod Middle 3.4 0.6—19.5





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 MA/EA 30.6 3.0—310.3

MLR L1AP.rank ns None-Mod Middle 1.0 0.3—3.2





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 MA/EA 31.5 5.3—187.4

MLR L5AP.rank ns None-Mod Middle 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 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


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


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


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.


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-


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.


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


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.


Figure 9.9: Scatter plots of neural canal sizes (PCA scores from imputed ML and AP datasets) against uncali-brated radiocarbon date.


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


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.


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.


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).


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-


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


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.


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


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;


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


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


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


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


(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


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


(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


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).


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


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


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


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


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 B

Appendix

207

Appendix B. Appendix 208

Table B.1: Demographic, geographic, and temporal variables of the full sample (page 1)


Table B.2: Demographic, geographic, and temporal variables of the full sample (page 2)


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).


TableB.4:Osteologicalm

easurements

andjointmod

ificatio

nvaria

bles

ofthefullsample(page1)


TableB.5:Osteologicalm

easurements

andjointmod

ificatio

nvaria

bles

ofthefullsample(page2)


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).


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.


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.


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).


Table C.5: Zero-order correlation coefficients for all osteological measurements.


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.


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.


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.


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).

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