A Multilevel Profile Evaluating Social Inequality
by
Irina Campbell, PhD, MPH
US Dept. of State Fulbright Scholar in Global Health
Kazakhstan, 2006-2007
email: [email protected]
© 2000, 2002, 2007
Irina Campbell, PhD, MPHColumbia University
All Rights Reserved
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SUMMARY
This report describes a multilevel profile of self-reported physical health in
Moscow, examining individual and contextual determinants. A random citywide sample
of Moscow adults with household telephones (N=2000) was collected Sept. 17-19,
1991, with a completed interview rate of 81.8%.
Central research goals were to estimate 1.) individual level determinants,
defined as life chances, health choices, and measures of social relations, including
social cohesion and capital, 2.) contextual level determinants, defined as average and
relative inequality; and 3.) to specify how both average and relative inequality were two
complementary aspects of contextual level social status which had direct and mediating
effects on individual health.
The components of physical health were influenced by different sets of
individual determinants. Choosing one specific or several outcomes has significant
implications for public health interventions.
Hierarchical effects of social status on health were found in logistic and
hierarchical regressions, independently of health choices. These effects were moderated
by urban inequality. On the whole, living in high inequality urban areas was associated
with a greater risk for poor physical health, independently of the health choices made by
area residents.
Greater social connectivity was expected to have a positive effect on physical
health; and a buffering effect on low status residents living in high inequality areas.
This effect was observed in the reverse direction, suggesting that the construct validity
of measures of social capital defined for market economies may be less applicable in
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Moscow.
The importance of assessing the individual in a social context was shown in
comparing logistic with multilevel models. The multilevel model permitted identifying
contextual effects of relative inequality as interactions between macro and micro level
determinants.
The health crisis in Russia cannot be reduced to individual health choices such
as drinking or smoking behaviors. This research demonstrated that individual health is
an integral part of a social matrix. The place one lives may foster health, independently
of individual health choices, through the quality of a civic society and social relations,
or else impede it in an environment of social inequality and compromised psychosocial
well-being.
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TABLE OF CONTENTS
LIST OF FIGURES .............................................................................................................8
LIST OF TABLES .............................................................................................................10
ACKNOWLEDGEMENTS.............................ERROR! BOOKMARK NOT DEFINED.
PREFACE ..........................................................ERROR! BOOKMARK NOT DEFINED.
CHAPTER 1: INTRODUCTION ....................................................................................16
STUDY GOALS .......................................................ERROR! BOOKMARK NOT DEFINED.
GENERAL TERMS:........................................................................................................21
INDIVIDUAL AND MULTILEVEL MODELS............................................................23
PROPERTIES OF SOCIAL STRUCTURE....................................................................27
INEQUALITY AS AN EMERGENT PROPERTY OF SOCIAL STRUCTURE:.........29
INEQUALITY AS MODERATING CIVIC COMMUNITY AND HEALTH ..............34
INEQUALITY AS MODERATING LIFE CHANCES AND HEALTH .......................36
URBAN HEALTH PROFILES AS POLICY .................................................................36
CHAPTER 2: CONSTRUCTS OF HRQOL DETERMINANTS ...............................40
MACRO-MICRO PROBLEM ........................................................................................41
THE CIVIC COMMUNITY .........................................................................................48
LIFE CHANCES ..........................................................................................................52
HEALTH CHOICES ....................................................................................................54
MULTILEVEL MODEL.................................................................................................56
SUMMARY OF RESEARCH HYPOTHESES..............................................................59
CHAPTER 3: CONSTRUCTS OF HRQOL OUTCOMES........................................64
GLOBAL AND DOMAIN SPECIFIC INDICATORS ..................................................69
STRUCTURE OF QOL...................................................................................................71
HAPPINESS AND SATISFACTION ............................Error! Bookmark not defined.
SELF-RATED HEALTH ..............................................................................................71
PHYSICAL HEALTH...................................................................................................72
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CHAPTER 4: HEALTH POLICY IN RUSSIA............... ERROR! BOOKMARK NOT
DEFINED.
HEALTH POLICY AND INEQUALITY................ERROR! BOOKMARK NOT DEFINED.
CHAPTER 5: HEALTH INEQUALITIES IN SOCIAL CONTEXT ........................75
INTERNATIONAL CONTEXT OF HEALTH INEQUALITY....................................77
NATIONAL CONTEXT - RUSSIA................................................................................83
MORTALITY ................................................................................................................86
POVERTY.....................................................................................................................90
MORBIDITY.................................................................................................................93
INCREASING INEQUALITY ......................................................................................94
URBAN CONTEXT........................................................................................................95
MOSCOW.....................................................................................................................97
AREA VARIATION ....................................................................................................104
CONCLUSION..............................................................................................................115
CHAPTER 6: METHODS..............................................................................................116
DATA COLLECTION ..................................................................................................119
CHARACTERISTICS OF THE SAMPLE POPULATION .......................................119
SURVEY INSTRUMENT............................................................................................125
MEASURES OF HRQOL OUTCOMES ...................................................................125
MEASURES OF HRQOL DETERMINANTS............................................................129
MODEL ASSUMPTIONS ............................................................................................139
LOGISTIC REGRESSIONS.......................................................................................141
HIERARCHICAL LINEAR REGRESSION ...............................................................143
SUMMARY ...................................................................................................................147
CHAPTER 7: RESULTS OF AN INDIVIDUAL MODEL OF HEALTH .............149
SELF-RATED HEALTH ..............................................................................................154
HAPPINESS AND SATISFACTION...........................................................................159
EFFECTS OF INEQUALITY ON QOL ....................................................................164
PHYSICAL HEALTH PROFILE..................................................................................171
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DIMENSIONS OF PHYSICAL HEALTH .................................................................177
DISABILITY AND CHRONIC CONDITIONS..........................................................177
ACUTE SYMPTOMS .................................................................................................183
ENERGY LEVELS......................................................................................................187
EFFECT OF INEQUALITY ON PHYSICAL HEALTH ...........................................190
DISCUSSION ................................................................................................................191
EFFECTS OF HEALTH CHOICES..........................................................................192
EFFECTS OF CIVIC COMMUNITY........................................................................195
EFFECTS OF LIFE CHANCES................................................................................196
CHAPTER 8: RESULTS OF A MULTILEVEL MODEL OF HEALTH...............199
SPECIFICATION OF THE MULTILEVEL MODEL.................................................204
RANDOM COEFFICIENT MODEL............................................................................205
RESULTS FOR CONTEXTUAL EFFECTS..............................................................207
RESULTS FOR FIXED EFFECTS............................................................................208
SUMMARY.................................................................................................................212
THE INTERCEPT-AND-SLOPES-AS-OUTCOMES MODEL.................................213
RESULTS FOR FIXED AND INTERACTIVE EFFECTS........................................215
RANDOM EFFECTS AND SUMMARY ...................................................................223
CHAPTER 9: CONCLUSIONS.....................................................................................232
SUMMARY OF HYPOTHESES..................................................................................232
MAJOR FINDINGS ......................................................................................................233
SUMMARY OF CONCLUSIONS ...............................................................................235
CONTEXTUAL EFFECTS OF INEQUALITY ON QOL..........................................235
CONTEXTUAL EFFECT OF INEQUALITY ON PHYSICAL HEALTH.................237
MULTILEVEL INTERACTIONS..............................................................................238
IMPLICATIONS ...........................................................................................................244
STUDY LIMITATIONS ...............................................................................................247
FUTURE RESEARCH ..................................................................................................249
APPENDICES ..................................................................................................................250
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APPENDIX 1: TOTAL PERMANENT MOSCOW POPULATION, 1989, UNWEIGHTED AND
WEIGHTED SAMPLING DISTRIBUTIONS, MOSCOW, 1991, BY SEX AND URBAN AREA .....250
APPENDIX 2: RESIDUAL ANALYSIS OF PHYSICAL HEALTH PROFILE.................. ERROR!
BOOKMARK NOT DEFINED.
APPENDIX 3: AVPLOTS OF PHYSICAL HEALTH PROFILE WITH FULL MODEL OF SOCIAL
DETERMINANTS ..........................................................ERROR! BOOKMARK NOT DEFINED.
APPENDIX 4: ORDERED LOGIT REGRESSION OF PHYSICAL HEALTH PROFILE BY LIFE
CHANCES, HEALTH CHOICES, AND CIVIC COMMUNITY .................................................251
APPENDIX 5: TYPE OF INEQUALITY IN URBAN AREA BY PHYSICAL HEALTH PROFILE
AND QOL: LOW-HIGH DEVELOPMENT OF NEW URBAN RESOURCES BY LOW-HIGH ACCESS
TO URBAN RESOURCES IN RESIDENCE AREAS OF MOSCOW, 1991;(N=1629) PERCENT ...253
APPENDIX 6: KISH TABLES.........................................................................................254
APPENDIX 7: OVERALL DESIGN EFFECT .....................................................................254
INTERVIEWER EFFECT..........................................................................................256
APPENDIX 8: RUSSIAN LANGUAGE QUESTIONNAIRE ..................................................267
APPENDIX 9: RUSSIAN LANGUAGE RESPONSE CODESHEET ...ERROR! BOOKMARK NOT
DEFINED.
APPENDIX 10: ALAMEDA PHYSICAL HEALTH PROFILE..............................................268
APPENDIX 11: MEASURES OF HRQOL (SELF-RATED HEALTH, LIFE SATISFACTION ,
LIFE HAPPINESS) AND LIFE CHOICES, LIFE CHANCES, AND CIVIC COMMUNITY ..........271
APPENDIX 12: CODEBOOK.....................................ERROR! BOOKMARK NOT DEFINED.
REFERENCES.................................................................................................................279
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LIST OF FIGURES
FIGURE 1: CONCEPTUAL MULTILEVEL MODEL OF URBAN HRQOL ..................................17
FIGURE 2: MACRO AND MICRO PROPOSITIONS ACCOUNTING FOR GEOGRAPHIC VARIATION
IN HEALTH .....................................................................................................................33
FIGURE 3: THE STRUCTURE OF A MICRO, MACRO, AND MULTILEVEL MACRO-MICRO
PROPOSITION .................................................................................................................57
FIGURE 4: A STRUCTURE OF SELECTED MULTILEVEL MACRO – MICRO PROPOSITIONS.....59
FIGURE 5: ADMINISTRATIVE UNITS (89) OF THE RUSSIAN FEDERATION, 1993 ...... ERROR!
BOOKMARK NOT DEFINED.
FIGURE 6: CHANGES IN MORTALITY RATES, 1970-1990/1993, SELECTED WEST AND EAST
EUROPEAN COUNTRIES .................................................................................................76
FIGURE 7: AGE-STANDARDIZED DEATH RATES BY SELECTED CAUSES IN 1993, EUROPEAN
STANDARD POPULATION; ALBANIA 1992 DATA, USA 1991 DATA.................... ERROR!
BOOKMARK NOT DEFINED.
FIGURE 8: TRENDS IN MALE LIFE EXPECTANCY AT BIRTH IN RUSSIA, FORMER SOVIET
REPUBLICS, CENTRAL AND EASTERN EUROPE (CCEE), 1970– 1994 ..........................80
FIGURE 9: MALE LIFE EXPECTANCY IN ESTABLISHED MARKET ECONOMIES (EME) AND
FORMER SOCIALIST ECONOMIES (FSE) BY PER CAPITA GDP OF COUNTRY, 1993.......82
FIGURE 10: FEMALE LIFE EXPECTANCY IN ESTABLISHED MARKET ECONOMIES (EME)
AND FORMER SOCIALIST ECONOMIES (FSE) BY GDP OF COUNTRY, 1993 ..................82
FIGURE 11: NUMBER OF BIRTHS, DEATHS, AND NATURAL CHANGE IN POPULATION SIZE
(PER 1000 PERSONS) BY DISTRICT, MOSCOW, 1995...................................................100
FIGURE 12: INFANT MORTALITY BY URBAN AREA, MOSCOW, 1995................................103
FIGURE 13: EXAMPLES OF MICRO-RAYION PLANNING FOR MOSCOW IN 1950S AND 1960S
....................................................................................................................................105
FIGURE 14: CITY ADMINISTRATIVE DISTRICT (33), MOSCOW, 1989 ................................106
FIGURE 15: LIVING SPACE IN SQUARE METERS PER PERSON BY DISTRICT, MOSCOW, 1989
....................................................................................................................................106
FIGURE 16: SOCIAL AREAS, MOSCOW, 1960 ....................................................................108
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FIGURE 17: GROUPING OF MOSCOW CITY DISTRICTS BY RESIDENCE PREFERENCES AND
SOCIAL VALUATION FACTORS, 1984. ..........................................................................110
FIGURE 18: PERSONS WITH HIGHER EDUCATION (%) BY DISTRICT, MOSCOW, 1989 .......111
FIGURE 19: PENSIONERS (%) BY DISTRICT, MOSCOW, 1989 ...........................................111
FIGURE 20: CANCER MORTALITY (PER 100,000 PERSONS), MOSCOW, 1989...................114
FIGURE 21: INFECTIOUS AND PARASITIC DISEASES (PER 100,000 PERSONS), MOSCOW,
1992 ............................................................................................................................114
FIGURE 22: STANDARDIZED FACTOR SCORES OF ACCESS TO SOCIAL RESOURCES IN URBAN
AREAS BY NEW DEVELOPMENT IN URBAN AREAS, MOSCOW, SEPTEMBER, 1991 .......135
FIGURE 23: PROBABILITY OF QOL BY PHYSICAL HEALTH ...............................................152
FIGURE 24: MEAN ALCOHOL CONSUMPTION AND MEAN PHYSICAL HEALTH BY
EDUCATIONAL LEVEL, MOSCOW, 1991.......................................................................226
FIGURE 25: MEAN ALCOHOL CONSUMPTION AND MEAN ACCESS TO SOCIAL RESOURCES BY
EDUCATIONAL LEVEL, MOSCOW, 1991.......................................................................228
FIGURE 26: INTERVIEWER WORKLOAD, MOSCOW HRQOL SURVEY, SEPTEMBER, 1991264
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LIST OF TABLESTABLE 1: SUMMARY OF THE CONCEPTS, INDICATORS, AND MEASURES OF INEQUALITY,
MOSCOW HEALTH PROFILE ..........................................................................................23
TABLE 2: HRQOL COMPONENTS OF THE MOSCOW CITY HEALTH PROFILE.....................70
TABLE 3 SELECTED CAUSES OF DEATH CONTRIBUTING TO CHANGES IN LIFE EXPECTANCY,
RUSSIA, 1987-1994, BY SEX, IN YEARS; ......................................................................87
TABLE 4: PERCENT OF SELF-RATED HEALTH AND LIFE EXPECTANCY, BY COUNTRY, 1990
......................................................................................................................................90
TABLE 5: SOCIOECONOMIC INDICATORS IN RUSSIA, MOSCOW, ST. PETERSBURG, 1990-
1995..............................................................................................................................96
TABLE 6: SOCIAL INDICATORS IN RUSSIA, MOSCOW, ST. PETERSBURG, 1990-1995.......98
TABLE 7: SELECTED CAUSES OF MORBIDITY, RUSSIA AND THE CITY OF MOSCOW, 1988-
1995............................................................................................................................101
TABLE 8: CAUSE-SPECIFIC DEATH RATES IN RUSSIA, MOSCOW, ST. PETERSBURG, 1989-
1997............................................................................................................................102
TABLE 9: COMPARISON OF CITY OF MOSCOW CENSUS, 1989, WITH WEIGHTED SAMPLE,
1991, BY MARITAL STATUS, SEX, AGE, AND EDUCATIONAL LEVEL. ...........................120
TABLE 10: PERMANENT* MOSCOW POPULATION, 1989, UNWEIGHTED AND POST-
STRATIFICATION WEIGHTED** SAMPLE DISTRIBUTIONS, BY AGE AND SEX, MOSCOW,
1991 ............................................................................................................................122
TABLE 11: ESTIMATED NUMBER AND PER CAPITA TELEPHONES BY SELECTED
ADMINISTRATIVE DISTRICTS OF MOSCOW, JAN., 1992 ..............................................124
TABLE 12: ALAMEDA COUNTY PHYSICAL HEALTH PROFILE COMPONENTS IN 7
CATEGORIES BY PERCENT WEIGHT OF CATEGORY IN OVERALL PROFILE ....................127
TABLE 13: FREQUENCIES OF MICRO MEASURES OF HRQOL BY GENDER, MOSCOW, 1991;
WEIGHTED SAMPLE (PERCENT)....................................................................................128
TABLE 14: FREQUENCIES OF MICRO MEASURES OF LIFE CHOICES AND LIFE CHANCES BY
GENDER, MOSCOW, 1991; WEIGHTED SAMPLE (PERCENT) .........................................131
TABLE 15: DISTRIBUTION (PERCENT) OF INDIVIDUAL MEASURES OF CIVIC COMMUNITY BY
GENDER, MOSCOW, 1991; WEIGHTED SAMPLE ...........................................................133
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TABLE 16: AVERAGE INEQUALITY INDICATORS (STANDARDIZED VARIABLES/1000 AREA
POPULATION); ROTATED FACTOR LOADINGS (VARIMAX) BY 33 ADMIN. DISTRICTS,
MOSCOW, 1989...........................................................................................................136
TABLE 17: DESCRIPTION OF MACRO MEASURES OF LIFE CHANCES, BY 33 ADMINISTRATIVE
AREAS, MOSCOW CENSUS, 1989 .................................................................................137
TABLE 18: ANOVA FOR PHYSICAL HEALTH.......................................................................147
TABLE 19: SPEARMAN CORRELATION OF HRQOL OUTCOMES ......................................150
TABLE 20 : LOGISTIC REGRESSION OF SELF-RATED HEALTH BY QOL, CIVIC COMMUNITY,
AND HEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT B, ODDS RATIO E^B)..........156
TABLE 21: LOGISTIC REGRESSION OF LIFE HAPPINESS BY QOL, CIVIC COMMUNITY, AND
HEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT B, ODDS RATIO E^B)..................158
TABLE 22 : LOGISTIC REGRESSION OF LIFE SATISFACTION BY QOL, CIVIC COMMUNITY,
AND HEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT B, ODDS RATIO E^B)..........161
TABLE 23: LOGISTIC REGRESSION OF JOB SATISFACTION BY QOL, CIVIC COMMUNITY,
AND HEALTH CHOICES, IN ALL MOSCOW AREAS (N=1629; LOGIT B, ODDS RATIO E^B)......163
TABLE 24: LOGISTIC REGRESSION OF HEALTH, HAPPINESS, AND SATISFACTION BY
DEMOGRAPHICS AND TYPE OF AVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO
RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS (|Z-STATISTIC|)
....................................................................................................................................166
TABLE 25: LOGISTIC REGRESSION OF HEALTH, HAPPINESS, AND SATISFACTION BY CIVIC
COMMUNITY, HEALTH CHOICES AND TYPE OF AVERAGE INEQUALITY IN URBAN AREA ,
(LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS
RATIOS (|Z-STATISTIC|)...............................................................................................168
TABLE 26: INTERACTIONS IN LOGISTIC REGRESSION OF HEALTH, HAPPINESS, AND
SATISFACTION BY TYPE OF AVERAGE INEQUALITY IN URBAN AREA, (LOW ACCESS TO
RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS (|Z-STATISTIC|)
....................................................................................................................................169
TABLE 27: ORDERED LOGIT REGRESSION OF ALAMEDA PHYSICAL HEALTH PROFILE BY
TYPE OF AVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH
DEVELOPMENT OF NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC
COMMUNITY (LOGIT B, ODDS RATIO E^B) .........................................................................172
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TABLE 28: ORDERED LOGIT REGRESSION OF DISABILITY BY TYPE OF AVERAGE
INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF
NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT
B, ODDS RATIO E^B) .........................................................................................................176
TABLE 29: ORDERED LOGIT REGRESSION OF CHRONIC CONDITIONS BY TYPE OF AVERAGE
INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF
NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT
B, ODDS RATIO
E^B)____________________________________________________________________...........180
TABLE 30: ORDERED LOGIT REGRESSION OF ACUTE SYMPTOMS BY TYPE OF AVERAGE
INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF
NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT
B, ODDS RATIO E^B) .........................................................................................................184
TABLE 31: ORDERED LOGIT REGRESSION OF LOW ENERGY LEVELS BY TYPE OF AVERAGE
INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF
NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT
B, ODDS RATIO E^B) .........................................................................................................186
TABLE 32: ORDERED LOGIT REGRESSION OF HIGH ENERGY LEVELS BY TYPE OF AVERAGE
INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF
NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT
B, ODDS RATIO E^B) .........................................................................................................189
TABLE 33: BIVARIATE CORRELATION MATRIX OF URBAN LEVEL VARIABLES, MOSCOW
CENSUS, 1989 (N=33) .................................................................................................203
TABLE 34: RANDOM COEFFICIENT MODELS OF PHYSICAL HEALTH................................211
TABLE 35: RANDOM COEFFICIENT MODELS, MEANS-AND-SLOPES-AS-OUTCOMES, URBAN
AREA INEQUALITY EFFECT ON MEAN PHYSICAL HEALTH.........................................214
TABLE 36: RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTS
OF URBAN INEQUALITY AND LIFE CHANCES ON AVERAGE PHYSICAL HEALTH* ......217
TABLE 37 : RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTS
OF URBAN INEQUALITY AND HEALTH CHOICES ON AVERAGE PHYSICAL HEALTH* .220
13
TABLE 38: RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTS
OF URBAN INEQUALITY AND CIVIC COMMUNITY ON AVERAGE PHYSICAL HEALTH*223
TABLE 39: INTERVIEWER EFFECTS FOR SELECT VARIABLES BY SEX, MOSCOW HRQOL
SURVEY, SEPTEMBER, 1991........................................................................................261
TABLE 40: INTERVIEWER EFFECTS FOR SELECT VARIABLES BY AGE, MOSCOW HRQOL
SURVEY, SEPTEMBER, 1991........................................................................................262
14
ACKNOWLEDGEMENTS
The Ford Foundation provided a grant for the preparation of parts of this
manuscript for publication as a Special Issue of Social Science and Medicine, v. 51,
November, 2000.
Thanks go to all those who took the time to discuss parts of this material which
were presented at:
The World Association for Public Opinion Research Annual Meeting,Copenhagen, 17 September 1993;
The International Conference on Survey Measurement and Process Qualityof the Royal Statistical Association and American Statistical Association, Bristol, UK,1-4 April 1995;
The 3rd International Congress of the International Council for GlobalHealth Progress, UNESCO Paris, France, 18-20 May 1998;
The 7th International Conference on Social Stress Research, Budapest,Hungary, 27-29 May 1998;
The World Health Organization International Healthy Cities Conference,Athens, Greece, 20-23 June 1998;
The 5th International Congress of Behavioral Medicine, Copenhagen,Denmark, 19-22 August 1998;
The 7th Biennial Conference of the European Society of Health and MedicalSociology, Rennes, France, 27-29 August 1998.
Portions were also published in:
Editor, McKeehan, I.V. "A Multilevel City Health Profile of Moscow", SocialScience and Medicine, n. 9, vol. 51 (November, 2000): 1295-1312.
McKeehan. I.V. ”Planning of National Primary Health Care and PreventionPrograms: The First health Insurance Law of Russia, 1991-1993”, in Gallagher, EugeneB. and Subedi, Janardan (Eds.). Global Perspectives on Health Care. Prentice Hall,Englewood Cliffs, New Jersey, 1995: 174-197;
McKeehan, I.V. "Measurement of Interviewer Error in a Moscow TelephoneSurvey", American Statistical Association: 1995 Proceedings of the International
15
Conference on Survey Measurement and Process Quality, Bristol, United Kingdom,April, 1995: 46-51.
McKeehan, I.V. “Health Status of the Moscow Elderly”. SotsiologicheskiyeIssledovaniye, n.3, (March, 1995): 109-114. (in Russian).
McKeehan, I.V. "Meta-Analysis of Soviet Survey Research Methods", AmericanStatistical Association:1993 Proceedings of the Section on Survey Research Methods,Vol. II, January, 1994: 1172-1177.
McKeehan, I. V., Campbell, R., and Tumanov, S.V. “Lifestyles and Habitsinfluencing the health of Muscovites before the implementation of the Health InsuranceLaw of Russia of 1991-1993”. Sotsiologicheskiy Issledovaniye, n.3, (March, 1993): 45-49 (in Russian).
16
CHAPTER 1: INTRODUCTION
After the presidential election of 2000 in Russia, where the mayor of Moscow
was a focal contender, the city has taken on an even more central role in defining the
flavor of democracy in Russia. This monograph reviews an investigation designed
during the dissolution of the USSR. It provides a public health picture of an urban
community for establishing a City Health Profile, four months before the
implementation of macro economic changes in January, 1992.
Central research goals of this multilevel health profile of Moscow (Figure 1)
were to: 1.) provide baseline data on the cross-sectional distribution of micro and
macro determinants of self-reported physical health; 2.) assess the effect, moderating,
additive or interactive, of social relations, in the form of social cohesion, formal and
informal networks on physical health outcomes; 3.) to examine the hypothesis that a
hierarchical effect on health of individual social status is significant, 4.) that the
distribution of contextual inequality is a determinant of individual health, in addition to
individual social status and health choices; 4.) establish the baseline for longitudinal
follow-up comparisons.
A random telephone sample of adults within 33 administrative districts of
Moscow was collected on Sept. 17-19, 1991, with a completed interview rate of 81.8%
(n=1629). The questionnaire replicated items from the California Alameda Study on
Health and Ways of Living, and the U.S. Health Interview Survey in a Russian
translation. Moscow State University sociologists organized instrument translation,
telephone sampling, and data collection.
The initial chapter introduces the theoretical issues of inequality as they relate to
17
a multilevel health profile and to the ongoing health crisis in Russia. The theoretical
components of the multilevel health profile, as outlined in Figure 1, are presented in
greater detail in chapters 2 and 3. Chapter 4 describes health inequalities in relation to
social context at the international, national, urban, and small area levels, and then tied to
the theoretical model in chapter 2. Chapter 5 describes the empirical methods and
measurement issues of the survey, including design error.
FIGURE 1: CONCEPTUAL MULTILEVEL MODEL OF URBAN HRQOLTYPE OF MEASURE
HEALTH OUTCOME HEALTH CHOICES LIFE CHANCES SOCIAL RELATIONSMICRO
Physical Healthdisabilitychronic conditionsacute symptomsenergy levels
MICRO
Personal healthpracticesbody mass indexsmokingalcohol intakephysical activity
Prevention
Utilization
Economic values
AGGREGATED MACRO
▪Area mean alcohol use
MICRO
EducationOccupationEmploymentHousing typeMobility in work, housing
GLOBAL MACRO
Average Inequalityarea mean factor score ofaccess to material resources
Poverty risksarea ratio Family 5 members
Relative Inequalityarea rate ratiosmall/average apt. sizearea rate ratio lo/hi educarea rate ratio bluecollar manual/ whitecollar nonmanual workers
MICRO
Social cohesionanomie
Social capital:Informal networksFormal groupsDensity ofnongovernmentalorganizations(NGOs)Voter turnout
AGGREGATED MACRO
▪Social CohesionArea mean anomie
Chapters 6 and 7 present the micro and macro level determinants of individual
health-related quality of life outcomes. Chapter 8 concludes this report with a
discussion of the implications of a multilevel profile of urban health and by suggesting
public health priorities which are derived from the Moscow health profile.
The objective of this research is to describe a cross-sectional multilevel health
18
profile of the city of Moscow, which was obtained before implementation of macro
economic changes of January, 1992, in a social epidemiological survey. The
development of a multilevel theory and model of health was undertaken in keeping with
the WHO Healthy City Program and policy for the twenty-first century of Health For
All: “by the year 2000, the actual difference in health status between . . . groups . . .
should be reduced . . . by improving the level of health of disadvantaged . . . groups”
(WHO, 1994). A city health profile provides a description of the social determinants of
health, including the distribution of social disadvantage or vulnerability.
A clearer understanding of the influence of social status differences in individual
level health, within the social context experienced by people, is useful for the
implementation of WHO policy. The city profile enables first, the identification of
health differences between individuals and between social groups in Moscow, secondly,
the recognition of specific structural conditions in the community which affect the
health of people living there, and thirdly, the separation of structural determinants of
health from the effects of individual psychosocial and health behaviors.
Social epidemiology has traditionally been concerned with the distribution of
morbidity or mortality in relation to a causal triad: group or personal characteristics,
geographic, social or community determinants, and change in occurrence over time:
who is exposed or susceptible to what, where, when, and why. These parameters are
included in the design of the health profile, which examines the social origins of health
status: the differential effect of community level social inequality, a characteristic of the
social context that was hypothesized to increase vulnerability for poor health, in
addition to the biopsychosocial risk factors of the individual. Health outcome was
19
defined in the Moscow survey by the Alameda County Physical Health Profile. A
prospective study design was based on the longitudinal Alameda County health studies.
This report describes the baseline data. Follow-up studies have been planned of the
Moscow community.
The social changes, associated with the August Coup of 1991 and subsequent
dissolution of the USSR, which ensued during the implementation of the survey, altered
the conditions of the research plan, delaying the follow-up of the 1991 Moscow cohort
currently underway. The 1991 survey is relevant not only as a necessary baseline for
subsequent longitudinal comparisons, but also as an independent urban sample in
addition to the only other two cross-sectional surveys of that pre-reform period
(Palosuo, Uutela, Zhuravleva & Lakomova, 1998; Lee, 1995).
The city health profile of Moscow gives a historical perspective on the relative
importance of community conditions that moderated the effect of individual risks on
health outcomes during a specific period of sociopolitical reorganization. This survey is
also unique as a sample which collected individual and ecological level data of
Moscow, providing valuable information not available elsewhere. There have not been
other multilevel community health studies done in Russia that can provide an insight
into the relationship between aggregate ecological (macro) factors and individual
psychosocial (micro) factors either recently or during the Soviet period. The impact of
the dynamic changes on the health of the sample population, which have occurred since
the survey was conducted, will be analyzed in a follow-up study.
A context of relative social inequality experienced by people living in different
communities in conjunction with poor health habits or lack of social connectivity can
20
have an additive effect on physical health. The effects of social context on individual
health may be mediated by intervening psychosocial processes that depend on the
characteristics of the social context (Hox, 1998). Some forms of social relations, such
as social cohesion, social support or social networks, can moderate the effects of
inequality on health. Two statistical models are employed to explore these research
questions: logistics regression at the individual level and multilevel or hierarchical
linear regression at both the individual and ecological levels.
Multiple indicators and multiple methodologies are used throughout the profile
to apply a comprehensive approach to urban health assessment. Methodological
pluralism increases certainty in the validity and reliability of conclusions which may be
drawn from a community sample survey.
Public health and personal health are two prongs of community health policy
based to varying degrees upon two fundamental issues: the right to health and the right
to happiness. Neither health nor happiness are principally based on privilege or earned
achievement but on justice and a moral imperative as part of the human condition.
Underlying the individual human rights issue is the general quality of life or material
standard of living in a community and the responsibility and cost to the state for
guaranteeing that sociopolitical conditions do not militate against citizens’ rights to
health and happiness. One part of this debate centers on the question of who is
responsible for personal and who for public health, given the mutual influences between
individuals and the social context in which they live. A second part of this debate
concerns the effect on health of the distribution of inequality in social and material
resources within a community.
21
GENERAL TERMS:Constructs, their operational indicators, and the measurement of the indicators
are distinguished in Table 1. Context may be understood in two principal ways:
statistically, as a measure of a macro level or socioenvironmental/ group effect upon the
micro level or individuals; and sociologically, as a measure of social structure, also a
macro level effect on individuals. In the interest of parsimony and pragmatism
important for the development of efficient health policy, social structure is defined as
the distributions of social groups, social relations, and socioeconomic positions.
Geographical place may be defined at any of various hierarchical levels: street
block, neighborhood, community, regional, national, or even by continent, which has a
resident population with social characteristics and relations. Although the parameters of
the geographical place may be more or less fixed, the demographic parameters are fluid,
depending on the extent of social change, mobility, or immigration and emigration in
the area. Social relations arise in a community from the interactions of residents from
various social groups and socioeconomic positions. Social groups are circumscribed by
gender, race, ethnicity, occupational status, or other homogeneous attributes such as
those related to employment, type of labor, property ownership, ascribed or achieved
status. Socioeconomic position is defined as the stratification of social groups on a
variety of hierarchical dimensions such as poverty, income, education, prestige, or
access to resources. Inequality refers to the position of groups or individuals on these
distributions in two ways: 1.) in relation to the mean of a specific distribution, and 2.)
relative to specific groups on a distribution.
Deprivation in health or happiness due to a relative position in the distribution of
social status or the standard of living in a community is termed here relative inequality.
22
Having marginal access to resources in contrast to mean access, for example, is related
to the position of individuals in the social structure of a community relative to other
groups and to the form which the distribution of resources itself takes. Such relative
deprivation may be due to the inequity of unnecessary, preventable, “unjust”
circumstances which some people face and others do not.
Deprivation may also be due to average inequality where the material standard
of living in a community, like income or other access to resources, refers to the overall
mean availability of resources to all residents. While relative inequality affects specific
groups and individuals differentially in relation to others in a hierarchical distribution of
social status, average inequality measures effect on everyone in a community at the
same level.
Poverty is a lack of resources below an average reference point. Poverty is used
as a proxy for an income covariate in this study. It is not synonymous with average or
relative inequality, but a measure of absolute economic status.
The average and relative distributions of standards of living within and between
communities changes. The relative position of individuals within communities is altered
due to changes in the macro distribution across communities, independently of
individual action. The size of the wealth and health gaps between nations affects the
gaps between individuals relative to each other, and to mean levels within communities.
Where people live and how they fit into their communities, as well as where their
communities stand in the larger global or sociogeographic arena is significant for
individual health. Although health is ultimately experienced at the most personal of
levels, the context of communities and whole societies may be decisive for the quality
23
of that experience. This issue is important when planning effective public health
programs focusing on the community or individual level.
TABLE 1: Summary of the concepts, indicators, and measures of inequality, MoscowHealth Profile
constructs Relative Inequality Average Inequality
theoretical concepts deprivation due to position insocioeconomic distribution disadvantage among specificsocial status groups greater impact of disadvantageon some groups than others lack of individual control overpreventable, avoidable, inequitableconditions
deprivation due to economicand material resources disadvantageous conditionsaffect everyone in group orcommunity at mean level some individual control unavoidable conditions
operational indicators relative position social status comparative difference betweenscores shifting reference point
average position access to material resources absolute score fixed reference point
measurements rate ratio of low/high socialstatus composition in urban area
mean access to materialresources available in urban area
relation betweenconcept and measure
indirect total impact of low social statuson outcome + effect of the range inthe distribution of social statusamong groups of different sizes macro
direct impact of average conditions macro
INDIVIDUAL AND MULTILEVEL MODELSPublic Health has traditionally been interdisciplinary. It combines the interests
of epidemiology, concerned with health assessments of populations, with social policy
or intervention to ensure the health of social groups, and sociology, as the study of
social processes within and between social groups, systems, structure and relations. The
biomedical model has contributed an overwhelming concern with the individual patient
and the curative, healing process. In addition, however, every individual lives in a
24
matrix of values, customs, traditions, language, mores, and norms of behavior, or
subjective and accepted ways of living, as well as a distinctive civic tradition and
political system, which comprise a collective context of lifestyle or culture.
Preventive health programs function within these contexts and, when
sufficiently funded, have the capability of monitoring and taking action to address the
differential gap in quality of life and health status among social groups and individuals.
Personal responsibility for health is often targeted with public educational programs,
while social responsibility is left more frequently to government legislation (for
example: professional certification, Food and Drug Administration labeling oversight,
regulation of chemicals and pharmaceuticals, occupational safety).
Strategies to maximize public health may include modifying those community
environments which have been shown to be effective in changing individual level
behavior, such as advertising for cigarettes or alcohol. The distributions of specific risk
factors which change at the community level need to be addressed within their social
context, insofar as risks have distinctive distributions in distinct communities.
The domain of preventive strategies in public health is the at-risk population as
a whole and that of medicine is the at-risk individual. Improvements in the quality of
life and public health depend in large part upon the development of a multilevel theory
of health which puts the individual back into the community context and provides an
assessment of micro and macro interactions of communicable, chronic and social
diseases. Personal risks to health under control by the individual and social risks to
health not directly controllable by the individual are situated in a larger community
context. To sift through such causal complexity requires maintaining local and national
25
data monitoring systems, which are multilevel, systematic and periodically recurring, to
assess the adequacy, equity, quality, accessibility, and outcome of health programs.
The human rights movement in health has raised the issue of inequality in health
status, which was tied to universal access and comprehensive medical and health
services. Universal access implies the availability of services to all individuals and
groups. Equality in access, measured as an input to the health system, however, does not
categorically imply equality in health status, as an outcome of the health system,
regardless whether measured by sociomedical indicators or mortality and morbidity
rates. Comprehensive treatment of disease also requires preventive services which
promote health through population-based programs such as education, nutrition,
sanitation, vaccinations, and occupational safety.
Multilevel models of health provide for disease patterns among individuals in
groups as a consequence of social relationships between groups and among individuals
within groups. Multilevel health profiles provide pictures of the interaction between
“sick populations” and “sick individuals”, rather than single macro or micro health
status. Multilevel models make a fundamental contribution to the research of disease
etiology and disease prevention conceptually, as well as methodologically.
Mortality data assume a biomedical model of disease causation: a cause, host,
agent, in an outcome chain. Mortality classifications characterize individuals rather than
populations and are thus prone to misattribute the multifactorial epidemiological
research triad (agency, host, environment). Considering fundamental causes of
mortality requires examining social structure, morbidity and psychosocial factors (Rose,
1995; Krieger, 1998), in other words, applying a sociomedical model of health.
26
In seeking to define a model of the complex causes of premature mortality and
increase in specific diseases in Russia, a distinction must be made between proximal
and fundamental factors, and the level of causal attribution should be sought in an
ecosocial context (Krieger, 1998). Social determinants may affect diverse health states,
like mortality or morbidity, directly and indirectly, through various mechanisms of
intervening, mediating, or contributing risk factors (Susser, 1976). Focusing on
immediate, proximal mechanisms may have the unintended consequence of minimizing
fundamental social causes and emphasizing personally controlled actions (compliance
with medical regimens, physical exercise, no drinking or smoking, healthy diet, etc.) to
the detriment of formulating cogent public policy.
The same social cause may operate simultaneously at the community and
individual level but through different paths and with multiple outcomes at each level, as
the following examples illustrate (Blaxter, 1990; Link, 1998; Matteson et al., 1998).
The lack of social support, as a type of relative deprivation, has been linked to increased
CHD (Siegrist, 1995). A loss of control in life and work conditions, or powerlessness,
has been associated with psychosocial symptoms, poor health, and mortality (Bobak,
1996; Seeman, 1995). Rapid social change, like the modernization within communities
which produces increased relative inequality, has been significantly related to heart
disease morbidity and mortality (Lasker et al., 1994). Job insecurity and organizational
change, during the modernization and specialization of a government
telecommunications agency, resulted in worsening self-rated health status, increases in
the body mass index, sleep problems, and clinical measures of hypertension (Ferrie, et
al., 1998). Policy strategies which inadvertently blame the victim or obscure the social
27
conditions which “put people at risk of risk” by emphasizing proximal causes cannot
reduce premature mortality due to increasing inequality within a community or living in
an “anti-modern, stressful” economy like Russia (Rose, #227, 1998).
PROPERTIES OF SOCIAL STRUCTUREMultilevel models of health analyze the emergent properties of social structure,
such as inequality, in conjunction with micro level properties, such as health status,
gender, ethnicity, educational level. Context or emergent properties of structure at each
level refer to those characteristics which exemplify aspects of the whole unit of
analysis and not the separate components of that unit (Blau, 1980). Contextual analysis
can explain the influences which the structure of a unit has within a hierarchy, and
multilevel analysis can focus on multiple hierarchies of units within the same model. It
is a prime objective of the sociological perspective of public health to assess the context
in which macro and micro units change, given the complex nesting of individuals in
groups, within neighborhoods, within cities or rural environments, and within societies
and nations.
Effective public health strategy aims at identifying groups at risk for poor health
and specific risk factors causing poor health. Emergent properties of social structure as
health determinants are often not considered. Health promotion policies involve
developing two intervention strategies by which health risks may be reduced for
vulnerable high risk groups as well as for the general population. High risk intervention
involves reducing rare high risks for a small number of individuals and population risk
intervention involves reducing pervasive low risks for large sectors of a population.
Application of both prevention strategies is necessary to avert the “prevention
paradox”: individual level intervention affects community health minimally, while
28
community level interventions have limited impact upon high risk individuals. The
“prevention paradox” is paralleled by the “risk intervention paradox”: the mass
exposure of a large number of individuals to low levels of negligible risks may produce
a larger number of disease cases than a small number of individuals exposed to high
risks. Treating only very sick individuals leaves scarcely sick populations without
health intervention. Mass prevention reduces negligible risk a little for many and not at
all for some, while most derive at least minimal benefits.
This phenomenon affects the development of public health policies for sick
populations rather than sick individuals (Rose, 1995). Often, barely sick populations are
left without intervention, although many would benefit even from minimal care, while
very sick individuals are vigorously sought out and treated, a cost borne by the
“healthier” community. Public health intervention differs from medical intervention
foremost in its emphasis on the socioenvironmental context of individual health status.
And secondly, public health recognizes that a continuous distribution in health status
characterizes populations, and is not restricted by the clinical designation of individuals
as potential treatment cases with or without disease.
A multilevel evaluation of health therefore gauges negligible and high risk
factors at both the population-level and individual-level. As abundant research has
shown, individual lifestyle behaviors, given specific level of socioeconomic
development, account for a majority of the risk factors for general well-being. A major
issue is to determine which risk factors are amenable to policy intervention and
manipulation. Mass levels of low exposure require mass levels of intervention even if
impact is negligible, since the community will benefit as a whole, and subsequently
29
individual members. The range of risk factors, which affect health, change as a whole
distribution at the community level. Public health policy therefore needs multilevel
research strategies which recognize the sui generis properties of communities, the
emergent properties of social structure, and their attendant risk factors, which cannot be
reduced to a collective aggregate of its individual members. Inequality may be
designated as a structural property of communities rather than individuals, and as such
is prior to individuals in a community context in which they must live.
INEQUALITY AS AN EMERGENT PROPERTY OF SOCIAL STRUCTURE:“Hippocrates, writing in the 5th century BC, advised anyone coming to a new
city to make inquiries in order to assess whether it was likely to be a healthy or an
unhealthy place to live, depending on its geography and water supply (‘soft, hard, or
salty’) and on the behavior of its inhabitants (‘whether they are fond of excessive
drinking and eating, and prone to indolence, or else fond of exercise and hard work’).
This notion, that healthiness is a characteristic of the population as a whole and not
simply of its individual members, lay dormant for a long time. It was revived and
developed by Durkheim, the great French sociologist of the last century…” (Rose,
1995). This spirit of Hippocrates which inquired about the socioenvironmental
conditions that contribute to individual and urban health is as germane today as it was
25 centuries ago.
Inequality produces structural obstacles to maximizing health – a proposition
which has preoccupied the public health research agenda for the past two decades in
Europe, especially after the seminal work of the Black Report established the strength
of this relationship. It was preceded by 140 years of socioeconomic variation in
mortality rates, and the current accumulation of evidence from comparative
30
international studies of almost every country in the world.
The Black Report (Macintyre, 1997) emphasized a typology of competing
theories of health inequality. Since the publication of the Black Report in 1980,
increased evidence about health inequalities has accumulated. Research has often been
organized around the typology of four broad explanations of the population
distributions of health inequalities: 1.) artefact of conceptual definition and
measurement error of health and social inequality; 2.) natural or social selection of the
healthiest by an occupational or social class structure; 3.) variation of material and
structural conditions of occupational positions with wealth and health; 4.) variation of
cultural and behavioral factors such as ideas, values, and individual lifestyle with
health. In each of these explanations, there is an interaction between individual health
and deprivation, mediated by a form of social hierarchy. Each one of these explanations
emphasizes how a different measurement or aspect of the individual – genetic or
biological, social, and personal – is related to social inequality to produce health
inequality.
There has been some debate on the lack of standard definitions and measurement
of health-related inequality as a risk factor or as an outcome, as a micro and macro level
indicator, or as a relative versus average indicator. Absolute standards of living as well as
income distributions have become conventional determinants of public health. Several
dominant definitions have referred to inequality as the relative distribution of an attribute,
such as social status or income, on a continuum from best to worst between individuals,
or in relation to a mean, median or ratio measure of the attribute (Murray et al., 1999;
Sen, 1997; Mackenbach and Kunst, 1997; Illsley and Baker, 1991; Frijters and van Praag,
31
1995). Inequality has been defined by a variety of measures (Pereira, 1990) such as the
distribution of occupation, salary, employment status, prestige, social group membership,
educational level, ownership of assets (housing, cars, etc.). Risk measures have been
distinguished by whether they are simple or complex; whether they can assess “relative”
or “absolute” differences in inequalities; or whether they can distinguish between the
effects of specific “levels” of high or low variation of inequality or the “total impact” of
socioeconomic inequalities upon the population distribution of health (Carr-Hill, 1990;
Mackenbach, 1997).
Health inequalities have been measured by a myriad number of population based
morbidity and mortality rates, prevalence rates, or individual subjective assessments of
self-rated health and quality of life indicators. Health inequalities vary consistently with
social inequalities regardless of type of measure, but the degree of inequality found is
affected by the specific indicator used to define either health or social inequality (Kunst
et al., 1998; Macintyre, 1997; Wilkinson, 1992). Inequality in health has been
successfully related to multiple dimensions of socioeconomic position: occupational
status and prestige, education, and income or access to resources (Siegrist, 1995). Each
dimension of social inequality may not only have a unique distribution in a community,
but be related to different sets of health determinants. The theoretical contribution of the
relative definition of social inequality addresses the structural issue that an individual has
a variety of social relations which are associated with a variety of social positions within
an array of social units (Blau, 1980).
An important consideration for assessing urban level health risks is the use of
area based indicators, for example - individual level (compositional aspect of area
32
where many of unemployed live, area unemployment is due to the individuals in the
area) or context-level (individual lives in an area with few opportunities for
employment thus is exposed to limitations on finding employment due to the area itself
since there are poor transportation conduits or no industries, no job skill retraining
facilities, etc.). Many individual level explanations have skirted the explicit
consideration of how the distribution of population characteristics may be affected by
the community as a whole.
The determinants of health variation between populations are of a different type
than the determinants of health variation between individuals within a population. For
example, life choices, such as exercise, nutrition, smoking or alcohol consumption,
made without hindrance, due either to self-control or to a social context affecting self
direction, need to be evaluated against structural obstacles, such as peer pressure,
advertising, marketing, or lack of adequate health information in a community.
The urban macro indicators of interest in this study are average and relative
inequality which affect individual health status. Analogous to the seminal Black
Report’s explanations of the distributions of health inequalities, there are two basic
propositions which have sought to explain the range, interaction, and direction of effect
between urban environment and individual health (Figure 2; Verheij, 1996): the
compositional hypothesis and the contextual hypothesis. In a compositional hypothesis,
direct and indirect selection can operate in the spatial variation of health-related
quality of life – either healthy people move or sick people stay in specific urban areas
(direct selection), or susceptible people with certain health traits move or stay in
specific areas (indirect selection).
33
FIGURE 2: MACRO AND MICRO PROPOSITIONS ACCOUNTING FOR GEOGRAPHIC VARIATIONIN HEALTH
Macro proposition:geographic variation due toContextual/social causationhypothesis:
Micro proposition:geographic variation due toCompositional/individual selectionhypothesis:
special variation in exposure toenvironmental/structuralfactors:inequality, poverty;pollution, traffic, housing;
quality, crime, recreational resources, sanitation, access to material or social resources
spacial variation in direct selection:sick/healthy people moving/staying in area:
poor people living in rundown areas; downward SES drift/mobility of sick concentration of sick around facilities concentration of healthy around parks, or “younger” areas
spacial variation in exposure tobehavioral factors:
drug/alcohol abuse, stress passive smoking, unsafe driving community group activities religious group membership
spacial variation in indirect selection:susceptible people with certain traitsmoving/staying in area –
large, younger, low-income families blue collar manual workers older persons w/low educational level
If composition of areas is the cause of health outcomes, then the variation in
health between urban areas is minimized when all individual variation in health-related
factors is taken into account, because the urban variation is composed from the specific
individuals in those areas. On the other hand, the contextual hypothesis accounts for the
spatial variation in health through exposure to socioenvironmental or structural factors..
The macro context exerts an independent and direct effect on individual health
outcomes. The contextual proposition is incorporated into the design of the multilevel
health profile.
Multilevel sociomedical theories permit the modeling of causes between social
structure and individual agency, permitting a broader perspective than linear social
selection (poor health causes low achieved social status) or downward drift (poor health
34
causes loss of social status) theories of poor health. The multilevel model takes into
account the interaction between area context, area composition, and interaction between
social context and individual behaviors. It is possible to examine propositions that
relative position on hierarchies of socioeconomic or social status has greater influence
on health than status itself by including macro-micro interactions in the modeling of
health determinants.
INEQUALITY AS MODERATING SOCIAL RELATIONS AND HEALTHStructural problems in public health often remain unsolved because they are
addressed at the individual level. Multilevel factors are not sufficiently specified, resulting
in medicalizing or “blaming the victim”. Promoting a “just say no to drugs” program to
adolescents, for example, but allowing advertising of cigarettes and alcohol which
legitimizes “permissible” drugs, assumes the individual is not embedded in a social
context. Such programs are ineffective and simply protect vested political and economic
interests. At a minimum, policy solutions for structural public health problems need to
outline the distal, mediating, and proximal influences on health at both the community
and individual level. Inequality in the social position of people in a community may act as
both a distal and mediating factor on individual health status. The mediating influence can
exist as both an intervening factor between macro and proximal determinants and a
moderating factor of proximal determinants on health outcomes.
A civic community, often understood broadly as social connectivity, has been
associated with sustaining individual well-being or physical health through social
integration of public and private spheres. It has been hypothesized that interpersonal,
informal-horizontal networks and formal-vertical networks “characterize how individuals
and groups are ‘connected’ to institutional, legal, political and economic structures”,
35
which is necessary for sustaining economic development (Rose, #278, 1998; Kaplan et al.,
1997; Kawachi et al., 1996; Lynch, 2000). The civic community plays an important part in
the pathway between the vertical and horizontal networks, affecting the biopsychosocial
level of health outcome. Despite the lack of consensus on which factors of a civic
community can be distinguished at the macro and micro levels as social cohesion, social
capital, social networks, social support, social integration, or social connectivity, the
literature clearly indicates a causal association with health status (Berkman and Kawachi,
2000).
The lack of formal networks, such as participation in religious and community
groups, the lack of informal networks, such as close friends and family, and the lack of
social cohesion have been associated with greater mortality from cardiovascular diseases
(Bruhn, 1979), declines in life expectancy (House et al., 1988), increases in homicides,
the infant mortality rate (Kawachi et al., 1997), and crime (Wilkinson et al., 1998).
Relative social inequality and surrounding poverty increase social disorganization through
lack of public trust and deterioration of social norms. Civic group memberships have been
related to social integration through the sustenance of social trust and maintenance of
norms, and consequently moderated health-related quality of life (Kaplan et al., 1996;
Kawachi and Kennedy, 1997a, 1997b; Berkman and Kawachi, 2000).
The various indicators of a civic community vary with sociopolitical and
geographic conditions, especially in a large and culturally diverse nation like Russia. The
effect of this diversity on outcome is lost if analyzed as a single sample at the individual
or ecological level. Multilevel models provide greater insight into how civic communities
moderate health given variations in the structure of communities.
36
INEQUALITY AS MODERATING LIFE CHANCES AND HEALTHA context of relative social inequality experienced by people living in different
communities in conjunction with poor habits or social connectivity can have an additive
effect on physical health. The effects of social context on individual health may be
mediated by intervening psychosocial processes which depend on the characteristics of
the social context (Hox, 1998). Social inequality can affect health by moderating some
forms of a civic community, such as lack of social cohesion, social support or social
networks, or some forms of social status.
Educational and occupational hierarchies have been related to health in what has
become known as the gradient effect of social status: higher status ensures better health.
Social hierarchies can define some types of relative inequality as a distribution of position
rather than resources. However, the interaction between education and occupation with
contextual inequality may have a direct effect on health apart from the main effects of
social hierarchies or inequality, whether in position or resources.
URBAN HEALTH PROFILES AS POLICY
The development of a multilevel health profile is a tool: an initial policy step,
which describes individual and community health status; facilitates problem definition;
outlines a strategy for public health programs. The importance for health policy of
addressing inequality not only involves the determination of priorities in problem
definition, program development, and local community action initiatives but also the
issue of ensuring that a social safety net includes health policy and functions properly to
protect basic human rights. Health policy based upon a city health profile which
describes the inequalities in community health status already provides many avenues of
political action for public health promotion and disease prevention.
37
A health profile which looks at inequality in health looks at solutions as well.
There is a need for community health profiles, especially during socioeconomic
transition periods in order to monitor the relationship between health status and social
policy, without “blaming the victim”. The reason for developing a Moscow city health
profile is to ground community health assessments in a policy framework. Moscow, as
the most economically advanced city within Russia, is central in defining the direction
of major policies and social change within Russia as a whole.
The relationship between health-related quality of life and inequality within a
community has concerned the World Health Organization since its inception in 1948,
continuing into the organization of the Healthy Cities Project. The Healthy Cities
approach has tied community assessment to local policy initiatives, such as specific
targets to achieve “Health for All”. WHO targets the complex urban problems of
inequality in health. The WHO strategy of “Health for All”, a policy adopted by the
European Region in 1984, has monitored progress by periodic evaluations, such as the
global assessment in 1990/1991, and delineation of principles of equity in health. The
WHO Healthy Cities Project has been instrumental in encouraging the organization of
ongoing comparative city-wide health information systems which specifically address
profiling inequity in health for the development of policy measures.
A health profile for Moscow lays the foundation for the development of a
multilevel Healthy City Profile for the World Health Organization Healthy Cities
Program. This focus on municipalitites was adopted in 1980 by the 32 member states of
the WHO European Region (including Russia). The primary objective was to reduce
inequality in health: “by the year 2000, the actual differences in health status between
38
countries and between groups within countries should be reduced by at least 25%, by
improving the level of health of disadvantaged nations and groups”(WHO, 1985).
The WHO-Regional Office for Europe has promoted a set of research guidelines
for the assessment of health, including measures of social inequalities applicable for
studying variation between international urban communities. Health policy can further
or hinder health promotion to the extent that such indicators are addressed. Economic
research of the health-inequality problem has contributed several innovative approaches
to conceptualizing the normative aspects of inequality as equity, the relative inequality
in distributional relationships. Distribution rules which have now become established in
the normative economics perspective on inequality (Pereira, 1982) include entitlement,
the decent minimum, utilitarianism, envy-free allocation, and more recently, “equity as
choice” and “health maximization” (Le Grand, 1987). However, normative economics
has still not been widely used in public health.
Economic measurements of inequality have most commonly steered away from
macro occupational or class definitions (Kunst et al., 1998), and suggested that
individual micro indicators of the distribution of wealth are more relevant than social
group measures. However, several applications of the more widely used macro
measures like the Gini coefficient, the Atkinson, as well as Robin Hood index, have
since been successfully applied to the study of inequality in health (Culyer, 1989;
Lambert, 1989; Mooney, 1987; Musgrove, 1986).
Measurement of inequalities in city-wide conditions which can be directly
addressed by policy are described by the Healthy Cities Project. These included such
urban indicators as types of municipal support for self-help organizations; health
39
education programs; percentage of six-year-old children fully immunized; population to
general practitioner ratio; population to nurse ratio; percentage of population with
health insurance protection; population proportion with access to emergency medical
services within 30 minutes by car; communication of health information.
The distribution of a set of environmental indicators, also dependent on policy,
was considered instrumental by WHO in assessing the ecological influences on urban
health: air and water pollution; household waste collection and treatment quality index;
public accessibility and relative area of green spaces in the city; industrial sites; sport
and leisure facilities; bicycling paths in the city; public transportation; crowding in
living space; emergency services provision. Socioeconomic indicators included square
meter of living space per resident in each city district; substandard dwellings;
unemployment rate; proportion of single parent families; illiteracy rate; crime rate;
percentage of city budget allocated to health and social programs.
Multilevel health profiles may include these macro measures of inequality and
contribute as well to the development of an ecosocial approach to community health
policy. Individually-based concepts of lifestyle as causes of disease may be
reformulated with contextual concepts of inequality as causes of disease due to the
distributions of socioeconomic resources within a community (Wilkinson, 1998).
40
CHAPTER 2: CONSTRUCTS OF HRQOL DETERMINANTS
Explanation of a social science phenomenon consists chiefly in identifying by
definitional specification the necessary and sufficient conditions for an event to occur.
The identification of specific conditions and their interactions within and between
individual and social levels is basic to establishing the theoretical utility of the
multilevel model as an explanatory construct of inequality, and as a predictor of health-
related quality of life. Historically, there have been several main perspectives of
lifestyle, such as the 19th and early 20th century traditions of sociology and
epidemiology, both of which examined individual action in a social context, but
methodologically did not have the appropriate tools, often emphasizing one to the
exclusion of the other.
Early methodological development of the linear statistical model circumscribed
the possibility of adequately assessing the weight of each factor, individual and social,
simultaneously within the same model. Phenomenological, empiricist, and positivist
approaches, with their concomitant methods, provided the dominant paradigms within
the framework of early 20th century social science and public policy, as did advances in
the biological and medical sciences. Theoretical perspectives argued within distinctive
qualitative and quantitative methodologies which formed disciplinary distinctions: the
humanities and cultural sciences; the social and psychological sciences; the natural and
physical sciences; as well as interdisciplinary biomedical and health sciences (Blalock,
1982).
On one hand, qualitative methods circumscribed the populations to which
41
models were applicable; examining individual, social, or historical levels separately;
and thus restricting the generality and universality of understanding individual action to
given specific temporal, cultural, and spatial conditions. On the other hand, the
limitations of a quantitative positivist, linear methodology could not address the
syncretic, phenomenological preservation and integration of the individual experience
as distinct from the social experience, a problem which gave rise to the micro-macro
dichotomy in sociological research and the atomistic-ecological fallacy in statistical
methods (Bottomore, 1975; Lilienfeld, 1976).
MACRO-MICRO PROBLEMBriefly, the micro-macro dichotomy was reflected in Durkheim’s rules of
sociological method which emphasized a realist and wholistic social order, where the
social fact acts as an external constraint, prior to individual action, is internalized by the
moral, socialized individual, and exists (such as a group average) in its own right
independent of individual manifestations (Durkheim, 1966).
Weber’s methodology of the social sciences insisted upon viewing the
individual within a partial social order, as an abstraction rather than a reality. The
verstehende analysis of social structure referred to a.) the subjective meaning of
individual action or b.) average meaning of group action oriented towards others, or c.)
to an ideal type of subjective meaning attributed to a hypothetical individual or group,
and d.) to the opportunities afforded to certain types of individuals to attain specific
positions in the social hierarchy through the operation of various objective and
subjective selective factors, but e.) not to a total social system (Weber, 1949).
Although it would be a gross oversimplification to reduce Durkheim’s position
as one concerned predominantly with the whole, while Weber concentrated on the parts,
42
the issue of how to relate social wholes, social parts or individual actors, as distinct
elements, occupied much of their and others’ sociological discourse of that industrial
age. However, these broad perspectives have exerted an important influence upon the
historical development of the lifestyle construct as currently used in modern social
research. Lifestyle was addressed by Weber directly as a characteristic of status
relationships, combining economic position with cultural expressions of consumption.
Economic classes and social status groups were stratified by Weber according to
production, acquisition, and consumption of goods. Durkheim, in contrast, emphasized
the concept of lifestyle as institutionalized social norms internalized by the individual
(Lynch and Kaplan, 2000).
Weber’s concept of lifestyle was composed of two aspects: life chances or social
opportunities at the macro level; and life choices or individual decisions at the micro
level. Briefly, Weberian lifestyles were collective phenomena associated with status
groups, patterns of consumptions, and individual self-directing choices made within the
constraints of the social opportunity structure. Differential health patterns may be
primarily explained by individual decisions to act in a specific manner which are
influenced by status group norms, the availability of socioeconomic options, and basic
physiological status. Given the historical period, other elements of the social
opportunity structure, such as sociopolitical values and ideology, civic culture, race,
gender, ethnicity, age, were only subsumed and not directly addressed by Weber
(Weber, 1978; Cockerham, 1997, 1993; Abel, 1993).
The important influence of macro level normative regulation of the social
opportunity structure and social cohesion, although not expanded upon by Weber, were
43
explicitly proposed earlier by Durkheim. Durkheim’s theory of normative control of
individual conduct through social institutions emphasized the coherent, stable,
predictable pattern of social norms. Social institutions exerted influence through a
common value system and a body of rules governing means and ends of individual and
group action, as well as an array of social opportunities or life chances. Durkheim
argued for the necessity of social restriction to unlimited individual desire for well-
being, the lack of individual self-control was replaced by external social control,
through the operation of a social conscience, internalized as individual morality. The
moral authority of society, in the name of the common interest, stipulates not only the
aspirations but the rewards offered and expected for every class of individual action
(Bellah, 1973; Durkheim, 1961).
There is a concomitant loss of individual control over achieving personal goals
with prescribed institutional means during a period of social deregulation or
destabilization. In periods of social crises, whether increasing or decreasing inequality,
for example, individuals become dislocated with respect to changing institutional values
and rules regulating the relationship to individual conduct. A loss of control over
individual effort or means, and lack of clarity about ends or attainment of goals, has
been associated with poor health status and to an increase in cardiovascular disease
(Bobak, 1999; Siegrist, 1996; Marmot, 1998).
The breakdown of socially normative regulation of individual action affects life
choices by often placing the individual in a social quandary of not being able act in
traditional and expected ways. New values and norms often arise in situations of social
crisis but the time lag before individuals assimilate the social changes into their
44
personal lives continues as a period of psychosocial disorganization. Command over
resources and attainment of goals are essential aspects of individual happiness which
are disrupted by massive social change or crises, resulting in health crises as well
(Veenhoven, 1996).
Durkheim points to a stratification of well-being, aspirations, and a way of
living considered acceptable or equitable by public opinion, a social mechanism of
regulation which determines the maximum and minimum degree of ease of living
legitimate for each social class or group. Public opinion, the social conscience, is not
immutable but subject to the vagaries of time and space, with relative standards of
luxury and poverty, health and disease. When the continuities of these relative standards
are stable, predictable, and harmonious, according to Durkheim, individuals accept the
equilibrium of social normative regulation of aspirations and satisfactions, characteristic
of healthy societies and healthy individuals, (Traugott, 1978; Cohen, 1959).
Maintaining stability and equilibria between individuals and social groups is
dependent upon social regulation of individual aspirations and expectations; economic
disasters produce rapid declassification, changing the relationship between the
satisfaction of aspirations and the levels of expectations, which has also been observed
to occur in modern high effort-low reward occupational situations (Siegrist,
1997;1996;1990; Theorell, 1992; Converse, 1972; Barnes and Inglehart, 1972).
The sudden acquisition of prosperity, power or wealth also changes social
standards of acceptable lifestyle norms. If one social group suddenly becomes rich, an
alteration occurs in the distribution of social wealth for other social groups, creating an
imbalance in the distribution of social positions, life satisfaction and expectations. This
45
relative inequality in the distribution of social positions is associated with the
disjuncture between aspirations, expectations, and satisfaction, which have been
associated with quality of life measures (Veenhoven, 1995, 1990).
As Durkheim puts it, the scale of the social hierarchy is upset, but a new scale
cannot be immediately improvised to reclassify men and things, thus there is no restrain
on aspirations. Profound disturbances may change social stratification, the principles of
distribution between status and occupational groups, as well as the attendant acceptable
norms of maximum and minimum degree of ease of living. This is a state of social de-
regulation, or anomie. When the stable, coherent pattern of public aspirations,
expectations, and attainments, or way of living, is disturbed at the institutional or social
level because of social crises or rapid transitions, malaise ensues at the individual level
because society has become sick, anomic (Durkheim, 1897).
Although Durkheim’s perspective is historically circumscribed, it has several
modern extrapolations in recent studies of public policy and community cohesion, in
relation to the promotion of health and civic culture (Hoffman-Nowotny, 1972;
Antonovsky, 1993a, 1993b; Stassen, 1988; Andrews, 1976; Bradburn, 1969; Campbell,
1976; Travis, 1993; Srole, 1956; Bell, 1957; Cloward, 1959; Giddens, 1971; Merton,
1964; Willis, 1982).
Another dominant concept of lifestyle relevant for a multilevel model and
utilized frequently in European health research is outlined by Giddens. Giddens tries to
overcome the traditional macro-micro dichotomy, and specifies lifestyle as having two
main aspects: 1.) a self-identity, created and changing in time and space, and 2.)
individual social practices which reflect personal, group, and socioenvironmental
46
characteristics of multiple group memberships. Social class and social milieu are both
important stratification parameters of socioenvironmental factors, as are status groups,
with specific aspiration norms and expected behavior patterns for individual lifestyle
choices. Giddens’ contribution lies in the emphasis on the influence of temporal and
spatial social change upon individual lifestyle patterns, and upon clusters of habits,
orientations, and social identities found in group memberships. These patterns are
constructed as elements of social structure through social interaction by individual
agency, within a cultural and historical context of hierarchical levels of complexity
(Giddens, 1984).
Gidden’s structuration theory emphasizes the reflexive duality of social structure
created by individual actors, who are not abstractions or hypothetical ideal types, or
reflections of internalized social norms, but concrete active agents. Since social
structure is both the means by which action occurs, and the result of individual action,
structuration theory integrates Durkheims’s objective (social) constraint of the
individual with Weber’s subjective (verstehende) and enabling effect upon individual
choice. Whereas Durkheim illustrated the effects of social norms upon individual
health, Giddens points to the social continuity of individual self-identity as a reflexive
element deriving from group membership, which also creates a civic community
through social interaction at various levels of complexity. While Weber was occupied
with the traditional social status hierarchy of socioeconomic position in industrial
society, Giddens addresses multifactorial interaction levels between the individual and a
complex society. Self-identity is expressed in a civic community as a mediating element
between life choices and life chances in the modern and post-modern period for
47
Giddens.
The civic community is an expanded dimensions of lifestyle subsumed by
structuration theory. It has been investigated as a macro effect on health status.
Characteristics of a civic community, such as social capital (civic activity and group
memberships), have been shown to have an important benefit for individual quality of
life (Wilkinson, 2001; Putnam, 1993). Durkheim’s social integration theory has also
been incorporated by research into the effects of a civic community on health primarily
through measures of trust and anomie. The importance of Weber’s measures of social
opportunities, derived from a distribution of socioeconomic positions and status
groups, and individual choices made within the constraints of the social opportunity
structure, have been extensively applied in the WHO distinction between avoidable and
unavoidable conditions of health inequalities. The constraints upon life choices by life
chances has been a challenge for empirical research. The factors resulting in a specific
health status outcome for individuals needs to be identified at the micro level within the
macro social level.
A pragmatic application of the civic community construct may explain
premature mortality and the health crisis in Russia, a natural experiment where
variations in personal and cultural life choices, as well as sociopolitical transformation
of life chances, can be assessed. Research has increased in this area since the collapse of
the Soviet system in 1989-1991. Russia has been described as an hourglass society,
lacking civic culture and especially trust; where an elite, disavowing social
responsibility, skims off the socioeconomic capital from a small middle-class and from
a large number at the bottom of the social hierarchy (Rose, 2000).
48
THE CIVIC COMMUNITYHRQOL has been related to indicators of a civic community, such as social
capital and social cohesion. Part of this research tradition has grown out of the
longitudinal California Alameda County health studies, which began investigating the
impact of anomie, social support and social networks on mortality beginning with a
1965 cohort (Hochstim, 1970). An extensive literature exists now on associations with
morbidity (Berkman and Kawachi, 2000; Berkman, 1985; Doeglas et al., 1996;
O’Reilly, 1988; Lomas, 1998; Berkman and Kawachi, 2000).
Although often used interchangeably, social capital may be conceptually
distinguished from social cohesion. Social cohesion may be a psychosocial subset of
social capital, while the latter may be derived from a political economy framework. One
dominant perspective has defined social capital as the instrumental allocation of goods
and services by individuals through mutual exchange networks of expectations and
obligations (Rose, #318, 1999). Institutional and interpersonal trust in a mutually
beneficial exchange is an underlying social axiom for cementing civic, economic and
political activity. The formation of organizations, small groups or networks as a result
of such normative activity may be one measure of the available stock of social capital in
a culture (Inglehart, 1997). Others have argued that social support and social networks
are only meaningful as interpersonal micro level measures, while both social capital and
social cohesion are intrinsically macro concepts (Berkman and Kawachi, 2000). On the
other hand, social inequality and distrust have been defined as indicators of a lack of
social capital, which is an aspect of social cohesion or social integration (Kawachi,
1999).
Regradless of the nomenclature, or risk of sliding into nominalism, the civic
49
community connotes social integration, in this study, characterized by measures of
horizontal interpersonal relations and vertical formal group relations. Social cohesion
may be understood as the normative social “glue” which links horizontal and vertical
relations.
While civil society implies an independence from state control, a civic
community implies integration with the state through social organizations like trade-
unions, clubs, professional and political participation. The civic sphere is suffused with
social cohesion, derived from values of trust and expected reciprocity, where the public
and private interact with mutual benefit. This normative area is especially conducive for
self-regulated professional activity with autonomy of occupational expertise, which
receives legitimation from the state and provides essential services to the state.
The difference between autonomy and independence from the state is an
important distinction, wherein individual self-interested partnership with the state,
rather than separation from the state, coincides with civic interest. A state with
numerous “independent” social and economic organizations, which are poorly
integrated and lack mutual reciprocity, may result in a corrupt, corporatist, and civil
rather than civic society (Blazer, 1996). Soviet state monopoly of sociopolitical
institutions precipitated demoralisation, separated the activity of citizens from
influencing the state, rather than fostering civic participation. This increased mistrust,
insecurity, loss of mastery over political and life conditions, and other types of personal
disillusionment with public life which have been shown to impact negatively on health
status (Kawachi, 1997).
Whereas social cohesion assumes the mutual social norms and obligations of a
50
civic community, social capital, as the instrumental exchange of goods and services, can
take several forms in either a civil or civic society. Informal relationships of social
capital in the form of friendships and family assumed greater importance in Russia
when social isolation from public participation increased for individuals, and when
formal organization of social capital, such as firms, nongovernmental groups or
voluntary community associations, entered into conflict with Russian state policies and
therefore became unsustainable.
Formal organizations, like professional or trade unions, have been connected to
political parties in post-Soviet Russia, replacing monolithic party control with
politicized groups, further weakening the civic relation between citizens, voluntary
nongovernmental organizations and the state (Kennedy et al., 1998). The lack of social
cohesion in Soviet and post-Soviet formal groups, so characteristic of the civil society
of Russia, was balanced by greater interpersonal reliance between individuals. The
positive association on physical and emotional health of trusting people and having
someone to rely upon when ill was extensively demonstrated in the New Russia
Barometer Social Capital Survey (Rose, #303, 1998).
Primary relationships of social connectivity in the form of friendships and
family assumed greater importance when secondary relationships of community
integration became unsustainable. Research of “healthy societies” has shown how
membership in various social groups is related to the greater prevalence of social trust, a
more effective government, and public well-being. Membership in various social
groups, from sports clubs to kindergarten meetings, has been related to keeping abreast
of political information in newspapers, voting behavior, and a more effective
51
government in Italy (Putnam, 1993). Alameda County studies of mortality have
demonstrated the important protective effects which the number and size of primary and
secondary group networks have upon longevity, even under circumstances of relative
social and economic deprivation (Berkman, 1979).
A sociopolitical model of health and democratization may be balanced by a
psychosocial model for predicting and explaining the increased mortality from
cardiovascular diseases in individuals and countries undergoing rapid social change and
economic transition (Putnam et al., 1993; Siegrist, 1995). He has shown how high effort
and low reward conditions in Germany, among some occupational groups of men, have
resulted in biobehavioural changes and increased cardiovascular disease.
Analogously, when expected social structures and norms are displaced by social
crises and a lack of social coherence is combined with loss of individual life control
(self-regulation), individual self-efficacy is disturbed since a sense of purpose is no
longer connected to social norms and personal success cannot be ensured; self-worth is
disrupted by a change in obtaining social rewards from occupational and economic
status (loss of savings from inflation, loss of wages from unemployment or unpaid
labor, loss of access to material and social resources from increased prices, etc.); self-
integration is disrupted by secondary and primary group disconnections and inability to
obtain social support, indirect economic assistance, or other forms of informal access to
social and material resources.
Certain tension alleviating behaviors, which act as central nervous system
depressors, such as smoking and alcohol consumption, may increase under conditions
of rapid social change. The chronic stress response of the autonomic nervous system
52
then precipitates physiological changes, cardiovascular or other disease, and premature
mortality. The social context of institutional environments and secondary group
relationships (such as occupational groups) is thus linked with mediating psychosocial
behaviors and physical responses at the micro level, as well as to macro level
community health status (Hertzman, 1996).
Cross-cultural research which searches for universal aspects is also essential in
pointing to differences in relationships between social contexts and individual health
(Kohn, 1988). Those relationships found among Western European countries may not
be as uniform for countries of the Former Soviet Union or Eastern Europe. Market
transitions increase the inequalities of the stratification structure within the FSU and
countries of CEE. There are concomitant increases in life uncertainties, individual
controls over the social environment are weakened during massive macro changes in the
political - economic structure. Distress levels may be translated into physiological
processes producing increased disparities in health at the individual, group, and
community levels. Cross-cultural research in public health is clearly important in
systematically clarifying the processes of social change which have resulted in such
structural and cultural variations of health-related quality of life.
LIFE CHANCESLife chances, the social opportunity structure, can be defined as having both
macro and micro aspects, each of which has been related to health status. The size of the
range in the distribution of socioeconomic resources has been demonstrated to be a
predictor of community mortality and morbidity patterns. Socioeconomic position is
understood as a social context which constrains individual opportunity (Kawachi and
Berkman, 2000).
53
Individual social status and socioeconomic position , however, in following
structuration theory, is not predetermined but created interactively with social context.
Individual social status dimensions of education, occupation, and income, as well as
individual health status, are situated within the distribution of production, acquisition,
and consumption of goods (Kaplan, 1997; Wilkinson, 1997; Marmot, 1997). Relative
inequality, such as the degree of steepness in hierarchies of social positions, social
marginality, and normative disorganization, have been more strongly associated with
mortality within countries than have absolute measures like average income or policy
norms (average price of a food basket). Lower deprivation between groups relative to
each other has been related to higher life expectancies, egalitarianism, smaller ranges in
the distributions of national income and access to social resources (Wilkinson, 1997).
The lifestyle construct was broadly based upon Weber’s distinction between
individual life choices constrained within a structural context of life chances, as well as
Durkheim’s theory of normative control of individual conduct through cohesive social
institutions. Social institutions govern individual and group action through a common
value system and a stable, predictable body of rules. Institutions also exert influence
upon the structural array of life chances, the social possibilities and probabilities within
which life’s actions occur. These common values and normative constraints form a
consistent and cohesive context, setting out relative social and cultural expectations and
permissible action.
Life choices or individual conduct occur within this institutional context and
have social consequences. However, individual behaviors are limited by a milieu often
beyond individual control, as in exposure to environmental pollutants, or by social
54
institutions, like professions, the job market or residential neighborhoods, where the
individual must negotiate partial control. Individual and community well-being is
disturbed during periods of sociopolitical crises; conflicting, inconsistent occupational
opportunity structure; or when the cohesion between social values and rules for action,
or social expectations and individual aspirations to acquire social rewards (goods and
services) is frustrated and disrupted. Changes in the range of social stratification due to
sociopolitical disruption of life chances and a civic community transform some
emergent properties of social structure which, in turn, affect public health and quality of
life.
HEALTH CHOICESAlthough chronic disease and morbidity surveys in populations have a long
history from the early part of the 20th century, the Alameda County studies were among
the first longitudinal studies of health and longevity (Syme and Berkman, 1976;
Breslow, 1996). They grew out of the tradition of the United States National Health
Survey, established in 1956, and the U.S. Commission on Chronic Illness, which
conducted county-level surveys on chronic disease in 1957 and 1959. The Human
Population Laboratory in Berkeley, California, was established by the National
Institutes of Health in 1959 to conduct the Alameda County studies, focusing on
assessing the interrelationships between the WHO health dimensions, demographic
characteristics, and lifestyle, including personal habits, socioeconomic, cultural, and
environmental factors (Belloc, 1971, 1972, 1973; Hochstim, 1968, 1971; Wiley and
Camacho, 1980).
This effort engendered nearly half a century of prospective longitudinal research
since the initial Alameda County sampling cohort in 1965. Mortality and health status
55
follow-ups were first conducted in 1974, and almost twenty times since the original
cohort. The Alameda County studies demonstrated what has now become accepted as
common knowledge, that lifestyle and social relations influence longevity and quality of
life, controlling for initial physical health status.
The debate defining lifestyle as an individual level risk factor for specific
populations has become inherent in the construction of health insurance benefit
packages and continues as one of the most controversial and fundamental issues in each
American presidential race, as well as in Russian parliamentary politics surrounding the
implementation of private health insurance legislation (McKeehan, 1995).
In the USA, the Surgeon General's Goals for the year 2000, continued to
emphasize the research results of the Alameda County Studies, identifying three broad
areas which influence population health status and require public policy intervention:
1.) first, health practices or Health Promotion - decreasing risk factors, personal habits
such as smoking, lack of physical exercise, poor diet, alcohol abuse; 2.) second,
ecological factors or Health Protection - decreasing occupational and environmental
toxic exposures, decreasing accidents; and 3.) third, medical care factors or Preventive
Health Services - increasing access to services such as prenatal care, infant programs,
family planning, hypertension control (CDC, 1991). Health promotion and disease
prevention indicators were included in the Alameda County survey instrument.
The long-term research objectives of the Alameda studies were replicated in the
construction of the Moscow HRQOL Survey instrument. Individual preferences and
personal health habits, such as physical exercise, smoking, and alcohol intake were
included as proximal determinants of health which were amenable to individual control
56
and change; initial physical health status was controlled by height.
Height (like age, sex, marital status, educational level) has been demonstrated to
be a direct measure of the average “net nutritional status of the members of a human
population during their childhood and adolescence”. It is an indirect measure of
childhood welfare, which is more comprehensive than income or wealth, having a
lifelong effect which influences mortality (Blaxter, 1990). The body mass index was
included as a choice variable indicating quality of nutrition and diet. Periodic nonacute
checkup visits to dentists and physicians were added as measures of health promotion
and prevention. The willingness to pay for private medical services was an item which
indicated changing economic values in the Russian public, accustomed to free
socialized medicine.
MULTILEVEL MODELA multilevel analysis of infant mortality and community risk factors
demonstrated that community context, like family poverty rates and local welfare
expenditures, influenced mortality as a “fundamental cause” (Matteson, et al., 1998). In
various studies, smoking, drinking, long-term illness, standardized mortality ratios, and
female heart disease death rates were reported to be affected by household or
neighborhood deprivation measures as independent main effects, controlling for a
variety of individual risk factors, like social class, age, ethnicity, car ownership, or pre-
existing health conditions (Duncan et al., 1999; Gould, 1996; Langford, 1996; LeClere
et al., 1998; Rice et al., 1998).
In a study controlling for ecological effects, mean area consumption of alcohol
was strongly correlated with the prevalence of heavy drinking in areas of England,
suggesting that social factors which increase the average consumption of alcohol in the
57
population of an area may increase the prevalence of heavy drinking among individuals
(Colhoun, 1997). Further, it has been shown that not taking into consideration
nonresponse rates can seriously bias the relationships found (or not found) within a
survey. Income inequality was not found to be significantly related to life expectancy in
a study of 13 OECD countries until substantial nonresponse rates were corrected in
subsequent analyses (Kawachi, 1997). However, such models are still left at either the
micro or macro level, precluding an examination of influences between levels on the
social dynamics of HRQOL, which may operate differentially yet simultaneously at the
community and individual level.
The hierarchical linear model has three different analytical propositions based
on different data levels: individual and group or contextual levels (there can be any
number of hierarchical levels); and micro propositions, macro propositions, and macro-
micro interaction propositions (Figure 3; Snidjers and Bosker, 2000).
FIGURE 3: THE STRUCTURE OF A MICRO, MACRO, AND MULTILEVEL MACRO-MICROPROPOSITION
There are various common types of contextual effects which may be modeled
with hierarchical linear regression. A simple macro-micro relationship, as depicted in
58
Figure 3 (Snidjers and Bosker, 2000), refers to the direct contextual effect on
individual members of the “context” or group. For example, ghetto crime rates induce
criminal behavior in ghetto teenagers. Or, national income inequality is associated with
premature mortality. A contingent macro-micro relation control for another micro level
factor, such as ethnicity, income, education or occupation. This modifies the
proposition, for example, so that ghetto crime rates induce criminal behavior in ghetto
teenagers, controlling for ethnicity. Or, income inequality is associated with premature
mortality, controlling for the development of a civic community.
The third common macro-micro relation is the cross-level interaction, where the
relation between micro factors x – y is dependent upon the contextual factor, Z, or in
other words, the relation between context Z and dependent micro variable y is
dependent upon micro variable x. The proposition would change, for example, to
considering the relation between ethnicity and crime is dependent upon ghetto context,
or in other words, the effect of ghetto context on teenager criminal behavior is
dependent upon ethnicity. Or, a civic community and premature mortality are
associated with national income inequality, in other words, national income inequality
and premature mortality are dependent upon the development of a civic community.
There are also reverse micro-macro relationships, where the individual factors
impact upon contexts, for example, poor individual health affects public health policy.
More complex macro-micro-micro-macro relations may also be modeled in a variety of
combinations (Snijders and Bosker, 1999). The proposition of how individual quality of
life affects public health policy may be schematized as follows: preventive policy
promotes implementation of equity which encourages access to social resources and
59
positive health lifestyles, which in turn leads to less morbidity and successful public
health policy (Figure 4; Snidjers and Bosker, 2000).
FIGURE 4: A STRUCTURE OF SELECTED MULTILEVEL MACRO – MICRO PROPOSITIONS
SUMMARY OF RESEARCH HYPOTHESES
This chapter described the social determinants of health, conceptualized as a
multilevel health profile of a civic community, life chances, and life choices.
Three dimensions of social determinants were related to a broad lifestyle
construct as predictors of individual health. The general construct of lifestyle was
derived from a broader sociological conception of individual action (Blaxter, 1990).
Lifestyle was defined as a collective and cultural pattern of ways of living, including
life chances, characteristics of a civic community, and individual life choices, in
keeping with the four Black Report explanations of health inequality. Multiple
dimensions of lifestyle were formulated to minimize measurement artefact due to single
indicators, increasing the reliability and validity of the construct (Sullivan, 1979;
Carmines, 1979).
60
Lifestyle incorporated first, a social selection and structural aspect (life
chances), the clustering of social characteristics among groups which influence
individuals’ options to live in a particular way within those groups; second, a
sociocultural aspect (civic community) which provides the normative content of specific
values and goal-oriented behavior; and third, individual health-related behavior (life
choices). The distribution of life chances served to differentiate between relative
inequality and average inequality among individuals, as well as urban areas.
The three dimensions of lifestyle were operationalized as shown in Figure 1:
1.) Macro indicators of life chances:
average inequality as a factor scale of access to material resources
between urban residential areas; the score was derived from 33 separate indicators;
the construction of this factor is described in the methods section;
poverty risks in urban areas; a proxy measure for economic status
since no direct measures of income at either the macro or micro levels were
available in the survey; poverty risk was assessed at the urban area level as the
ratio of families with five or more members in each area. Poverty was most likely
among large families in Russia, with 3 or more children. Based on the average
family size of 3.1 in 1991, families with five or more members could be
considered to be at risk of being poor. It was assumed that the likelihood of areas
having a greater concentration of poverty or other form of deprivation if they had a
greater proportion of large families was reasonable in lieu of a more direct
measure of average income levels.
relative inequality as multiple indicators of the composition of social
61
status in urban areas, including education, occupation, and employment. At the
community level, ratios of blue-collar manual to white-collar non-manual residents
within areas, ratios of below average to above average living space per person, and
ratios of low to high educational levels were measures of relative deprivation or
inequality between the 33 administrative areas in the city of Moscow. The
composition of areas was defined by the simple proportion of white collar workers,
blue collar workers, educational level, family size, or apartment size. In and out-
migration by social characteristic was not available for urban areas to examine
direct selection of specific persons in urban areas. The rate ratios were contextual
indicators of the relative distribution of social groups to each other within urban
areas.
2.) Micro indicators of a civic community:
social cohesion as a scale of social anomie;
social support as a scale of the quality of marital relations; and
social capital as primary group connectivity with friends and family,
and secondary group community connectivity with group memberships or
activities.
3.) Macro and micro indicators of health choice:
a set of micro level personal health practices, including smoking,
alcohol consumption, physical fitness, body mass index; medical utilization of
preventive physician visits and acute care visits; and economic values such as the
willingness to pay privately for physician services.
an aggregated macro indicator of alcohol intake within each area.
62
A multilevel theory of health explains how multidimensional health outcomes
may be determined by behaviors of individuals within groups, as a consequence of
social relationships between networks or groups, within a larger social context. A
multilevel statistical model explains the variation in physical health by apportioning the
effect directly to characteristics of the individual, to community contexts, or to the
interaction between the individual and community context, without losing information
about the independent effect of each level of determinant on health outcomes.
Multilevel modeling is one parsimonious method, among several more sophisticated
estimation procedures, which can provide a robust analysis of the components in social
hierarchies, networks, or causal paths, which integrates conceptual and statistical
clustering.
Based on the research goals outlined in Chapter 1, the following hypotheses
were formulated to estimate 1.) individual level effects and, 2.) contextual level effects
on health-related quality of life:
1.) a.) the components of HRQOL outcome, life satisfaction, life happiness,
self-rated health status, and physical health, were expected to be influenced by different
sets of individual determinants; the components of HRQOL contribute unique
information for assessing outcome; components can not be eliminated or conceptually
substituted for each other without losing significant information on outcome. Choosing
one specific or several outcomes has significant implications for public health
interventions.
b.) it was posited that there was a hierarchical effect of life chances on HRQOL
outcome and that it was stronger than the effect of poor health choices; people with less
63
education or lower status occupations were at greater risk for poorer HRQOL even if
they made all the healthy choices.
c.) the hierarchical effects of individual life chances on HRQOL outcome are
expected to be moderated by the effects of a civic community, in the form of social
cohesion, social support, formal and informal networks, controlling for health choices.
2.) a.) it was posited that the distribution of inequality was an urban-level
contextual determinant of individual health; characteristics of the social context,
measured as average and relative inequality, were determinants independent of
individual risk factors such as social status and health choices;
b.) because of the effect of cumulative risk factors, poor health choices among
resident in high inequality areas would have a greater impact on physical health than
among residents in low inequality areas who made the same choices;
c.) living in a civic community which connects individuals by social capital,
social support, social cohesion was expected to have a positive effect on physical
health; and a protective or buffering effect on those living in high inequality areas,
controlling for social status and health choices; the buffer effect was expected to be
greater for low status individuals living in high inequality areas;
d.) in sum, it was expected that the risk for poor HRQOL was a cumulative
function of living in high inequality areas, which were less likely to exhibit the
parameters of a civic community, and more likely to have residents with low social
status rather than individual health lifestyle choices.
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CHAPTER 3: CONSTRUCTS OF HEALTH OUTCOME“Scientific” operational definitions rather than popular, self-reported definitions
tend to look at behaviors in terms of consequence and not internal states or processes,
exemplifying the difficulty of verifying the validity of a concept in various cross-
cultural populations, where meanings may differ due to social context. Different health
status indexes reflect differences in measurement and conceptualization, and thus have
different referent phenomena. Social concepts are especially subject to the "Uncertainty
Principle", which operates beyond the upper limit of measurement accuracy. This raises
the question of how useful it is to measure the degree of HRQOL with indicators which
are fuzzy or imprecise, without treating the degree of fuzziness itself as a separate
variable. Otherwise the fuzziness is referred to as residual error, as the unspecified part
of the conceptual definition which may confound measurement and theory.
The degree of social fuzziness in cross-cultural comparisons may be captured by
self-perceived, self-rated indicators, which describe populations from their own vantage
points rather than those of the expert. In other words, HRQOL is both methodologically
and conceptually a multidimensional, general, and abstract construct rather than one
which is unidimensional, unique, and concrete.
In cross-cultural research, empirical relations such as reliability and validity
between concepts and indicators may be different for different populations. The
generalizations drawn from population-based research to theoretical concepts are not
universal given that the rules of inference are statistical (Coleman, 1986). It is argued
that the validity of indicators must be established in each population. Operational
definitions of indicators are often confused with theoretical constructs and linguistic
terminology. Theory requires a meaningful specification of explanatory, conceptual
65
interrelations, not simple quantification of a phenomenon.
Global, self-perceived indicators of health status and quality of life transcend the
issue of whether linguistic terms have phenomenal referents in society, since concepts
can exist in language and not in society, but still have meaning for the individual. The
confounding of measurement and theory is often due to the reification of concepts:
concepts may have the same meaning in different cultures but operational indicators of
concepts may have different meanings in different cultures. Nonetheless, indicators
should be applied appropriately for a specific culture, so that a study of polygamy in
Great Britain would be difficult to entertain. Analogously, measuring health and
happiness among women in Somalia and Great Britain by the questions, “how would
you rate your health” or “how happy are you”, will assume cultural referents within the
self-perceived indicators. The cultural referents need to be defined explicitly only in a
comparative study which investigates the influence of that specific cultural phenomenon
(Becker et al., 1978).
A central problem in theory construction is the definition of the phenomenon to
be investigated. This introduces language as a central category of theoretical concern in
cross-cultural research. The more abstract or complex the phenomena, the more the
conceptual terms correspond to common-language categories, rather than those of
expert, professional jargon, and have greater relevance for the everyday life of people
who perceive and define them. The meaning of everyday terms has greater variability,
less uniformity and increased subjectivity, therefore multitheoretical constructs
operationalize such everyday concepts, which are not amenable to a single, unified
explanatory theory (Elder, 1976).
66
The search for a common scientific terminology has led to a premature demand
for the cross-cultural identity of meaning as the golden criterion for construct validity.
Equivalent but nonidentical concepts have often been confused with being incongruous
or invalid. Cross-cultural equivalence does not necessarily imply identity, even under
conditions of ceteris paribus (all other things being equal). Sociocultural norms are
tacitly imbedded in health assessments and sociomedical indicators. What is accepted as
healthy in rural Siberian villages, for example, may not be tolerated as healthy to the
same extent in Moscow or on an Arkansas farm. The universalism of scientific validity
has often been assumed to be independent of culture, although Merton (1959) and
researchers such as Kohn (1987) and Elder (1976) have long argued that scientific
activity is situated in a sociocultural community.
Comparative research is thus not only confounded by the social context of
research methods, but also by the social context of the research problem. Some concepts
are intrinsically more comparative than others and are thus better suited for application
to cross-cultural research. Health and quality of life are such comparative categories of
daily living, abstract and non-specific, and therefore more suited for cross-cultural
research.
Common dimensions of QOL, such as objective, macro, community in contrast
to subjective, micro, individual measures do not imply social rather than psychological
dimensions, but rather different levels of abstraction and generality/specificity. QOL
research requires simultaneous work in theoretical and methodological development,
particularly looking at the problem of the relative rather than absolute distribution of
well-being. It is necessary to isolate the endogenous and exogenous variables affecting
67
different dimensions of QOL as a dependent, outcome variable, as well as an
independent, predictor variable. (Siegrist, 1987).
Cross-cultural research in health-related quality of life has been grounded in the
social indicators movement, where often expert and "professional" definitions of
indicator content have been supplemented by lay "self-perceived, subjective" indicators
rather than "official, objective" data. Quality of life and health status have been defined
both at the “subjective” and “objective” level (Katz, 1987). Most researchers in the field
define “quality” as a subjective rating on a continuum, scale, ranking, or ordinal range
from high to low or from better to worse. The term “life” subsumes both objective and
subjective dimensions. Objective dimensions include environmental conditions, work,
community, food, shelter, socio-political, cultural, and ecological. Subjective
dimensions include attitudinal conditions of life, such as global or domain-specific
feelings, affective, cognitive, or value-orientations (Schuessler, 1985; Armer and
Grimshaw, 1974).
Global self-perceived indicators minimize the problems of cross-cultural
translations and maximize functional and semantic equivalence, insofar as the concepts
are abstract enough to leave room for individual meaning without losing the element of
general applicability over various populations. The issue of selecting equivalent
measures for cross-cultural research tautologically assumes the validity of the original
concept as a universal: it is comparable only if equivalent, if equivalent therefore
universal; similarities between cultures are emphasized and not differences. But it is
thus essential to look at concepts with divergent methods and indicators.
It is of questionable validity to emphasize similarities in comparative research,
68
as it necessitates the use of equivalent concepts. There needs to be an increased
emphasis on demonstrating the validity of concepts at various levels of specification:
not only subjective and objective, specific and global, but micro and macro.
Standardized methods and literal translations of instruments are useful only if
conceptual equivalence is assumed to exist a priori, but this is itself a restrictive
research strategy, given that validity is dependent upon independent replication with
multiple methods (Grimshaw, 1969).
The mistaken emphasis upon equivalent measures further assumes the validity
of the measures, and that there exists a single best way of measuring concepts instead of
multiple ways. Various measures of the same concept may also be valid, as in
translations (Deutscher, 1968). It is important to consider whether an indicator measures
the “same concept” in different cultures, i.e. conceptual equivalence, or a culturally
specific concept, i.e. indicator equivalence of measures. For example, intelligence is a
universal phenomenon (conceptual equivalence), but the IQ test may measure exposure
to certain educational systems, cultural ideas, or standardized test taking techniques
(indicator equivalence).
The validity of a concept may be demonstrated in one culture and within another
culture, even with different operational indicators. If a construct is valid in one culture,
and valid within another culture, then it is valid cross-culturally but may still be
nonequivalent. This may be especially true for consensual rather than standardized
translations.
Translations may be seen as nonequivalent measures, especially of difficult or
linguistically unique concepts. Translations into different languages may therefore
69
produce different measures of the same concept from the original language, and in
another language the translated measure is nonequivalent both as concept and indicator.
Standardized “authorized translations” are useful only if conceptual equivalence has
been demonstrated already. Otherwise, the concept of cross-cultural validity itself must
be revised to include the preeminence of intra-cultural validity.
Some concepts are culture-specific, other concepts are inter-cultural, that is,
“universal“ or “transcultural”, rather than culture-bound. Global indicators, such as
general QOL or self-rated health, are such linguistically abstract transcultural concepts.
Any given language is in and of itself cultural, therefore an increased level of linguistic
abstraction is related inversely to cultural specificity (such as mathematics or symbols).
However, the problem of operationalizing a concept reduces abstractness, necessitating
the use of multidimensional constructs such as the global indicators of happiness,
satisfaction and general self-rated health to capture what was a single term, such as
quality of life.
Global, self-perceived indicators, which have the greatest abstractness within
cultures, tap the “universal dimension” or transcultural level with cross-cultural
referents (Werner and Campbell, 1970).
GLOBAL AND DOMAIN SPECIFIC INDICATORSThe following indicators were included in the outcome measures of the Moscow
health profile: general life satisfaction and life happiness as global components; specific
domain satisfaction with job and domain happiness with family; general self-rated
health as a global component and domain specific physical health (Table 2).
70
TABLE 2: HRQOL COMPONENTS OF THE MOSCOW CITY HEALTH PROFILE
HEALTH DIMENSIONSGLOBAL DOMAIN SPECIFIC
Self-reportedphysical health
Profile score disability/impairmentschronic conditionsacute symptomshigh energy levelslow energy levels
The communist ideology attempted to eliminate money as a mechanism of
“private property”. The command economy in Soviet Russia was organized to function
with social and economic planning, rather than a supply-demand based price system.
The post-Soviet economy of Russia has not yet made a successful transition to a
monetized market economy. Major research of income in Russia, after January, 1992,
the moment when price controls were lifted and the transition to a market economy was
officially initiated, examined the association between money income and household
welfare or well-being (Rose, #215, 1993; RLMS, 1992-1999).
These surveys have demonstrated that alternative means to official money
income have developed in Russia in order for households to acquire and maintain their
welfare: barter; reliance on social capital; participation in alegal, nonmonetized social
economies (like household production of food); and working in illegal, second
economies (like having social connections to the access of foreign currency). The
application of various quality of life indicators in Russia as outcome measures of
“objective” and “subjective” well-being have greater utility for public policy than the
use of traditional economic measures of household income.
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STRUCTURE OF QOL global indicators may be single items or additive sums of specific domains like:
family life; housing, health, job, occupation, environment, etc.;
global indicators vary less for ascribed, stable group characteristics, such as gender
or ethnicity; variation increases among social status characteristics, such as income,
education, and other fluctuating situations in health, family life, social relations, etc.;
SELF-RATED HEALTHThe definition of health as optimum physical, mental, and social well-being was
reconceptualized as a 1.) global, self-rated general health status, and 2.) domain-specific
component of quality of life - self-perceived and self-reported functional limitations,
impairments, chronic conditions, acute symptoms, and energy levels as indicators of
physical status derived from the Alameda County Physical Health Profile.
Health status has been traditionally measured as a multidimensional index plus a
global index because the global index captures "unquantifiable" elements or residual
nonspecific content associated with health. Global self-perceived health status cannot be
reduced only to a residual category of HRQOL. Self-rated health has been associated
with health outcome independently of medical, physiological, psychosocial, or
behavioral risk factors (Idler and Benyamini, 1997).
The global health rating gained popularity after the Alameda Studies reported
the usefulness of lay definitions of health. Wholistic, lay definitions of health status
were also related with exposing the “iceberg” phenomenon of under-reported morbidity
in a simple, direct, and individually-meaningful way (Kaplan and Camacho, 1983;
Verbrugge and Ascione, 1987). An overwhelming number of health surveys in the
1980’s included the self-rated health indicator. It has also performed well in cross-
72
cultural and comparative replications, providing invaluable information as a global
dimension of health status.
Self-rated health was included in the Moscow health profile for the following
reasons. Domain specific dimensions of health status capture information specific to
physical, functional, physiological, mental/ psychological health:
then global health, as a residual category, measures something “more” than
medical evaluation and needs to be included to partial out remaining variation; self-
perceived health status has been shown to be an independent predictor of health
outcome and not only a proxy measure of “objective” health status;
then global health is a “subjective” domain wherein an individual has access to
information, which is important in cross-cultural studies, where the appropriateness and
equivalence of indicators are additional methodological considerations for validity and
reliability;
self-perceived health has been shown to be one of the strongest predictors of
mortality in many studies, independent of age, sex, initial physical health, or medically
evaluated “objective” health status;
self-perceived health can be included not only as an outcome measure but also as a
self-assessed nonspecific risk factor.
PHYSICAL HEALTHIn addition to global health, domain specific dimensions were included in the
Moscow health profile based on the four major domains of the Alameda Physical
Health Profile: disability/impairment, chronic conditions, acute symptoms, and energy
levels. The Alameda Physical Health Profile was developed as a measure of general
physical health for community-based samples. The Alameda Physical Health Profile is a
73
multidimensional, non-disease-specific, self-perceived measure. It has been applied in
cohort studies of mortality, controlling for a variety of psychosocial and demographic
factors in numerous community studies.
The Alameda profile was one of the first operational quantitative approaches to
the WHO definition of health. This concept of health constructed a multidimensional
spectrum with three axes: the physical, mental, and social, where an individual could be
simultaneously evaluated on any one or all three axes. The statistical formulation was
first developed by the Alameda County studies as part of a project to measure the
morbidity in a general community, using sample survey methods (Breslow, 1972).
While other parts of the Alameda instrument measured mental and social health,
morbidity was operationalized by the physical health profile, consisting of specific
chronic diseases, symptoms, disabilities, functional status, and energy levels
The Alameda Physical Health Profile may be viewed as an “objective”
component of health, insofar as the scale is composed of multiple observable items.
Global evaluations of self-rated health may be viewed as the "subjective" component of
health insofar as the rating is non-observable self-perceived assessment. The Physical
Health profile is weighted toward an absence of disease continuum, whereas self-rated
health operationalizes well-being and taps into nonspecific, unlabeled conditions, as
well as a state of being.
The Physical Health Profile has been successfully used as a measure which
compares individuals, social groups, or entire communities over time (Brock et al.,
1988; Gottlieb, 1984; Metzner, 1983; Pope, 1982; Reed, 1983; Wilson and Elinson,
1981), and was applied for the first time in Russia within the Moscow Health Profile.
74
The post-Soviet Russian Federation consists of 21 autonomous republics, 55
regions (including 6 “krais”), 10 autonomous districts and 2 cities which have the legal
status of regions, Moscow and St. Petersburg (Figure 5). Together, they make up 89
constituent administrative units of the Russian Federation. Many of these units are little
known outside Russia, even though several are bigger than many European countries.
The health care crisis in Russia represents one of the most significant challenges to
public health policy today. The primary question for post-Soviet health policy centered
on the extent to which public health functions could be privatized.
75
CHAPTER 5: HEALTH INEQUALITIES IN SOCIAL CONTEXT
This chapter will review the international, national, urban, and small area
variation of health in relation to socioeconomic inequality. Health is an international
phenomenon, situated in the larger context of a global community. A plethora of
comparative studies have sought to account for a growing gap of health inequalities
within and between nations. The East-West health gap is related to the geographic
distribution of nations, as well as to their political, social, cultural, and economic
profiles (Figure 6). The health gradient among nations has been shown to follow the
distribution of egalitarianism; economic; sociopolitical choice; or personal freedom
(Hertzman, 1995).
The mortality differential among nations has been associated with various
theories of the East-West health gap, some of which are dependent on policy:
socioeconomic transformation; environmental pollution; lack of an adequate social
safety net; relative poverty, socioeconomic deprivation; historical, generational effects
of a Soviet heritage; regional disparities; psychosocial stress; and others are dependent
on individual lifestyle – poor health practices and violent behavior (Hertzman, 1995).
Public health is affected not only by the gradient of public and personal wealth
but also by the social stress of political turbulence and uncertainty. The rapid rate of
social change among the eastern European countries and the Newly Independent States
(NIS) in the transition from socialist to market relationships has accelerated
deterioration of health status. There are common factors and profound lifestyle changes
occurring in both eastern Europe and the NIS, accompanied by new values, the
reexamination of acceptable norms and the creation of socioeconomic institutions.
76
However, the complex structure of social factors which might explain the East-West
health gap may differ within and between East European countries, and differ from the
factors salient in Russia.
FIGURE 5: Changes in Mortality Rates, 1970-1990/1993, selected West and East Europeancountries
(source: WHO Health For All database)
77
INTERNATIONAL CONTEXT OF HEALTH INEQUALITYGeneralizations derived from data in one specific country, for example Poland
or Albania, cannot be applied without additional verification for Russia. In addition, the
issue of data quality and mortality/morbidity classification consistency between
countries must be considered when comparing data (Figure 7;Goldstein et al., 1996).
Mortality rates for eastern Europe do not show as severe fluctuations for the same age,
sex, and cause-specific death rates as trends for Russia or other NIS countries,
particularly after the sociopolitical failures of the Perestroika Period . Mortality
increases were substantially larger for Russia than eastern Europe in 1990-1994,
principally due to cardiovascular disease and external causes of death - homicides,
alcohol poisoning, accidents and violence. Self-rated health was worse and
dissatisfaction with income was greater in countries of eastern Europe than in the West
(Carlson, 1998).
It is noteworthy that 1991 is a focal period for health status changes, as well as
sociopolitical and economic changes, for Russia and the NIS but less so for countries of
central and eastern Europe. Another East-West contrast is the integration of East and
West Germany. After 1989 and German unification, the life expectancy of former East
Germany (DDR) seems to have initially fallen, recovered, and then made substantial
progress toward former West German (FRG) levels, as compared to pre-1989 levels and
to other countries in eastern Europe.
At the global level, income and life expectancy are strongly associated within
and between countries. Life expectancy is associated less with average deprivation as
measured by the Gross Domestic Product among economically similar countries (for
78
example, established western market economies (EME)), and more with relative
deprivation, when comparing economically dissimilar countries (for example, former
socialist economies (FSE)) with economically similar countries (Figures 8) .
The relation between average income, whether GDP or per capita, and life
expectancy is attenuated after a certain standard of living is achieved as has been the
case in market economies. Average income is weakly related to mortality within
wealthier countries, like the United States, which has one of the highest standards of
living. The relative distribution of income has been demonstrated to be more strongly
associated with differences in death rates within wealthy countries, given uneven
distribution and concentration of resources (Wilkinson, 1997). Mortality tends to be less
in countries with more egalitarian distributions of income like those of Scandinavia
where relative deprivation is less pronounced.
Health inequality is experienced differentially by gender in the former socialist
economies of Russia and East European versus West European countries (Goldstein et
al., 1996) . This health disadvantage has affected the productive male population
(Figure 9) in lower GDP countries to a greater degree than women (Figure 10), and in
a different manner. Russian male life expectancy has plummeted below all other former
socialist countries, disproportionate to decreases in average inequality or GDP. The
relation between life expectancy and GDP among men in FSE countries is not as clearly
linear as among men in market economies, suggesting factors other than economic may
be more influential in the loss of life expectancy.
The life expectancy of men in former socialist economies appears to have almost
an inverse relation to average income as compared to either men in market economies
79
or women in former socialist economies. The life expectancy of men in the wealthiest
of the former socialist economies is also the lowest.
Although Figure 10 indicates a linear relationship between increasing national
income (per capita Gross Domestic Product) and increasing life expectancy among
women in countries with western economies, that is not the case among women in
countries with former socialist economies. This suggests that women in FSE countries,
like FSE men, are at risk for a lower life expectancy due to factors other than income.
The effect of average income on the life expectancy of men and women in western
economies, however, is consistent with a direct relationship between wealth and health.
80
FIGURE 6: Trends in male life expectancy at birth in Russia, Former Soviet Republics,Central and Eastern Europe (CCEE), 1970– 1994
(source: Goldstein et al., 1996: 14).
81
These data may be accounted for by a differential distribution of inequality and
variation in the factors comprising inequality among women in different countries as
contrasted to men. The differences in life expectancy between FSE and EME countries
may also be accounted for by the relative inequality in sociopolitical institutions
(Wilkinson, 1997). Clearly, structural factors of nations such as development of
egalitarian institutions in addition to relative socioeconomic position must be
considered, for example among gender groups, to explain such striking international
differences in health status. This is more so in the case of Russia, which appears to
have had the greatest changes in male life expectancy in the 1990’s.
Several hypotheses have been offered as explanations for the East-West health
gap phenomenon. Notable are the models outlining the impact on health and well-being
of a civic community, which integrates individual citizens and encourages participation
with the state in a mutually beneficial relationship, in contrast to a society which
segments public and private spheres by a hegemonic state (Blazer, 1995). The structure
of soviet culture separated the individual from involvement with the state and reinforced
personal powerlessness, diminished personal initiative, and divided the private and
public norms of individual values and action (Bobak, 1999), in the context of state
rhetoric which identified personal good with state ideology. There was a lack of
institutional integration of the family or community into the soviet vision with a
concomitant exclusion of the individual from participating in the state monopoly over
public and private life. The Perestroika period prior to the collapse of the Soviet Union
was plagued with an intensification of disparity between personal achievements and
satisfactions possible within a socialist socioeconomic and political structure when
82
FIGURE 7: Male Life Expectancy in Established Market Economies (EME) and FormerSocialist Economies (FSE) by per capita GDP of country, 1993
FIGURE 8: Female Life Expectancy in Established Market Economies (EME) and FormerSocialist Economies (FSE) by GDP of country, 1993
83
personal aspirations escalated and emulated those in the West. The information
explosion which followed the breakup of the Warsaw Pact in 1989 contributed to a
markedly increased awareness of relative deprivation (Hertzman, 1995).
NATIONAL CONTEXT - RUSSIAThe health status of nations deteriorated as social cohesion deteriorated with the
ideological and market transformations of the 1980’s. Britain and eastern European
countries experienced increases in relative poverty, income inequality and premature
mortality among the working-age populations (Wilkinson, 1996). A similar dynamic
operated in Perestroika Russia, when widening income differentials within the country
were due to fiscal policies which cut back the communist welfare state to stimulate
economic growth and privatization (Kaplan, 1998), changing the distribution of social
status (Rimashevskaya, 1997).
Dissolution of the Soviet Union and international competition also weakened the
market position of those with inadequate education and job training, while enabling
others to flourish in an unregulated neoliberal market, which increased social
stratification and income inequality. Working-class male laborers in Russia, with
minimal vocational education, have been at greatest risk for unhealthy lifestyles and,
consequently, poor health (Cockerham, 1999). Life expectancy in Russia has been
associated with increased inequality of income between regions (Walberg, BMJ),
independently of per capita income (Kennedy, 1998).
Deterioration in the economic transition period after 1989 was characterized by
a drop in real wages, increased unemployment, unpaid state employment, and an
overall fragmentation of social services in Russia. There was also an increase in real
poverty and a larger concentration of wealth, resulting in a steeper hierarchy of income
84
distribution within Russia (Eberstadt, 1999). Russia has been undergoing an erratic
transformation from a communist state since 1991, reorganizing its military, political,
economic, and social policy. The issue of a deteriorating social safety net as a
consequence of transition to a market economy has been a crucial factor in decisions of
the IMF and World Bank for providing economic support for post-Soviet Russia.
Russia’s public health crisis is historically unprecedented. No other
industrialized country has ever experienced such a severe and prolonged deterioration
during peacetime. The loss of life from this health crisis in Russia has been a
catastrophe of historic proportions, which has been estimated by the World Health
Organization, for the period 1992-1995, as exceeding 1.8 million deaths, more than died
during the entire first World War (Eberstadt, 1999).
A substantial gender gap of increased mortality among Russian men was evident
in the cause-specific death rates for each year from 1970 to 1995. After an increase in
life-expectancy in 1985-1987 of 3 years in men and 1.5 years in women, male life
expectancy in Russia decreased by 1.1 years between 1987-1991, and by 6 years in
1992-1994. Women’s life expectancy was stable in 1987 and decreased by 3.2 years in
1992-1994. There was a 45% increase in the absolute number of deaths per year
between 1989-1994; 79% of this increase was attributable to premature deaths while
21% were due to the natural aging of the population (Shkolnikov, 1995).
However, life expectancy is composed from infant mortality rates and adult
mortality rates (Bobak, 1996). Within the countries of the Former Soviet Union, the
infant mortality rate, an accepted indicator sensitive to average deprivation, is better
than the adult mortality rate. This suggests that the changes in life expectancy are due
85
not so much to average deprivation in income alone as to other factors which affect
adult mortality, such as changes in political and social structure, levels of social stress,
and concomitant changes in personal lifestyles.
After decades of stagnation, and post-Soviet crony capitalism, Russia’s health
profile in the 1990s no longer resembled that of a developed country but was worse than
many Third World countries. In 1997, overall life expectancy in Russia, below 67 years,
was lower than forty years earlier. Mexico, even after major natural disasters and
political problems in the 1990s, had a life expectancy six years higher than Russia.
(Zdavookhraneniye Rossiyskoy Federatsii, 1999; World Development Report 1998/99;
World Health Statistics Annual, 1993, 1996). Death rates for Russian men in their early
30s were twice as high in 1994 as in 1964. For men in their early 50s, death rates were
almost two and a half times higher in 1994 (Eberstadt, 1999; Vishnevsky, 1995).
Between 1987-1994, death rates increased 1.96 times for working-age women, between
40-44 years (Notzon et al., 1998). However, infants and pensioners were not affected to
the same extent as middle-aged men and women.
In the post-Soviet decade, Russia suffered outbreaks of typhus, typhoid, cholera,
and diphtheria, even in Moscow. Tuberculosis has more than doubled since 1990, and
has been formally designated an epidemic by WHO. The impact of infectious diseases
has been primarily due to laxity in immunizations, dearth of vaccines, and migration of
infected persons through porous borders from Asian regions. Deaths from infectious
and parasitic diseases were relatively small, however, about 2 percent of the overall
age-standardized death rate. The main decrease in life expectancy, between 1987-1994,
was due to mortality from cardiovascular diseases and external causes of violence,
86
trauma, and accidents (Table 3; Vishnevsky, 1995).
MORTALITYThe two principal causes of death, cardiovascular and external factors, account
for two-thirds of the Russian overall mortality rate. The epidemic of heart disease in
Russia, for men and women alike, is higher than the death rate in the U.S. for all causes
combined. In Europe and America, cardiovascular mortality reached a high in the late
1950s and 1960s, and subsequently declined, while in Russia, high rates have continued
to climb. Even Russia’s female CVD mortality rate was roughly twice as high as male
rates from countries like Canada, Italy, and Spain in 1996 (World Health Statistics
Annual, 1996).
Mortality from external causes in post-Soviet Russia is twice what it was during
the Brezhnev and Gorbachev eras, and four times as great as the U.S. death rate
attributed to injuries and poisonings. The risk for a Russian male born in 1995 was
nearly 1 in 4 for dying from external trauma, while in Britain, the risk was about 1 in 30
(World Health Statistics Annual, 1996). Several other countries have a history of critical
drops in life expectancy: Spain in 1936-39, Western Germany in 1943-46, Japan in
1944-45, and South Korea in 1950-53. These mortality crises were transient and a direct
consequence of international or civil wars. In 1944-45, male life expectancy at birth in
Japan was under 25 years, yet three decades later, Japan reported one of the highest
male life expectancies in the world. Death rates from CVD are nearly twice as
87
TABLE 3 SELECTED CAUSES OF DEATH CONTRIBUTING TO CHANGES IN LIFE EXPECTANCY,RUSSIA, 1987-1994, BY SEX, IN YEARS;
Year Cause of deathAll causes Circulatory
diseasesAccidents/traumas
Respiratorydiseases
Othercauses
Men1987-1991 -1.42 -0.11 -1.32 0.13 -0.101992 -1.44 -0.23 -0.84 -0.08 -0.211993 -3.06 -1.09 -1.29 -0.28 -0.161994 -1.49 -0.68 -0.46 -0.09 -0.15
Women1987-1991 -0.03 0.38 -0.31 0.14 -0.27
1992 -0.53 -0.15 -0.25 0.02 -0.111993 -1.81 -0.94 -0.49 -0.13 -0.151994 -0.83 -0.53 -0.17 0.02 -0.07
high for modern Russians as they were for postwar Japanese; death rates for injury and
poisoning, over three times higher (Eberstadt, 1999).
The death rate from homicides in Russia, between 1985 and 1995, increased
directly with alcohol consumption rates (Shkolnikov, 1995). Homicides were fifty
times higher in 1993, following the precipitous drop in living standards, disorganization
of basic public services, and the crime wave which swept Russia after price
deregulation and other economic changes initiated in January, 1992.
The entrenchment of the health care crisis in Russia at the beginning of the new
millenium represents a significant challenge to public policy concerned with the
widening health inequities of the past decade. The changes in demographic, life
expectancy, and mortality patterns within Russia and across Central and Eastern Europe
have been extensively documented (WHO, 1994, 1998). There have been about 1.3 to
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nearly 2.0 million more deaths observed than expected between 1989-1994 within
Russia, resulting in a disproportionate loss of person years for the male working age
population through year 2025 as well as a critical demographic imbalance in family
structure. The impact of the mortality crisis has been gender-specific. A greater
population ratio of women to men, diminished marriage and fertility rates, greater
divorce rates among women, more children growing up in single-parent families, and an
increasing number of lone elderly women have all been projected to affect the future
human capital potential and economic productive capacity for at least the next two
decades in Russia (Rose, 1998; Bennett, 1998).
A study of 450,000 death certificates in the city of Moscow, for the period 1993-
1995, showed that external causes and alcohol-related deaths were unequally distributed
and greater among certain population groups: the least educated, blue-collar workers,
manual laborers, and the unmarried. Increased mortality was related in the study to
causes which existed prior to 1992: an increase in alcohol abuse and malnourishment,
poverty, or the economic transition. Psychosocial stress was associated to the inability
of individuals to cope with the transition in Russia, leading to unhealthy behaviors
which increased the risk of premature mortality. Recommendations by the study were
made for more specific micro level research on mortality in Russia, which would permit
a change in the medical classification of causes of death to a classification of
psychosocial and economic determinants (Shkolnikov, 1996). However, such
explanations are left at the individual level, precluding an examination of more
fundamental social causes of premature mortality in Russia.
Social factors have been consistently associated with increases in Russia’s death
89
rates (Kennedy, et al., 1998). A drop in male life expectancy, between 1990-1994, was
found to be region specific, associated with the Robin Hood Index, an increase in the
relative income inequality of urban areas such as Moscow and St. Petersburg, increased
rate of labor turnover and crime. These factors are related to modernization,
employment insecurity, organizational and social change, mentioned above. Decreases
in life expectancy was related to death rates from accidents (1.67 years), alcohol related
causes (0.84 years), and cardiovascular disease (0.52 years) in Russia.
The relative deprivation of persons in high income areas which exhibited growth
in crime, impoverishment and stress, related to labor market changes, may explain the
greatest fraction of the fall in life expectancy among Russian males after 1992
(Walberg, 1998). Although individual lifestyle has been implicated in the deterioration
of health indicators, the apportionment of effect size between social factors and
proximal psychosocial factors, such as alcohol abuse, has not yet been made. Recently,
more detailed analyses of interregional patterns of life expectancy and cause-specific
mortality rates between the 49 oblasts of Russia were conducted. The effects of
individual lifestyle, such as level of alcohol consumption, were found to have had less
significant impact on life expectancy in comparison to sociodemographic factors, such
as abortion, marriage, and birth rates (Becker, 1998).
Reliable death certificate validation in Russia has not been routinely conducted
and access to individual level mortality and morbidity statistics is still strictly controlled
by official government agencies. It is therefore difficult to conduct independent
replication of Russian mortality research. The accuracy of recording the cause of death
in the death certificate is affected by the coding and location of death: only about 24%
90
of deaths in Russia occurred in hospitals in 1985, and over 6% were certified by
feldshers or medics (Elliott et al., 1996). In addition, coding procedures in Russia were
followed regionally, not centrally, until the mid-1990s (Shkolnikoff, 1997). Mortality
data should thus not be viewed as any more “objective” than self-reported health or
morbidity data (Table 4; Rose, 1995).
TABLE 4: Percent of self-rated health and life expectancy, by country, 1990
Country % rating health“very good”
Life expectancy(years)
Ireland 48 71.6USA 40 71.6UK 39 72.4Sweden 38 74.2Australia 36 73.2France 19 72.6Japan 9 75.9USSR 3 65.1
Although the causes of mortality differ from those of morbidity, recent
availability of non-governmental survey data has permitted more extensive
investigation into the social determinants of morbidity associated with the health crisis
in Russia, such as income inequality, social cohesion, and social capital (Bobak et al.,
1998; Rose, 1994; Rose, #304, 1998a; Rose, #303, 1998b; Bobak and Marmot, 1996;
Frijters and van Praag, 1995).
POVERTYThe general relationship between poverty and poor health has been well
documented (Klugman, 1997). Before the disintegration of the Soviet Union, and up to
1992, the greatest prevalence of poverty was among families with pensioners and single
91
heads of household (40%), and families with children (50%) (Rimashevskaya, 1997).
Between 1991 and 1994, inflation increased by over 1300%, gross industrial output and
real wages fell over 40%. In October, 1996, the highest overall poverty rates in Russia
were among children (44.5%) and the lowest among the elderly (30.6%); the latter,
however, experienced the greatest number falling into poverty between 1992 (1.2%)
and 1996 (19.0%) (Zohoori et al., 1998). Between the end of 1991 and 1992, the value
of the average pension decreased by 47% and the average household income by 39%
(McAuley, 1994). Every third Russian, about 51.7 million in the first half of 1999, was
reported by the Russian government statistics agency, Goskomstat, to live below the
poverty line, in comparison to 22% in 1998. During this same time period after the
August, 1998, financial crash, there was a 46% drop in imports and 11% drop in exports
(Kruiderink, 1999).
Income from employment in state-controlled enterprises dropped from 41.5% in
1992 to 22.9% in 1996, while cash and noncash income from home production and the
informal sector only rose from 9.4% to 22.4%. Nearly 50% of Russians, in 1993, were
participating in unofficial economic transactions to supplement their official incomes.
Earnings from official economy jobs did not meet the daily needs of over two-fifths of
Russian workers in 1992, rising to over two-thirds in 1994 (Rose, #227, 1994). Private
sector income sources increased only 7% from 1992 to 1996. About two-fifths of
working age Russians were working without being paid in 1994, which increased to
over 50% in 1996. In 1998, the Social Capital survey of Russia found that more than
three in five Russians did not routinely receive wages, pensions or economic
entitlements (Rose, #304, 1999).
92
The World Bank reported that, in 1992, 63% of poor households in Russia were
employed; the earnings of substantial numbers of employees did not rise concomitantly
with inflation and fell below the cost of the subsistence minimum (Milanovic, 1994).
The subsistence minimum was introduced in 1992 as a measure of poverty and was
based on a minimum nutritional food basket, as recommended by WHO and national
Russian agencies (Mikhalev, 1996). The subsistence minimum provided for 68% of
earnings to be allocated for food (80% among pensioners), allowing for only one-third
to one-fifth of personal earnings to be paid for medications and health services, clothes,
shoes, and other consumer goods. Persons with incomes below the subsistence
minimum were considered to be below the poverty line. In 1992-1993, the average
pension was about equal to the subsistence minimum, and average salaries, if paid at all,
in cash and not in kind, were less than three times the poverty line (McAuley, 1994).
A representative sample of Russia in 1994, estimated poverty conditions to
affect 58% of families (Zaslavskaya, 1994), and most of the poor were of working age
with low wages, not pensioners. Three-quarters of large families with three or more
children were designated as receiving earnings below the subsistence minimum in 1994;
and from 22-29% of poor households had adult dependents or disabled family members.
The Russian Longitudinal Monitoring Survey reported that older persons, living with
families, became poorer with socioeconomic changes: in September, 1992, 22.3% were
at the poverty level, in December, 1994 – 27.6% (Popkin, et al., 1996). Privatization of
the medical care system, low wages and short cash supply, inflated prices, marketisation
of housing, electricity, water, telephones and other utilities, increased the cost for the
minimum standard of living after 1992. This economic situation constrained many
93
working families, with children, older dependents, or disabled, to ration necessities like
clothes and medicines.
MORBIDITYRisk factors for poverty are also determinants of chronic conditions and
disability. Unpredictable, rapid transformations in the political economy, social stress
and anomie, depreciating social capital, impoverishment, and an institutional, policy,
and administrative vacuum characterized post-1991 Russian society and have been
implicated in poor health status. The New Russia Barometer found that severe material
deprivation and lack of life control predicted poor physical health and poor self-rated
health. (Rose, 1995; Rose, 1998). The structural factors determining mortality and the
factors determining chronic conditions and disability may be the similar, vary in
severity, social intervention, and by specific disorders. Disability may not be related to
premature mortality at all for some causes (i.e., external causes), it may be the
consequence of better disease treatment protocols, or the precursor of the normal aging
process. However, it is difficult to claim individual-level health habits as causes of
macro-level distributions of chronic conditions and disability without a multilevel
analysis which includes the distribution of social inequality.
Economic changes radically increased the inequality in the distribution of
incomes and occupations. The Gini coefficient increased by a fifth in one year from
0.27 to 0.32 at the end of 1992. This magnitude in the change of income inequality took
ten years to occur in Great Britain, between 1976-1986 (McAuley, 1994). Real per
capita income fell to 1970’s levels by 40% between 1991-1992, and the distribution of
income was less uniform, varying 11-fold between the highest and lowest deciles in
1993 (Shkolnikov, 1996). The minimum wage lost a fifth of its value by 1992, and the
94
minimum pension was worth 48.3% of the subsistence cost of living. The
impoverishment of the Russian population, after 1992, may be attributed to
organizational change such as privatization without adequate job security;
unemployment, low, and unpaid wages; increase in single-parent families; and an
increase in the number of families in which dependents exceeded wage-earners
(Rimashevskaya, 1997).
Impoverishment and economic uncertainty have been accompanied by a radical
shift in the birth/death ratio of the Russian population: increased mortality among adult
working-age males and a stunting in fertility or the desire to have children. The birth
rate influences the infant mortality rate, which together with adult mortality rates are
related to life expectancy, and the construction of generational life tables.
INCREASING INEQUALITYRussia’s health trends today also exemplify what has been termed "negative
momentum". In Spain, Western Germany, Japan, and South Korea, health improved
for several decades before their respective political crisis occurred. Rebounds in health
status were rapid after the end of the crisis period. This has not been the case for Russia,
where health conditions had been stagnating for several decades prior to the political
and socioeconomic policies of post-Soviet reforms.
At the end of Perestroika, the Russian Federation was the world’s fifth largest
nation. In the first two decades of the twenty-first century, according to United Nation
projections, Russia’s population size will drop to ninth place in the world and the GDP
to about twentieth. The political and strategic repercussions of the health crisis in Russia
may have even greater implications for future global security. Disproportionate
increases in mortality among the employed population compromise the integrity of
95
national intellectual, competitive, and creative resources. Life expectancy is strongly
associated with income and for Russia, it is a good predictor of productivity and per
capita GDP. Even with a smaller population in two decades, and an improved life
expectancy, a future Russia could attain a real GDP of only about a trillion U.S. dollars
at the current rate, assuming 2.5 percent average growth per year (World Development
Report, 1998/1999). This could derogate Russia to the sidelines of global decision-
making, after occupying the first ranks since World War II.
URBAN CONTEXTIt is important to consider the regional diversity of the Russian Federation
separately in policy and health outcome debates, given the disparity between Moscow
and other geographic areas (Table 5). Regional and urban environments vary
sufficiently to warrant separate analyses to minimize a “regression to the mean” effect
when generalizing from federal level data. Health profiles at the urban level decrease
the array of intervening individual and fundamental social factors accounting for the
inequality of health in a community (Molinari et al., 1998).
A 1992-1994 analysis of health profiles from 47 cities across Europe, which
were members of the WHO Healthy Cities Project, indicated that cities, as coherent
collective wholes, have population distributions with characteristics distinct from the
sum of its individual members. The international and community-wide implications of
the health disparity between cities has been systematically investigated by WHO with
the aim of reducing health inequalities.
To orient policy priorities to resolve health inequalities, it is important to
distinguish between identifying intervening individual risk factors (like secondary
smoke or drinking and driving) and fundamental social conditions (like the lack of
96
TABLE 5: SOCIOECONOMIC INDICATORS IN RUSSIA, MOSCOW, ST. PETERSBURG, 1990-1995RUSSIA MOSCOW ST.PETERSBURG1990 1995 1990 1995 1990 1995
N telephones/100 families 35.7 46.0 92.0 102.8 76.6 87.1N sq.m. living space/ per capita 16.4 18.1 18.0 20.0 17.7 18.8% privatized apts/all apts whichcould become privatized 0.16 36.0 0.1 41 0.01 29N private autos/1000s 58.6 92.7 70.6 153.5 56.2 119.3% employees with wages delayed,not paid in full** n/a 51 n/a 39+ n/a 37+Ave. monthly wage(1000 rbls in value of that yr)* 303 472 267.6^^ 584 n/a 443Ave. monthly wage+social payments(1000s rbls) 220.4# 531.6 325.5 726.7 212.2 506.8% with per capita income >1000rbls/mos (1000s rbls) n/a 10.5 n/a 40.1 n/a 13.9Ave. per capita income(1000s rbls/month) 215 532.9 691.1 1707.8 223.2 599.5% with total income in top quintile n/a 46.9 n/a 59.2 n/a 44.3Ave. cost of living/capita(1000s rbls/month) 1.9^ 264.1 107.8 328.5 86.9 262.2% with total income belowave. cost of living 22.4## 24.7 13.7 19.1 23.0 20.0% with per capita income 2 belowave. cost of living n/a 37.6 n/a 12.4 n/a 13.2
# data for the year 1993; ## data for 1994; ^ data initially available for the year 1992 in value of ruble forrespective year (Goskomstat Rossii, 1996). ^^ data for 1989;*excludes any social payments in 1995 (Munro, N., Regional Database\maps for web\rusregions.htm,1998);** VCIOM, Russian Nationwide Surveys, 1996, 1997 (Munro, N., 1998);+ data for the Central Region (Moscow, capital); Northwest Region (St. Petersburg, capital).
education, emergency services, or employment). Health policy which seeks to
eliminate only the intervening mechanisms or individual factors in health inequality will
not eliminate the relationship between the disease and fundamental social conditions
(Fiscella, 1997). Fundamental social causes of disease influence access to resources
such as “money, knowledge, power, prestige”, as well as interpersonal resources like
social support, social cohesion and social capital. Differential access to resources which
affect the “risk of being at risk” for health outcomes results in inequality of health status
(Link, 1995). Access to resources, as has been discussed earlier, is related to geographic
97
location.
Cities in eastern Europe and Russia have a younger population structure but a
lower life expectancy than western and northern European cities. Many more eastern
European cities reported substantially greater levels of air and water pollution, less
relative area of green space and less living space for residents than those in the west and
north. St. Petersburg, Russia, had the highest standardized mortality ratio of 14 cities in
the world. Almost two-fifths of Moscow residents complained that there were
insufficient green parks in the city in 1995, three-fifths said that there was not enough
fresh air (www.mos.ru , 1998). St. Petersburg and Minsk were the only two out of 45
cities in the world which reported not having any sport and recreational facilities for
every 1000 inhabitants. Out of 38 cities, St. Petersburg ranked fifth highest for having
single parent families; second in reporting a young median age for a first birth; and the
only city in the world to report an abortion rate over 100% of the birth rate (WHO,
Healthy Cities, 1998).
Although the health status of Moscow is mirrored in the health profile of its
sister city, St. Petersburg, Moscow has distinct characteristics: a higher income, greater
percentage of the population with higher education, lower unemployment and job
insecurity, less crime, smaller divorce rate, and the best working conditions in the
Russian Federation (Table 6).
MOSCOWA health profile of Moscow is specific to its historical and geographical context.
It is the capital of the Russian Federation and chief city in the European region of
Russia. Moscow, which celebrated its 850th anniversary in 1997, has always
98
TABLE 6: Social Indicators in Russia, Moscow, St. Petersburg, 1990-1995
RUSSIA MOSCOW ST.PETERSBURG1990 1995 1990 1995 1990 1995
Pop size (millions) 148.200 147.739 8.967^ 8.625 5.035 4.774Pop. change +2.2 -5.7 +10.2^ -8.9 +9.7^ -8.9Ave. household size 3.2 2.84# 3.1 2.74# n/a 2.79#N divorces/1000 marriages 424 619 505 565 n/a 623% high/prof educ% sec/tech educ%sec/gen educ% general primary educ
16.3^^31.3^^33.9^^15.0^^
18.433.034.312.4
33.222.027.517.3*
39.830.524.54.8
n/a 32.635.125.35.7
N crimes/100,000 1240 1860 669 1066 1144 2104N striking workers, in 1000s 99.5 489.4 0.3 0 0.1 26.5Unemployed, as % employed 8.4^^ 8.7 5.2 5.2 7.4 9.8% Unemployed With high/prof educ With sec/tech educ With sec/gen educ With general educ
13.7**27.2**37.2**19.2**
9.228.643.617.1
n/a 22.726.640.79.3
n/a 21.537.229.511.5
% workers With unhygienic cond. With heavy phys labor With unsafe cond.
32.5^^6.6^^3.3**
21.42.71.0
11.6#1.2#1.1#
11.81.50.3
n/a 17.91.10.4
(source: Goskomstat, 1996; Rossiiskii Statisticheskii Ezhegodnik; Munro, 1998).
# data for the year 1994; ^ data for 1989; pop. change calculated in comparison to1979 census (Moskva v Tzifrakh, 1990); all other pop. change given with reference toprevious year; * includes incomplete sec/gen; ^^ % officially registered unemployed in1991 when data first reported; ** data for 1992
been Russia’s seminal city, the seat of the Tsars and the bureaucratic elite, which
controlled national resources and wealth (Klugman, 1997). Moscow's unique economy,
the centralized location of Commonwealth, Federation and municipal governments,
accounts for the largest percentage of the city's revenues, and does not exemplify any
other metropolitan area in the Former Soviet Union.
The capital had a gross domestic product (GDP) more than 13% above the
Russian Federation mean and wage levels 27% above the mean in 1995 (Munro,
99
Regions web site, 1998). Comparable GDP data for Moscow in 1989-1991 were not
published during the soviet period census. It is likely that the economic status of the city
relative to other regions was not substantially different then. Moscow remains the major
cultural mecca, housing universities, museums, theatres and art institutions, as well as
almost three times the number of students in higher education the Federation.
Advantageous social conditions in the city as compared to the region or
federation, however, have not provided a cushion for residents’ health. Moscow and St.
Petersburg had the highest life expectancies in the Russian Federation in 1978-1979 but
the life expectancy of Moscow residents in 1990 was ten years below what it had been
in 1970 (State of the Environment, 1998; St. Petersburg Profile, 1995; Shkolnikov,
1996).
Goskomstat, the government statistical agency, produced several population
projections for Moscow into the next millenium. The long range prognosis was not
favorable, even with the slight decrease in mortality during 1995-1997. Goskomstat
estimated that the population of the city would fall to 7.577 million people by the year
2010. This is about one and a half million less inhabitants than in 1990, a depopulation
due to the imbalance from the birth:death ratio (Figure 11; Goskomstat, 1996a), rather
than migration. This was an alarming indicator of a compromised quality of life and
loss of productivity when, overall, more inhabitants died in Moscow than were born at
the close of Perestroika (Goskomstat, 1998).
Mortality and morbidity data were officially published in Moscow for only a
few conditions before 1990 (Vishnevsky, 1995; Goskomstat, 1996). Published
morbidity data were based on Soviet era concern with the volume and quantity of health
100
services rather than health status. Incidence of morbidity was defined by Goskomstat as
the number of persons with an initial diagnosis of illness received after a medical visit,
FIGURE 9: Number of births, deaths, and natural change in population size (per 1000persons) by district, Moscow, 1995.
classified according to the ICD-9 standard (Table 7). Officially recorded diagnoses
depended on utilization behavior, thereby underestimating the prevalence of illness in a
general community, if those in need did not seek medical care. Most studies of
morbidity have therefore been based on a limited number of surveys, which have only
been conducted on a wide scale since 1992 (Palosuo, 1995; RLMS, 1992).
101
TABLE 7: Selected causes of morbidity, Russia and the City of Moscow, 1988-1995
(initial diagnosis per 100,000 polyclinic visits)Conditions Russia Moscow
1988 1990 1995 1988 1989 1990 1990# 1997#All causes ofmorbidity
66000 65120 67880 n/a 63351 56743 122088 153900
Infectious diseasesScarlet feverDiphtheriaPertussisMeaslesViral hepatitsHIV-carriers
3420132.3*0.8*28.8*130.5*227.0*n/a
349083.60.816.912.4226.7n/a
473048.324.114.04.5167.50.72
n/a189.8#0.5#128.3#140.7#73.5#n/a
2434199.0#1.1#78.6#18.1#76.3#n/a
2233 3216148.04.462.922.484.70.20
35652223.6^^4.05^^48.4^^6.1^^97.258.5
Circulatory systemIncl.IHD+hypertension
1044
369.9*
1123
423.6
1330
391.2^
n/a 1173
476
1124
447
23829
16457
27260
n/aRespiratory system 36450 33620 29530 n/a 34297 29239 34312 60500Digestive system 2750 2720 3630 n/a 1587 1574 10006 11790Congenital anomalies 58 70 110 n/a 15 15 132 3077Traumas andpoisoning
8210 8520 8800 n/a 10229 10511 9979 n/a
Malignant Neoplasms n/a 265 279 302 355 367 2102 n/aAlcoholismNarcotic addiction
266*2.1*
1524.3
15616.9
1504.4
2303.2
1412.9
164634
n/an/a
Gonorrhea STD**
148* 259 354 n/a 211 173 203 n/a644.7
Syphilis (all forms) 9.8* 10.8 355.3 n/a 14.5 16.3 253.6 197^^Active Tuberculosis 41.2* 34.2 57.9 n/a 28.1 22.9 145.2 32.1^^
(source: Vishnevsky, 1995; Moskva v Tsifrakh, 1990; Goskomstat, 1996; Dept ofPublic health, Moscow City Council, Bureau of Medical Statistics, 21 March 1991.Mean Moscow Indicators of the Medical Dept. of Mosgorispolkom 1989-1990.Moscow, 1991).
*data for 1985; ^ data for 1994; #prevalence rates (all registered cases, including initialdiagnoses); derived from the City of Moscow Government Report about “The State ofHealth in Moscow, 1997”, Moscow, 1998; ^^cases of initial diagnosis; ** selectedSexually Transmitted Diseases (exclude gonorrhea) include chlamydia, gardnerella,urogenital herpes, uroplasmosis= 229.4 - 419.3/100,000; and trichomonas=225.4/100,000;
Cardiovascular diseases contributed the largest segment to decreasing life
expectancy in Moscow, but deaths from unexplained external and other causes
accounted for a substantial proportion of total mortality (Table 8). Of these, the
102
greatest percentage of deaths was due to the residual category of “other” and injuries of
undetermined cause, largely consisting of people found dead with head injuries where
the cause could not be specified (McKee, 1998).
TABLE 8: Cause-specific death rates in Russia, Moscow, St. Petersburg, 1989-1997
(per 100,000)RUSSIA MOSCOW ST.PETERSBURG
1990 1993 1995 1989 1993-1995*1996 1997 1990 1993Life expectancyMalesFemales
6474
5972
5872
6674
58^^72^^
n/an/a
61.373.6
65.8^74.3
62.673.6
Birth rate/1000 14.6^ 9.4 9.3 11.8 8.0^^ 7.9 7.8 10.0 7.0^^Death rate/1000 10.7^ 14.5 15. 12.4 16.9^^ 15.0 14.5 12.2^ 15.9^^Infant mortality/1000 live births 17.4 19.9 18.1 19.2 16.5 13.1 14.4 16.2^^^ 16.2All causes of death/100,000 1116.7 14446.4 1496.4 n/a 1584.4 1515.4 1444.5 1222.1 1754.8Cardiovascular/100,000 617.4 768.9 790.1 n/a 518.8** 864.4 851.3 709.1 1000.5Neoplasms/cancer100,000 194.0 206.9 202.8 n/a 256.8 260.2 258.8 268.5 288.2Accidents, trauma,poisonings/100,000 133.7 227.9 236.6 n/a 193.8 191.9 162.3 113.6 258.6Respiratory/100,000 59.3 74.5 73.9 n/a 50.2 n/a n/a 30.1 61.5Digestive/100,000 28.7 38.3 46.1 n/a n/a n/a n/a 36.4 49.2Infectious/parasitic/100,000 12.1 17.3 20.7 n/a 17.1 n/a n/a 10.1 28.3
(sources: St. Petersburg Health Profile; Goskomstat, 1996; Moscow Gorkomstat 1989-1998; Munro, 1998; Environment of Moscow, 1998; M.McKee, 1998).
^ data for 1989; ^^ data for 1995; ^^^ data for 1991; * data is a three year average, January, 1993-December, 1995, divided by three yearaverage Moscow population (8700.0 - Jan.,1994; 8625.4 - Jan.,1995; 8572.4 -Jan.,1996), mortality data for 1993-1995 was kindly shared by Prof. M. McKee, LondonSchool of Hygiene and Public Health; population size and mortality, 1989, 1996, 1997,was reported in Goskomstat Rossii, Moscow City Committee of Statistics,Administrative Raions of Moscow in 1997, 1998.**data is only available for Ischemic heart disease, which is only a subset ofcardiovascular diseases.
103
The infant mortality rate, a traditionally sensitive indicator of deprivation, was
higher in Moscow than in other regions after macro economic reforms began in 1992;
birth and death ratios were substantially greater than in the Russian Federation;
congenital anomalies at birth were greater; chronic morbidity was higher than the mean,
especially for cardiovascular diseases. More than a decade after Perestroika collapsed,
post-Soviet Moscow was the STD/HIV capital of Russia.
Moscow, as the primary urban center in the Russian Federation, clearly exhibits
the health risks of a variety of factors which accompany any megalopolis of over 8
million inhabitants. In sum, Moscow appears to have an overall health profile distinct
from the Russian Federation mean. Although the wealthiest city, it is by no means the
healthiest, especially in several urban areas (Figure 12; Goskomstat, 1996a).
FIGURE 10: Infant Mortality by urban area, Moscow, 1995
104
AREA VARIATIONBefore the Revolution of 1917, Moscow’s population was one of the fastest
growing in Europe, having increased to about 1.6 million by 1917. (Smith, 1994).
Moscow was developing factories and an indigenous nascent proletariat, as well as
migrant peasant populations searching for employment (Smith, 1989). By 1989, the
built up areas of metropolitan Moscow, including those outside the ring road, had about
13 million inhabitants.
Like London and Paris, other centers of the West which served as loci for the
rise of industrial capitalism, Moscow developed greater geographical intra-city
inequalities in living standards, social and spatial stratification at the turn of the century.
The Soviet leadership tried to eliminate the inequalities of a market distribution of
goods and services by abolishing private property. After the 1917 Revolution, the state
confiscated all housing, nationalized land, and took ownership of economic production.
Inequality and poverty in Moscow were regulated to some extent by urban planning of
housing, transportation, services, and economic infrastructure. The soviet state
developed 5 year plans for economic growth and redistribution of goods and services, a
central allocation system for such resources as housing, and central planning for
recreational, educational, and health care facilities (French, 1995).
A housing construction boom was inititated in Moscow after World War II
during the Khrushchev era. Moscow was designed as a “model communist city” with
the development in the 1950’s of the urban micro-raiyon, homogeneous and “liberated”
from problems like segregation, ethnic living quarters, concentrations of poverty and
wealth (Figure 13; Smith, 1989).
Micro-raiyons were nested within larger administrative districts. The planning of
105
the micro-raiyon included self-contained neighborhoods of about 15,000 people with
educational, cultural, health, and retail services. Within raiyons, per capita norms were
devised for services and staff, such as square meters of shopping space, educational,
health facilities, and numbers of teachers and medical personnel.
FIGURE 11: EXAMPLES OF MICRO-RAYION PLANNING FOR MOSCOW IN 1950S AND 1960S
1950s 1960s
By 1989-1991, there were 33 administrative districts for Moscow (Figure 14;
Hamilton, 1993). The contrasts of the quality of life between and within the districts
were amplified with time and a dominant mechanism of controlling perks, the allocation
of services and housing through employing institutions. Housing was also important in
the state planning of egalitarian living standards with the specification of per capita
space entitlements. Housing was a major employment benefit before 1992 and an
important measure of personal wealth with the spread of privatization and purchase of
apartments after 1992.
106
FIGURE 12: City administrative district (33), Moscow, 1989
FIGURE 13: LIVING SPACE IN SQUARE METERS PER PERSON BY DISTRICT, MOSCOW, 1989
;
107
The 1922 standard for Moscow was 9 square meters per person. This was
achieved only on the average by the 1970s after the Khrushchev constructionboom. The
implemented distribution of per capita space however in the 1980s and by 1991 was not
as egalitarian as initially planned, ranging from a high of about 20 sq.m./person to a low
of nearly 7 sq.m./person (Figure 15; Hamilton, 1993). In addition, housing was divided
into public and cooperative ownership.
Allocation and planning were the purview of governmental, industrial, and other
employing agencies of the state. Rather than ensuring equity of access to resources, the
allocation process produced substantial inequality and geographic clustering of high
quality goods and services with high social status within housing areas (Smith, 1998).
In 1989-1991, Moscow public housing, about nine-tenths of total stock, was
owned and allocated to their employees by the municipal government (75%), by local
industries (16%), and by other ministries. A significant source of social inequality
stemmed from cooperative apartments, about one-tenth of Moscow housing. Coops
were constructed for groups of people within a workplace, who had collective
ownership. A substantial cash deposit was required for coop membership, with monthly
payments over twice as large as that for public housing. Families often pooled resources
to buy cooperative apartments for their children, which could be transferred through
inheritance, unlike public housing. The economic resources necessary to make such a
purchase could only be made by the more advantaged, adding to the stratification of
housing quality (French and Hamilton, 1979; French, 1987).
Some districts developed into socially distinct areas of prestige as more or less
desirable places to live (Figure 16, French, 1995). This roughly paralleled the
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development of rich and poor, middle-class, or suburban neighborhoods in western
cities, created largely by a market economy.
FIGURE 14: SOCIAL AREAS, MOSCOW, 1960
During the transition in 1991 from a centrally planned state to a market
economy, the contrasts of lifestyle among Moscow districts were even more amplified
primarily through the privatization of housing. Muscovites themselves described where
and why they preferred living in specific city areas in a 1984 survey (Vasil’yev and
Privalova, 1984). City districts were combined into seven larger areas on the basis of a
factor analysis of census data (Figure 17; Smith, 1989). The five factors of urban
differentiation were: 1.) proximity to functional-spatial factors (roads, transport access,
shops, cultural events, etc.); 2.) development of urban construction (characteristics of
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streets, buildings, traditional buildings, historical establishments, etc.); 3.) availability
of employment establishments (concentration of factories, industries, economic activity,
etc.); 4.) transportations conduits and interconnections (convenience in transferring
between highways and city thoroughfares, subways, buses, suburban trains, etc.); 5.)
ecological quality (air pollution, distribution of forests, parks, playgrounds, recreational
areas). Areas were rated as better (1) or worse (0) on each factor. The survey examined
15,000 applicants for exchanging apartments. Relative preference ratings for living in
areas were calculated based on the ratio per 1000 families who wanted to move out of
an area and into another area. The central area I was preferred the most by apartment
hunters and scored the highest on the urban desirability factors. The central district is
also were the soviet and post-soviet elite work as part of the administrative, managerial,
political, military, academic, and cultural echelons. Northern and southern peripheral
areas VII were the least preferred and least desirable (Smith, 1989).
A Moscow longitudinal survey on housing and economic characteristics,
conducted annually by the Urban Institute between 1992 and 1995, indicated a growth
in relative income inequality between Moscow residents: the largest increase in income
(38%) occurred in the highest income group as opposed to only 8.7% in the lowest
income group. The Urban Institute survey estimated that the richest one-fifth of
Moscow residents owned about half the wealth, including most real property (Lee,
1996). The highest income groups were also most likely to exhibit the greatest mobility
in acquiring housing units after 1992, and to be least likely to experience overcrowding
(less than 10%). In contrast, nearly 50% of the lowest income households in Moscow
were overcrowded.
110
FIGURE 15: GROUPING OF MOSCOW CITY DISTRICTS BY RESIDENCE PREFERENCES ANDSOCIAL VALUATION FACTORS, 1984.
Almost 30% of municipal housing units and over 20% of state employer-owned,
departmental housing was privatized between 1992 and 1995. Three-quarters of all
privatized apartments by 1995 were owned by the non-pensioner, white-collar,
university educated , who were also in the highest income groups in Moscow (Lee,
1996). A 20% sample of permanent male residents of the 1916-1935 birth cohort from
Moscow showed that individual survival was best among the highest educated in 1990-
1994, as compared to the lowest educated, who also exhibited the poorest survival rates
(Shkolnikov, 1996).
Municipal maps of Moscow indicate the extent to which the highly educated
(Figure 18; Smith, 1989) were both younger and living in the more desirable areas of
111
FIGURE 16: Persons with higher education (%) by district, Moscow, 1989
FIGURE 17: Pensioners (%) by district, Moscow, 1989
112
Moscow (Figure 19; Smith, 1989) in1989. These social groups were already living in
distinct districts, adding to the spatial stratification of Moscow after 1989.
In 1992, Moscow was rezoned into 10 districts, combining the 33 administrative
districts into larger coterminous sectors. The reorganization makes it difficult to
compare Soviet with post-Soviet Moscow with any precision since the 10 districts do
not coincide with the original 33 districts. The Moscow districts, both before and after
rezoning, have had specific characteristics reported by the city census.
With the transfer of government property into private ownership, there was a
concomitant increase in the breach of the city sanitation code by industries and firms.
Most code violations involved food handling enterprises, garbage disposal, toilet
amenities, antiquated technology and machines, inadequate ventilation and crowded
working space and facilities. Moscow was the site of several large automobile factories,
heavy industrial plants, construction companies, chemical and oil refineries.
Automobiles and industrial production within the city were cited as primary causes of
pollution (Fomichev, www.mos.ru, 1999). Related to such employment sites, there was
an increase in the number of persons working under unhygienic, unsafe, and physically
difficult conditions, of which between 34%-40% were women. Moscow residents
ranked poor street sanitation, air and drinking water pollution as major ecological
problems in the city. Public surveillance programs somewhat reduced the
occupationally-related illnesses in Moscow between 1993 and 1997 (Government
Report, 1998).
In fact, several Mayoral annual reports have provided geographical mapping of
quality of life, cancer mortality (Figure 20; www.mos.ru ) and infectious disease
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morbidity (Figure 21), indicating that there was substantial variation not only of
education, age, and housing, but also health by urban area. In all maps of Moscow, the
Kremlin is in the very center of the inner circle, from which all other areas fan out as
from a focal point.
The uneven variation of risk factors and health status was noted by a Mayor’s
Report, pointing to the existence of specific neighborhood pockets of poor health
(www.mos.ru, 1997). Most of the industrial plants in Moscow are located in the eastern,
southeastern, and southern districts, where artesian water has been polluted the most.
These are also the districts with the greatest risk of radioactive contamination, and
necrosis of vegetation, since 1982.
Morbidity rates were influenced by the location of specific health facilities
which report utilization data as illness rates. The NW district is located near one of the
most traditionally prestigious areas in Moscow, which was a high income, high status
neighborhood before and after 1991. Many specialty hospitals were concentrated in the
NW and central areas. The central district of Moscow, adjacent to the NW district, was
also the shopping nexus, attracting many transients, foreign tourists, and migrants from
other parts of the Russian Federation. Many people came to Moscow specifically to
seek specialty health care. However, the social characteristics for areas are based on
census data by residency and were not significantly affected by transient migration.
Although district health status may have been influenced by location of medical
resources, urban areas still varied by important health risks. Although the NW district
recorded the lowest cancer mortality, it had an incidence of scarlet fever which was 1.6
times higher than other areas in 1989; epidemics of paratyphus were registered in NW
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FIGURE 18: CANCER MORTALITY (PER 100,000 PERSONS), MOSCOW, 1989
FIGURE 19: INFECTIOUS AND PARASITIC DISEASES (PER 100,000 PERSONS), MOSCOW, 1992
115
and southern district schools; drug-related HIV infections and viral hepatits B were also
recorded at above average levels in these city districts. The NE district of Moscow had
lower immunization levels among children than the city average and higher measles
incidence; viral hepatitis C infections and cancer mortality. In 1992, the SW district had
the highest overall prevalence of infectious and parasitic diseases.
CONCLUSIONIn summary, mapping of poor ecological conditions, mortality, apartment size,
housing desirability, education, age and living preferences illustrated substantial
geographic variation. The central districts were the most desirable for housing and
employment. The furthest peripheral districts were also the least desirable. Such
geographic distribution of social and environmental conditions among urban areas of
Moscow made it likely that inequality in health status of residents would also follow a
similar gradient. The inequality in the distribution of social characteristics among the
urban areas, in addition to the characteristics of individual residents, are the contextual
and compositional determinants of urban health. The following section describes the
theoretical constructs of the inequality, health and socioeconomic dimensions of the
multilevel model
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CHAPTER 6: METHODS
The multilevel model of the city health profile, as described in Chapters 1 and 2
of this report, provides the statistical methods for examining the effects of both the
individual and social contexts on health outcomes. Although this is, to the author’s
knowledge, the first application of a multilevel model to develop a health profile, it has
become an established model of choice when data have a variety of thedoretical or
statistical hierarchically nested levels. The methodology has already been extensively
described (Bryk and Raudenbush, 1992; Bryk et al., 1996; Duncan et. al., 1996; Duncan
et. al., 1998; Kreft and de Leeuw, 1998; Goldstein, 1995).
The use of multilevel models is not limited by nested data if the question of
interest is also the multiple levels of determining effects on outcome (Diez-Roux,
1998). Exploratory analysis of the effects of social context upon individuals requires the
application of the statistical and theoretical multilevel model. If an apriori multilevel
data design is not available, a grouping of individuals into conceptually relevant levels
is appropriate in existing data if theoretical parameters justify grouping data.
Correlation between individual observations in clustered or stratified data requires the
multilevel model on statistical grounds to accurately estimate the sampling variance.
Individuals within groups can be treated as random samples from a larger distribution,
both theoretically and statistically.
For increased accuracy in estimating the standard error in multilevel data, the
number of groups is more relevant in multilevel models than the size of the groups. The
sample size at the individual level is relevant only for the individual level estimates.
The relationship between group sizes and number of groups to standard error estimates
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havae been examined in several simulation studies (Snijders and Bosker, 2000; Hox,
2000; Kreft and de Leeuw, 1998). It has been demonstrated that at least 30 groups are
required for an adequate maximum likelihood estimate, which still underestimates the
confidence interval by about 9% as compared to 50 groups. The size of each group
should have at least 5 observations for the t-test/Wald-test, used in multilevel
regression, to be robust under conditions of small samples. The “30-30 rule” has been
suggested as a rule of thumb for multilevel designs: 30 groups with 30 observations per
group (Hox, 2000). For stable cross-level interactions to be detected, about 50 groups
with at least 20 observations per groups should be considered as adequate.
In terms of surveys which are often designed as clustered, stratified, or complex
multistage samples, not adjusting for the design effect produces misleading significance
tests. This can lead to finding spurious relationships, such as type I errors (erroneously
rejecting the null hypothesis). This is especially true of sample surveys in Russia, which
routinely use multistage sampling, such as the All-Russian Center for Public Opinion
(VCIOM) surveys, on which the New Russian Barometer is based (University of
Strathclyde, Glasgow – Russia project) and the Russian longitudinal Monitoring Survey
(RLMS), a joint US-Russia project.
Standard errors and the large nonresponse rates typically found in Russian
surveys are not systematically reported in the published literature. Exact sampling
information is rarely provided by sampling points or strata. Missing values are usually
never reported separately, although often response rates are as low as 50% of the
original multistage sampling frame.
Multilevel models have not been applied, for example, in the New Russian
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Barometer Surveys I – V, which have different multistage sampling frames. Survey I of
1992 was restricted to a random sampling of 34 cities and towns, with stratification of
smaller areas within urban areas, and selection of individuals within households. Survey
II was stratified into 15 Russian regions, then into urban and rural areas within region,
urban populations were stratified proportionate to population in oblasts and kray
capitals, resulting in 153 primary sampling units; within sampling units, households
were selected and individuals within households. Subsequent surveys have had
similarly complex multistage sampling structures (Rose, #256, 1995).
The Russian Longitudinal Monitoring Survey, on the other hand, was
implemented as a time-series with the assistance of the U.S. Agency for International
Development and the World Bank, in conjunction with Goskomstat of Russia,
beginning in 1992. It is a nationally representative sample of 6500 households, not
individuals, clustered within 21 primary sampling units. The RLMS involved a three-
stage sample of addresses, derived from the stratification of 2335 raiyons by 10 quality
of life indicators and percentage urbanicity. This resulted in 21 primary survey sites,
within which 10 districts were selected proportionate to size, resulting in 210 secondary
sampling units. Within the latter, household addresses were selected at random and all
members of the household were interviewed.
Both the New Barometer Survey of Russia and the Russian Longitudinal
Monitoring Survey claim to compare adequately with the last Russian 1989 census. Not
one of the reports from these major surveys, to the author’s, have published any
analyses of the design effect correction of the standard error or how the hierarchical
structure of the multistage sample might have impacted on the statistical results. This
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health profile of Moscow suggests that exploring the theoretical and statistical
application of multilevel models can be more efficient for making contributions to
social epidemiology and evidence-based health policy.
DATA COLLECTIONA random sample of Muscovites with telephones was collected, September 15-
17, 1991. Only adults 18 years and older were interviewed. The total sample size of
nearly 2000 telephone numbers (n=1991) had a completed interview rate of 81.8%
(n=1629). There was a two-stage sample selection of respondents. The first stage was a
random sample of telephone numbers proportionate to the size of the 33 Moscow
administrative districts, provided by the City of Moscow Telephone Network; the
second stage was the random selection of one respondent in each telephone household
using Kish probability tables (see Appendix). The sample is thus proportionate to the
size of the population which owns household telephones within urban districts.
All findings are restricted to the Moscow population with telephones. Personal
telephone ownership was estimated at 92 telephones per 100 Moscow families or about
4.6 telephones for every five families (Goskomstat, 1996). Only 83% of all families
lived in separate apartments in 1990, without sharing their living quarters with others.
The average family size in Moscow was 3.1 persons in 1989, which did not include an
extended family member such as a grandparent. The size of apartment households, thus,
was larger, with a greater number of adults, than family size. The number of adults
having access to telephones within apartment telephone households was also therefore
greater than 92%.
CHARACTERISTICS OF THE SAMPLE POPULATIONThere were 5.4% (n=88) of the sample who reported residing in communal
apartments. Of those in communal apartments, 11.5% were the elderly, 65 years or
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older - only 4.2% of all the elderly sampled. A higher percentage of elderly, from 9.8%
- 18.2%, have been reported in the census as living in communal apartments in 1991,
indicating that there was an undersampling of this group. This may have been affected
by a lack of telephones among the elderly generally, and in communal apartment
dwellers specifically, where telephones may have served several families.
TABLE 9: Comparison of City of Moscow census, 1989, with weighted
sample, 1991, by marital status, sex, age, and educational level.
Moscow census Moscow samplePop. 15 yrs.old N=7,213,120 (100%) N=1991* (100%)Sex Men Women Men Women
44.9 55.1 44.2 55.8Marital statusMarriedDivorcedwidowed
71.56.82.9
54.912.118.0
72.86.92.6
59.513.015.9
Age (years)**20-2930-3940-4950-5960-6970
**16.117.513.112.5 7.8 5.0
**14.016.012.113.111.910.8
19.425.018.015.113.6 8.9
18.323.917.014.814.312.0
Educational levelHigher completeHigher incompleteSecondary/technicalSecondary/generalSecondary incomplete
26.43.619.924.815.6
22.83.220.721.314.8
37.16.323.720.012.9
33.65.226.120.714.4
* 18 years; ** % of total population of all ages=8,875,579 (see Table 10; Appendix 1)
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The number of government and cooperative apartment dwellers for the sample,
however, was 85.8%, consistent with government statistics on the number of Moscow
families occupying separate apartments (Moskomstat, 1990). The weighted telephone
sample, when compared by age, sex, educational level (Table 9, Table 10), and urban
area, approximates the Moscow population distribution (see Appendix 1; Table 11).
DESIGN EFFECT
Several measures were taken to examine survey errors. Adjustment of selection
probability within telephone households with poststratification weights was used to
check the effect of sampling bias (Table 10). It is clear from the skew of the
unweighted sample in comparison with the census population that noncoverage error
was not random. A 10% quality control reinterview of the sample was conducted, which
reduced the effective interview rate to 81%. Accurate sampling probabilities could not
be determined because the Moscow Telephone Ministry did not provide any
information about the derivation of the sampling frame and selection probabilities.
Probability weighting could therefore not be applied in any estimation procedures.
Although the resultant sampling distribution is not a random sample of the general
Moscow population, it is a sample of a fixed universe, the city of Moscow. Fixed
models of inference may be applied.
In addition to sample frame noncoverage, interviewers over sampled women,
30-49 years old, as compared to the general Moscow population enumerated by the
census. Three age groups of men were under sampled. It is likely that procedures for
applying Kish random selection tables within telephone households, as well as varying
time of call and follow-up of "not at homes", were not followed correctly.
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For countries with developing research infrastructures, such as Russia in 1991, it
was important that survey design and interviewer training address data quality by
decreasing specific components of error through random allocation of workloads;
increasing training and supervision of specific interviewers; and increase training of
interviewers in the random selection of respondents. Subsequent large scale surveys,
such as the RLMS, have incorporated extensive interviewer training into their
fieldwork, although no studies have been published testing data quality to my
knowledge. Further experimental research is also needed in Russia of interviewer
attitude and ideology, as well as item interaction, with respondent and interviewer traits
on responses obtained (see Appendix 3 for details of the effect of interviewer error).
TABLE 10: PERMANENT* MOSCOW POPULATION, 1989, UNWEIGHTED AND POST-STRATIFICATION WEIGHTED** SAMPLE DISTRIBUTIONS, BY AGE AND SEX, MOSCOW, 1991
_____________________________________________________________________________________
CENSUS* UNWEIGHTED SAMPLE WEIGHTED SAMPLE**____________________________________________________________________AGE Total % M% F% Total % M% F% TOTAL % M% F%______________________________________________________________________________________MOSCOW 8875579 100 44.9 55.1 1629 100 29.9 70.1 1629 100 44.1 55.9 0-14 1662459 18.7 9.6 9.1 0 015-19 539136 6.1 3.0 3.1 71 4.4 1.8 2.6 121 7.4 3.7 3.720-24 572508 6.5 3.1 3.4 99 6.1 2.3 3.8 131 8.0 3.9 4.125-29 751852 8.5 4.1 4.4 140 8.7 3.0 5.6 173 10.6 5.1 5.530-34 775131 8.7 4.2 4.5 188 11.6 4.1 7.5 175 10.7 5.1 5.635-39 700160 7.9 3.7 4.2 215 13.3 3.7 9.6 160 9.9 4.6 5.240-44 600006 6.8 3.2 3.6 177 11.0 3.7 7.3 135 8.3 3.9 4.445-49 512091 5.8 2.7 3.1 114 7.1 2.2 4.8 116 7.1 3.3 3.850-54 610956 6.9 3.1 3.8 147 9.1 2.8 6.3 137 8.4 3.8 4.655-59 528703 6.0 2.5 3.5 99 6.1 1.8 4.3 119 7.3 3.1 4.260-64 552706 6.2 2.4 3.8 121 7.5 1.9 5.6 126 7.7 3.0 4.765-69 339707 3.8 1.1 2.7 100 6.2 1.0 5.2 76 4.7 1.4 3.370-74 267809 3.1 .8 2.3 61 3.8 .8 3.0 60 3.7 1.1 2.675-79 257178 2.9 .8 2.1 39 2.4 .2 2.2 66 4.0 1.5 2.580-84 135918 1.5 .4 1.1 34 2.1 .5 1.6 30 1.8 0.4 1.4>=85 65939 .7 .2 .5 10 .6 0 .6 4 0.3 .1 0.2_____________________________________________________________________________________
*The distribution of the permanent 1989 Moscow population of all ages (Goskomstat, 1990);
** the sampling distribution was based only on adults aged 18 years and older, the referentMoscow population for weighting was calculated from all those age 15 years(N=7213121) andwas thus more than the permanent working age Moscow population; age-sex proportions werederived by dividing the number in age-sex groups by the base referent population 15 years;
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Weights=the proportion referent population in age-sex cells divided by proportion of sampledistribution in age-sex cells; poststratification weights were used because the number of menand women in each household was available only for completed interviews (n=1629), and notfor all selected telephone numbers (n=1991).
Interviewer error varies by mode of data collection and was compounded by
several factors: a telephone sampling frame not representative of the Moscow
population, violation in the random selection of respondents within telephone
households, and inadequate supervision of field work. Interviewers, often employed as
contract piece-workers for telephone polling by local firms in Russia, were
predominantly female, college-students, who studied or worked full-time elsewhere.
Interviewers performed their jobs customarily at home without supervision, using their
own or a friend’s private telephone. In September 1991, when the Moscow Health
Profile survey was conducted, there was a dearth of centralized, monitored telephone
facilities with full-time shift workers manning individual telephones. The quality and
validity of Moscow survey information was dependent upon the subjective probity,
training, and experience of each individual interviewer.
There was scarce information on hand in Moscow about the exact distribution of
telephones, after the August Coup of 1991, a time when still no complete telephone
books were publicly available. The technology to implement Random Digit Dialing or
Computer Assisted Telephone Interviewing was neither available nor economically
feasible. Accurate and verifiable enumeration of telephones within each of 33 Moscow
administrative districts was not accessible, insofar as the government telephone network
refused to divulge details on the exact distribution of telephones.
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TABLE 11: ESTIMATED NUMBER AND PER CAPITA TELEPHONES BY SELECTEDADMINISTRATIVE DISTRICTS OF MOSCOW, JAN., 1992
____________________________________________________________ AREA MOSCOW CENSUS HOUSEHOLD TELEPHONES # Total N % POP N=2865806 percapita
____________________________________________________________5- 143528 1.6 221068 1.527- 146822 1.6 208654 1.410- 134560 1.5 134372 1.018- 218456 2.5 189352 0.929- 260501 2.6 224849 0.912- 344241 3.9 266593 0.816- 311749 3.5 237542 0.814- 320526 3.6 185661 0.613- 384643 4.3 177116 0.59- 582524 6.6 283334 0.524- 452568 5.1 204243 0.511- 655941 7.4 259062 0.425- 97437 1.1 43015 0.432- 503699 5.7 222022 0.433- 158815 1.8 47883 0.330- 161267 1.8 8923 0.1
____________________________________________________________
Table 11 (Goskomstat, 1992) illustrates, for selected Moscow administrative
districts, that any telephone survey in 1991 sampled only a portion of the population,
biasing population estimates and claims to generalizability. The urban areas closest to
the administrative nerve center of Moscow had the largest number of telephones per
capita.
TRANSLATION
A standard international approach to translation was undertaken. Translations
and back-translations were done independently by bilingual speakers into their native
languages. This minimized problems of arriving at conceptual and technical
equivalence of the instrument in Russian. Equivalence of meaning was arrived at
through a decentering and qualitative procedure with a conference to reach consensus
on the final version of the translation. Two blind back-translations were done on the
final version, compared, and revised in the consensus conference (Werner and
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Campbell, 1970). The author translated the initial instrument and discussed the nuances
of meanings in all consensus procedures.
Four major types of systematic bias occurred in this sample survey: mode effects
and frame noncoverage from the use of the telephone method of interview; unit
nonresponse within the sampling frame, resulting in an unequal probability of selecting
a respondent within telephone households; and item nonresponse error was related to
missing values, response effect, instrument effect and interviewer effect. Responses,
such as "don't know", "refuse to answer", blanks were often miscoding by Moscow
interviewers, who worked at home unsupervised. Instrument effect was also related to
the translation of an American questionnaire into the Russian language. Missing values
were corrected by regression imputation, using the STATA6 program.
SURVEY INSTRUMENTThe Moscow Health Profile questionnaire was based on items drawn directly
from existing sources: the California Alameda County Study on Health and Ways of
Living (Berkman and Breslow, 1983), and the U.S. NCHS Health Interview Survey
(Adams and Benson, 1991). The questionnaire of 128 items was grouped into four
outcome categories of health-related quality of life outcomes, and four social
determinants of HRQOL: life choices, life chances, civic community and demographic
indicators (see Figure 1).
MEASURES OF HRQOL OUTCOMESThe dependent variables measured HRQOL as life satisfaction, life happiness,
self-rated health, and the Alameda County Physical Health Profile.
Life satisfaction was measured by the question, “How often are you completely
satisfied with your life?”; the available responses were rarely or never, sometimes, and
often. The responses were dichotomized into often and sometimes/never. A second
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measure of satisfaction (jobsat) was domain specific, measured by the question, “How
satisfied are you with your present job?”; responses were dichotomized as described
above. A third measure of satisfaction (unsat) was an ordinal count of all 5 satisfaction
items. Cronbach’s alpha was .42, indicating that this scale was not a reliable measure of
satisfaction.
Life happiness was measured by the question, “All in all, how happy are you
these days?”. There were three available responses which were dichotomized: not so
happy/ pretty happy and very happy. A second measure of happiness (unhap) was an
ordinal count of all 5 happiness items. Cronbach’s alpha was .46, indicating that this
scale was not a reliable measure of happiness.
Self-rated health was measured by the standard ordinal indicator, “How would
you describe your overall general health?”. The responses were also dichotomized into
excellent/good and fair/poor.
PHYSICAL HEALTH SCALE
The physical profile consisted of specific chronic diseases, symptoms,
disabilities, functional status, and energy levels. The Physical Health Profile (Table 12;
Belloc et al., 1971) was constructed from a series of questions concerning impairments,
disability, 13 specific chronic conditions, 11 specific symptoms, and three energy
levels. These dimensions were combined into a mutually exclusive seven-point ordinal
count spectrum, based on frequency of conditions within the past 12 months: from
optimum health of having 1.) high energy; to 2.) low/medium energy levels; 3.) one or
more symptoms; 4.) one chronic condition or impairment; 5.) two or more chronic
conditions or impairments; 6.) restricting activities, type or hours of work for 6 months
or longer; and 7.) severe disability, reported as difficulty with feeding, dressing,
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mobility, or inability to work for 6 months or longer.
TABLE 12: ALAMEDA COUNTY PHYSICAL HEALTH PROFILE COMPONENTS IN 7CATEGORIES BY PERCENT WEIGHT OF CATEGORY IN OVERALL PROFILE
% inprofile
N of itemsin category
Category Components of category
7 4 disability - severe reported trouble with feeding, dressing,climbing stairs, getting outdoors, orinability to work for 6 months or longer
8 2 disability – less did not report above, but reportedchanging hours or type of work or cuttingdown on other activities for 6 months orlonger
9 15 chronic conditions did not report any disability, but reportedtwo or more impairments or chronicconditions (list of 12 chronic conditionsincluding heart trouble, asthma, hernia,and 6 impairments given) in the past 12months
19 15 did not report any disability, but reportedone chronic condition or impairment in thepast 2 months
28 11 symptomatic did not report any disability, impairmentor chronic condition but reported one ormore symptoms (list of symptomsincluding tightness in chest, pains in backgiven)
23 4 without complaints low to medium energy level – fewer than3 “high energy” answers to questions
6 4 high energy level – at least 3 “highenergy” answers
The seven categories were not weighted by the severity of loss of optimum
health. Each category was assumed to have a weight equal to all others, simplifying the
comparison between various other health measures. Although the Alameda County
studies employed a ridit scaling technique (Relative to an Identified Distribution), the
physical health profile as applied in the Moscow survey was calculated as either a
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nominal scale or as a simple ordinal count, without making any assumptions about
equal widths between categories (Belloc et al., 1971).
To investigate a possible differential effect of social determinants on the
components of physical health, four separate ordinal count variables were constructed
from the 13 items of chronic diseases (Cchronic, alpha=.65), the 11 items of acute
symptoms (Cacute, alpha=.72), and the 4 items of energy (Hienergy, Loenergy,
alpha=.62). In addition, the physical health profile was collapsed into 4 ordinal/nominal
categories: impairments/disabilities; chronic diseases; acute symptoms; and energy
levels (Table 13). Dummy variables were constructed from each category.
TABLE 13: FREQUENCIES OF MICRO MEASURES OF HRQOL BY GENDER, MOSCOW, 1991;WEIGHTED SAMPLE (PERCENT)
Total Men WomenHEALTH-RELATED QUALITY OF LIFE OUTCOME % (n=1629) % (n=717) % (n=908)Self-rated Health – how would you describeyour overall general health?ExcellentGoodFairPoor
3.144.642.88.8
4.957.731.95.5
1.533.553.911.2
Life Satisfaction – How often are youcompletely satisfied with your life?RarelySometimesOftenJob Satisfaction – How satisfied are you withyour present job?Not satisfiedSomewhat satisfiedVery satisfied
29.748.019.8
13.250.519.7
26.251.422.5
14.058.327.6
34.647.517.9
13.656.929.4
Life Happiness - All in all, how happy are youthese days?
Not so happypretty happyvery happy
30.359.0 7.8
28.364.4 7.3
32.858.6 8.6
Alameda Physical Health ProfileDisabilityFunctional limitations2 Chronic conditions or impairments1 chronic condition, no impairments1 acute symptomslow energyhigh energy
5.1 6.733.824.118.7 9.9 1.8
3.9 5.321.126.221.518.7 3.3
5.5 7.339.223.217.5 6.1 1.1
(percentages do not sum to 100% due to missing values)
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MEASURES OF HRQOL DETERMINANTSMICRO INDICATORS
LIFE CHOICE
Life choices were defined as patterned ways of living, including a set of
personal health practices: smoking, physical activities, alcohol intake, seeking medical
care privately (Table 14). Body mass index (BMI) was included as a choice variable,
due to the body weight component, although height has been argued to be a direct
measure of average “net nutritional status of the members of a human population during
their childhood and adolescence”, and an indirect measure of childhood welfare which
has been shown to influence mortality (Blaxter, 1990). Height was included in the
model without the BMI index. All health practice variables were dichotomized into
none or any. In addition, two control variables were investigated in the model: number
of illness days in the past month (daysill) and number of work days lost due to illness in
the past month (dayoffjob).
LIFE CHANCES
Life chances were defined at the micro level as education and occupation (Table
14). Dummy variables were created from all educational categories: primary,
incomplete secondary, general secondary, technical secondary, incomplete higher, and
higher education.
Occupation was measured by 16 response categories, specific to the division of
labor categories in Russia, and used extensively by the sociologists at Moscow
University. The responses were collapsed into 5 dummy variable categories:
▪ professional/engineers (research worker, college teacher, industrial manger,
engineer, service professional-physician, lawyers; creative professional-writer,
artist, journalist);
130
▪ white collar/service (government worker, social worker, trade and retail
worker, coop or joint-venture worker, service worker-registered nurse,
accountant, laboratory technician);
▪ manual (workers, army);
▪ not employed (students, college students, housework, temporary
unemployment);
▪ pensioners
Interactions between the 6 education and 5 occupational categories were
constructed with dummy variables.
In addition, two mobility variables were included in the model: number of times
a respondent changed jobs in the past 10 years (Nwork) and number of times a
respondent changed place of residence in the past 5 years (Naddress).
131
TABLE 14: FREQUENCIES OF MICRO MEASURES OF LIFE CHOICES AND LIFE CHANCES BYGENDER, MOSCOW, 1991; WEIGHTED SAMPLE (PERCENT)
Total Men Women(n=1629) (n=717) (n=908)
LIFE CHOICES % % %Body Mass Index (BMI=WT in kg./HT*2 in m.)
underweight =<18.6average weight= 18.6-25.0overweight =>25.1-30.0obese =>30.1
2.345.138.114.6
1.251.241.3 6.3
2.742.436.718.2
Smoking* – If you smoke regularly, how much doyou smoke in one day?
Any cigarettes 28.4 28.9 28.2Physical fitness – How often do you do any ofthese things: exercises,sports, work on dacha?
N activities – almost everyday/sometimes:
0123
35.436.717.310.6
30.733.320.515.5
37.538.215.8 8.5
Alcoholn* - How many drinks of alcohol did youhave in the past month (in gms.)?
0.25 liter.50 liter
.50-1.0 liter>1 liter
43.931.911.2 5.7 7.3
40.234.6 9.8 6.7 8.7
45.530.811.8 5.3 6.7
Alcoholx* - How many times did you drink wine,beer, or liquor in the past month?
01/week2/week3/week
44.045.4 5.9 4.7
39.648.2 6.3 6.0
45.944.2 5.7 4.1
Pay MD – If you could see any doctor you choseto see, would you pay todo so if necessary?
Yes 83.6 87.9 81.8Free MD – Do you think the government shouldprovide all medical servicefree of charge?
Yes 72.1 66.6 74.5LIFE CHANCESEducational level
PrimaryIncomplete secondary
General secondaryTechnical secondary
Incomplete higherHigher
4.0 8.619.922.4 7.836.9
2.2 7.118.318.1 8.945.3
4.8 9.720.726.1 5.233.6
Occupational levelProfessional/engineer
White collar/service workersManual workers
PensionersNot employed/Students/Housework
MobilityNaddress – During the last 5 years, how manyaddresses have you lived at?
1>1
Nwork – During the last 10 years, how manytimes did you change your place of work?
1>1
34.620.613.420.311.1
68.831.2
56.943.1
38.814.226.011.6 9.4
63.636.4
46.953.1
32.823.4 7.924.011.9
71.128.9
61.238.8
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SOCIAL COHESION
The Alameda County instrument also included measures on social cohesion.
There were 7 questions which addressed various aspects termed social anomie. Anomie,
discussed in Chapter 2, as social deregulation and loss of integration was argued by
Durkheim to be a state of social disequilibrium and disorder, the disintegration of social
normative controls over individual conduct. The perceived social cohesion scale
(Canomie, alpha=.71) is an ordinal count of the 7 anomie items assessing perceptions
about social disorder, nostalgia, lack of faith, hopelessness, uncertainty, and confusion
about social rules. The scale (Table 15) was examined as two dimensions: a count of
normative items (Cnorm, alpha=.60) and a count of meaning items (Cmean,
alpha=.55). Because of the lower reliabilities of the separate dimensions, the complete
scale was used.
A distress variable was included as a covariate of anomie. The distress scale was
an ordinal count composed from two dimensions: mood (Cmood, alpha=.66), a count of
7 items relating to depressed feelings; and malaise (Cmalaise, alpha=.81), a count of 9
items relating to depressed physical activity. Although anomie and mood/malaise were
significantly correlated at r=.2840/.2359, this was not substantial. The intercorrelation
between mood and malaise was r=.6204, suggesting that the two components should be
combined into one distress scale when entered into a regression model.
SOCIAL SUPPORT
Social support (Cnegspo, alpha=.93) was measured by an ordinal count of 7
items concerned with the quality of the marital and family relationship (Table 15). A
second measure was the number of divorces, which was dichotomized into none and
any.
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TABLE 15: Distribution (percent) of individual measures of civic community by gender,Moscow, 1991; weighted sample__________________________________________________________________________________________
total men women%(n=1629) %(n=717) %(n=908
CIVIC COMMUNITY________________________________________________________________________SOCIAL COHESIONSocial Anomie - meaningfulness
“old kind of friendship that lasted for a lifetime are lacking today”71.8 64.9 74.8
“things our parents stood for are going to ruin before our eyes”86.2 80.1 88.9
“everything so uncertain these days, anything could happen”91.4 85.4 94.1
“today most people really don't believe in anything”80.2 69.3 84.9
Social Anomie - norms“in a state of disorder, don’t know where you stand from one day to the next”
67.5 59.8 70.8“better off in the old days when everyone knew how he was expected to act”
60.4 50.1 64.9“changes so quickly now, trouble deciding which are right rules to follow”
61.3 48.0 67.1SOCIAL SUPPORTScale of marital quality-6 items relating to emotional support, affection,understanding, expectations in relationship; number of negative responses:
0 25.8 22.4 27.41 4.6 6.1 4.02 19.7 23.6 18.03 20.2 22.6 19.24 30.0 25.4 31.5
Number of divorces0 74.7 75.2 4.51 22.3 22.0 22.42 3.0 2.8 3.0
SOCIAL CAPITALInformal networksNumber of close friends (people that you feel at ease with, can talk to about privatematters, and can call on for help)?none 3.4 12.0 12.7Number of relatives that you feel close to?none 8.9 10.8 7.3
Formal networksGo to religious services (church, synagogue, muslim)
46.2 29.1 52.8Member of any group1-labor union 69.0 9.9 31.12-commercial or trade group 3.4 8.2 1.43-professional association? 3.2 6.7 1.74.-group concerned with children? 2.4 2.9 2.25.-social or recreational group? 2.3 3.4 1.8
______________________________________________________________________
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SOCIAL CAPITAL
There were two dimensions of social capital: informal networks – measured by
the number of close friends (npal), number of close relatives (nfam), frequency of
contact; formal networks – measured by group membership in religious organizations
(religrp), labor unions (union), commercial or trade groups (protrade), social or
recreational groups, groups concerned with children (kids). The informal networks were
frequencies. The contact variable was dropped from the model due to multicollinearity.
The formal network measures were dichotomized into none or any. Social and
children’s groups were combined into one variable.
MACRO INDICATORS
There are two main types of macro variables which can be used in multilevel models:
global and aggregate. Global variables are contextual and measured from group level
characteristics; aggregate variables are averages of the individual observations within the
group level, and measure the mean characteristic. Several variables were not available as
contextual measures and aggregate variables were created from the sample for mean area
alcohol consumption (Xetohn) and mean area anomie levels (Xanomie).
The social composition of the areas was defined by the census simple proportion of
white collar workers, blue collar workers, educational level, family sizes, or apartment sizes.
Relative ratios were constructed of high to low characteristics within each area. In and out-
migration by social characteristic was not available for urban areas to examine direct selection
of specific persons in urban areas. However, a demographic profile of relative ratios of
persons living in high access versus low access areas indicated that some form of selection
operated to make it more likely that persons with certain social characteristics lived in
135
particular areas. All global variables were derived from the 1989 census for each
administrative area, standardized by population size of area.
AVERAGE INEQUALITY
Two factors were extracted of existing access to resources and the development
of new resources in urban areas (Table 16). The two factors had an inverse relationship
and varied between the central areas with existing access to resources and little
development and the peripheral areas with less access but greater new development in
areas (Figure 22).
FIGURE 20: Standardized factor scores of access to social resources in urbanareas by new development in urban areas, Moscow, September, 1991
(c=central areas surrounding Kremlin; p=peripheral areas surrounding central areas; seeFigure 15 for map)
Access by New Development of social resources in urban areas
std.
sco
re f
or a
cces
s
std. score for new development-1.03 0.00 1.14
-0.58
0.00
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136
The variables included area specific distributions of educational establishments,
industries, restaurants and cafeterias, retail trade shops; construction of new housing,
moving into housing of better quality; higher, secondary, elementary and preschool
facilities; cultural and recreational facilities; polyclinics, hospitals and physicians
(Table 16). It is implicit in the new development factor that there were inadequate
resources existing in the peripheral areas, leaving room for additional, new
construction. Two mean factor scores were derived for each of the 33 administrative
districts of Moscow and linked to the respondent’s district of residence.
TABLE 16: Average inequality indicators (standardized variables/1000 areapopulation); rotated factor loadings (varimax) by 33 admin. districts, Moscow, 1989
______________________________________________________________________ACCESS NEW DEVELOPMENTTO RESOURCES OF RESOURCES
--------------------------------------------------------- N govt cafe | 0.95756 N public cafe | 0.86182 N edhigh students | 0.73689 N social clubs | 0.69193 N libraries | 0.95965 N hosp beds | 0.70667 N doctors | 0.93737 N clinic visits | 0.91473 N new housing moves | 0.85689 N food stores | 0.85029 N nonfood stores | 0.81610 N edgen students | 0.70319 N preschool students| 0.81809 N new trade built | 0.75509 N new houses built | 0.73744 ______________________________________________________________________________________ eigenvalues 8.64 2.51
The 3 global macro dimensions of inequality in life chances are described in Table
17. Average and relative inequality were distinguished from area composition. The social
context of individual HRQOL was indicated by the administrative district of residence in
Moscow. Life chances are affected by individual and neighborhood social status. Urban
residential area has been used as an indirect component or gradient of social class differences
(Blaxter, 1990). Access to resources within each of the 33 administrative areas was measured
137
by standardized factor scores, obtained from 15 urban area level indicators from the 1989
City of Moscow census. Two factors of average inequality were obtained.
TABLE 17: Description of macro measures of life chances, by 33 administrative areas,Moscow census, 1989
________________________________________________________________________________________
AVERAGE INEQUALITY:
RISK FOR POVERTYFamily size - ratio of % families 5 members in urban areas to all families
living in urban areaDivorce - % divorced/1000 population in area
INEQUALITY IN URBAN DISTRIBUTION OF MATERIAL RESOURCES/1000 persons in area
N ed high - number of students in higher educational institutions N ed gen - number of students in general basic education, grades 1-10 N food - square meter space in food trade stores N nonfood - square meter space in nonfood trade stores N govt cafes - number of places in government cafeterias/ restaurants N public cafes - number of places in public cafeterias/ restaurants N hospital - number of hospital beds/10000 persons in each district N clinic visit - number of ambulatory polyclinic visits/shift/10000 persons N doctors - number of medical physicians all specialties N clubs - number of cultural clubs N libraries - number of public libraries N new moves - number of residence changes for better housing N new trade - square meter space of trade stores newly constructed N new houses - square meter space of residential houses newly constructed N preschools - number of students in kindergartens and nursery schools
RELATIVE INEQUALITY:
INEQUALITY IN URBAN AREA SOCIAL STATUS COMPOSITIONBlue collar – ratio of %blue-collar to %white-collar workers living in areaApt size–ratio of below mean(<10 sq.m./person)apts. to below the mean(10
sq.m./ person) apts. in area Education level – ratio of %loed to %hied residents in area (secondary general, incomplete
secondary, and lower vs. higher, incomplete higher, and secondary technical)______________________________________________________________________
RELATIVE INEQUALITY
Relative inequality was defined as the relative distribution of the composition of
138
social status in urban areas, which affected quality of life, and which was not under
personal control of individuals. Relative inequality was measured by three rate ratios.
The importance of housing in Soviet and post-Soviet Moscow, described in chapter 2,
was related to social status and the distribution of income as a non-monetary perk by
employing government agencies or factories. Areas in which larger apartments
predominated were higher social status areas. Education has been shown in numerous
comparative research studies to be related to health status and mortality (Kunst and
Mackenbach, 1994a; Kunst and Mackenbach, 1994b). Although basic education was
universally available in Russia, higher education was accessible through competition or
the perk system. Chapter 5 demonstrated how areas varied by the proportion of highly
educated to lower educated residents, and other social status measures such as blue or
white collar occupations (Figures 15, 16, 17).
Finally, social class has often been measured by occupational class, and
operationalized in available international classification systems as manual versus non-
manual categories (Kunst, et al., 1998). A survey of 11 European countries found that in
all countries men in manual classes had higher mortality rates than in non-manual
classes, across all age groups. This socioeconomic distinction was available from the
city of Moscow census by area. The proportion of blue-collar or manual workers to the
proportion of white-collar or non-manual workers, who resided in an area, was a third
measure of area social status. Relative inequality was thus measured by three basic
dimensions of social status: prestige in occupational position, educational skills and
level, and access to housing or material goods. The ratio indicators measured the
relative distribution of social status between areas. The effect of the distribution of
139
inequality in the social status of areas was included in the hierarchical linear regression
on individual physical health status.
MODEL ASSUMPTIONSThe use of ordinary least squares (OLS)models requires that basic assumptions
of OLS are met: linearity, normality of residuals, independent and uncorrelated
distribution of residuals, constant variance of residuals, model specification. If the
observations are not sampled independently from each other, the variances are not
homogeneous, and the residual errors are not independent, the significance tests derived
from the gaussian distribution will be inaccurate. If individual observations are
correlated due to the effect of social context within urban areas, the assumptions of the
OLS model are not met. The multilevel model includes a complex error term which
accounts for the dependent observations between micro-level individuals and contexts
or sampling units from various macro levels. This permits examining several macro-
micro relationships not possible with the basic OLS model.
To examine whether the OLS assumptions were violated by the data, the
distribution of all the variables was inspected using STATA6 software. Several
continuous variables were normalized with log (access and new development factors;
malaise and mood) and square root transformations (npal, nfam, age, daysill). The
ordinal dependent variable with 7 categories, Physical Health Profile (Physindx), was
nearly normally distributed and a boxcox normality transformation was not significant.
An ANCOVA model was estimated for Physical health. Interactions between education
and occupation, gender and age, gender and education, gender and health practices, age
and occupation were included. The full model was significant at the p<.0000 level and
had an adjusted R2 of .39. The factor score for lack of resources in urban areas, new
140
development, was significant, indicating that physical health varied by area of
residence.
In order to proceed with model diagnostics, a regression was estimated. The
STATA command, ovtest, examines model fit by testing whether any higher order
effects exist among the predictors which may have been omitted from the model. The
test was not significant and the null hypothesis that the model had no omitted variables
could not be rejected. Multicollinearity was tested with the variance inflated factor (vif).
Age was found to be strongly correlated. After deleting the untransformed age variable
from the model, as well as all the interaction terms, all vif values were below ten and
acceptable, indicating that the fitted model was not collinear.
The studentized residuals had a skewness of .203, and a kurtosis of 3.38,
indicating that the distribution was nearly normal, which was illustrated by a kernel
density plot of the distribution of residuals as compared to the normal curve (see
Appendix Residual Analysis). A normal probability plot indicated a straight line
without noticeable deviation from normality. Partial regression plots of continuous
variables in the model did not indicate any great departure from linearity.
The Cook-Weisberg test for heteroscedasticity using fitted values of the Physical
Health Profile was highly significant. A linktest for adequately specifying the dependent
variable and predictor model was alsosignificant. A graph of physical health with the
access and new development factors indicated that there were three areas (pE, pSW, and
pNW) which were significant outliers. An examination of residuals by fitted values also
indicated that the largest residuals were located in the same three areas, while the
greatest leverage was also exerted by observations in two of the same areas (pE, pNW,
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cW, cN2). Partial regression plots of the two area factors indicate substantial
heteroscedasticity, although the regression line is almost straight about zero. A Cooks
D statistic indicated that it was not individual observations but all those in area pE and
pNW which were significant outliers with substantial leverage. Figure 22 illustrates that
these two areas have the greatest average inequality scores: low access to material
resources and lack of resources in new development. Ordinarily, one might consider
deleting the outliers and reestimating the model. Because of the conceptual importance
of the urban areas, this indicates that a multilevel model is warranted.
The basic assumptions of the OLS model were met for the “continuous”
physical health profile, with the exception of constant variance and dependent variable
specification (see Appendices). A linktest indicated that the Poisson distribution was not
appropriate but that an ordered logit distribution of physical health (Physindx) was a
better model of the data.
LOGISTIC REGRESSIONSIndividual level stepwise ordered logistic regressions were conducted for all
health-related quality of life outcome variables by using the ordinal count variables in a
full model, removing nonsignificant variables at the (p.< .10) level to reduce the model.
The full model included all dummy variables, two-level and three-level interaction
terms, covariates, and main effects. Linktests were done after each regression to check
the model specification for the dependent variable. The reduced models were on the
whole more accurate (the predictors were significant and the hatsquared were not) than
the full models (often both predictors and hatsquared were not significant).
The individual level logistic regressions were run with STATA6 software,
(STATA, 1998). The appropriateness of the proportional odds assumptions of the
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STATA6 ordered logit regressions were tested with the –omodel- command, which
examines whether the OR is constant between ordinal categories of the dependent
variable. If the proportional odds assumptions are not met, a multinomial logit model or
ordered logit without the proportional odds assumption (gologit) are more appropriate.
Self-rated health was examined as an ordinal variable, but was rejected by the omodel
test. The distribution of self-rated health indicated that it is best dichotomized, given the
small numbers for excellent health and poor health. Life happiness, life satisfaction, and
job satisfaction were all indicated by questions which could be dichotomized.
The Alameda Physical Health Profile was examined as an ordinal variable, as
were the composite dimensions of the profile. Because of the complexity of the profile
construction, the ordinal assumptions were met for the composite dimensions but not
for the profile as a whole. The ordered logit model for the physical profile was not
rejected by the linktest, but a constant odds ratio between ordinal levels could not be
assumed. However, the separate ordinal count variables of Impairment/Disability,
Chronic Conditions, Acute Symptoms, Low Energy and High Energy, which comprised
the physical profile, were not rejected by the linktest for the ordered logit model, or by
the omodel test for meeting the proportional odds assumptions. The physical profile and
its subcomponents were therefore examined with an ordered logit model.
Basic stepwise logistic and ologit regressions were performed for all outcome
variables. Relationships between all health profile dimensions were analyzed in terms of
odds ratios (OR) and percent change in the odds ratio for one unit change in the
predictor variables. Models are reported for each outcome variable, including the
separate dimensions of physical health. Models are also reported separately for low
143
access and high new development urban areas. Only significant odds ratios are reported.
Each HRQOL model was associated with different sets of significant variables. A
multivariate regression was explored with the count variables of happiness and
satisfaction, but the results were not interpretable. This was due to the low reliability of
the scales constructed from several questions, as discussed earlier, and the rejection of
the ordinal distribution, supporting the logistic modeling of these QOL dimensions.
HIERARCHICAL LINEAR REGRESSIONPhysical health was examined in a multilevel model, including age, sex,
education, health habits, anomie and civic community variables at the individual level,
and the macro dimensions of poverty risks, average and relative inequality at the urban
level. A separate multilevel file of macro and micro dimensions was created with HLM
software (Bryk et al., 1996) to separate out the fixed and random variations at the
individual and urban area levels in a hierarchical linear regression.
The multilevel propositions explored by the hierarchical linear regression were
whether living in areas which were characterized by a greater prevalence of poverty and
inequality effects on individual HRQOL outcomes, independently of personal
demographic and psychosocial factors, including indicators of a civic community. The
forms of the proposition were schematized in Figure 3. The contextual variables
included for each of the 33 administrative districts of Moscow were described in Table
17 (family size; access to resources; ratio of lower educated residents to higher educated
residents in areas, ratio of blue collar men to white collar men among area residents,
and ratio of above the mean to below the mean apartment size within areas).
Although individual areas were not homogeneous, a strong correlation between
inequality and poverty risks differentiated between urban areas. Historically high status,
144
high access urban areas were geographically centrally located and lower status areas,
lacking resources were growing in the periphery of Moscow (see Chapter 2/3 maps).
The main multilevel outcome variable was the Alameda Physical Health Profile,
a numeric count scale. As described earlier, the physical health measure was close to
normal, only slightly kurtotic due to underlying ordinal scoring. The hierarchical linear
model is robust enough to handle such a mild kurtosis and because the Boxcox lambda
transformation for normality was not significant, the dependent variable was not
transformed. In the interests of model parsimony and interpretability, only significant
variables were selected based on the stepwise ordinal logistic regressions: age, sex,
physical fitness, social cohesion, social support, and components of social capital.
Interactions were included to investigate possible cross-level interactions between
individual and urban area characteristics.
The initial multilevel equation for the Alameda Physical Health Profile as
outcome was: Yij was: Yij = 00 + p0Xpij + 0qZqj + pqZqjXpij + u1jXpij + u0j + eij
where:
p is the number of explanatory variables X at level L1 (individuals),
q is the number of explanatory variables Z at level L2 (urban areas), and
ij is individual level L1 observation i in level L2 (urban areas) j ;
combining terms produces the following general hierarchical linear equation which
separates the fixed and random elements:
Yij =[ 00 + p0Xpij + 0qZqj + pqZqjXpij ]+[ u1jXpij + u0j + eij ]
Fixed part of equation - Random part of equation - invariate between areas residual variance between areas after fixed variables controlledand where:
145
Zqj is the cross-level interaction = value of Y-X slope at level L1
(individuals) with Z at level L2 (urban areas);
eij is the between individuals, random residual, mutually independent,
mean=0, homoscedastic, normally distributed, constant across macro units, random
effect = unexplained variability of dependent variable at micro level;
u0j is a between macro unit random residual, mutually independent,
mean=0, homoscedastic, normally distributed, random effect of intercept = unexplained
(by micro level intercept) variability of dependent variable at macro level;
u1jXpij is the random interaction between macro unit and X; u1j is a
between macro unit and micro unit random residual, independent from the individual
level residuals but correlated to the macro level residuals, random effect of slopes =
unexplained (by micro level slopes) variability of dependent variable at macro level.
The basic difference between the ordinary least squares regression model (OLS)
and the hierarchical linear model is the complex random residual term, [ u1jXpij + u0j +
eij ]. The contextual effects or unexplained variance of the outcome due to macro units
as estimated by the random residuals, u0j and u1j , are assumed to be independent
between macro units but correlated within macro units; independent of the micro level
residuals; with population mean = 0, a multivariate normal distribution, and constant
covariance (Snidjers, 2000; Bryk et al., 1996;.Kreft and De Leeuw, 1998).
Whereas an OLS multiple regression would have to produce a separate
regression for each macro unit to adequately represent the variability of the micro units
within each macro unit, and a separate regression only at the macro level to estimate
146
variance between macro units, the hierarchical linear regression can summarize this
variability in the complex residual term.
The fixed portion of the general hierarchical linear equation relates to the micro
relationships and does not vary randomly between macro units; it is the modeled
variance or explained variability in the outcome due to the micro factors and non-
randomly varying macro factors included in the equation.
The proportion of variance which is accounted for by the urban area level in the
multilevel equation may be estimated by the intraclass correlation coefficient, I ,
derived from a random effects analysis of variance (ANOVA) model: Yij = + Uj + Rij
The total variance of Yij is the sum of the between-group variance and the
within-group variance. The intraclass correlation is defined as the proportion of the
population variance between-macro units from the total variance: I = 2 / (2 + 2 ) ;
this correlation coefficient denotes the correlation between two randomly selected micro
units within the same randomly selected macro unit or the proportion of total variance
due to variability between macro units. If the macro units do not vary, as in a simple
random sample where 2 (population between-group variance) = 0, the intraclass
correlation is equal to 0, which indicates that there is no macro effect on micro unit
variance. The residual variance which is attributable to the micro level is estimated by
2 / (2 + 2 ).
The intraclass correlation, estimated in a null model without predictor variables,
provides an indication of macro unit differences and the necessity for using a multilevel
rather than a unilevel model. The intraclass correlation coefficient was calculated with
the Stata6 loneway command: a random effects way of variance (Table 18).
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_______________________________________________________________________
TABLE 18: ANOVA FOR PHYSICAL HEALTH
One Way Analysis of Variance for Nphyslow: Nphyslow=poor physical health
Number of obs = 1629 R-squared = 0.0274
Source SS df MS F Prob > F----------------------------------------------------------------------------Between moscow areas 7889.32 32 246.54 1.41 0.0664Within moscow areas 279985.82 1596 175.43----------------------------------------------------------------------------Total 287875.14 1628 176.82748
Intraclass Asy. correlation S.E. [95% Conf. Interval]
------------------------------------------------ 0.00822 0.00723 0.00000 0.02239
Estimated SD of moscow effect 1.206172 Estimated SD within moscow 13.24499 Est. reliability of a moscow mean .2884365
________________________________________________________________________________________
Although the greatest variation in physical health may be accounted for at the
individual level, the small ICC is still a significant indication of a social context effect
on physical health due to the urban area of residence (Snijders and Boskers, 1999; Kreft
and De Leeuw, 1998).
SUMMARYThe health profile of Moscow was estimated in several stages. First the
individual level logistic model was used to describe health status in the Moscow
population with STATA6 software. Secondly, a multilevel regression was used to
describe the effect of the social context of inequality on individual physical health.
Initially, a null model was estimated to obtain the intraclass correlation coefficient with
HLM5 software for the HLM modeling, followed by a random intercept model to test
for the significance of covariates and their random or fixed variance; third, each
individual micro level variable was examined separately for significant random
variation between urban areas, controlling for gender, age, and education as covariates.
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Nonsignificant variables were dropped from the model at each stage. Fourth, the macro
urban area variables were added to the model and finally, cross-level interactions
between micro and macro variables were fitted.
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CHAPTER 7: RESULTS OF AN INDIVIDUAL MODEL OF HEALTH
Self-rated health and the physical health profile were modeled with stepwise
ordered logit regression (p<.10 to retain). Life satisfaction , job satisfaction, life
happiness, and self-rated health were modeled with stepwise logistic regression (p<.10
to retain). Interactions with gender, education, occupation, and civil community
dimensions were included with all main effects and covariates. In addition, interactions
between gender, educational levels and life choices were entered. Only significant
variables are reported.
A Pearson 2 for model goodness-of-fit was performed for each estimated model
using STATA6 lfit for logistic models and linktest for all dependent variable
specification. A residual analysis was performed for the physical health profile
(Appendix 2 and Appendix 3), which indicated that an ordinal model of physical
health could be more appropriate but not essential; that residuals were distributed
normally, and heterogeneity of variance was present due to urban area.
Predictive power of each logistic model was checked with a lroc graph, which
tests the number of predicted successes as a graph of specificity against 1-sensitivity
(Stata6, vol.2, 1999). Approximate likelihood-ratio test of proportionality of odds across
response categories was performed for ordinal outcomes: if Ho was rejected; the logit
coefficient b was not constant across categories, and a general ordered logit model was
more appropriate.
All dependent variables were coded to have a negative direction in outcome:
poor self-rated health, unhappiness, dissatisfaction. Ordinal variables were also counted
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for negative outcome: the physical health profile changed from high energy levels to
impairments; increasing counts of chronic, acute and low energy levels. Self-rated
health changed from excellent, good, fair, to poor. The only positively coded outcome
was the increasing count of high energy items. All dummy variables were coded 0 for
absence and 1 for the presence of the factor. Due to small numbers in either tail of self-
rated health, the ordinal variable was dichotomized into poor/fair and good/excellent
health.
Spearman pairwise correlations between all dependent variables indicated only
slight association, except for two components of the physical health profile, chronic and
acute conditions, which were moderately intercorrelated (Table 19). The five HRQOL
outcomes had only a trivial, although significant, amount of covariation, and could
therefore be included as covariates in the regression models.
TABLE 19: SPEARMAN CORRELATION OF HRQOL OUTCOMES
PoorPhysicalHealthProfile
Numberchronicconditions
Numberacutesymptoms
NumberlowEnergyitems
NumberhighEnergyitems
Poorselfratedhealth
LifeNotsatisfied
JobNotsatisfied
LifeNothappy
Poor Phys.Health
1.00 .7954 .6106 .3772 -.3942 .4845 .1413 -.0632 .1437
N chronicconditions
(.0000) 1.00 .6353 .3778 -.3931 .4755 .1616 -.0871 .1508
N acutesymptoms
(.0000) (.0000) 1.00 .3923 -.4026 .5070 .1555 -.0195 .1211
N lowEnergy
(.0000) (.0000) (.0000) 1.00 -.9695 .4075 .1198 .0524 .1202
N highEnergy
(.0000) (.0000) (.0000) (.0000) 1.00 -.4144 -.1301 -.0467 -.1283
Poor ratedhealth
(.0000) (.0000) (.0000) (.0000) (.0000) 1.00 .1817 -.0171 .1674
Life notsatisfied
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000) 1.00 .0447 .3190
Job notsatisfied
(.0108) (.0004) (.4308) (.0343) (.0598) (.4901) (.0447) 1.00 .1591
Life nothappy
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000) (.0000) (.0000) 1.00
(n=1629; pairwise correlations above diagonal, significance in parentheses below diagonal)
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This indicated that the outcomes should be considered independent and could
illustrate the differential effect patterns of average and relative inequality, psychosocial
factors, and material conditions.
Health-related quality of life, in contrast to disease-specific mortality and
morbidity rates, includes the general parameters of death, disease, disability,
discomfort, dissatisfaction (Elinson, 1980). The latter three dimensions represent the
hidden portion of the iceberg phenomena of illness, addressed by self-reported health
measures, which are independent of an exclusively clinical, functional, or professional
evaluation. Prior research has pointed out that “one disorder is not a proxy for all
disorders”, prompting the examination of several outcomes in this study (Thoits, 1995).
It is clear from comparing outcomes that the same set of factors does not affect either
psychological, social, or physical health to a similar extent.
This study demonstrates that including several dimensions of HRQOL better
isolates the set of determining factors which might be common for multiple outcomes.
The effect of different types of inequality on life happiness, satisfaction, self-rated and
physical health may be mediated differently by psychosocial determinants: life chances,
health choices, or the prevalence of a civic community .
It is often difficult to obtain conceptually and statistically intelligible models
given complex social relations. For example, the covariation of education, income, and
occupation are persistently vexing problems in specifying the effect of social status or
hierarchical relations on health. Studies which argue for the predominant effect of a
single determinant, such as income inequality, rather than joint effects on one outcome
to the exclusion of others, often suffer from misattribution by commingling variation
152
from absent or proxy factors. The macro effects of cultural, geographic and historical
factors further compound model complexity in social epidemiology.
For example, in addition to its face value, job satisfaction may be considered a
proxy variable for life control or for the contextual effect of the workplace, acting as an
outcome or determinant in specific models (Figure 23), with different conceptual
interpretations.
FIGURE 21: PROBABILITY OF QOL BY PHYSICAL HEALTH
Probability of QOL by Physical Health Profile
Pro
ba
bility
se
lf-ra
ted
he
alth
1=
po
or;2
=fa
ir;3
=g
oo
d;4
=e
xce
l
Physical health Profile
Pr(healhilo==1) Pr(healhilo==2) Pr(healhilo==3) Pr(healhilo==4)
hienergloenergacute chron=1chron>=2jobdysdisable0
.2
.4
.6
.8P
ro
ba
bility
life
ha
pp
ine
ss 1
=n
ot h
ap
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=so
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;3=
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=m
iss
Physical health Profile
Pr(Msatisf8==1) Pr(Msatisf8==2) Pr(Msatisf8==3) Pr(Msatisf8==4)
hienergloenergacutechron=1chron>=2jobdysdisable0
.2
.4
.6
Pro
ba
bility
life
sa
tisfa
ctio
n 1
=n
ot sa
t;2
=so
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;3=
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tisfie
d;4
=m
iss
Physical health Profile
Pr(Msatisf3==1) Pr(Msatisf3==2) Pr(Msatisf3==3) Pr(Msatisf3==4)
hienergloenergacutechron=1chron>=2jobdysdisable0
.2
.4
.6
Pro
ba
bility
job
sa
tisfa
ctio
n 1
=n
ot
sa
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=so
me
;3=
sa
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=m
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Physical health Profile
Pr(Msatisf9==1) Pr(Msatisf9==2) Pr(Msatisf9==3) Pr(Msatisf9==4)
hienergloenerg acute chron=1chron>=2jobdysdisable0
.2
.4
.6
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Types and levels of inequality may affect the extent of life control and social
integration which could lead to decreased opportunities for life happiness or
satisfaction, but not poor physical health at specific threshold levels (Marmot, 2002).
Negative and positive outcomes, although often dichotomized from ordinal variables,
should not be assumed to be indicators of inverse models.
Not only are multidimensional and better measures of social status, social
differentiation and social integration needed, but so are measures of multiple outcomes
at multiple levels. Control of various pathways to multiple outcomes at multiple levels
increases the certainty germaine for policy intervention.
Figure 23 illustrates the interrelated distributions of the ordinal HRQOL
variables for each response category. The odds for a negative response, being
dissatisfied or unhappy with life, were nearly the same, whereas the likelihood for
positive responses varied: satisfaction was substantially more probable than happiness,
controlling for the same level of physical health. It appears that dissatisfaction and
unhappiness, as negative outcomes, are about as likely at the positive as well as
negative endpoints of the physical health scale. Satisfaction and happiness, as positive
outcomes, varied substantially only at the well-being or energy endpoint of physical
health, whereas there was minimal variation of QOL with disabling physical conditions.
Life satisfaction was three times as probable as happiness given better physical health
but nearly as likely as happiness when worse physical conditions prevailed.
One can be in excellent/good health, have high energy and be satisfied with life,
yet also three times less likely to be happy. On the other hand, if one has more than two
impairments or chronic conditions or is disabled, the odds for dissatisfaction or
154
unhappiness with life are nearly equal as are the odds for satisfaction or happiness.
This suggests that satisfaction and happiness are orthogonal and provide additive
information about QOL, while dissatisfaction and unhappiness may provide similar
information. Further, the negative responses are not simple inverse relationships to the
positive responses, given an ordinal distribution. The likelihood of life happiness should
be modeled separately from life unhappiness to examine the links to inequality, life
chances, or social integration, holding physical health constant. It further suggests that
the social determinants of the negative response, life unhappiness, may be similar to life
dissatisfaction. Figure 23 illustrates how job satisfaction was more likely with worse
physical health and job dissatisfaction was more likely with better physical health. This
suggests an interaction with education, occupation, or other main effect.
SELF-RATED HEALTHSelf-rated health was bimodally distributed, and highly predicted by the physical
health profile. Having one impairment or chronic condition was the sample mode, thus
the likelihood for rating oneself with either excellent/good or fair/poor health was
similar [~pr(.4)]. As a whole, over 6 out of 10 had fair self-rated health given at least
two or more chronic conditions or worse physical status and about 8 out of 10 had good
self-rated health given better physical health than reporting experiencing any acute
symptoms (Figure 23). Self-rated health, as a global indicator, did not vary as much
between different sets of social determinants of health as did specific dimensions of the
physical profile. The usefulness of global indicators are not only dependent on the scale
of the research question but also on the specificity of the model (Table 20).
The 6th New Russia Barometer survey found that lack of optimism, collapse of
informal networks, material deprivation, and loss of life control influenced poor self-
155
reported health in a national sample of Russia (Rose, 1998). This suggests that self-
rated health is a global dimension of QOL which includes main effects with residual
variance measured by stress, optimism, social dysfunction, and self-assessed status.
Self-related health was associated directly with age, gender, and educational
level. Women were 157% more likely than men to be in poor health. Older women
were at less risk for poor health than younger women, as were older residents of urban
areas undergoing new development. Job satisfaction was not significantly linked to self-
rated health, but life unhappiness and life dissatisfaction were risks for poor health.
There were several interactions between educational level and occupational
status. Pensioners were included as an occupational category by the Moscow University
question format, which could be confounded with age. Controlling for age, interactions
between gender and age, age and education, age and occupation, however, still
indicated an inverse gradient between educational level and poor health among
pensioners. Pensioners with a completed university or higher education had the greatest
odds (5.34) for poor health, whereas those with an incomplete secondary education had
the least odds (3.49). Professionals were also 41% at greater risk for poor health than
other occupational groups. The exception was women with a higher education, working
in white collar jobs, who were 33% at greater risk for poor health than others. This was
related to age, since significantly older individuals worked in white collar positions.
Union participation had a protective effect on health for the lowest educational
levels but other factors had an impact on health with increased education. The odds for
poor health among individuals with an incomplete or general secondary school
education who participated in union activities were about 55-60% less than among
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TABLE 20 : LOGISTIC REGRESSION OF SELF-RATED HEALTH BY QOL, CIVIC COMMUNITY,AND HEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT b, ODDS RATIO e^b)
__________________________________________________________________________________________ Odds of poor/fair health vs. good/excellent health: | b z P>|z| e^b % change in OR__________________________________________________________________________________________HRQOLPhys. Health | 0.60865 10.080 0.000 1.8379 83.8Life unhappy | 0.35507 2.382 0.017 1.4263 42.6Life dissatisfy | 0.24970 1.697 0.090 1.2836 28.4DEMOGRAPHICSAge | 0.03117 3.510 0.000 1.0317 3.2Female | 0.94308 1.838 0.066 2.5679 156.8Female*age | -0.02805 -2.831 0.005 0.9723 -2.8Age*urban NewDev | -0.00338 -2.300 0.021 0.9966 -0.3Educational LevelHigh ed6, complete | -0.45421 -2.396 0.017 0.6350 -36.5High ed5, incompl |Tech second ed4 |Gen. Second ed3 |Incomplete sec ed2 |Female * educationalFemale*gen sec ed3 | 0.54421 1.843 0.065 1.7232 72.3Occupational StatusProfessional | 0.34281 1.647 0.100 1.4089 40.9White collar |Manual |Pensioner |Age * OccupationAge*white collar | 0.02110 2.769 0.006 1.0213 2.1Educational level * occupational statusHigh ed6*pension | 1.67565 3.771 0.000 5.3423 434.2Tech Sec ed4*pension| 1.43819 2.482 0.013 4.2131 321.3Gen sec ed3*pension| 1.32539 2.584 0.010 3.7637 276.4Inc Sec ed2*pension| 1.24851 2.811 0.005 3.4851 248.5Female * education * occupationF*highC ed*white col| 0.84581 2.077 0.038 2.3299 133.0HEALTH CHOICESPhysical Activity | -0.85990 -3.430 0.001 0.4232 -57.7Any smoking |Any alcohol |Overweight |F * sport | 0.59302 1.995 0.046 1.8094 80.9CIVIC COMMUNITYInformal NetworksN friends contact |N family contact |Formal Networks/ group membershipsProfess/trade grp |Child/social grp |Religious grp |Union grp | 0.79572 3.899 0.000 2.2160 121.6Education * Group membershipGen sec ed3*union | -0.81195 -2.724 0.006 0.4440 -55.6Inc sec ed2*union | -0.90075 -2.632 0.008 0.4063 -59.4Occupation * Group membershipWhite collar*union | -0.76963 -2.189 0.029 0.4632 -53.7White*profess/tr grp| -0.82762 -1.675 0.094 0.4371 -56.3Manual*relig grp | 0.64497 2.047 0.041 1.9059 90.6Social Cohesiondistress | 0.13442 2.658 0.008 1.1439 14.4F * distress | 0.09468 1.654 0.098 1.0993 9.9__________________________________________________________________________________________logistic model goodness of fit test 2 =1516.5 p>.9450; area under ROC curve = 0.8463;dependent variable link test [hat coeff= p<.000][hatsq coeff= p<.229];pseudo R2 = 0.3055; N obs = 1627;--------------------------------------------------------------------------------------------------------------------------------------------------------------b = raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = odds ratio for unit increase in X; % = percent change in odds for unit increase in X;
157
other educational groups. This was also the case for white collar workers participating
in union or professional activities. No other formal or informal network participation
was significantly related to self-rated health, except religious involvement. People in
manual occupations who participating in religious activities were more likely to have
poor health than other groups, which could be due to an interaction with age.
A threshold effect of education on health which could be linked to social
participation may exist similar to that reported for income (Marmot, 2002). Group
memberships may have stronger effects at certain educational levels. In lieu of income
measures, this could be expected to obtain for occupational groups as well. It is unclear
why professionals, on the whole, were more likely than other occupations to rate
themselves in poor health. This was not due to a gender or age effect.
It is possible that the chaotic political situation during the Coup and direct
involvement of professionals as government functionaries was responsible for increased
stress. A stressful workplace setting has been repeatedly associated with poor health,
independently of social status (Siegrist, 1996). There was an indication for such an
effect in that those who felt distressed were 14% at greater risk for poor health than
those who did not experience distress. Distressed women were only 10% more likely to
report poor health than unstressed women. This would suggest that men may have borne
the brunt of the health effects linked to the political context during the survey.
Health choices, apart from any sports or physical activities, were not associated
with self-rated health. Engaging in sports was associated with 60% less odds to be in
poor health than those who did not participate in any physical activities. This
relationship held for men since physically active women had nearly twice the odds for
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TABLE 21: LOGISTIC REGRESSION OF LIFE HAPPINESS BY QOL, CIVIC COMMUNITY, ANDHEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT b, ODDS RATIO e^b)
__________________________________________________________________________________________ Odds of unhappy life vs happy life:
| b z P>|z| e^b % change__________________________________________________________________________________________HRQOL
Physical Health | 0.09733 1.624 0.104 1.1022 10.2Poor rated health | 0.39752 2.565 0.010 1.4881 48.8Life unsatisfy | 1.18167 9.030 0.000 3.2598 226.0Job unsatisfy | 0.76724 5.066 0.000 2.1538 115.4DEMOGRAPHICSurban new dev | 0.18032 2.756 0.006 1.1976 19.8Educational LevelHigh ed6, complete |High ed5, incomplete |Tech sec ed4 | 1.27283 2.708 0.007 3.5709 257.1Gen sec ed3 | 2.55253 3.015 0.003 12.8395 1184.0Incomplete sec ed2 |Female * educationalF*gen sec ed3 | -1.99007 -2.084 0.037 0.1367 -86.3Occupational StatusProfessional |White collar |Manual |Pension | 2.36032 2.150 0.032 10.5944 959.4Age * educationAge*tech sec ed4 | -0.02031 -2.173 0.030 0.9799 -2.0Age*gen sec ed3 | -0.05732 -2.522 0.012 0.9443 -5.6age * occupationAge*pension | -0.03536 -2.198 0.028 0.9653 -3.5Female * age * educationF*age*gen sec ed2 | 0.04581 1.872 0.061 1.0469 4.7Education * occupationHigh ed6 compl*pension| -2.64537 -2.634 0.008 0.0710 -92.9Female * education * occupationF*high ed6 * pension | 2.97856 2.858 0.004 19.6594 1865.9HEALTH CHOICESSport |Any smoking |Any alcohol |Overweight |CIVIC COMMUNITYInformal NetworksN friends contact |N family contact | -0.01692 -2.228 0.026 0.9832 -1.7Formal Networks/ group membershipsProfess/trade grp |Child/social grp | 0.80964 2.479 0.013 2.2471 124.7Union grp |Religious grp |Occupation * group membershipProfession*child grp | -1.54367 -2.649 0.008 0.2136 -78.6Education * group membershipTech sec ed4*union | -0.49934 -1.738 0.082 0.6069 -39.3Social CohesionF*anomie | 0.08631 2.306 0.021 1.0901 9.0distress | 0.19874 4.469 0.000 1.2199 22.0F*distress | -0.12650 -2.704 0.007 0.8812 -11.9Social SupportPoor marriage | 0.37487 8.407 0.000 1.4548 45.5F*poor marriage | -0.14594 -2.995 0.003 0.8642 -13.6__________________________________________________________________________________________
Logistic model goodness-of-fit test 2 =1568.4 p>.7772; area under ROC curve = .8163; dependentvariable linktest [hat coeff=.949… p<.000][hatsq coeff=-.0499 p<.167]; pseudo R2 = 0.2435; N obs = 1610;b = raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = odds ratio for unit increase in X; % = percent change in odds for unit increase in X
159
poor health as physically inactive women. One possible explanation might be that
physically active women could be older or engaged in strenuous occupations, while for
men sports was a leisure activity.
HAPPINESS AND SATISFACTIONAge increased the odds for happiness, while marriage was a greater risk for
dissatisfaction with life. There was an interaction between age, gender, educational
level, and occupational status. Several three-way interaction levels between gender,
education, and occupation were not significant and several cells were too small for
estimation: lower educated women in manual, white collar, or professional
occupations, and higher educated women in manual jobs (Table 21).
Happiness was related to gender and occupation: women pensioners with a
university education were 19 times at greater odds for unhappiness than men, as were
retired professional women. Again, this relationship did not hold for life satisfaction.
Men with a general secondary education, one of the lowest educational levels, were at
much greater risk for unhappiness than women, controlling for interactions with age and
education. This was not the case for dissatisfaction with life. As a whole, pensioners
were over 10 times more likely to be unhappy than non-pensioners, but retired men with
university educations were about 90% less likely, although they were at greater risk for
poor health.
Men were 86% at greater risk than women for unhappiness due to poor marital
support and distress but less likely to report anomie. This suggests that distress and
anomie are not confounding concepts. Psychological distress should be considered as a
covariate with any measure of social cohesion.
Social networks in the form of supportive family and social participation were
160
positively associated with happiness. The odds were greater among those with only a
technical secondary education if they were union members, which was also evident in
better self-rated health. It is unclear why participating in a social or child related group
would increase the risk for unhappiness among all groups except professionals. The
same effect was not found for satisfaction.
In contrast to happiness, both men and women were at risk for dissatisfaction
with life because of poor marital relationships. Dissatisfaction with work was not
associated with lack of social cohesion or marital support (Table 22).
Pensioners with lowest and highest education had greater odds for a positive
association with life satisfaction, as well as job satisfaction. Pensioner with incomplete
higher and technical secondary education were dissatisfied with life. This may have
been due to the interaction between pension levels and occupational category, which
was most often dependent on educational credentials. There was an interaction between
professionals and education. Those with a lower general secondary education but
working in a professional capacity were more likely to be dissatisfied, as were those
with a university education. But those in professional occupations as a whole were 63%
less likely to be dissatisfied. White collar workers with higher education and women
white collar workers were more likely to be satisfied, but white collar men were 50%
more likely to be dissatisfied.
Finally, in distinction to health and happiness, union membership was associated
with life dissatisfaction among those with a mid-level educational attainment.
Participation in sports was the only health choice to be positively associated
161
TABLE 22 : LOGISTIC REGRESSION OF LIFE SATISFACTION BY QOL, CIVIC COMMUNITY,AND HEALTH CHOICES, ALL MOSCOW AREAS (N=1629; LOGIT b, ODDS RATIO e^b)
__________________________________________________________________________________________ Odds of dissatisfied vs. satisfied with life:
| b z P>|z| e^b % change__________________________________________________________________________________________HRQOLPoor rated health | 0.30493 2.232 0.026 1.3565 35.7Life unhappy | 1.10712 8.565 0.000 3.0256 202.6DEMOGRAPHICSUrban access | 0.11130 1.910 0.056 1.1177 11.8married | 0.30632 1.807 0.071 1.3584 35.8Educational LevelHigh ed6, complete | 1.35756 4.705 0.000 3.8867 288.7High ed5, incomplete |Tech sec ed4 |Gen sec ed3 |Incomplete sec ed2 |age * educationage*gen sec ed3 | 0.01634 2.545 0.011 1.0165 1.6age*incompl sec ed2 | 0.01491 2.304 0.021 1.0150 1.5Occupational Statusprofessional | -0.98371 -2.292 0.022 0.3739 -62.6white collar | 0.83136 2.385 0.017 2.2964 129.6pension | 1.04713 3.484 0.000 2.8494 184.9manual |Age * occupationAge*profession | 0.02224 2.569 0.010 1.0225 2.2Female * occupationF*white collar | -0.69631 -2.015 0.044 0.4984 -50.2Educational level * occupational statusHigh ed6*white col | -1.09048 -2.804 0.005 0.3361 -66.4High ed6*pension | -0.86890 -2.077 0.038 0.4194 -58.1Gen sec ed3*profession| 0.85945 1.818 0.069 2.3619 136.2Gen sec ed3*pension | -1.26593 -2.383 0.017 0.2820 -71.8Incom sec ed2*pension| -1.14019 -2.115 0.034 0.3198 -68.0HEALTH CHOICESAny smoking |Any alcohol |Overweight |Physical activity | -0.21716 -1.742 0.081 0.8048 -19.5CIVIC COMMUNITYInformal NetworksN family contact |N friends contact | -0.01478 -2.073 0.038 0.9853 -1.5Formal Networks/ group membershipsProfess/trade grp |Child/social grp |Union grp |Religious grp |education al level * formal network/group membershiphigh ed5,incompl*union| 0.66942 1.797 0.072 1.9531 95.3tech sec ed4*union | 0.96334 3.809 0.000 2.6204 162.0Social CohesionN anomie | 0.15636 4.071 0.000 1.1692 16.9N distress | 0.04933 2.761 0.006 1.0506 5.1Social SupportPoor Marriage | 0.11741 3.958 0.000 1.1246 12.5__________________________________________________________________________________________
Logistic model goodness-of-fit test 2 =1597.3 p>.5910; area under ROC curve = .7579;dependent variable linktest [hat coeff=.8649… p<.000][hatsq coeff=-.1300 p<.033];pseudo R2 = 0.1435; N obs = 1627; b = raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = odds ratio for unit increase in X; % = percent change in odds for unit increase in X;
`
162
with life satisfaction and self-rated health.
Job dissatisfaction was substantially affected by a gender, education, and
occupation interaction. Women were 91 times at greater risk for being dissatisfied with
their jobs than men. Older women and younger highly educated women were more
likely to be satisfied. Educated women with pensions were also satisfied with their jobs,
suggesting that they were deriving income and satisfaction from continued employment
after age 50. This is consistent with the Soviet era practice of permitting full pensions
from one position to be kept after moving to another position, especially among senior
management.
Although university educated men had greater odds for dissatisfaction with work
than women, white collar higher educated women and women employed in manual
labor with a secondary education were from 600% - 700% more likely than men in to be
dissatisfied with their jobs.
Women engaging in sports had greater odds to be satisfied with their jobs,
although they were also more likely to be in poor health. This is consistent with the
organization of Moscow health services around restricted employer-based clinics, in
addition to lower quality neighborhood polyclinics available to everyone. A further
indication of the effect of job based health facilities is the greater odds for
dissatisfaction with a willingness to pay out of pocket for medical services.
The effect of formal networks on job satisfaction was also related to three-way
interactions. Participating in professional or trade organizations was associated with
greater odds for job satisfaction, especially among women, having a general secondary
education, as well as those with professional occupations. Women who participated in
163
TABLE 23: LOGISTIC REGRESSION OF JOB SATISFACTION BY QOL, CIVIC COMMUNITY, ANDHEALTH CHOICES, IN ALL MOSCOW AREAS (N=1629; LOGIT b, ODDS RATIO e^b)
__________________________________________________________________________________________ Odds of dissatisfied vs. satisfied with job:
| b z P>|z| e^b % change__________________________________________________________________________________________HRQOLLife unhappy | 0.79712 5.546 0.000 2.2191 121.9DEMOGRAPHICSage | 0.04504 2.917 0.004 1.0461 4.6F * age | -0.08788 -4.457 0.000 0.9159 -8.4female | 4.52008 4.261 0.000 91.8431 9084.3Educational LevelHigh ed6, complete | 3.35140 3.853 0.000 28.5426 2754.3High ed5, incomplete | 2.27114 3.474 0.001 9.6904 869.0General second ed3 | 2.13510 2.110 0.035 8.4579 745.8Female * educational levelF*high ed6, complete | -4.19905 -3.394 0.001 0.0150 -98.5F*tech second ed4 | -2.58028 -2.520 0.012 0.0758 -92.4F*gen second ed3 | -4.48830 -3.253 0.001 0.0112 -98.9age * educationage*high ed6, complete| -0.07634 -4.291 0.000 0.9265 -7.3age*gen sec ed3 | -0.05650 -2.440 0.015 0.9451 -5.5age*incomplete sec ed2|-0.01593 -2.565 0.010 0.9842 -1.6female * age * educationF*age*high ed6, compl | 0.06714 2.954 0.003 1.0694 6.9F*age*tech sec ed4 | 0.02816 1.599 0.110 1.0286 2.9F*age*gen sec ed3 | 0.09492 3.185 0.001 1.0996 10.0Occupational StatusProfessional |White collar | 0.82631 2.721 0.007 2.2849 128.5Manual | 2.47169 2.723 0.006 11.8425 1084.2Pension | -0.91853 -2.013 0.044 0.3991 -60.1age * occupationage * manual | -0.03554 -1.811 0.070 0.9651 -3.5female * occupationF * professional | 1.33485 4.341 0.000 3.7994 279.9F * pension | 1.93627 3.417 0.001 6.9329 593.3educational level * occupational statushigh ed6*white collar | -1.38456 -2.625 0.009 0.2504 -75.0high ed5*white collar | -2.08648 -2.268 0.023 0.1241 -87.6gen sec ed3*white coll| -1.05453 -2.203 0.028 0.3484 -65.2high ed5*professional | -2.27820 -2.787 0.005 0.1025 -89.8tech sec ed4*pension | -1.27700 -2.501 0.012 0.2789 -72.1incom sec ed2*manual | 2.17342 3.116 0.002 8.7883 778.8Female * education * occupationF*highed6*white collar| 2.00740 3.117 0.002 7.4439 644.4F*high ed5*pension | -3.04751 -2.820 0.005 0.0475 -95.3F*tech sec ed4*manual | 2.08178 1.842 0.065 8.0187 701.9F*gen sec ed3*profess | -1.53787 -2.403 0.016 0.2148 -78.5F*gen sec ed3*pension | -1.73731 -2.377 0.017 0.1760 -82.4HEALTH CHOICESPay private MD | 0.38756 2.382 0.017 1.4734 47.3F*physical activity | -0.34181 -2.195 0.028 0.7105 -29.0CIVIC COMMUNITYfemale * formal network/group membershipF * union | -0.64787 -2.365 0.018 0.5232 -47.7F * profess/trade grp | -0.81873 -2.305 0.021 0.4410 -55.9educational level * formal network/group membershiphigh ed6,incom* union | 0.56170 1.928 0.054 1.7537 75.4tech sec ed4 * union | 0.84667 2.490 0.013 2.3319 133.2gen sec ed3 * union | 1.36718 3.541 0.000 3.9243 292.4gen sec ed3*prof/trade| -2.05136 -3.159 0.002 0.1286 -87.1incompl sec ed2*relig | 0.79740 1.844 0.065 2.2198 122.0occupational status * formal network/group membershipprofessional*relig grp| -0.43782 -2.118 0.034 0.6454 -35.5manual * union grp | -0.95121 -1.641 0.101 0.3863 -61.4Social CohesionDistress | 0.07666 3.985 0.000 1.0797 8.0
164
__________________________________________________________________________________________
Logistic model goodness-of-fit test 2 =1568.4 p>.7772area under ROC curve = .8163dependent variable linktest [hat coeff=1.062… p<.000][hatsq coeff=-.039 p<.477];pseudo R2 = 0.1210; Observations = 1627b = raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test;e^b = exp(b) = odds ratio for unit increase in X;% = percent change in odds for unit increase in X__________________________________________________________________________________________
union were more satisfied with their jobs than men. But unions did not ensure job
satisfaction among the educated at the secondary or higher level (Table 23).
EFFECTS OF INEQUALITY ON QOLInequality of resource development between urban areas was associated with
unhappiness among area residents. There was almost a 19% greater likelihood for life
unhappiness among residents in new development areas, which lacked social and
material resources, than among other area residents. Life happiness was not related to
access as strongly to lack of resources. Life dissatisfaction was related to living in areas
with greater access to material resources. Experiencing lack of fulfillment or rising
frustrated expectations may underlie these seemingly disparate findings.
Low access and high development factor scores were two different indicators of
average inequality within urban areas. Although the two types of average inequality
were almost orthogonal, the composite factors were independent of each other and
varied among urban areas. Some areas could have high access/low development, high
development/low access, or high access to resources and high new development of
resources. The particular urban area of residence accounted for the variation in QOL
outcomes being associated with average inequality controlling for demographic and
psychosocial factors.
In order to investigate the effect of average inequality, logistic regressions were
calculated for each outcome within low access and high development areas (Table 24).
165
Seemingly unrelated estimation was used to check the significant differences between
the model for low access areas and high development areas with STATA6 –unest-
command (Stata6 v. 4, STB52, sg120). The regression coefficients of all eight logistic
regressions were significantly different within and between dependent variables, as well
as between areas. This was a further indication that there was a significant variation
between urban areas which could be examined in a multilevel model, even though the
between group variation (ICC) was small.
There were distinct patterns of determinants for health, happiness and
satisfaction, dependent upon the type of average inequality in the urban areas.
Informal networks, social support, and distress did not differ markedly by
inequality in areas. Life chances, social participation, health choices, and social
status/gender interactions varied significantly by type of inequality. HRQOL within
urban areas varied by social status and inequality: the low access areas had the worst
health status and high development areas had greater likelihood for job dissatisfaction
among some groups and life unhappiness among other groups. The variation of job
dissatisfaction among social status indicated the importance of the workplace context
and life control as an indirect effect on health through life happiness.
Generally, average inequality was associated with a discernible hierarchical
effect of social status on QOL. The odds of poor self-rated health varied by education
and occupation, as well as average inequality. Although not consistent among the eight
outcome groups because of complex interaction terms, higher education had better odds
for good health, happiness and job satisfaction than lower education.
166
TABLE 24: LOGISTIC REGRESSION OF HEALTH, HAPPINESS, AND SATISFACTION BYDEMOGRAPHICS AND TYPE OF AVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TORESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS (|Z-STATISTIC|)
_______________________________________________________________________SELF-RATED HEALTH LIFE LIFE JOB
FAIR/POOR UNHAPPY UNSATISFIED UNSATISFIED____Low High Low High Low High Low Highaccess develop access develop access develop access develop
__________________________________________________________________________________________
HRQOLLow rated health 1.506 1.421 1.491
(2.29)* (2.15)*(1.81)^life unhappy 1.648 1.601 3.259 3.612 2.182 1.498
(3.05)* (2.08)* (7.84)** (6.48)** (5.83)** (1.90)^life unsatisfy 1.385 3.360 3.536
(1.88)^ (7.84)** (6.31)**job unsatisfy 2.153
(4.28)**physical health 1.067 1.059 1.013 1.019profile/n (9.19)**(5.78)** (1.96)*(2.28)*
URBAN AREAResources Access 3.715
(2.145)*Age*Access 0.975 1.004
(2.19)*(2.02)*Resources New Dev 1.25
(2.50)*Age*new dev 0.997 0.995 0.997
(1.69)^(2.18)* (1.87)^DEMOGRAPHICSfemale=1 5.793 12.815
(4.35)**(4.97)**age/yr 0.977 0.962 1.019 1.042
(3.46)**(3.51)** (2.94)**(4.22)**married=1 1.637
(2.47)*fem * age 1.025 0.969 0.975 0.957
(2.46)* (3.53)** (4.60)**(3.47)**EducationHigh ed6,compl 0.200 0.212 1.828 4.491 1.77 0.561
(2.50)*(3.32)** (2.00)* (4.74)**(2.59)* (2.21)*Hi ed5,incompl 0.069 2.946 3.91
(2.90)* (1.79)^(2.39)*Tech sec ed4 2.136 2.218
(2.53)* (2.23)*Gen sec ed3 2.966 3.579 0.094
(3.05)** (2.96)* (3.03)**incompl sec ed2 2.597
(2.31)*OccupationProfessional 2.894 5.369 4.677
(2.36)*(3.32)** (4.64)**white collar 3.16 3.686
(3.12)* (3.13)*manual 1.788 2.942
2.61)* (4.14)**pension 7.154 2.292
(3.85)** (2.33)*MobilityAny job change 0.586 0.634
(2.52)* (1.97)*Any housing change__________________________________________________________________________________________
167
The odds varied significantly by average inequality. The odds for life dissatisfaction
were greater for higher educated residents of low access areas. Involvement in union
activities increased the odds for good health for all educational groups in low access
areas but increased the odds for life dissatisfaction among union members with mid-
level education. Life happiness and job dissatisfaction were not markedly affected.
The effect of occupational status on QOL varied by inequality of area and was
moderated by education and social capital. Pensioners of all educational levels were at
greater risk for poor health if they lived in low access areas but not high development
areas. Professionals and university educated white collar workers had greater odds for
poor health in new development areas. Manual workers with incomplete secondary
school in low access areas were the highest risk group for poor physical health among
those working, but they were not the unhappiest group.
Occupational status and education were not linearly related in Moscow, thus the
effect on outcomes was not consistent over type of inequality. The odds for life
unhappiness were affected by a strong hierarchical relation between an interaction of
gender with occupation and inequality in new development areas. Although women
manual workers had 19 times greater odds than men and 16 times greater odds than
professional women for unhappiness, there was no similar effect on self-reported health,
life satisfaction, or job satisfaction.
Participation in formal networks by occupational status varied by type of
inequality and QOL outcome measure. A readily discernible and consistent pattern
could not be estimated due to the complexity of third or even fourth order interaction
patterns, some of which had null cells given the sample size (age * gender * education *
168
occupation * group membership) (Tables 25/26).
TABLE 25: LOGISTIC REGRESSION OF HEALTH, HAPPINESS, AND SATISFACTION BY CIVICCOMMUNITY, HEALTH CHOICES AND TYPE OF AVERAGE INEQUALITY IN URBAN AREA ,(LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS
(|Z-STATISTIC|)_______________________________________________________________________
SELF-RATED HEALTH LIFE LIFE JOBFAIR/POOR UNHAPPY UNSATISFIED UNSATISFIED____Low High Low High Low High Low Highaccess develop access develop access develop access develop
__________________________________________________________________________________________
CIVIC COMMUNITY
Informal NetworksN friends 0.980 0.977 0.980
(2.38)*(2.07)* (2.37)*N family 0.984
(1.81)^Formal Networks/ Group MembershipUnion group 8.79 2.364 2.106
(3.46)** (2.97)*(2.12)*child/social grp 2.392
(1.75)^religious grp 1.57 4.526 0.653
(1.80)^ (3.59)** (2.31)*Profess/trade grp 0.068 0.476
(4.14)**(1.78)^Social SupportPoor marriage 1.532 1.479 1.142 1.079
(7.63)**(7.31)** (3.86)**(2.16)*F * poor marriage 0.827
(3.50)**
Social CohesionAnomie/n 1.158
(3.17)**Distress/n 1.224 1.278 1.070 1.263 1.047 1.059 1.064 1.134
(7.26)**(6.00)** (2.96)**(3.03)** (2.23)*(1.98)* (2.93)*(3.82)**F * anomie 1.119 1.267
(2.52)* (3.68)**
HEALTH CHOICESPay private MD 1.578
(2.58)*Physical Activity 0.693 0.382 0.547
(2.31)*(3.43)** (2.85)**Any smoking 2.921
(2.51)*female * health choicesF * obesity 0.579
(2.02)*F * smoke 0.247
(2.78)*F * active 2.005 0.537 1.822 0.474
(2.61)* (2.75)* (2.94)** (2.84)*
______________________________________________________________________
169
TABLE 26: INTERACTIONS IN LOGISTIC REGRESSION OF HEALTH, HAPPINESS, ANDSATISFACTION BY TYPE OF AVERAGE INEQUALITY IN URBAN AREA, (LOW ACCESS TO
RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS (|Z-STATISTIC|)_______________________________________________________________________
SELF-RATED HEALTH LIFE LIFE JOBFAIR/POOR UNHAPPY UNSATISFIED UNSATISFIED____Low High Low High Low High Low Highaccess develop access develop access develop access develop
__________________________________________________________________________________________
Female * educational statusF*high ed6 2.254
(3.24)**F*high ed5 7.593 0.286
(2.44)* (1.98)*F*tech sec ed4 0.517
(2.12)*F*gen sec ed3 3.695 0.393 0.389
(3.38)** (2.56)* (2.79)*Female * occupational statusF*professional 3.100 0.414
(2.14)* (2.90)*F*white collar 4.939 0.312 0.342
(2.43)* (2.72)* (2.16)*F*manual 7.115 2.68
(2.75)** (2.07)*F*pension 19.532
(3.54)**female * formal networks - group membershipF*union 0.467 0.373 0.401
(2.49)*(2.49)* (2.69)*F*child/social grp 2.25
(2.17)*Educational level * occupational statusHigh ed6*professional 2.08
(2.28)*Techseced4*prof 0.147 2.508
(2.85)* (1.98)*Gen seced3*prof 2.52
(2.00)*High ed6*whitecollar 9.96 0.335 0.237
(3.54)** (2.65)* (2.41)*Techseced4*white 0.301
(2.45)*Gen sec ed3*white 0.342 0.276
(2.21)* (2.41)*Inc seced2*manual 3.461 0.289
(1.96)* (2.27)*High ed6*pension 5.133 0.238
(2.85)* (3.16)*High ed5*pension 11.37
(2.28)*Tech sec ed4*pens 4.988 0.134
(2.24)* (3.06)*Gen sec ed3*pens 3.799
(1.96)*Inc sec ed2*pens 4.522 4.129
((2.95)* (2.91)*
170
(CONTINUED) INTERACTIONS IN LOGISTIC REGRESSION OF HEALTH, HAPPINESS, ANDSATISFACTION BY TYPE OF AVERAGE INEQUALITY IN URBAN AREA, (LOW ACCESS TO
RESOURCES; HIGH DEVELOPMENT OF NEW RESOURCES), ODDS RATIOS (|Z-STATISTIC|)_______________________________________________________________________
SELF-RATED HEALTH LIFE LIFE JOBFAIR/POOR UNHAPPY UNSATISFIED UNSATISFIED____Low High Low High Low High Low Highaccess develop access develop access develop access develop
__________________________________________________________________________________________
Educational level * formal networks - group membershipHigh ed6*prof/trade grp 8.553
(3.03)*High ed6*union 0.173
(2.48)*High ed6*religious 0.172
(3.62)**High ed5*union 0.327 3.637
(2.46)* (2.27)*Tech seced4*union 0.292 2.740
(1.96)* (3.18)**Tech seced4*relig 0.185 0.363
(3.57)** (3.07)*Gen seced3*relig 0.202
(3.05)*Gen seced3*union 0.083 1.978 4.438
(3.64)** (2.36)*(3.12)*Incomseced2*union 0.0422
(3.95)**Incomseced2*relig grp 0.158
(3.07)*Occupational status * formal networks\ group membershipProfession*protradegrp 0.262
(2.10)*profession*relig grp 0.580
(2.12)*profession*union grp 2.279
(2.56)*whitecol*prof/trade grp 4.74
(2.42)*whitecol*social/child grp 0.262
(1.96)*whitecol*relig grp 0.481 2.49
(2.19)* (3.02)**whitecol*union grp 2.700 5.164 3.409
(2.74)** (3.55)** (3.29)**manual*prof/trad 5.371 14.65 0.130
(1.79)^(1.88)^ (2.39)*manual*soc/child grp 0.119
(1.98)*manual*union grp 2.827
(3.09)**__________________________________________________________________________________________Observations= 1236 688 1215 684 1229 681 1221 681R2sq.= 0.3176; 0.3635; 0.2495; 0.1728; 0.1616; 0.1728; 0.1056; 0.1661;Lfit=2= 1175.6; 597.7; 1190.6; 651.17; 1215.4; 668.00; 1220.8; 629.4;
(p<.66); (p<.49); (p<.42); (p<.49); (p<.44); (p<.49); (p<.25); (p<.16)Lroc= 0.8542; 0.8745; 0.8236; 0.8193; 0.7683; 0.7792; 0.7154; 0.7666;
Absolute value of z-statistics in parentheses;* p<.05; ** p<.001; ^ p<.051-.09
171
PHYSICAL HEALTH PROFILEThe social determinants of each health outcome, whether QOL, mortality,
disability, morbidity, self-rated health or functional limitations, has a specific set of
unique predictors which may not be generalized to other outcomes, although a subset of
predictors may be common for a variety of outcomes. It is therefore important to
considering the structure of physical dimensions as a whole, as well as specific chronic
and acute conditions or diseases, when looking at the social and lifestyle determinants
of population health status.
The Alameda Physical Health Profile was conceptualized as a spectrum of
health from disability or functional incapacity to excellent health or functioning with a
high level of energy. As described in the methods chapter, the physical profile was
aggregated in terms of specific diseases, symptoms, conditions, discomforts, or
disabilities (see Appendix for exact items/ questions). The physical health profile can be
disaggregated into its components, each of which can be examined separately in
conjunction with social determinants (Table 27).
The major components of the physical health profile are mutually exclusive and
include: any disability; no disability but any job dysfunction; no disability or job
dysfunction but one impairment or chronic condition; none of the previous categories
but two or more impairments or chronic conditions; no impairments or chronic
conditions but one or more acute symptoms; and last, those who report no specific
problems with their health and can function with a low level of energy or a high level of
energy in everyday activities. The link test of the physical health profile indicated that
inequality was related to the specification of the profile. The constructed profile was
correct for low access areas but not for high development areas.
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TABLE 27: ORDERED LOGIT REGRESSION OF ALAMEDA PHYSICAL HEALTH PROFILE BYTYPE OF AVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH
DEVELOPMENT OF NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVICCOMMUNITY (LOGIT b, ODDS RATIO e^b)
--------------------------------------------------------------------------------------- low access to resources high development of new resources---------------------------------------------------------------------------------------Physical Health| b z P>|z| e^b b z P>|z| e^b---------------------------------------------------------------------------------------HRQOLLife unhappy | 0.235 1.930 0.054 1.265 0.312 1.931 0.053 1.366Low rated health | 1.306 10.109 0.000 3.692DEMOGRAPHICSUrban Access | -0.556 -2.140 0.032 0.572Female | 0.730 1.869 0.062 2.076 1.998 3.880 0.000 7.379married | 1.041 2.690 0.007 2.834
* Female | -1.176 -2.593 0.010 0.308age | 0.047 6.784 0.000 1.049 0.039 5.776 0.000 1.040
* Female | -0.017 -2.207 0.027 0.982Educational LevelIncompl seced2 | -1.195 -3.249 0.001 0.302 -1.006 -2.617 0.009 0.365Gen sec ed3 | -1.107 -2.899 0.004 0.330Tech sec ed4 | -1.005 -2.730 0.006 0.366Higher ed6, | -0.569 -1.738 0.082 0.565 -0.420 -1.899 0.058 0.656Tech sec ed4 | * Female | 0.678 3.066 0.002 1.971Occupational StatusPension | 0.958 3.499 0.000 2.608white collar | -0.460 -1.926 0.054 0.631Female * occupational status *manual | 0.687 2.297 0.022 1.989Educational level * occupational statusGen seced3*pens | 0.622 1.710 0.087 1.863Tech seced4*pens | 0.655 1.656 0.098 1.925Gen seced3*white | 1.028 2.765 0.006 2.797Tech seced4*white | 0.661 1.972 0.049 1.937High ed5*profess | -1.123 -2.059 0.039 0.325High ed6*profess | -0.499 -2.091 0.036 0.606MobilityMove house | -0.231 -2.003 0.045 0.793 -0.306 -1.914 0.056 0.735Move job | 0.269 1.715 0.086 1.309HEALTH CHOICESAlcohol | -0.461 -2.325 0.020 0.630
* Female | 0.381 1.615 0.106 1.463Obesity | 0.236 1.987 0.047 1.267
CIVIC COMMUNITYInformal NetworksN friends contact | 0.015 2.432 0.015 1.016Formal Networks/ group membershipsUnion group | -0.804 -2.364 0.018 0.447Social/child grp | 0.517 1.670 0.095 1.678occupation * formal networks/group membershipsPension*child grp | 1.075 1.969 0.049 2.930Manual*child grp | 0.943 1.719 0.086 2.569Manual*union grp | -0.445 -1.782 0.075 0.640Professional*union | 0.379 2.008 0.045 1.462education * formal network/group membershipIncoml seced2*union| 1.662 3.456 0.001 5.270 1.615 3.544 0.000 5.030Gen sec ed3*union | 1.042 2.402 0.016 2.837Tech sec ed4*union | 0.909 2.112 0.035 2.483Higher ed5*union | 0.602 1.623 0.105 1.826Higher ed6*union | 0.803 1.984 0.047 2.233Gen seced3*religious|-0.551 -2.143 0.032 0.576High ed6*religious | 0.677 2.537 0.011 1.969female * formal networks/group membershipsF*religious group | 0.512 3.675 0.000 1.670
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(CONTINUED) ORDERED LOGIT REGRESSION OF ALAMEDA PHYSICAL HEALTH PROFILE BYTYPE OF AVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH
DEVELOPMENT OF NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVICCOMMUNITY (LOGIT b, ODDS RATIO e^b)
-------------------------------------------------------------------------------------- low access to resources high development of new resources
-------------------------------------------------------------------------------------Physical Health| b z P>|z| e^b b z P>|z| e^b-------------------------------------------------------------------------------------Social CohesionAnomie | 0.097 2.098 0.036 1.102Distress | 0.210 5.323 0.000 1.234 0.222 4.255 0.000 1.249
* Female| -0.095 -2.246 0.025 0.909 -0.101 -1.816 0.069 0.903
Social SupportPoor marriage| 0.140 2.085 0.037 1.151
* Female| -0.055 -2.410 0.016 0.946 -0.188 -2.479 0.013 0.828------------------------------------------------------------------------------------- Coef. Std.Err. Coef. Std.Err. _cut1 | -1.794 .487 -.031 .572 _cut2 | .331 .455 2.365 .521 _cut3 | 1.937 .460 3.768 .534 _cut4 | 3.466 .469 5.240 .551 _cut5 | 5.992 .483 7.595 .580 _cut6 | 6.980 .493 8.620 .598-------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds across responsecategories: chi2(111) = 259.81 Prob > chi2 = 0.0000 (Ho rejected; b not constant)--------------------------------------------------------------------------------------link test for model and dependent variable specification: N of obs = 1237; R2 = 0.1617; N of obs = 689; R2 = 0.1433;Nphyslow | Coef. Std.Err. P>|z| Coef. Std.Err. P>|z|---------+--------------------------------------------------------------------------- _hat | 1.142 .1327 0.000 1.646 .2959 0.000 _hatsq | -.022 .0195 0.256 -.0626 .0279 0.025(Ho not rejected for low access model; Ho rejected for high dev. model, transformdependent variable or respecify model)---------+--------------------------------------------------------------------------- b= raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = factor change in odds for unit increase in X-------------------------------------------------------------------------------------
The physical health profile, not adjusted for inequality of urban area, indicated
a strong hierarchical relationship with life chances, education and occupation,
especially among and between gender groups (see Appendix). Third-order
interactions with gender and second-order interactions were included and significant.
Professional women, on the whole, were five times more likely to report poor
physical health than other occupations but highly educated professional women were
from 72%-84% more likely than other education/occupation groups to have better
physical health. On the other hand, men with higher education, incomplete higher,
174
and technical secondary educations, employed in a professional capacity, had odds
from almost 4-7 times (OR=3.57, 5.95, 6.89) greater for poorer physical health.
Hierarchy in social status was related to physical health. The risk gradient for
professional men was lower for the most educated and higher for the least educated.
Professional men were at greater risk than women. This finding is consistent with
data on the mortality and life-expectancy gender gap in Russia, discussed previously,
which illustrates to what extent men have suffered the health effects of living in
transitional economies. In addition, obesity, distress, and lack of marital support were
associated in men with a significantly greater risk for poorer physical health than in
women. An anomalous finding was the greater risk of poorer physical health with
alcohol intake among women (OR=0.1.71) but not among men.
Mobility in housing and hierarchy in education were positively associated
with physical health. Higher status occupations, professionals and white collar
workers, who participated in professional and trade organizations were at
significantly less risk for poorer physical health by about 75% than others. Manual
workers involved with unions were also at half the risk for poorer health than others.
However, this positive effect on health was reversed for union members
within an educational gradient. Union members with the lowest educational level,
incomplete secondary school, were almost 6 times more likely to have poorer
physical health, those with general secondary school – 2.5 times, and union members
with completed higher education – 2 times. A third-order interaction between
education, occupation, and formal group membership could not be estimated due to
small cell sizes (5x4x4 matrix). It is likely that negative health of union members is
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associated with specific work environments, as well as gender and education.
When type of inequality in urban area was controlled, these relationships
followed the educational and occupational gradient, but varied by type of inequality
(Table 28). Union members with the lowest educational level were at 5 times greater
risk for poorer physical health than other groups in both low access and high
development areas. Union membership among professionals or those with incomplete
higher education, as well as lack of social cohesion and mobility in workplace, were
risks only for residents of high development areas.
The education-occupation hierarchy was evident particularly in low access
areas. Professionals with higher education were 40%-70% less likely to have poorer
physical health, while white collar workers with secondary educations were almost 2-
3 times at greater risk than other groups.
The significant positive effect of mobility in housing on physical health
remained for both inequality areas. But women, as a whole, and married men were at
greater risk for poorer physical health if they lived in high development areas.
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TABLE 28: ORDERED LOGIT REGRESSION OF DISABILITY BY TYPE OF AVERAGEINEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF NEWRESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT b, ODDS
RATIO e^b) low access to resources high development of new resources--------------------------------------------------------------------------------------- disability | b z P>|z| e^b b z P>|z| e^b---------------------------------------------------------------------------------------HRQOLLow rated health | 0.651 4.146 0.000 1.919Life unsatisfy | 0.281 2.007 0.045 1.329 0.435 2.303 0.021 1.545Job unsatisfy | -0.473 -2.346 0.019 0.623LIFE CHANCES/ DEMOGRAPHICSage | 0.051 5.816 0.000 1.054 0.038 6.100 0.000 1.039 * Female |-0.023 -2.335 0.020 0.977female | 1.062 2.012 0.044 2.895 1.579 2.136 0.033 4.854Urban New Development | 0.332 3.707 0.000 1.394 * age | 0.002 1.771 0.077 1.004Educational LevelHigh ed6, complete| -0.834 -2.368 0.018 0.433Occupational StatusManual | -0.899 -2.337 0.019 0.406female * occupationF * white collar | 0.616 1.790 0.073 1.858educational level * occupational statusHigh ed6 * professional| 0.627 1.806 0.071 1.872Gen sec ed3 * white | -1.340 -2.631 0.009 0.261Gen sec ed3 * pension | 0.724 1.852 0.064 2.062Incomplete seced2*pens| -0.983 -2.311 0.021 0.373HEALTH CHOICESF * physical activity | 0.277 1.722 0.085 1.324CIVIC COMMUNITYFormal Networks/ group membershipsProfessional/trade grp| 0.743 2.862 0.004 2.109Social/Child group | -0.618 -1.824 0.068 0.530 -2.590 -2.363 0.018 0.075Religious group | 0.322 2.386 0.017 1.386 -0.433 -1.664 0.096 0.648educational level * formal network/group membershipHigh ed6*religious grp| 0.816 2.052 0.040 2.261female * formal networks/group membershipsF*social/child group | 3.037 2.546 0.011 20.856Occupational status * formal networks/group membershipWhite collar*union | -0.982 -2.789 0.005 0.375White collar*religious| 0.611 1.850 0.064 1.842Manual * religious | 1.345 2.495 0.013 3.839Pension*social/child | 1.995 3.174 0.002 7.359Social CohesionAnomie | 0.390 3.419 0.001 1.478F * anomie | -0.354 -2.706 0.007 0.701distress | 0.138 3.143 0.002 1.149F*distress | -0.092 -1.954 0.051 0.915 0.076 2.754 0.006 1.080--------------------------------------------------------------------------------------- coeff. std.err. coeff. std.err. _cut1 | 4.208 .4466 _cut1 | 4.375 .7524 _cut2 | 6.787 .7850 _cut2 | 6.473 .4762
-------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds IMPAIR across responsecategories: chi2(37) = 47.88; Prob > chi2 = 0.1085; (Ho not rejected; b constant)--------------------------------------------------------------------------------------link test for model and dependent variable specification: N of obs = 1237; R2 = 0.1161 N of obs = 689; R2 = 0.1389------------------------------------------------------------------------------ Oimpair | coef. z P>|z| coef. z P>|z|---------+-------------------------------------------------------------------- _hat | .585 1.548 0.122 .350 0.800 0.424 _hatsq | .057 1.109 0.268 .083 1.488 0.137(Ho not rejected for low access model; Ho not rejected for high dev. model)------------------------------------------------------------------------------------- b = raw coefficient; P>|z| = p-value for z-test;e^b = exp(b) = factor change in oddsfor unit increase in X
177
DIMENSIONS OF PHYSICAL HEALTHIndividual agency in combination with social factors change health outcomes.
Downward drift or social selection, due to initial or changing health status, are not
necessarily the antecedent causes of proximal risk factors for all outcomes of disease.
One set of determining factors cannot be generalized from one outcome to various
other outcomes unless specifically demonstrated within the same model. The factors
which were associated with disability and chronic conditions varied by inequality
context and psychosocial determinants.
The structures of disability and chronic diseases, as opposed to acute
symptoms or daily functioning with high energy, illustrate to what extent global
indicators need to be supplemented by information from specific indicators. Specific
indicators also need to be balanced by global assessments of health, which do not
tend to exclude multiple, complex, polymorphous illness states affecting and affected
by the functional levels of everyday activities.
DISABILITY AND CHRONIC CONDITIONSAn international comparison of disability-free life expectancy in 7
industrialized countries, before 1990, indicated that the proportion of disability years
varied between a low 11% in men to a high of 24% in women. Disability was greatest
among the poorest, and was affected the most by diseases of the circulatory system
(Robine, et al., 1991). The ranking of diseases which affect mortality, morbidity, or
disability are not the same. This order can vary by region and country. The most
important diseases affecting disability-free life expectancy in the West were first,
skeletomuscular and locomotor disorders, second, circulatory disorders, and third,
diseases of the respiratory system.
The threefold increase in disability, between 1980 and 1995 in Russia, was
178
due primarily to an increase in diseases of the circulatory system among the working
age population. Almost one in ten adults was classified as disabled by survey data,
substantially greater than official statistics. This was most likely due to the contrast
between self-reported assessments of the surveys and medically certified data
collected by local polyclinics for government agencies.
The reasons for disability in Russia were not completely consistent with the
rank order of disease-specific causes of death. Trauma, accidents, and homicides
ranked first among causes of death while diseases of the circulatory system accounted
for the greatest number of disabled. Disability and mortality for neoplasms, the third
major cause of death and the second cause of disability, have not shown any major
trend changes for this period. While disability has increased only slightly for diseases
of the respiratory and digestive systems, mortality rates, however, have more than
doubled.
Disability and chronic diseases were intimately linked in Moscow because
57% of all mortality was due to cardiovascular disease, the primary cause of
disability, and the prevalence rates for heart disease were higher in Moscow than in
Russia as a whole. Occupational accidents did not account for increased disability in
Moscow. This implies that personal behavior may be relevant to mortality from
external causes, but that disability is related to other than personal factors.
Conceptualization and measurement of disability in Russia has traditionally
been linked to physical dysfunction in the performance of a socially necessary role.
Disability has been defined as the loss of working capacity either completely or over
a protracted period because of trauma or chronic disease. There is some overlap
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between the physical health profile conceptualization of disability and that of the
official Russian statistical agency which maintain public health data. Disability was
considered by each as impairment of working capacity by illness or injury .
The definition used by the Alameda Physical Health Profile was constructed
from multiple self-reported items, summarized in Tables 15 and in detail in
Appendix 10. The disability category was the assumed to have the worst health, and
consisted of 6 questions covering functional limitations, mobility, as well as being
unable to work or needing to cut down on hours worked due to some illness or injury.
Chronic conditions consisted of a check list enumerating specific observable
impairments or conditions.
Hypertension, respiratory and heart disease were prevalent among 34% of
adults in the HRQOL survey. Arthritis, hepatitis, and gastritis occurred in 10% to 16%
of Moscow adults. Type of inequality was associated with specific diseases: 40% of all
hypertension, respiratory and heart disease cases were located in low development-low
access urban areas; 9% in high development-high access areas; and 16% in low
development-high access areas; tuberculosis was significantly more prevalent in low
access urban areas. Poor self-rated health was a significant risk for disability and
chronic conditions in low development-low access areas.
Women were almost 5 times more likely than men to have disabilities if living
in high development areas and 3 times more likely if living in low access areas. There
was no direct gender effect for chronic conditions (Table 29).
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TABLE 29: ORDERED LOGIT REGRESSION OF CHRONIC CONDITIONS BY TYPE OFAVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENTOF NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGITb, ODDS RATIO e^b)____________________________________________________________________ low access to resources high development of new resources--------------------------------------------------------------------------------------- N chronic conditions| b z P>|z| e^b b z P>|z| e^b-------------+-------------------------------------------------------------------------HRQOLLow rated health | 1.181 8.922 0.000 3.259Life unhappy | 0.426 2.616 0.009 1.531Job unsatisfy | -0.237 -1.809 0.070 0.788LIFE CHANCES/ DEMOGRAPHYage | 0.041 8.537 0.000 1.041 0.052 9.151 0.000 1.054Educational LevelGen second ed3 | -0.398 -2.276 0.023 0.671Tech second ed4 | -0.573 -2.581 0.010 0.563Higher ed6, completed | -0.902 -2.671 0.008 0.405Occupational StatusProfessional | -0.824 -2.339 0.019 0.438educational level * occupational statusIncompl seced2*pension| -0.521 -1.783 0.075 0.593Tech seced4*pension | 0.829 1.940 0.052 2.292Tech seced4*profession| 0.949 2.995 0.003 2.584High ed5*white collar | -1.593 -2.190 0.028 0.203High ed6*white collar | -0.700 -2.267 0.023 0.496MobilityMove house | 0.304 1.794 0.073 1.355HEALTH CHOICESObesity | 0.227 1.830 0.067 1.254Any smoking | 0.215 1.599 0.110 1.240Any alcohol | -0.599 -3.402 0.001 0.549 -0.398 -2.568 0.010 0.671
* Female | 0.296 1.680 0.093 1.344CIVIC COMMUNITYInformal NetworksN friends contact | 0.011 1.601 0.109 1.011Formal Networks/ group membershipsUnion group | -0.502 -1.672 0.095 0.605 -0.701 -2.682 0.007 0.496Religious group | 0.328 2.822 0.005 1.388Social/Child group | 0.657 1.979 0.048 1.929female * formal network/group membershipF * union group | 0.679 3.219 0.001 1.972educational level * formal network/group membershipIncompl sec ed2*union | 0.977 2.852 0.004 2.658Tech sec ed4*religious| 0.663 2.732 0.006 1.941High ed6*religious grp| 0.548 1.972 0.049 1.730High ed6*union group | 0.793 2.114 0.035 2.212occupation * formal networks/group membershipsManual * union group | 0.740 2.337 0.019 2.096Pension * union group | 0.956 2.530 0.011 2.602 1.093 2.844 0.004 2.983White col*social/child| 0.978 2.449 0.014 2.660White collar*union grp| 0.892 2.787 0.005 2.440Profession*union group| 0.883 1.977 0.048 2.420Social CohesionAnomie | 0.106 2.173 0.030 1.112Distress | 0.148 8.343 0.000 1.160 0.174 6.794 0.000 1.191-------------------------------------------------------------------------------------
coef. std.err. coef. std.err. _cut1 | 2.250 .353 _cut1 | 2.956 .435 _cut2 | 3.550 .363 _cut2 | 4.164 .451
_cut3 | 4.951 .375 _cut3 | 5.498 .470-------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds CHRONIC across responsecategories: chi2(123) = 154.30; Prob > chi2 = 0.0294; (Ho rejected; b not constant)--------------------------------------------------------------------------------------link test for model and dependent variable specification: N of obs = 1237; R2 = 0.2017; N of obs = 689; R2 = 0.1959;Ochronic | coef. std.err. P>|z| coef. std.err. P>|z|
181
---------+----------------------------------------------------------------------------- _hat | 1.087 .158 0.000 1.159 .253 0.000 _hatsq | -.014 .023 0.564 -.019 .031 0.513(Ho not rejected for low access model; Ho not rejected for high dev. model)---------+----------------------------------------------------------------------------- b = raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = factor change in odds for unit increase in X---------------------------------------------------------------------------------------
There was no consistent hierarchical relation of education or occupational
status with disability or chronic conditions, controlling for type of inequality.
However there was a significant interaction between inequality, education and
occupation. University and higher educated professionals had the greatest risk for
disability in high development areas but least risk for chronic diseases in low access
areas.
Men were at greater risk than women for disability due to psychological
distress in low access areas and due to lack of social cohesion in new development
areas. Housing mobility, anomie and distress were risk factors for chronic conditions
in both men and women in new development areas.
Membership in various social groups is associated with social trust, a more
effective government, and public well-being (Putnam, 1993). Alameda County, and
other longitudinal studies, have demonstrated, since 1965, the important protective
effects which the number and size of informal and formal group networks have upon
longevity, even under circumstances of relative social and economic deprivation
(Mossey, 1982; Idler, 1992; Marmot, 2001; Marmot, 2002).
Participation in social groups has been related to better health in Russia (Rose,
1998). The New Russian Barometer reported that the odds for poor physical
functioning were greater for those persons who could rely only on formal institutions,
such as the state, employer, charity, or church, for help with problems (Rose, 1998).
182
This indicates that participation as a risk in Moscow might be due to the lack of other
resources. This emphasizes the importance of family and friends to health status.
Chronic conditions were more likely among those who reported more
interactions with friends (but not family), and formal groups, such as unions and
religious groups. This indicates that, given the ubiquity of the extended family,
friends may be more important in procuring help. It is possible that only certain group
memberships may be important for health, such as those which provide
psychologically or economically-related support.
On the whole, social participation in new development areas had a protective
effect against disability and chronic conditions, except for women’s involvement with
social or child-related groups. These women were 21 times more likely to have
disabilities. It is unclear what this significant relation really indicates unless group
memberships are health related and the children are also disabled. Data on children
was not available in this sample.
Those participating in religious groups were on the whole at greater risk for
disabilities and chronic conditions. White collar union members in low access areas
were less likely to have disabilities, but more likely to have chronic conditions. Union
members in all occupational groups were at greater risk for chronic conditions if they
lived in low access areas, as did union members with the lowest and highest
educational levels in high development areas.
Several lifestyle factors, associated with mortality, were not found to be
associated with disability in Moscow. Obesity, smoking, and alcohol consumption,
were associated with chronic conditions in low access areas. Drinking alcohol
183
predicted health outcome independently of other factors in a direction inconsistent
with mortality studies. Drinking alcohol was related to less risk for chronic conditions
among men but not women, and less risk for acute symptoms for both men and
women.
On the whole, the extent to which social factors, such as marriage, education,
social integration, as well as health choices, influence physical health is determined,
not only by model specification (Duncan et al., 1993), but by a larger culturally-
specific context (Blaxter, 1990; Macintyre, et al., 1996; Seeman, 1988). The disabled
were 12% of the sample and chronic conditions were the most prevalent, comprising
58% of the sample. This was consistent with other surveys and Goskomstat data
(Maximova, 1995). Type of average inequality, life chances, lack of social cohesion
and social participation were significant risks for disability and chronic conditions.
ACUTE SYMPTOMSGenerally, there were significant interactions between gender, education,
occupation. Women in new development areas were almost 5 times more likely to have
acute symptoms than men. Health choices, distress, poor marital support, and mobility
in workplace were associated with greater risk for acute symptoms in all urban areas.
Drinking alcohol reduced the risk for acute symptoms, as did smoking for women.
An inequality and education gradient effect was significant among pensioners.
Those with incomplete secondary school had 50 times greater odds for acute symptoms
than pensioners with a completed higher education in new development areas (Table
30).
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TABLE 30: ORDERED LOGIT REGRESSION OF ACUTE SYMPTOMS BY TYPE OF AVERAGEINEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF NEWRESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT b, ODDS
RATIO e^b) low access to resources high development of new resources
------------------------------------------------------------------------------------- N Acute Symptoms | b z P>|z| e^b b z P>|z| e^b-------------------------------------------------------------------------------------HRQOLLife unhappy | -0.383 -2.814 0.005 0.681 -0.359 -1.972 0.049 0.698Low rated health | 1.298 10.062 0.000 3.663LIFE CHANCES/ DEMOGRAPHICSage | 0.020 4.764 0.000 1.020 0.027 3.163 0.002 1.028female | 1.584 6.487 0.000 4.876married | 0.517 3.084 0.002 1.678 0.420 1.864 0.062 1.522Urban New Dev | 0.470 1.871 0.061 1.600 * age | -0.009 -1.636 0.102 0.990Educational LevelIncomplete second ed2| 0.708 1.692 0.091 2.030 -4.790 -3.636 0.000 0.008Gen second ed3 | -1.651 -2.070 0.038 0.191Tech second ed4 | -2.121 -2.512 0.012 0.119High ed5, incomplete | -2.331 -2.621 0.009 0.097High ed6, complete | -3.973 -4.193 0.000 0.018Female * educational statusF * incompl sec ed2 | -0.967 -2.035 0.042 0.380Occupational StatusManual | -0.750 -2.358 0.018 0.471MobilityMoving jobs | 0.277 2.300 0.021 1.320 0.558 3.298 0.001 1.748Female * occupational statusF * manual | -0.775 -1.799 0.072 0.460F * pension | -2.019 -3.696 0.000 0.132educational level * occupational statusInc second ed2*white | 1.479 1.646 0.100 4.391Inc second ed2*pension| 4.018 3.246 0.001 55.619Gen second ed3*pension| 1.339 1.998 0.046 3.818Tech sec ed4*pension | 1.487 1.782 0.075 4.426High ed6*pension | 3.139 3.930 0.000 23.102High ed6*white collar| 2.110 3.256 0.001 8.255High ed6*professional| 1.718 2.972 0.003 5.577Health ChoicesAlcohol | -0.204 -1.685 0.092 0.814 -0.321 -1.879 0.060 0.724Any smoking | 0.695 3.342 0.001 2.004 0.672 2.392 0.017 1.959 * Female | -0.754 -2.976 0.003 0.470 -0.580 -1.642 0.101 0.559F *Physical Activity | 0.278 1.964 0.050 1.320CIVIC COMMUNITYFormal Networks/ group membershipsUnion group | -0.508 -2.377 0.017 0.601 -2.456 -2.401 0.016 0.085Social/child group | 1.107 2.526 0.012 3.025female * formal networks/group membershipsF*social/child group | 1.563 2.336 0.020 4.773F * religious group | 0.321 2.358 0.018 1.379F * union | 0.626 2.746 0.006 1.870occupation * formal networks/group membershipsPension*union group | 0.511 1.617 0.106 1.667 1.012 1.845 0.065 2.752Manual*union group | 0.690 1.876 0.061 1.994 0.704 1.948 0.051 2.022White col*religious | -0.692 -2.251 0.024 0.500White col*social/child|-1.263 -2.074 0.038 0.282Profession*soc/child | -0.994 -1.735 0.083 0.369education * formal network/group membershipIncompl sec ed2*union| 5.077 3.542 0.000 160.339Gen sec ed3 * union | 1.778 1.693 0.090 5.918Tech sec ed4*religious| 0.891 2.613 0.009 2.438Tech sec ed4*union | 2.244 2.062 0.039 9.438High ed5,incom*union | 2.793 2.374 0.018 16.336High ed6,compl*union | 2.226 2.089 0.037 9.263High ed6,compl*relig | 0.481 1.680 0.093 1.617
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Social CohesionF * anomie | 0.053 1.608 0.108 1.054Distress | 0.237 10.728 0.000 1.267 0.359 10.660 0.000 1.432Social SupportPoor marriage | 0.070 2.376 0.018 1.073 0.117 2.977 0.003 1.124---------------------------------------------------------------------------------------- Coef. Std.Err. Coef. Std.Err. _cut1 | .966 .316 _cut1 | -.264 .918 _cut2 | 2.091 .3196 _cut2 | .996 .920 _cut3 | 3.071 .325 _cut3 | 1.912 .923 _cut4 | 3.922 .3331 _cut4 | 2.745 .925----------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds ACUTEacross response categories: chi2(123) = 136.71 Prob > chi2 = 0.1879(Ho not rejected; b constant) linktest: link test for model and dependent variable specification: N of obs = 1237; R2 = 0.1745; N of obs = 689; R2 = 0.2023;---------------------------------------------------------------------------------- Oacute | Coef. Std.Err. P>|z| Coef. Std.Err. P>|z|---------+-------------------------------------------------------------------- _hat | 1.203 .167 0.000 1.224 .127 0.000 _hatsq | -0.031 .024 0.204 -0.051 .024 0.033(Ho not rejected for low access model; Ho rejected for high dev. model, transformdependent variable or respecify model)---------+---------------------------------------------------------------------------- b= raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = factor change in odds for unit increase in X--------------------------------------------------------------------------------------
The risk for acute symptoms varied by type of average inequality in urban areas
and by interactions between education and occupations and between education and
group memberships. Women participating in unions (in low access areas) and religious
groups or social/child-related groups (in new development areas) were at greater risk
for acute symptoms. Manual workers or pensioners who were union members had
greater odds in new development areas for acute symptoms. Professional and white
collar workers who participated in social or child-related groups (in low access areas)
and white collar workers involved in religious activities (in high development areas)
were at half the risk for acute symptoms.
The hierarchical and interaction effects of education were more pronounced
with union participation in high development areas. Union members with incomplete
secondary education (OR=160.34) were over 150 times at greater risk for acute
symptoms than union members with a completed higher education (OR=9.26).
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TABLE 31: ORDERED LOGIT REGRESSION OF LOW ENERGY LEVELS BY TYPE OFAVERAGE INEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH
DEVELOPMENT OF NEW RESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVICCOMMUNITY (LOGIT b, ODDS RATIO e^b)
--------------------------------------------------------------------------------------- low access to resources high development of new resources---------------------------------------------------------------------------------------N low energy symptoms | b z P>|z| e^b b z P>|z| e^b---------------------------------------------------------------------------------------HRQOLLow rated health | 1.077 8.609 0.000 2.94Life unsatisfy | 0.303 1.856 0.064 1.35Job unsatisfy | 0.450 3.521 0.000 1.57 0.521 3.080 0.002 1.68LIFE CHANCES/ DEMOGRAPHICSage | 0.027 5.247 0.000 1.027F * age | 0.018 4.719 0.000 1.019F * married | 0.241 1.774 0.076 1.273Educational LevelIncomplete second ed2 |-1.529 -2.178 0.029 0.216 -2.917 -4.705 0.000 0.054Tech second ed4 |-0.828 -3.141 0.002 0.436Higher ed6, completed | 0.867 2.616 0.009 2.379Female * educational levelF * incomplete sec ed2| -1.121 -2.236 0.025 0.325F * gen second ed3 | -0.980 -3.594 0.000 0.375F*high ed6, completed | -1.063 -3.508 0.000 0.345Occupational StatusManual | -0.965 -3.323 0.001 0.381White collar | -0.733 -2.191 0.028 0.480female * occupational statusF * manual | 0.889 2.280 0.023 2.433F * white collar | 0.813 2.982 0.003 2.255 1.088 2.857 0.004 2.970F * professional | 0.955 3.083 0.002 2.600educational level * occupational statusInc sec ed2*pension | 1.651 3.006 0.003 5.213 1.989 3.338 0.001 7.309Gen sec ed3*pension | 0.918 2.343 0.019 2.506Gen sec ed3*manual | -1.373 -3.653 0.000 0.253Gen sec ed3*profess | -1.271 -2.472 0.013 0.280Tech sec ed4*pension | 1.664 3.572 0.000 5.285Tech sec ed4*manual | 1.284 3.359 0.001 3.612High ed5*white collar| -1.105 -1.758 0.079 0.331High ed6*white collar| -1.153 -3.195 0.001 0.315High ed6*profession | -0.827 -2.608 0.009 0.437HEALTH CHOICESPay private MD | 0.375 2.403 0.016 1.455Any smoking | -0.419 -2.622 0.009 0.657
* Female | -0.288 -1.919 0.055 0.749Physical Activity | -0.737 -3.464 0.001 0.478
* Female | 0.766 3.534 0.000 2.151
CIVIC COMMUNITYInformal NetworksN family contact | 0.016 2.450 0.014 1.016N friends contact | 0.018 2.102 0.036 1.018Formal Networks/ group membershipsSocial/child group | -0.531 -2.090 0.037 0.587 -0.633 -1.822 0.068 0.530Union group | 0.767 3.045 0.002 2.153occupation * formal networks/group membershipsPension*union | -0.561 -1.626 0.104 0.570Manual*social/child | -1.304 -1.605 0.109 0.271Manual*religious grp | -0.624 -2.062 0.039 0.535Whitecollar*religious| -0.596 -2.461 0.014 0.551Profession*religious | -0.542 -2.133 0.033 0.581Profession*union grp | -0.868 -3.104 0.002 0.419education * formal network/group membershipInc second ed2*union | 1.619 2.935 0.003 5.051 2.019 3.386 0.001 7.532Gen sec ed3*soc/child| 1.033 1.651 0.099 2.809Gen sec ed3*union grp| 0.650 2.247 0.025 1.915High ed5*union group | -0.656 -1.959 0.050 0.518
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High ed6*religious grp| 0.545 2.055 0.040 1.724female * formal networks/group membershipsF * religious grp | -0.605 -3.515 0.000 0.545F * union group |-0.677 -2.584 0.010 0.508
MobilityMove housing | -0.240 -2.021 0.043 0.785Social CohesionDistress | 0.156 8.377 0.000 1.168 0.157 6.620 0.000 1.170------------------------------------------------------------------------------------- _cut1 | .0202325 .3062224 -1.937179 .4033964 _cut2 | 1.520407 .3080538 -.2646692 .3614377 _cut3 | 3.239231 .3190224 1.18773 .358052 _cut4 | 2.944496 .3740368-------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds LOENERGY
across response categories:
chi2(123) = 136.88
Prob > chi2 = 0.1852(Ho not rejected; b constant)
link test for model and dependent variable specification:. linktest: N of obs = 1237; R2 = 0.1397; N of obs = 689; R2 = 0.1076;------------------------------------------------------------------------------Oloenerg | Coef. Std. Err. P>|z| Coef. Std. Err. P>|z|---------+-------------------------------------------------------------------- _hat | 1.191366 .1546489 0.000 1.037461 .1859431 0.000 _hatsq | -.0410135 .0308527 0.184 -.0096686 .0437886 0.825(Ho not rejected for low access model; Ho not rejected for high dev. model)---------+-------------------------------------------------------------------- b= raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = factor change in odds for unit increase in X------------------------------------------------------------------------------
The religiously active with technical secondary school (40% of white collar workers) or
with higher education (23% of white collar workers) had 1.5 to 2.5 times greater risk
for acute symptoms in new development areas, in contrast to white collar workers in
religious groups. Not all the interactions between education, occupation, and formal
networks were estimable due to small cell sizes.
Job satisfaction was not a significant risk for acute symptoms, although it was a
risk for chronic conditions in high development areas.
ENERGY LEVELSSocial determinants of high and low energy levels varied by type of
inequality. Poor self-rated health, life unhappiness, life dissatisfaction, and job
dissatisfaction were significant risks for low energy. Self-rated health and life
satisfaction, although associated with low energy, were not related to high energy.
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Life unhappiness and job dissatisfaction were about two-thirds less likely to be
associated with high energy than happiness and satisfaction. High and low energy
dimensions were defined by the same variables but with different response categories,
the patterns can be interpreted much as the responses of happiness and satisfaction
(Figure 23) which were affected by a large midpoint category (“somewhat”),
precluding an inverse relationshop.
On the whole, living in low access than in high development areas was a greater
risk for low energy. A significant interaction between gender, education, occupation
and group membership also followed a hierarchical gradient in low access areas.
Women in all occupations were over 2 times more likely to have low energy than
employed men. However, men in the lowest and highest educational categories,
excluding mid-level education, were more at risk for low energy functioning than
women of the same educational levels (Table 31).
In low access areas, lower educated pensioners (incomplete secondary, general
secondary and technical secondary school) were 2.5 to 5.3 times at greater risk for low
energy than higher educated professionals (OR=0.44) or white collar workers
(OR=0.32). There was also a significant hierarchical effect of social status on low
energy: union members with incomplete secondary school had more than 5 times
greater odds for low energy (OR=5.1) than those with higher education (OR=0.52).
Participating in social networks was inversely associated with low energy to the
same extent among professionals, pensioners, manual and white collar workers who
were involved in either union or religious groups. Being less likely to have low energy
was not synonymous with having better health. On the contrary, it could be interpreted
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TABLE 32: ORDERED LOGIT REGRESSION OF HIGH ENERGY LEVELS BY TYPE OF AVERAGEINEQUALITY IN URBAN AREA (LOW ACCESS TO RESOURCES; HIGH DEVELOPMENT OF NEWRESOURCES) AND LIFE CHANCES, HEALTH CHOICES AND CIVIC COMMUNITY (LOGIT b, ODDS
RATIO e^b)----------------------------------------------------------------------------------------- low access to resources high development of new resources-----------------------------------------------------------------------------------------N High Energy symptoms| b z P>|z| e^b b z P>|z| e^b-------------+---------------------------------------------------------------------------HRQOLLife unhappy | -0.283 -2.212 0.07 0.75 -0.405 -2.436 0.015 0.66Job unsatisfy | -0.424 -3.192 0.001 0.65 -0.460 -2.565 0.010 0.63LIFE CHANCES/ DEMOGRAPHICSage | -0.015 -3.108 0.002 0.98 -0.029 -5.383 0.000 0.97F * married | -0.250 -1.738 0.082 0.77 -0.494 -2.358 0.018 0.69Educational LevelIncomplete second ed2 | 0.908 2.829 0.005 2.47 0.967 3.493 0.000 2.63High ed6, completed | -0.700 -2.475 0.013 0.49female * educational levelF * high ed, completed| 0.907 2.821 0.005 2.47 0.524 2.433 0.015 1.68Occupational StatusManual | 1.301 4.397 0.000 3.67White collar | 0.921 2.473 0.013 2.51Professional | 1.441 4.395 0.000 4.22female * occupational statusF*pension | -0.932 -3.259 0.001 0.39F*manual | -0.904 -2.424 0.015 0.44F*white | -1.169 -2.863 0.004 0.31F*professional | -1.236 -3.297 0.001 0.29educational level * occupational statusgen sec ed3*manual | 1.296 3.870 0.000 3.65gen sec ed3*profession| 1.096 2.226 0.026 2.99tech sec ed4 * manual | -0.804 -2.371 0.018 0.44MobilityMoving jobs | 0.211 1.734 0.083 1.23HEALTH CHOICESAny smoking | 0.411 2.438 0.015 1.58Physical Activity | 0.362 1.742 0.081 1.43 0.640 2.544 0.011 1.89
* Female | -0.405 -1.694 0.090 0.66 -0.676 -2.349 0.019 0.58CIVIC COMMUNITYInformal NetworksN family contacts | -0.017 -2.595 0.009 0.98N friends contacts | -0.021 -2.296 0.022 0.97Formal Networks/ group membershipsSocial/child group | 0.863 2.514 0.012 2.37occupation * formal networks/group membershipspension*union group | -1.417 -3.474 0.001 0.24white collar*profession| 0.846 1.812 0.070 2.33education * formal network/group membership ed2union | -1.002 -2.480 0.013 0.36 ed6relig | -0.418 -1.868 0.062 0.65female * formal networks/group membershipsF * religious group | 0.315 2.058 0.040 1.37 0.706 3.801 0.000 2.02Social CohesionF * anomie | 0.090 2.447 0.014 1.09distress | -0.193 -10.030 0.000 0.82 -0.170 -6.582 0.000 0.84Social SupportF * Poor marriage | -0.084 -2.351 0.019 0.91----------------------------------------------------------------------------------------- Coef. Std.Err. Coef. Std.Err. _cut1 | -2.284 .338 _cut1 | -3.222 .413 _cut2 | -0.666 .331 _cut2 | -1.457 .395-----------------------------------------------------------------------------------------Approximate likelihood-ratio test of proportionality of odds HIENERGY across responsecategories: chi2(123) = 146.85; Prob > chi2 = 0.0702 (Ho rejected; b not constant)-----------------------------------------------------------------------------------------link test for model and dependent variable specification: N of obs = 1237; R2 = 0.1342; N of obs = 689; R2 = 0.1470;
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Coef. Std.Err. P>|z| Coef. Std.Err. P>|z| _hat | 1.136 .1488 0.000 .821 .265 0.002 _hatsq | 0.039 .0388 0.309 -.035 .051 0.487(Ho not rejected for low access model; Ho not rejected for high dev. model)----------------------------------------------------------------------------------------- b= raw coefficient; z = z-score for test of b=0; P>|z| = p-value for z-test; e^b = exp(b) = factor change in odds for unit increase in X__________________________________________________________________________________________
as being more likely to have chronic or acute conditions, unless the energy scale was part
of the physical profile as the positive tail. Further, women involved with unions or
religious groups had smaller odds for low energy than men, consistent with the
interpretation that a decrease in low energy meant more chance of poor health.
Greater likelihood for having high energy levels in everyday activities was
associated with a gradient in occupational status among men, but not women, living in
low access areas, and among men who engaged in physical activities (Table 32). High
energy was also more likely with mobility in workplace; involvement in religious
activities for women and professional/trade groups for white collar workers.
EFFECT OF INEQUALITY ON PHYSICAL HEALTHThe effect of educational or occupational hierarchy on physical health may have
been due to the contextual effect of inequality or to variation in the composition of
urban areas. The latter is consistent with the patterns of demographic mobility and
urban growth in Moscow. The new development of employment and housing markets in
peripheral areas of Moscow selected for a different demographic profile of people than
in central areas. However, the effect of residing in peripheral or new development areas
on physical health was not only dependent on individual characteristics but also on the
workplace, housing, or neighborhood context. A logistic model cannot distinguish
between these effects of context and composition.
The indirect effects of composition and context on physical health were
particularly evident in the moderating the effect of social status. The effects of
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psychosocial factors were consistently significant as major determinants of poor health.
In comparison, health choices had a negligible effect, although positive for physical
fitness, smoking and alcohol consumption. A multilevel model of this relationship will
be explored further in the next chapter.
DISCUSSIONThe quality of the experience of living in urban areas characterized by inequality
was dependent on gender and social status in Moscow, rather than health behaviors or
the prevalence of a civic community. A social status effect was significant in relation to
inequality and physical health, controlling for health choices, social cohesion, and social
participation. Generally, those living in low access areas were more at risk for a poor
quality of life than people in high development areas.
Occupational mobility was associated with greater job satisfaction and better
health in high development areas. This suggests that life control or workplace context
were associated with health, consistent with other survey findings (Marmot et al., 1997;
Bosma et al., 1997).
In the QOL models and the five sub-models of the physical health profile, both
the global and specific health indicators have significant and independent effects on
each other, demonstrating that they measure unique health aspects, and, although
overlapping, are not synonymous. Self-rated health was significantly predicted by
gender, age, distress and physical health, as well as participation in formal organizations
such as religious groups or unions.
This basic pattern was consistent with those found on examining logistical
models of the separate components of the physical health profile: high energy levels,
low energy levels, acute symptoms, chronic conditions, and disability. Age was a
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consistent predictor, in the expected direction, for all five physical health components.
Gender was less consistent: although women and men had the same odds for reporting
disabilities or acute symptoms, and some gender-education-occupation interactions
were significant, men were more likely to function at a high energy level.
The difficulty in obtaining a unified picture of which individual determinants
were most significant for specific outcomes was due to the multiple interactions
between micro effects, contextual models, and several outcomes. To adequately
estimate all effects, a single regression model is required which includes direct and
interaction effects at three levels: multiple outcomes within individuals at level 1; micro
determinants of persons at level 2; and urban level inequality at level 3. Such a
multivariate multilevel model is further complicated by different measurements of the
outcome variables, which are binary, ordinal, and polytomous. The following chapter
will describe a preliminary, simpler multilevel model, which assumes the continuous
dependent variable to be physical health.
EFFECTS OF HEALTH CHOICESRemarkably, several lifestyle factors, associated with mortality, were not
associated with poor physical health. Problems with sleeping were measured as part of
the distress factor, which was a significant risk for all HRQOL outcomes. Hours of
daily sleep, snacking, or eating breakfast regularly were not significantly related to
health status. Being overweight, sedentary, not drinking alcohol or smoking, were
risks for health disorders. Except for sports, health choices did not significantly change
the odds for happiness or satisfaction. Physical fitness, in particular, was associated
with gender: greater odds for good self-rated health, less odds for disability and acute
symptoms, among men; greater odds for job satisfaction among women; and greater
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odds for life satisfaction and high energy for both men and women. Interactions
between health choices, such as smoking drinkers or overweight smoking drinkers,
were not estimated due to insufficient cell sizes.
Nutritional status was measured by the bodymass index which was calculated
following WHO recommendations. Those with high energy and no physical health
problems were most likely to have normal weight (BMI=18.6-25.0 wt/ht2), while the
overweight and obese were more likely to be disabled. Being underweight or having
inadequate food intake was not a problem in Moscow before 1992. A nutritional study
of the elderly between 1992 and 1994, based on the Russian Longitudinal Monitoring
Survey which applied the same BMI categories in national samples, found overall
increases in weight between 1992 and 1994. There were statistically significant
increases among the obese, over 60 years old, between 1992-1993; and increases (not
significant) among the normal and underweight groups (Popkin et al., 1996).
Drinking more alcohol was related to having acute symptoms, while drinking
less predicted chronic conditions. Drinking any amount was not a risk for poor self-
rated health. Similar to the findings in this survey, the New Russian Barometer survey
established, in 1996, that current smokers and those drinking alcohol had better physical
functioning (Rose, 1998). Residence in Moscow or in an urban center, having less than
a higher education, drinking at least twice a month, and reporting most severe material
deprivation were associated with being a current smoker (Bobak, 1998b). These
relationships were related to a lack of life control in some social groups.
Comparisons of odds ratios could not be made directly with the New Russia
Barometer survey because alcohol consumption was measured as monthly frequency
194
rather than quantity by the latter. Reexamination of the alcohol frequency variable for
the HRQOL data (coded as frequency of drinking once/week to 4 times/week) found a
significant odds for having chronic conditions among those drinking less frequently
(0.87 [95%CI:0.76-0.99, p<.049]), independently of other factors.
Not smoking predicted high energy but not good self-rated health. It is
noteworthy that the odds for poor physical function and smoking in the New Russia
Barometer survey (OR=0.92; 95%CI=0.56-1.52) was similar to that found in this data
for disability (OR=0.79; 95%CI=0.53-1.19) and high energy/ no health disorders
(OR=0.70; 95%CI=0.45-1.08). The odds were not significant for smoking and poor
self-rated health in the New Russia Barometer (OR=1.29; 95%CI=0.87-1.89), as well as
in the HRQOL study (OR=0.87; 95%CI=0.65-1.16).
Having a high energy level and no significant health disorders was associated
with making a smaller number of healthy choices than those with acute symptoms,
chronic conditions, or disabilities. Total lifestyle effect was calculated as a count of all 7
risk factors: body mass index, smoking, hours of sleep at night, daily snacking, eating
breakfast, participation in sports, and alcohol consumption. Those with a greater
number of low risk behaviors were more likely to have disabilities and chronic
conditions. The differences between the health groups, however, were not great, and
significance was marginal. This is consistent with similarly anomalous results, due to
smoking and alcohol intake, from multiple community surveys in Russia, as described
earlier.
For example, a longitudinal investigation of personal health practices among the
elderly, replicating the operational definitions of the Alameda County Studies, found no
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overall relation of lifestyle to mortality if the elderly had reached their seventh decades.
Among elderly men, not one of the personal health choices was associated significantly
to mortality, while among women, only never having smoked predicted longevity. The
disparity between lifestyle effect in middle aged adults and the elderly was conjectured
to be due to an initially better health status, and secondly, to the diminished importance
of health practices after a certain stage in life had been reached (Branch, 1984). This
may also be the case in Russia, except that the lifespan is shorter and the lack of
lifestyle effect or variations in expected effect may also be evident at earlier ages.
Patterns of personal lifestyles have been demonstrated to have varying effects in
different social status groups as well as cultures (Bourdieu, 1990; Cockerham, 1997).
Dietary, sleep, and work patterns are culturally specific and particular biomedical
consequences are neither necessary nor uniform across social groups or nations. The
prolonged experience of poor health may develop resilience, not only chronic stress
syndrome, in host immune systems.
Similarly, some choices, such as drinking alcohol, which have a dose-response
relationship, may have a beneficial effect when combined with social factors under
specific conditions (Marmot, 1996). Whereas “drinkers” may be lonely drinkers in
Western cultures, in Russia, for example, sharing a drink is a custom linked with
hospitality, friendship and sociability.
EFFECTS OF CIVIC COMMUNITYCivic community dimensions, on the whole, appeared to be related to happiness
and health but not satisfaction. Distressed men were at greater risk for poor health.
Union membership was positively associated with health at lower educational levels.
The effect of professionals who were union members or manual workers who were
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professional/ trade group members could not be estimated because the group sizes were
not sufficiently large in the sample. A complex interaction between gender, education,
and occupation with participation in formal networks was reflected in the various effect
patterns for different QOL outcomes.
Life control or workplace context had an indirect effect on poor health through
unhappiness, if dissatisfaction with one’s job is interpreted as an indicator for lack of
control or unsatisfactory workplace setting.
Social cohesion had a direct effect independently of psychological distress and
was consistently related in the same direction for health, happiness, and satisfaction.
Among the indicators of social capital, adjusted for inequality, membership in religious
and professional/ trade organizations was associated predominantly with disability,
whereas union membership was a risk for overall poor HRQOL. Union members were
at greater risk for poor self-rated health, dissatisfaction with their job, unhappiness with
life, chronic conditions, acute symptoms, low energy, and less likely to experience high
energy in daily activities.
Friends, rather than family members, were associated with having chronic
conditions, low energy, but satisfaction with life and job.
EFFECTS OF LIFE CHANCESA persistent effect of inequality and hierarchy in occupation and education was
significant across HRQOL. Although the gradient was significant, no consistent pattern
was associated for all QOL outcomes. Lowest educated manual workers had 9 times
greater odds for job dissatisfaction than other educational-occupational groups, although
this group did not report life unhappiness or life dissatisfaction. Professionals were most
likely to be in poor health but satisfied with their jobs and life. In contrast, those with
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university and higher educations were likely to have good self-reported health, but the
greatest likelihood for job and life dissatisfaction. Professional men were at risk for
poor health due to stress. This suggests that gender and education interactions were
important qualifiers to the direct effect of inequality and occupation on QOL.
There was a dissatisfaction gradient related to gender and occupational group
within educational level. Women, as a whole, were at greater odds to be dissatisfied
with their jobs than men. Women in all occupational groups were at greater odds for life
unhappiness than men if they lived in high development areas.
The occupational structure of socialist Moscow in 1991 had not yet begun its
reorganization to a market transition at the time of the survey. It is important to keep in
mind that the most important professional or trade organization in the Moscow of 1991
was still the Communist Party. The Party was linked to occupations through
“voluntary” membership and the most accepted and prevalent mode for economic
mobility. Party membership and the Komsomol, a social group for children and
adolescents, similar to the Scouts, may have confounded the formal network measures.
On the other hand, party affiliation provided rewards which could be seen on health
status. The confounding may exist in that formal networks in Moscow have diverse
effects, the specific group is not known and party affiliation was not asked.
As was described in earlier chapters, several socioeconomic factors have had a
negative impact on health in post-Perestroika Moscow: the disintegration of a centrally
planned economy, including the health sector, and general impoverishment of the
working population. About three-fifths of the population were the working poor. The
average pension was near the poverty level and average salaries were three times lower
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than the subsistence minimum. Low wages and short cash supply from being paid in
kind, inflated prices, marketisation of medicine, housing, electricity, water, telephones
and other utilities, increased the cost of the minimum standard of living for many
families after 1992. Such increases in economic inequality could only exacerbate the
effect of a social status hierarchy on HRQOL.
The factors associated with poor HRQOL raise some doubts about lifestyle,
especially alcohol consumption, as the primary mechanism of premature mortality in
Moscow. The pattern of HRQOL challenges the definition of the health crisis in Russia
as a personal problem, and reconceptualizes it as a social problem.
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CHAPTER 8: RESULTS OF A MULTILEVEL MODEL OF HEALTH
The working hypothesis posits that social status (education, occupation), health
choices, social support, and social capital, such as participation in formal networks
(religious groups; professional or trade groups) are related to physical health differently
in some urban areas due to the distribution of average and relative inequality across
urban areas.
Multilevel analysis explores the direct effect of individual level variables and
urban level inequality on physical health. Furthermore, the effect of urban inequality on
individual physical health can be examined as a moderating effect on individual level
predictors. The Random Coefficient cross-level model illustrates the moderating effect
of urban factors on the specific individual factors which impact on physical health.
Based on the conceptual model discussed in Chapter 1 (Figure 1) and tested in logistic
and ordinal logit regression models in Chapter 7, four hypotheses were examined in the
multilevel model:
1.) it was expected that low status individuals living in high inequality areas
would have poorer physical health than if they lived in low inequality areas, controlling
for poor health choices;
2.) it was expected that poor health choices among resident in high inequality
areas would have a greater impact on physical health than among residents in low
inequality areas;
3.) it was expected that social capital, social support, social cohesion would have
a protective or buffering effect on the physical health on individuals living in high
inequality areas, controlling for social status and health choices; the buffer effect was
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expected to be greater for low status individuals living in high inequality areas;
4.) finally, it was expected that average inequality would have an overall effect
on physical health, and relative inequality would increase the hierarchical effect of
individual social status on physical health.
There were poverty pockets in Moscow which were related to social status and
geographic location. The distribution of inequality mirrored a geographical gradient
from the city center to the city outskirts. There were some compositional differences
between areas. The peripheral areas of Moscow, with predominantly lower educated
blue-collar residents, also had a greater proportion of younger, married families, and
less single, older women than the central areas around the Kremlin. About three-fifths
of all families in Moscow were estimated to be poor. Three-quarters of larger than
average families, with three or more children, lived below the poverty level; and one-
third of poor households were larger than average due to the presence of adult
dependents or disabled family members (Shaw et al., 1994) The average family size in
1989 was 3.1 members. Urban areas with larger than average families (over 5 members)
were associated with smaller than average apartments, greater ratio of lower educated to
higher educated residents, and greater numbers of blue collar to white collar workers
(Table 33).
Relative inequality was also related, particularly in the periphery, to the two
measures of average inequality in urban areas. Urban areas with a higher proportion of
lower educated and manual workers were also undergoing new development. Greater
mean alcohol consumption areas were also strongly associated with larger families and
blue-collar workers. There was substantial average and relative inequality in Moscow at
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the close of the Perestroika period, controlling for compositional differences.
The geographic gradient of central and peripheral areas was primarily due to the
historical significance of the areas surrounding the Kremlin (in the center of the city).
Administrative and government functions were located around the Kremlin, while some
large petrochemical and automobile factories were established in outlying areas. With
urban growth, older factories were also located closer to heavily populated areas.
On the whole, the two average inequality measures were geographically
orthogonal: centrally-located high access areas did not have any new development, and
development of new resources occurred in low access peripheral areas. There were 4
outliers to this general pattern which may have affected the average inequality effect on
physical health and the normality assumption. Normality was not affected significantly
(see Appendix 2). Since relative inequality measures were not affected, and because
maintaining the largest number of groups was necessary to detect cross-level
interactions, the outliers remained in the sample.
The two outliers in the central areas (cE, cN1) exhibited high access to social
resources and high development of new resources (Figure 22). This was consistent with
the Mayor’s environmental report, which indicated that the central eastern
administrative areas had the poorest ecological condition (www.mos.ru). Two
peripheral areas (pNW, pE) had low access to resources and low development of new
resources. This also was consistent with the reported concentration of historically
established facilities in northwest, and with the location of industrial plants and
radioactive contamination in the peripheral east and southeast (Figures 20, 21).
The bivariate correlation matrices of macro (Table 33) and micro variables
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(Table 19) indicate that there was only a subset of 5 education and blue collar measures
which had a significant covariation between p=0.4 and 0.8. Other macro variables were
not significantly intercorrelated. Therefore, census rates for separate education and
occupation status by gender for each urban area were included in the multilevel model.
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TABLE 33: Bivariate correlation matrix of urban level variables, Moscow census, 1989 (n=33)* significance level = p<.05
acc dev obese etoh smoke sport anomi progr kidgr union relig famsiz blm blf apt edf edm----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------
AVERAGE INEQUALITY
Access to Resources 1.00
New Development -0.121 1.00
AGGREGATE URBAN AREA MEANS
Bodyweight 0.364 -0.220 1.00
Alcohol 0.147 0.176 -0.074 1.00
Smoking 0.197 0.080 0.246 0.323 1.00
Sports -0.032 0.101 -0.224 0.116 -0.091 1.00
Anomie -0.046 -0.240 0.345* -0.266 -0.231 0.053 1.00
Prof/Trade grp -0.027 -0.160 -0.164 -0.287 -0.249 0.084 0.392* 1.00
Child/Social grp -0.329 -0.091 0.040 -0.183 0.103 0.407* 0.128 -0.047 1.00
Union grp -0.294 -0.057 0.035 0.035 0.034 0.153 0.270 0.146 0.351* 1.00
Religious grp -0.020 0.016 -0.065 0.129 0.241 0.177 -0.291 -0.061 -0.010 0.234 1.00
POVERTY
Family Size -0.492* 0.280 0.024 0.293 0.202 0.113 -0.216 -0.261 -0.061 0.106 0.160 1.00
RELATIVE INEQUALITY
Blue Col M -0.559* 0.530* -0.118 0.219 0.034 0.086 -0.275 -0.310 0.116 0.121 0.158 0.614* 1.00
Blue Col F -0.457* 0.568* -0.122 0.251 0.056 0.059 -0.329 -0.329 0.050 0.112 0.150 0.634* 0.969* 1.00
Apt Size -0.556* 0.239 0.031 0.237 0.023 -0.129 -0.233 -0.280 0.068 0.132 0.149 0.643* 0.809* 0.807* 1.00
Lo Educ F -0.642* 0.038 -0.071 0.168 0.075 0.022 -0.015 -0.197 0.281 0.265 0.117 0.461* 0.611* 0.593* 0.721* 1.00
Lo Educ M -0.631* 0.109 –0.001 0.163 0.068 0.091 0.011 –0.202 0.164 0.257 0.163 0.501* 0.703* 0.694* 0.751* 0.912* 1.00
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SPECIFICATION OF THE MULTILEVEL MODELThe multilevel model was estimated for the Moscow sample. Physical health was
modeled as a normally distributed variable in a two-level hierarchical linear model using
HLM version 5.04 software.
A random coefficient model was used to test indivdual level and urban inequality
variables and compare the unexplained variance at each hierarchical level. Since each level
in a hierarchical model is assumed to be a separate population, urban areas were assumed to
be a sample from a hypothetical population of areas, characterized by average and relative
inequality, about which inferences could be made. The random coefficients represent the
residual or unexplained variance at each level.
Individual level predictors were allowed to be random to test between area
differences in average physical health. It was expected that the effect of individual level
social status, health choices, social capital, social support, and social cohesion on health
would be different across urban areas because of average and relative inequality in urban
areas. The means-as-outcome model estimated the effect of urban inequality on individual
physical health; the slopes-as-outcome model estimated the effect of individual level
predictors, within and between urban areas; the random cross-level model estimated the
moderating effect of urban inequality on the individual level predictors.
Table 34 compares the random intercept models estimated, while Table 35 describes
the random coefficient cross-level interaction model. Continuous variables were centered
around the grand mean. A group centered model which included aggregate group means
(Table 33) was estimated to test for compositional effects. The aggregate variables were
found to be nonsignificant and group centering was therefore not applied (Kreft and de
Leeuw, 1998). All macro level variables were centered around the grand mean.
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The linear regression assumption of homogeneity of level 1 variances was tested
with each estimated model. Heteroscedasticity of residual within-area variances was not
found to be significant.
The relationship between the power to find significant relationships with a dataset is
related to sample size and method for estimating standard errors. Level 1 estimates depend
on the total number of observations (n=1629 in this case), while level 2 depends on the total
number of groups (N=33 in this case). Simulation studies have indicated that about 30 (but
at least 20) groups with 30 observations/ group are required to find significant cross-level
interactions. Although smaller group sizes or a smaller number of groups decrease the power
of finding a significant relationship, simulation studies have also shown that small group
sizes (n=10) have a lower probability of making a Type I error than larger group sizes. There
were an adequate number of groups to conduct a multilevel analysis. Group sizes across the
33 urban areas varied from one low of 9 to a high of 123, with average size of 49.4
individuals. The smallest group of 9 individuals was dropped as a separate observation and
included in a geographically adjacent area, for a total of 32 macro level groups. Maximum
likelihood estimates have been shown to be the most efficient method for maximizing power
and estimating standard errors in multilevel models. Therefore all random coefficient
models and comparison of deviance between models were estimated with ML.
Even if a larger sample produces more robust results, the significant relations found
in this smaller study indicated that a more balanced sample with less sampling or response
bias could produce stronger contextual effects (deLeeuw, 1998; Hox, 1998; Raudenbusch,
1992).
RANDOM COEFFICIENT MODELThe multilevel analysis progressed in three basic steps: first a null random intercept
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model was estimated, then a random coefficient means-as-outcome model, followed by a
random coefficient slopes-as-outcome, and finally, the random coefficient means-and-
slopes-as-outcome model. The null model provides the intraclass correlation coefficient as a
baseline for assessing “explained” in contrast to “unexplained” variance for subsequent
HLM models. Both the random effects ANOVA (Table 18 )and the Random Intercept
model indicated that the random variation between urban groups was significant (Table 34,
model 1).
The ICC [ = τ00/(τ00 + 2)] for the null model and the RANOVA was equal to
0.008, suggesting that, although significant, urban area heterogeneity was minimal and a
negligible proportion of variance was due to group structure. About 99% of the variance in
physical health was due to individual level effects. Because the ICC is affected by
unbalanced designs, number of groups and group sizes, the statistical effect may not be
large. However, the theoretical model which specifies the contextual effects of average and
relative inequality on physical health can still be tested with this data given a significant
ICC.
The grand mean of physical health across areas was 4.184 ± 1.96 (.0394). The 95%
confidence interval was 4.1068 ≤ γ00 ≤ 4.2612. The variability of urban area means in
physical health was τ00 = u0j = 0.0143. The null hypothesis that τ00 = 0 was rejected at p<.05
level, indicating that average physical health was significantly different between areas,
although the ICC was less than 1%.
A significant intraclass correlation coefficient also indicates that if the clustered data
is not taken into account, the violation of the linear regression model assumption of
independent observations within groups will result in inflated standard errors of coefficients,
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and the likelihood of seeing nonexistent relationships (Type I errors). Even as small an
intraclass correlation as 0.01, with about 50 observations within each group, will inflate the
Type I error from the posited =.05 to about =.11, which increases to about =.17 for 100
observations per group (Kreft and de Leeuw, 1998; Snidjers, 2000).
Goodness of model fit was assessed by the deviance statistic with a 2 distribution.
A random coefficient means-as-outcome model was investigated (Table 34: model 2),
unconditional at level 1, included all the urban area predictors, to estimate how much
between group variance in the mean of physical health was due to the average and relative
inequality factors.
A random-slopes-as-outcome model, unconditional at level 2, included all the
individual level predictors, was estimated to obtain an average intercept and slope across the
32 urban areas, and to examine the amount of variation in individual coefficients across
areas (Table 34: model 3). Individual level interactions were not entered due to small cell
sizes, continuous variables were centered around the grand mean, dummy variables were not
centered.
RESULTS FOR CONTEXTUAL EFFECTSProportional reduction in variance [τ00 (RANOVA) - τ00 (model 2)] / τ00 (RANOVA)
= 0.9864] indicated that 99% of between urban area variance in the mean of physical health
was accounted for by the macro factors of average and relative inequality (Table 34: model
2). There was no longer a significant random variance across areas, indicating a good fit by
the macro model. The conditional ICC, adjusted for average and relative inequality,
indicated that all the variation in physical health was across individuals within groups,
except for 0.01%. The null hypothesis that physical health is constant between urban areas,
given average and relative inequality as the macro predictors, could not be rejected.
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The variation of average inequality measured by new development in urban areas
had a significant direct effect on the mean of physical health in areas, whereas inequality of
access to resources did not. It was removed from subsequent models. Relative inequality,
measured by global ratios of blue collar to white collar residents in an area, lower educated
to higher educated residents, and smaller apartment sizes to larger apartments impacted on
average urban physical health. Poverty, measured by the global ratio of family size, did not
vary significantly across groups and did not significantly affect physical health as a direct
effect. It was dropped from subsequent models.
RESULTS FOR FIXED EFFECTSTo examine how much individual level regressions varied between urban areas, in
both their intercepts and slopes, the random coefficient slopes-as-outcome model was
estimated as unconditional at level 2, fitting all the components of social status, health
choices, social cohesion, social support, and social capital as random coefficients (Table 34:
model3). Those variables which did not have a significant random effect were rerun as
fixed, and if not significant again, dropped from the cross-level interaction model
(incomplete higher education, marital status). All other nonsignificant variables were
conceptually important and retained in the subsequent model as nonrandomly varying. The
goodness-of-fit deviance statistic indicated that the individual level variables were a
significantly better fit than the null model to account for the variance of physical health.
The equation for modeling the within-area variability of individual level predictors
on physical health took the following form: Level-1 Model:
Y = B0 + B1*(social support) + B2*(social cohesion) + B3*(social capital-informal network-nfamily) + B4*(social capital-informal network-nfriends) + B5*(social capital-formal network-childgroup member) + B6*(social capital-formal network-professional/trade group member) + B7*(socialcapital-formal network-union group member) + B8*(social capital-formal network-religious groupmember) + B9*(health choice – pay doctor) + B10*(health choice – drink alcohol) + B11*(healthchoice-bodyweight) + B12*(health choice-smoke) + B13*(health choice-sports) + B14*(female) +
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B15*(age) + B16*(married) + B17*(life chance-mobility in housing) + B18*(life chance-mobility inwork) + B19*(life chance-occupation-professional) + B20*(life chance-occupation-manual) +B21*(life chance-occupation-white collar) + B22*(life chance-occupation-pension) + B23*(lifechance-education-complete higher) + B24*(life chance-education-incomplete higher) + B25*(lifechance-education-technical secondary school) + B26*(life chance-education-general secondary school)+ B27*(life chance-education-incomplete secondary) + R
The equation for modeling contextual effects separated the intercept and area-
dependent residual in the following form: Null Level 2 model: β0j = γ00 + u0j .
Average physical health was significantly different between urban areas, leaving an
unexplained variation between areas, given the direct effect of individual level predictors.
Two dimensions of social capital had a direct effect on physical health within areas,
with random effects: participation in professional or trade groups and being active in
religious groups. The significant random slopes indicated that the direct effect on health was
not constant across groups, a variation not entirely explained by group participation.
Social capital as membership in either child, professional, or religious groups, was
associated with poor physical health, without a random effect, which was consistent with the
basic results of the logistic regressions. Although social capital is hypothesized to have a
positive relationship to health, the Russian context may have cultural aspects which explain
this apparent reversal in expected results, and will be addressed in a later section.
The direct effect of educational or occupational status on physical health was null, on
average, within areas but varied significantly between areas. In some urban areas, specific
educational and occupational groups had significantly different physical health status. A
nonsignificant fixed effect and a significant random effect indicated a contextual interaction
could be expected in the cross-level model.
Mobility in work or place of residence, on average, were significantly related to
physical health within urban areas, a relationship which was constant across areas. Mobility
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in employment was positively associated with poor health, while mobility in place of
residence was negatively associated with physical health. People who had moved from jobs
were, on average, in poorer health than people who moved their housing accommodations.
Social cohesion had a significant direct effect on health, while social support had a
random effect and no direct effect.
Among health choices, paying a physician out-of-pocket for medical care was
positively associated with poor physical health within areas and varied between areas.
Obesity also had a random effect. Participating in sports and drinking any alcohol, on
average, were negatively associated with poor physical health within groups. Those who
were active in sports or drank alcohol were in better health than those who did not,
regardless of the urban area of residence.
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TABLE 34: RANDOM COEFFICIENT MODELS OF PHYSICAL HEALTH
Physical Health Profile (scoring from high energy to severe disability)
Random InterceptNull model
Null at L1Null at L2Model 1
Random CoefficientMacro variables forMeans-as-outcomeNull at L1All at L2Model 2
Random CoefficientMicro variables forSlopes –as-outcomeAll at L1 ^Null at L2Model 3
Est. (SE) prob est. (SE) prob. est. (SE) prob est. (SE) prob
Fixed effects Individual Level 1 Fixed effect of slope within areas
Random effect of slope between areas
physical health 4.184 (.039) p<.000 4.193 (.029) p<.000 3.387 (.185) p<.000
COVARIATESSex (female=1) 0.441 (.056) p<.000 0.0002 (.014) p<.014Age* 0.025 (.003) p<.000 0.213 (.461) p<.001Married=1EDUCATIONAL LEVELHigher, completed =1Higher, incomplete =1Tech. Secondary =1General Secondary =1Secondary, incomplete =1
0.356 (.596) p<.016
0.293 (.541) p<.0360.261 (.511) p<.0380.311 (.558) p<.082
OCCUPATIONAL STATUSProfessional =1White collar =1Manual =1Pensioner =1 0.418 (.153) p<.011
0.204 (.451) p<.0820.152 (.389) p<.0340.211 (.459) p<.0720.344 (.587) p<.086
MOBILITYAny change in place of work =1Any change in residence =1
0.125 (.054) p<.029-0.108 (.063) p<.097
HEALTH CHOICESObese =1Smokes =1Any sport *Drinks Alcohol/mos*Pays MD =1
-0.129 (.044) p<.007-0.116 (.051) p<.0330.158 (.083) p<.067
0.064 (.253) p<.007
0.093 (.305) p<.049SOCIAL COHESIONAnomie * 0.079 (.019) p<.000SOCIAL SUPPORTPoor marriage * 0.003 (.055) p<.030SOCIAL CAPITALSees N family/mos*Sees N friends/mos*Union grp member =1Child/social grp member =1Professional/trade grp =1Religious grp member =1
0.399 (0.091) p<.0000.266 (0.114) p<.0260.249 (0.072) p<.002
0.237 (.487) p<.0910.106 (.325) p<.031
^ the overall intercept is interpreted as the expected outcome for a man, average age of45.22 years, with a primary education, not working; lacking formal networks but having averageinformal networks; average social support; not overweight, not smoking, not paying for medicalcare, drinking on the average 0.264 liters/month, engaging in average number of physicalactivities; experiencing average number of anomie symptoms; not married, not mobile, etc.
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CON’T: RANDOM COEFFICIENT MODELS OF PHYSICAL HEALTHPhysical Health Profile (scoring from high energy to severe disability)
Random InterceptNull model
Null at L1Null at L2Model 1
Random CoefficientMacro variables forMeans-as-outcomesNull at L 1All at L2Model 2
Random CoefficientMicro variables forSlopes-as-outcomeAll at L1Null at L2Model 3
Est. (SE) prob est. (SE) prob. est. (SE) prob
Fixed Effects Urban Area Level 2POVERTYRatio large/average Family size
RELATIVE INEQUALITYRatio small/average Apt size
Ratio Blue/White collar MRatio Blue/White collar FRatio Low/High Ed MRatio Low/High Ed F
AVERAGE INEQUALITYNew Dev of resources, area Access to resources, area
-0.036 (.027) p<.202
-0.155 (.057) p<.012
-0.053 (.139) p<.706 0.303 (.138) p<.038-0.235 (.071) p<.003 0.278 (.066) p<.000
-0.074 (.036) p<.048 0.019 (.031) p<.530
Random effectsLevel 1 residual(within areas) variance σ 2 (sd) 1.754 (1.33) 1.749 (1.32) 1.025 (1.013)Level 2 residual(between areas)intercept variance (sd) chi sq. (df) prob. level
0.0147 (.121)χ2=44.97 (df=2)p<.05
0.0002 (.014)χ2=27.87 (df=23) p<.22
0.396 (.629)χ2=18.06 (df=19)p>.50
Deviance
Var-Cov components testChi. sq. stat (df) p-value
5552.91 (df = 2) 5531.52 (df =11)
χ2=21.39 (df=9)p<.011
5008.39 (df=407)
χ2=544.52 (df=405)p<.000
Homogeneity of L1 var Chi-sq. stat (df) p-value 25.599 (31) p>.500 25.59 (31) p>.500 20.199 (19) p<.383
*centered around grand mean; all level 2 predictors are centered around grand mean;
SUMMARYAverage physical health varied significantly between urban areas. The mean of
physical health was not constant across urban areas. Average and relative inequality, as a
characteristic of urban areas, accounted for the variation in average physical health across
areas, without including the effect of any individual level predictors. Poverty due to family
size and average inequality as access to resources were not associated with physical health
in urban areas and were dropped from the subsequent cross-level model.
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The relationship of individual level education and occupational status to physical
health varied significantly between urban areas. Among health choices, the effects on
physical health of obesity and paying out-of-pocket for medical care were significantly
different between urban areas. In addition, social support and social capital were
significantly associated with health which varied between areas. This indicated that
contextual effects were present which could be investigated in a cross-level model by the
distribution of inequality across urban areas.
The cross-level model, discussed in the following section, posits that the direct
effect on physical health of individual level life chances, health choices (obesity, paying for
medical care), social support, and participation in formal networks varies by urban area of
residence due to the area level of average and relative inequality.
THE INTERCEPT-AND-SLOPES-AS-OUTCOMES MODELThe random intercept-and-slopes-as-outcomes model includes cross-level
interactions (Table 35) to fit average and relative inequality as predictors of the random
variation in physical health between urban areas, controlling for the individual level
variables.
The cross-level random coefficient model took the following general form:Alameda Physical Health Profile = [00 + 10 (women) + 20 (age) + 30 (educ) + 40
(occup) + 50 (health choices) + 60 (social cohesion, social support) + 70 (social capital)+ 01 (urban average inequality) + 02-05 (urban relative inequality) + .. + 111 (level 1 *level 2 interactions)] + [ u0jXpij + u0j + eij ]; and where:
physical health is scored between 1=high energy to 4=chronic conditions and7=severe disability;
eij is the individual level residual term; the macro level residual terms [u1jXpij + u0j]; the overall mean effect is 00 ; and u0j is the conditional deviation of each urban area from the overall
mean, which remains unexplained, given the urban level predictors and individuallevel predictors in the model (context effect) ;
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the overall slope effect is 0q , the direct effect of the urban level predictorson the overall mean outcome;
and u1j is deviation of each urban area slope from the overall slope, whichremains unexplained, given the urban predictors, individual predictors, and micro-macro interactions in the model (context effect).
TABLE 35: RANDOM COEFFICIENT MODELS, MEANS-AND-SLOPES-AS-OUTCOMES, URBANAREA INEQUALITY EFFECT ON MEAN PHYSICAL HEALTH
Random CoefficientMeans-and-Slopes-as-OutcomeCross-level InteractionsModel 4aFixed effect Coeff Std.Err prob
Urban Level Direct Effects on Average Physical Health*MEAN PHYSICAL HEALTH G00
POVERTYRatio large/average Family size
RELATIVE INEQUALITYRatio small/average Apt size G02
Ratio Blue/White collar M G03Ratio Blue/White collar F G04Ratio Low/High Ed F G05Ratio Low/High Ed M G06
AVERAGE INEQUALITYNew Dev of resources, area G01Access to resources, area
3.471 0.178 0.000
-0.243 0.484 0.618
-0.717 0.682 0.304 0.334 0.624 0.594 0.157 0.455 0.733 0.415 0.456 0.371
0.494 0.223 0.036
Total Random effectsLevel 1 residual(within areas) variance σ 2 (sd) 0.9945 (0.989)Level 2 residual(between areas)intercept variance (sd) chi sq. (df) prob. level
0.158 (.398)χ2=20.39 (df=18)p<.310
Deviance
Var-Cov components testChi. sq. stat (df) p-value
4772.79 (df=534)
χ2=235.60 (df=127)p<.0000
Homogeneity of L1 var Chi-sq. stat (df) p-value 31.472 (21) p<.066
* all level 2 predictors are centered around the grand mean; effects are controlled for all othervariables in Table 35 and Table 38; (due to size the single cross-level model was split into fourseparate tables)
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RESULTS FOR FIXED AND INTERACTIVE EFFECTSURBAN AREA AVERAGE PHYSICAL HEALTH
Average inequality was positively associated with average area physical health,
whereas none of the measures of relative inequality were significantly related (Table 35:
model 4a). The overall average of physical health for the base comparison category
equaled was 3.47 for men of average age (44.74 years), with a primary education,
average social support, average anomie, average numbers of family and friends, drinking
an average of 0.266 liters of alcohol in the past month. Physical health was poorer in
areas undergoing development of new resources. Mean health in areas was not affected
directly by more smaller than average apartments, by more blue collar than white collar
residents, or by more low educated than high educated residents in areas. Because in
multilevel models there may be an overall average null fixed effect while variation across
levels can still be present, interactions between the individual and urban areas can be
significant. Although the intercept of model 4 was not significantly reduced by the
variables in model 4, the deviance indicating model fit was significantly smaller. There
were a number of interactions which indicated that the level of relative inequality in
urban areas had significant effects on physical health by moderating the effect of several
measures of social capital, health choices, occupational status, and education.
Women were on average in significantly poorer health than men ( = 3.92), and
the health of women was different across urban areas (Table 36: model 4b). As expected,
there was a significant direct and random effect of age on average physical health.
LIFE CHANCES
On the average, there were no significant direct effects of educational level or
occupational status on mean physical health. However, all educational and occupational
216
levels varied significantly across areas. There was an interaction effect which increased
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TABLE 36: RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTS OFURBAN INEQUALITY AND LIFE CHANCES ON AVERAGE PHYSICAL HEALTH*
Random Coefficient Means-and-Slopes-as-OutcomeCross-level InteractionsModel 4bFixed effect Coeff StdErr prob.
Random effect SD(uij) Var(uij) prob(uij)
PHYSICAL HEALTH INTRCPT1, B0 G00 3.471 0.178 0.000
U0 0.378 0.143 0.045
COVARIATES
Sex (female=1) INTRCPT2, G130 Slope B13
0.448 0.075 0.000U13 0.155 0.024 0.104
Age* INTRCPT2, G140 Slope B14
0.027 0.003 0.000U14 0.012 0.0005 0.001
EDUCATIONAL LEVELHigher, completed =1 INTRCPT2, G210 Slope B21
Tech. Secondary =1 INTRCPT2, G220 Slope B22
General Secondary =1 INTRCPT2, G230 Slope B23
Secondary, incomplete =1 INTRCPT2, G240 Slope interaction B24 *rel.inequality LOW ED F G245
-0.010 0.144 0.944
0.077 0.131 0.562
-0.104 0.133 0.441
0.143 0.1619 0.386
0.811 0.458 0.089
U21 0.505 0.255 0.001
U22 0.374 0.140 0.015
U23 0.387 0.150 0.002
U24 0.454 0.206 0.050
OCCUPATIONAL STATUSProfessional =1 INTRCPT2, G170 Slope B17
White collar =1 INTRCPT2, G190 Slope B19
Manual =1 INTRCPT2, G180 Slope interaction B18 *rel.inequality APT SIZE G182
Pensioner =1 INTRCPT2, G200 Slope interaction B20 *rel.inequality LOW ED F G205 LOW ED M G206
-0.136 0.119 0.266
-0.110 0.124 0.382
-0.095 0.136 0.490
0.898 0.432 0.048
0.275 0.167 0.112
-0.826 0.441 0.073 0.797 0.443 0.084
U17 0.220 0.048 0.033
U19 0.259 0.067 0.009
U18 0.240 0.057 0.063
U20 0.454 0.206 0.017
MOBILITYChange in place of work =1 INTRCPT2, G150 Slope interaction B15 *rel.inequality APT SIZE G152 BLUE COLLAR F G154
Any change in residence =1 INTRCPT2, G160 Slope interaction B16 *rel.inequality APT SIZE G162
-0.070 0.069 0.324
0.623 0.193 0.004-0.708 0.248 0.009
0.127 0.068 0.076
-0.326 0.184 0.089
U15 0.124 0.015 >.500
U16 0.1494 0.022 0.073
* controlling for all other variables in Table 35 through Table 38; (due to size the single cross-level model was split into four separate tables)
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the risk for poor physical health by almost 23% among men with the lowest educational
level, incomplete secondary school, who lived in urban areas which had greater ratios of
lower educated women as residents, and 36.3% for women. If manual workers lived in
areas with smaller than average apartments, the risk for poor health was increased by
26% for men , and by an additional 13% for women. The overall effect on physical health
of being a manual worker, holding urban inequality constant, was not significant.
The logistic regressions indicated that two-and three-level interactions were
significant. These could not be included in the multilevel model due to sample size
limitations and model complexity. However, given the random variances for education
and occupation, it is likely that a larger sample and a better specification of occupation
and gender-occupation-education interactions at both the micro and macro levels could
give a better model fit.
Relative inequality affected the risk for poor physical health among pensioners by
an increase of over one-fifth if they lived in urban areas with more poorly educated men,
and a decrease of nearly one-fourth if they lived in urban areas with more poorly
educated women. This apparent inconsistency is most likely due to the prevalence of
extended families with members providing economic and social support for pensioners,
which had a beneficial effect on male health in areas with more low to high educated
women (Ymen=2.65) and a negative effect in areas with more low to high educated men
(Ymen=4.27). If the pensioner was a woman, the risk for poor physical health was 13%
greater than for retired men in either area (YwomenY= 3.09, YwomenY=4.72, respectively).
The gender effect is consistent with the multiple caretaker roles of women which take
their toll on women’s health, especially if the surrounding neighborhood also lacks the
219
support provided by other women.
Mobility in housing had a negative main effect on physical health, increasing poor
health given a constant urban inequality. However, changing housing and living in areas
with smaller than average apartments had a beneficial interactive effect on health
indicating that people probably moved to better conditions. Mobility in employment was
not associated with a significant increase in average poor physical health, holding
inequality constant. However, if men changed jobs and lived in urban areas with smaller
than average apartments, their risk increased by 18% and for women by 31%. This could
be an indicator of downward drift in employment. However, mobility in employment
varied significantly across areas with more blue-collar women: reducing the risk for poor
health by 20% for men and by 7.5% for women. This could be an indication of upward
mobility in blue-collar neighborhoods. Living in areas with small apartments and more
blue collar women, reduced the risk for men by only 2.5% if they changed jobs and
increased for women by 10.5%. Although mobility in employment may be an indicator
of misfortune in Moscow rather than changing to more favorable circumstances, the
urban gender effect is consistent for that observed in occupations. The presence of
women in a Moscow neighborhood rather than men is associated with acting positively
on individual physical health for those changing jobs.
HEALTH CHOICES
Three out of five health choices varied significantly across urban areas, and four
were significantly associated with relative inequality in areas (Table 37: model4c); only
paying for medical services out-of-pocket varied directly with poor physical health,
holding urban inequality constant. Smoking did not have a fixed effect and varied
marginally across areas at p<.09. Regardless of the type of urban inequality, health
220
choices had a positive effect on physical health, with one exception. Obesity, drinking
alcohol, physical activity, and paying for a private physician decreased the risk for poor
physical health.
TABLE 37 : RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTSOF URBAN INEQUALITY AND HEALTH CHOICES ON AVERAGE PHYSICAL HEALTH*
Random Coefficient Means-and-Slopes-as-OutcomeCross-level InteractionsModel 4cFixed effect Coeff StdErr prob.
Random effect SD(uij) Var(uij) prob(uij)
HEALTH CHOICESPays MD =1 INTRCPT2, G90 Slope interaction B9 *rel.inequality BLUE COLLAR F G94 LOW ED M G96
Obese =1 INTRCPT2, G100 Slope interaction B10 *rel.inequality BLUE COLLAR F G104
Smokes =1 INTRCPT2, G110 Slope B11
Any sport * INTRCPT2, G120 Slope interaction B12 *ave.inequality G121 *rel.inequality APT SIZE G122
Drinks Alcohol/mos* INTRCPT2, G250 Slope interaction B25 *rel.inequality APT SIZE G252
0.174 0.082 0.045
0.468 0.282 0.109-0.712 0.241 0.007
0.058 0.072 0.430
-0.605 0.269 0.033
0.003 0.074 0.959
-0.083 0.062 0.191
-0.143 0.081 0.087
-0.299 0.174 0.098
-0.170 0.111 0.137
-0.527 0.3162 0.107
U9 0.118 0.013 0.188
U10 0.207 0.043 0.011
U11 0.152 0.023 0.088
U12 0.083 0.006 0.439
U25 0.308 0.095 0.025
* all level 2 predictors are centered around the grand mean; effects are controlledfor all other variables in Table 35 through Table 38; (due to size the single cross-levelmodel was split into four separate tables);
The one exception was for those who paid for private physician services and lived
in neighborhoods with a high ratio of blue to white collar women : mean physical health
for men was 3.94 and for women, 4.39. This effect was constant and marginal (p<.109)
across urban areas and may have been due to poorer initial health status or model
instability. For those paying medical costs out-of-pocket and living in areas with a higher
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ratio of low to high educated men significantly reduced the mean of average physical
health (γ = -0.712) . This is consistent with a positive health choice effect on individual
physical health. However, it is also likely that the choice effect may be confounded by
individual economic status: people had greater economic resources at their disposal and
could afford to pay for physicians in urban areas with a high ratio of low to high educated
men, or there were more intact families with dual incomes in these areas. This is also an
indicator of a moderating effect by an urban context on individual health choice.
Obesity, however, was positively associated with physical health in areas with
more blue to white collar women (γ = -.605). Relative inequality in apartment size was
also positively associated with health for those who were physically active or drank any
alcohol. The effects were however marginal and need to be re-examined with a larger
sample. Those who were physically active and lived in urban areas characterized by
average inequality had a significantly smaller risk for poor physical health (Ymen = 3.33;
Y women = 3.78). This relationship did not vary across urban areas.
SOCIAL COHESION AND SOCIAL SUPPORT
Social cohesion had a significant negative effect on average physical health,
holding urban inequality constant, and varied across urban areas (Table 38). The effect of
social support on average physical health varied by the level of average inequality and
relative inequality within urban areas and was constant across urban areas. Inequality
significantly moderated the social support effect on health and provided an adequate
explanation for this effect. Health was poorer among those with unsatisfactory marriages
who lived in areas with educational inequality among women.
SOCIAL CAPITAL
Table 38 illustrates the measures of social capital with significant fixed and
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random effects on average physical health: membership in social or child associated
groups, professional or trade organizations, and religious groups. Average inequality in
urban areas was not significant but ratios of low educational levels and blue collar
residents increased the risk for poor health among those participating in religious or
professional/trade groups. Religious group membership compounded the risk on poor
health as a direct and interactive effect, especially among women living in urban areas
with greater ratios of low to high educated men: Y men = 4.151; Y women = 4.599.
Urban area inequality in apartment size was positively related to average health
among men and increased the risk for poor health among women with greater family
contacts. This effect of social capital may also be due to the benefits provided by an
extended family to male members. Union membership increased the risk for poor health,
holding inequality constant. Having friends to rely on when necessary was not a
significant predictor within or between groups.
223
TABLE 38: RANDOM COEFFICIENT MODEL, MEANS-AND-SLOPES-AS-OUTCOME, EFFECTS OFURBAN INEQUALITY AND CIVIC COMMUNITY ON AVERAGE PHYSICAL HEALTH*
Random Coefficient Means-and-Slopes-as-OutcomeCross-level InteractionsModel 4dFixed effect Coeff StdErr prob.
Random effect SD(uij) Var(uij) prob(uij)
SOCIAL COHESION*Anomie INTRCPT2, G20 Slope B2
0.087 0.0213 0.000U2 0.068 0.004 0.042
SOCIAL SUPPORT*Poor marriage INTRCPT2, G10 Slope interaction B1 *ave.inequality G11 *rel.inequality APT SIZE G12 LOW ED F G15
-0.006 0.011 0.586
-0.028 0.014 0.072
-0.092 0.035 0.017 0.075 0.039 0.036
U1 0.021 0.0005 0.353
SOCIAL CAPITALSees N family/mos* INTRCPT2, G30 Slope interaction B3 *rel.inequality APT SIZE G32 LOW ED F G35F LOW ED M G36
Sees N friends/mos* INTRCPT2, G40 Slope B4
Child/social grp =1 INTRCPT2, G50 Slope B5
Professional/trade grp =1 INTRCPT2, G60 Slope interaction B6 *rel.inequality BLUE COLLAR M G63
Union grp member =1 INTRCPT2, G70 Slope B7
Religious grp member =1 INTRCPT2, G80 Slope interaction B8 *rel.inequality LOW ED M G86F LOW ED F G87
0.002 0.003 0.940
-0.016 0.009 0.086F 0.026 0.009 0.009
-0.020 0.010 0.037
0.002 0.004 0.561
0.422 0.138 0.006
0.139 0.131 0.326
0.826 0.481 0.098
0.168 0.083 0.053
0.226 0.077 0.008
0.454 0.224 0.053 0.371 0.211 0.091
U3 0.009 0.00008 0.348
U4 0.011 0.001 0.312
U5 0.317 0.101 0.079
U6 0.358 0.128 0.003
U7 0.231 0.053 0.315
U8 0.279 0.078 0.003
* all level 2 predictors are centered around the grand mean; effects are controlled for all othervariables in Table 35 through Table 38; (due to size the single cross-level model was split intofour separate tables);*L1 centered around grand mean: social support=2.78 items; anomie=5.25 items;nfamily=16.11 persons; nfriends=16.25 persons; age=44.74 years; alcohol=0.266 liters/pastmonth; all level 2 predictors are centered around grand mean
RANDOM EFFECTS AND SUMMARYAverage physical health was only partially explained by average inequality, the
224
residual variance was significant across urban areas. Relative inequality did not
account for overall average health but did vary significantly with specific slopes of
social status, mobility in housing, health choices, and social capital. The ratio of
apartment size was the most prevalent measure of relative inequality. The measures of
average and relative inequality accounted for different types of variation in physical
health: the main contextual effect on the overall intercept (average inequality) and the
direct and interaction effects of within area slopes which varied across areas (relative
inequality).
The addition of average and relative inequality to the Random Coefficient
Slopes-as-Outcome model 3 (Table 34) significantly decreased the variance in the
mean of physical health, and the residual between areas was no longer significant.
When the deviance was compared between model 4 and model 3, the cross-level model
was a significantly better fit (Table 35).
LIFE CHANCES
The effect of educational level or occupational status was not explained entirely
by the average and relative inequality measures. A significant residual variation
remained between urban areas. The expectation that low status individuals living in
high inequality areas would have poorer health was observed among pensioners, manual
workers, and the lowest education group.
HEALTH CHOICES
Among health choices, obesity and drinking alcohol had significant between
area residual variances, indicating that the contextual effects of inequality, as in the case
of life chances, contributed to explaining a portion of the effect on average health. The
random variation for smoking disappeared when the model was rerun with fixed effects
225
specified for those variables with insignificant random effects.
The proposition that poor health choices among residents in high inequality
areas would have a greater risk for poor health was observed for choosing to pay for
private medical services, but was not evident in the expected direction for obesity,
drinking alcohol, or smoking. Urban areas with high inequality of apartment size
differed only marginally from urban areas with low inequality in the strength of the
positive effect of individual alcohol consumption and physical activity on individual
health. Other factors remain to be considered in explaining why alcohol had a beneficial
effect on the health of men. The effect of physical activity on health was in the expected
direction but living in high inequality areas did not reverse this direction. Obesity had a
positive effect on women’s and men’s health in high inequality areas. The random
effect indicates that perhaps additional nutritional factors need to be considered to
explain this anomaly. Paying for private medical services was moderated by relative
inequality: the risk for poor health was decreased even in areas with greater ratios of
low to high educated residents. This suggests that this positive choice cushioned the
negative effect of urban inequality on physical health, consistent with the research
hypothesis.
ALCOHOL ANOMALY
The alcohol anomaly was examined in greater detail, given the significant
unexplained random residual for alcohol in the multilevel model and the established
relationship of alcohol to education and poor health and premature mortality in Russia
(Cockerham, 2000; Shkolnikov, 2001). To explain the residual, an aggregated group
mean was calculated for alcohol consumption in each area and included as a random L2
variable.
226
The effect of education on physical health status was found to be significantly
moderated by average alcohol consumption levels in urban areas. Individual differences
in educational level and physical health were influenced by macro alcohol consumption
levels as follows: in low consumption areas, physical health was poorer among the
higher educated and better among the lower educated; in high consumption areas,
physical health was better among the higher educated and poorer among the lower
educated.
At the individual level, mean physical health within educational category was
poorer with increased alcohol consumption among the lowest educational groups, and
those with incomplete higher education (Figure 24). This is evidence of a hierarchical
effect of social status on physical health, given alcohol consumption.
FIGURE 22: MEAN ALCOHOL CONSUMPTION AND MEAN PHYSICAL HEALTH BYEDUCATIONAL LEVEL, MOSCOW, 1991
Mean alcohol consumption and mean Physical Health
sta
nd
ard
ize
d v
alu
es
educational level-.2
-.1
0
.1
.2 Standardized values of (etohn) Standardized values of (xphyslow
primary incomple general technica incomple higher
227
The interaction between average and relative inequality and urban area mean
alcohol can be better illustrated by Figure 25. Areas with high access to resources but
low inequality in education among residents tend to also be average alcohol
consumption areas, and these are mostly central areas of Moscow. High inequality in
education among area residents is also associated with living in above average alcohol
consumption areas, which are peripheral areas, and have the least access to material
resources. At the individual level, higher alcohol consumption prevalence is related to a
lower educational level and urban inequality in access to resources. Alcohol
consumption is even more prevalent among those with incomplete higher education but
greater access to material resources.
It appears that for those lower educated individuals living in low alcohol
consumption areas, better physical health is related to a social advantage effect at the
macro level: greater educational egalitarianism in areas, as well as residing in areas with
greater access to material resources. For individuals with higher education living in high
alcohol consumption areas, better physical health was related to an advantage effect of
higher education at the micro level. Those with completed higher education had
prevalence rates of alcohol consumption close to the mean. It was also likely that these
individuals resided in areas which had higher access to material resources.
Poor physical health among the lower educated was associated with living in
high alcohol consumption areas, which were on the periphery of Moscow and had
greater relative inequality and less access to material resources, a cumulative
disadvantage effect.
228
FIGURE 23: MEAN ALCOHOL CONSUMPTION AND MEAN ACCESS TO SOCIAL RESOURCES BYEDUCATIONAL LEVEL, MOSCOW, 1991
[between areas, top 3 graphs, and within areas, bottom histogram]area ratio loed/hied lo inequality=0 hi inequality=1
me
an
are
a a
lco
ho
l co
nsu
mp
tio
ng
ran
d m
ea
n=
0.2
69
Educational inequality and alcohol by Moscow areasstd. values of mean access to social resources
s_edmf2==0
-0.92 0.00 1.00 2.00 3.24.1
.2
.269.3
.4
cen cenw
cenw
cen
cen
swcen
e
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n+zel
cen
cen
cen cen
s_edmf2==1
se ene
e
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s
w
cen
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s
n+zel
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Total
-0.92 0.00 1.00 2.00 3.24.1
.2
.269.3
.4
cen cenw
cenw
cen
cen
swcen
e
cencen
n+zel
cen
cen
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se ene
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mean alcohol consumption and mean access to social resources
stan
dard
ized
val
ues
educational level-.2
-.1
0
.1
.2 Standardized values of (etohn) access33
primary incomple general technica incomple higher
229
Finally, those higher educated persons with poor physical health who lived in
lower alcohol consumption areas may have been those with incomplete higher
education and greater prevalence of individual level drinking, a negative personal
effect.
SOCIAL COHESION, SOCIAL SUPPORT, SOCIAL CAPITAL
Social support and group membership in child oriented, religious, or
professional/trade groups had significant residual variation remaining after the
contextual effect of average and relative inequality on physical health. High inequality
in urban areas increased the risk for poor health given social capital measures, contrary
to expectations. There was no beneficial effect on health from formal group
membership in either a direct or interaction effect. The increased risk for poor health
was observed across inequality measures and across social capital measures.
Aggregate variables for all formal network types were included in a separate
model to test for group mean and compositional effects. None of the area mean
aggregates of child, union, professional/trade, or religious groups was a significant
predictor. The random variation suggests that other factors, such as inadequate
measurement of occupational status, or additional interactions between gender,
education, occupation, and social capital at the individual level should be considered.
Some political groups prevalent in post-Coup Moscow, such as the Communist Party,
Komsomol youth groups, other party affiliations, unsanctioned religious affiliations,
were important influences on occupational status and obtaining access to resources but
could not be specifically included in the survey questionnaire.
230
CONTEXTUAL EFFECTS
The significant contextual effects were average inequality in urban areas,
characterized by the geographic gradient of new development of resources and relative
inequality: peripheral areas, characterized by low access and development of new
resources, had a greater ratio of small apartments to large apartments, a greater ratio of
blue collar to white collar workers, and low to high educated residents. These areas
were also associated with having larger families and higher than average alcohol
consumption. The contextual effect of urban inequality on individual physical health
was significant and independent of micro level factors.
The geographic distribution of average inequality acted directly on health, while
relative inequality acted indirectly by moderating the effects of life chances, health
choices and civic community measures. A strong gender effect suggests that perhaps
separate models for physical health should be tested, considering gender –specific
personal and contextual determinants: the health of women was poorer on the average
for all variables but contextual variation, as well, was related to gender-specific
inequality.
A contextual effect was found for the area mean of alcohol consumption as an
interaction with educational level: individuals with less education, controlling for
alcohol intake, had poorer physical health if they lived in urban areas with higher
average alcohol consumption. The risk for poor physical health was decreased if these
individuals lived in urban areas with low mean alcohol consumption. The effect of
urban area mean alcohol consumption contributed an additional effect on health above
that of individual alcohol intake.
The urban inequality effect varied nonrandomly on physical health with mobility
231
in housing, paying private physicians, engaging in physical activities, social support,
and informal family networks. For all other variables, random variation remained to be
explained by factors in addition to average and relative inequality.
232
CHAPTER 9: CONCLUSIONSChapters 1, 2 and 5 described the theoretical multilevel problem of linking
individual, social and cultural parameters; Chapter 6 outlined the methodological issues
of atomistic and ecological fallacies in measuring these psychosocial and structural
parameters in a community survey. Chapter 3 described one possible solution by
applying subjective, self-perceived, self-reported indicators and two types of urban-
level inequality measures to investigate the multiple pathways of effects on multiple
health-related quality of life (HRQOL) outcomes in multilevel models. The results
reported in chapters 7 and 8 indicated that HRQOL was associated with the distribution
of average and relative inequality between the urban areas of Moscow, independently of
the following individual determinants: health choices, life chances, and parameters of a
civic community. The distribution of health was due not to statistical artifact, data
collection procedures, or sampling variation but to the unequal distribution between
urban areas of average access to material resources and relative social status.
SUMMARY OF HYPOTHESESThe basic research questions addressed in this paper were:
1.) to identify the micro and macro level risks significant for poor HRQOL
among individuals in the city of Moscow;
2.) to identify whether the determinants of each of the HRQOL outcomes
indicated the same pathway of effect or different sets of factors;
3.) to assess the additive and interactive effects on HRQOL of two dimensions
of micro level risks – personal health choices and psychosocial behaviors, such as a the
influence of social relations of a civic community, formed by social cohesion, social
support, and social capital; and
233
4.) to examine the broad hypothesis that the distribution of average and relative
inequality at the urban level moderated the physical health of individuals as a main and
joint effect with personal health choices and psychosocial behaviors.
The debate surrounding whether health effects were primarily due to absolute
material position or a comparative socioeconomic position in relation to others was
reformulated to specify that these were two aspects of social position, which had effects
on health at two different points in the distribution of social status (Marmot, 2002;
Marmot and Wilkinson, 2001): the intercept (the mean effect) and the slope (the
strength of association).
It was conjectured that absolute position influenced average health while relative
position varied with multiple social groups and multiple psychosocial determinants. It
was assumed that the average level of HRQOL was directly influenced by a social
context of average inequality and through the psychosocial pathway of health choices,
low social status, and inadequate social integration. Indirect effects arose through the
pathway of relative inequality in urban areas, which moderated the effects of
psychosocial behaviors within areas.
MAJOR FINDINGS▪ Significant individual level effects and contextual effects of average and
relative inequality on health-related quality of life were found. These micro and macro
effects varied by whether outcome was measured as life happiness, life satisfaction, job
satisfaction, self-rated health, or physical health;
▪ The distribution of inequality was an urban-level contextual determinant of
individual health independent of individual risk factors such as social status and health
choices;
234
▪ There was a significant hierarchical effect of life chances on HRQOL,
controlling for health choices; people with less education or lower status occupations
were at greater risk for poor HRQOL regardless of which health choices were made;
▪ Poor health choices did not vary consistently with urban inequality,
controlling for psychosocial behaviors;
▪ The direct hierarchical effects of individual life chances on HRQOL were
independent of the effects of a civic community, in the form of social cohesion, social
support, formal and informal networks, controlling for health choices. Overall
interactions between education with social group membership and occupation with
social group membership were significant but not generally in the direction of having a
buffer effect on health. Social group membership in professional/trade groups, unions,
or social/child-related groups increased the risk for poor health in urban areas of high
relative inequality.
▪ The risk for poor HRQOL was a cumulative function of living on the periphery
of Moscow, in high inequality areas, which had a lack of social and material resources,
were undergoing new development, and more likely to have residents with low social
status;
▪ Average inequality had an overall average effect on mean physical health, and
relative inequality moderated the hierarchical effect of individual social status on
physical health. In sum, personal physical health was affected by where people lived as
well as how they lived.
▪ Although the ICC was so small that it could have been effectively ignored, the
logistic regression results did not agree with multilevel results on many dimensions
235
because of the significant variation between urban areas. The urban context of
inequality made a significant contribution to explaining poor physical health, and
provided an insight into which and how specific psychosocial behaviors change due to
inequality. This study demonstrated that the variable inequality, comprised of two
measures of average inequality and four measures of relative inequality, may be applied
as a macro global independent variable for social class, which combines (as suggested
in Susser, 1973) the components of education, occupation, and place of residence.
SUMMARY OF CONCLUSIONSCONTEXTUAL EFFECTS OF INEQUALITY ON QOL
This study demonstrated that there was substantial variation in the different sets
of contextual and individual psychosocial determinants of multiple outcome indicators
of HRQOL in post-Coup Moscow. Although a multivariate multilevel model could not
be estimated with this dataset, seemingly unrelated estimation (-suest-)was used to
check the significant differences between the models by type of average inequality in
urban areas. Logistic regressions were calculated for each outcome within low access
and high development urban areas. Significant variation between urban areas was
examined further in a multilevel model, which differentiated between the contextual
effects of average and relative inequality on psychosocial determinants of health
outcome.
The logistic models indicated distinct patterns of psychosocial determinants for
physical health, self-reported health, happiness and satisfaction, dependent upon the
type of average inequality in urban areas. The effect of different types of inequality on
life happiness, life satisfaction, or physical and self-rated health varied with
psychosocial behaviors.
Life chances, participation in social groups, health choices, and social
236
status/gender interactions varied significantly by type of average inequality in urban
area. HRQOL varied with social status and inequality: low access areas had the worst
self-rated health status and high development areas had a greater likelihood for job
dissatisfaction and life unhappiness. Job dissatisfaction was a strong risk factor for life
unhappiness. Life unhappiness was a significant factor in life dissatisfaction; both
dissatisfaction and unhappiness with life were risks for poor self-rated health,
controlling for physical health. Women were 12 times more likely in new development
areas and 5 times more likely in low access areas to be dissatisfied with their jobs than
men. The variation of job satisfaction indicates that well-being in work environment
and life control have significant indirect effects on health, which can explain a portion
of the overall gender effect in the health status of women. Job satisfaction as a health
effect was particularly relevant in socialist and post-socialist Moscow where there was
little unemployment among women.
Generally, average inequality was associated with a discernible hierarchical
effect of social status on QOL. The odds of poor self-rated health varied by education
and occupation, as well as average inequality. Although not consistent among the eight
outcome groups because of complex interaction terms, higher education had better odds
for good health, happiness and job satisfaction than lower education. Education showed
a clearer linear effect than occupation. Hierarchical effects on HRQOL were also
evident in the interaction between occupation with education, occupation with social
group memberships, and education with social group memberships.
The lack of a consistent pattern among occupational groups was most likely due
to the collapse of 17 groups into 4 large categories. The measurement of occupation in
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this dataset by a functional classification system preferred by Moscow researchers and
+did not correspond to a traditional hierarchical grouping used in western studies. The
usefulness of the occupation measure was further complicated by the inclusion of
pensioners as part of the classification, without also asking about a second job. This
group comprised 20% of the sample and had a distinct age (99% ≥ 45 years old), gender
(24% women) and educational distribution (60% with primary, incomplete secondary,
and general secondary education). It was therefore examined as a separate category
which affected the consistency of hierarchical effects.
CONTEXTUAL EFFECT OF INEQUALITY ON PHYSICAL HEALTHThe effect of social status hierarchy on physical health was pronounced for the
interaction between education and occupation and between education and social group
membership with average inequality in urban areas. The hierarchical effect may have
been due to the urban context of inequality or to the variation in the composition of the
sample obtained from urban areas with high inequality. The latter is consistent with the
patterns of demographic mobility and urban growth in Moscow. The new development
of employment and housing markets in peripheral areas of Moscow encouraged specific
people to relocate. A causal pathway can only be suggested in a cross-sectional survey.
Although the relationship between health and social hierarchy may be recursive, the
effect of residing in peripheral areas on physical health was not only dependent on
individual characteristics but also on the workplace, housing, and other neighborhood
factors not subject entirely to personal control.
The effects of social factors were consistently significant as a major determinant
of poor health in comparisons across several logistic regressions. Health choices had
inconsistent effects: physical activity, smoking, obesity and alcohol consumption did
238
not act in the expected direction for all outcomes within and across inequality areas.
The quality of the experience of living in urban areas characterized by inequality
was dependent on the interaction between type of inequality, gender and social status in
Moscow. On the average, those living in low access areas were more at risk for a poor
quality of life than people in high development areas, while physical health risks were
more prevalent in new development areas. Codifying the results of multiple logistic
models was not a parsimonious or accurate method for assessing the differential
consequences for health of direct and interaction effects between macro and micro
determinants, for which the multilevel model is more appropriate.
MULTILEVEL INTERACTIONSThe multilevel model posited that the direct effect on physical health of individual
level life chances, health choices, social support, and participation in formal networks
varied by urban area levels of average and relative inequality. Average physical health
was found to vary significantly between urban areas.
The measures of average and relative inequality accounted for different types of
variation in physical health: the main contextual effect on the overall intercept (average
inequality) and the direct and interaction effects of within area slopes which varied
across areas (relative inequality). The ratio of apartment size was the most prevalent
significant measure of relative inequality. The urban inequality effect varied
nonrandomly on physical health with mobility in housing, paying private physicians,
engaging in physical activities, social support, and informal family networks. For all
other variables, random variation remained to be explained by factors in addition to
average and relative inequality.
The addition of average and relative inequality to the null Random Intercept
239
model significantly decreased the variance in the mean of physical health, and
accounted for the residual variation between areas. The deviance of the cross-level
model as compared to the null model or to the slopes-as-outcome model (the basic
logistic regression) indicated a significantly better fit.
LIFE CHANCES
The hierarchical effect of social status on health was not explained entirely by
the interaction effect of relative inequality. A significant residual variation in social
status remained between urban areas. The expectation that low status individuals living
in high inequality areas would have poorer health was observed among pensioners,
manual workers, and the lowest education group.
HEALTH CHOICES
Among health choices, obesity and drinking alcohol were only partially
explained by relative inequality. The proposition that poor health choices among
residents in high inequality areas would have a greater risk for poor health was observed
for choosing to pay for private medical services, but was not evident in the expected
direction for obesity or drinking alcohol. The effect of physical activity on health was
in the expected direction but living in high inequality areas did not reverse this
direction, indicating the lack of an additive effect of inequality. A random effect for
obesity indicated that possibly additional nutritional factors should be considered.
Paying for private medical services was moderated by relative inequality: the
risk for poor health was decreased even in areas with greater ratios of low to high
educated residents. This suggests that this positive choice cushioned the negative effect
of urban relative inequality on physical health, consistent with the research hypothesis.
It is possible that this health choice is also an indicator of economic status. The
240
association between average inequality, poor HRQOL and willingness to pay for
medical services may, however, have measured the ability to purchase services, as well
as need for services which could not be obtained from the “free” public health system in
any other way.
ALCOHOL ANOMALY
The alcohol anomaly was examined separately given a significant random
residual for alcohol in the multilevel model and the established relationship of alcohol
to education and premature mortality in Russia. Urban area mean alcohol consumption
levels, an aggregate variable, was added to the model.
A hierarchical effect of social status on physical health, given alcohol
consumption levels in urban areas, was observed. There was an interaction effect
between average and relative inequality with urban area mean alcohol consumption
levels. For those lower educated individuals living in low alcohol consumption areas,
better physical health was related to a social advantage effect at the macro level: lower
urban inequality due to smaller educational differences between residents, as well as
greater access to material resources. For individuals with higher education living in high
alcohol consumption areas, better physical health was related to an advantage effect of
higher education at the micro level. Poor physical health among the lower educated was
associated with living in high alcohol consumption areas, which were on the periphery
of Moscow and had greater relative inequality and less access to material resources, a
cumulative disadvantage effect.
SOCIAL COHESION AND SOCIAL SUPPORT
Social cohesion in Moscow included assessments of social chaos and conflict, as
well as a psychological sense of powerlessness or loss of control. There was a
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convergence of factors which reflected the interaction of perceived anomie and
membership in various social groups on health status. The variation between health and
the slope of social support, measured by the quality of the marital relationship, was
accounted by psychosocial factors and the level of average and relative inequality in
urban areas.
Anomie is not a construct which reflects an internal locus of control or a
particular coping stratagem of the individual, but a personal view of the structural
components of social order: expected and organized rules, values, or norms of social
behavior as means to attain individual goals. In this respect, perceived social cohesion
measures the structural order or disorder of social life from the perspective of the
individual. Individual reports of cohesion are not internal states within the individual
but aspects of the environmental context in which individual and social life occurs and
which are perceived as controllable, to some extent, by the individual to meet personal
goals. A lack of social cohesion was consistently significant for poor health-related
quality of life with all types of urban inequality present.
The structure of a civic community is salient for the theory of health-related
quality of life and institutional cohesion, demonstrating how the integration, rather than
segmentation of the public and private spheres is essential for sustained community
health. It is important to note that lack of social cohesion was the sole civic community
measure which was consistently related to poor HRQOL in all the logistic and
hierarchical linear models. Lack of social support was not significant in the logistic
regression of physical health, but did have a small, direct, and significant effect in the
random coefficient regression. The formal networks of social capital exhibited
242
significant effects on physical health when urban area effects were controlled.
SOCIAL CAPITAL
Residual variation remained in addition to the slope effect of group membership
in child oriented, religious, or professional/trade groups on physical health, which was
not entirely explained by inequality. This random effect may have been due to
inadequate measurement of occupational status, or omitted interactions between gender,
education, occupation, and social capital. High inequality in urban areas increased the
risk for poor health given social capital measures, contrary to expectations. There was
no beneficial effect on health from formal group membership in either a direct or
interaction effect. A general increased risk for poor health was observed across
inequality measures and across social capital measures.
It is likely that the negative effect on physical health was due in part to
professional and trade group membership being tied to occupational status. The
government bureaucracy in Moscow in 1991 was still intact and a dominant employer
of educated professionals. It is possible that professional group membership was also
connected to communist party membership either directly or through party control of
the organization, and that social/child-related groups were linked to the Komsomol.
Respondents may have assumed the group membership question to mean party
affiliation. Communist party membership was more common among the established,
older elite, or among pensioners and veterans, which would be consistent with poorer
health status. In addition, distress levels and poor health were significant in logistic
regression among professional men who were probably suffering from trauma and
genuine threats to personal security during the August Coup.
Another perspective, which is inherent in the dimensions of the civic
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community, suggests an explanation for the lack of a buffer effect on health of social
capital, as measured in this dataset. Explanation of poor health and premature mortality
have been related to cardiovascular diseases rates in Moscow and Russia, as well as
other Eastern European countries undergoing rapid social change and economic
transition.
Occupational stress can result in increased cardiovascular disease. Individual
self-regulation, self-efficacy, self-worth and self-integration are disrupted by a change
in obtaining social rewards from occupational and economic status (in the case of
Moscow - loss of savings from inflation, loss of wages from unemployment or unpaid
labor, inhibition of access to material and social resources due to social status or
increased prices). Self-integration is affected by the inability to retain social capital due
to loss of social status and access to material resources. The psychosocial elements of
loss of self-regulation, self-efficacy, self-worth, and self-integration after the August
Coup of 1991 may be reflected in the overwhelming sense of normlessness measured by
the social cohesion scale of the Moscow health profile (Siegrist, 1995; Putnam, 1992;
Rose, 1999; Marmot, 2002; Wilkinson, 2001).
The lack of social cohesion was noted in public opinion polls of Muscovites as
predicting lower voter turnout, which in turn retards the growth of a civic society.
Instead of participation in public life, Muscovites have turned to informal means to
achieve their goals. When individual choice and collective choice are diametrically
opposed, the growth of an “uncivil” society is fostered where citizens are alienated
from the political arena which is either nonresponsive or oppressive. They do not vote
even if enabled, separate themselves from public decision-making, and inadvertently
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cultivate the continuation of the very social mechanisms which create greater relative
inequality.
It has been pointed out by the New Russia Barometer Social Capital Survey that
in a complex “anti-modern” society, like Russia, formal organizations, although
numerous, fail to operate according to the rule of law. As a consequence, alternative
methods for obtaining goods and services were formed by Russians, such as the social
networks of a “second economy” and a “second polity” in a civil society, separated
from the state by self-interested, rather than civic activities. The poor health
consequences of a civil society can be seen in the Moscow health profile, while the
persistence of such “anti-modern” civil networks have retarded the development of a
civic community.
IMPLICATIONSThe main implication derived from the logistic and multilevel models was a
consistent demonstration of how average and relative urban inequality was associated
with cumulative disadvantage. Relative inequality in the composition of social status
between urban areas, at the micro and macro level, was associated with average
inequality in having access to material resources, which resulted in a cumulative
disadvantage for residents of peripheral areas in Moscow.
Structural influences exist independently of individual level behaviors, so that
the social distribution of health is affected by the social distribution of inequality
between geographic areas. Those who lived in peripheral Moscow areas were
surrounded by the effects of a greater prevalence of small apartments, low educational
level of residents, larger than average families, and a large proportion of blue-collar
workers, who were also more likely to live in higher than average alcohol consumption
245
areas. These multiple factors can act as cumulative risks for the adoption of certain
types of unhealthy lifestyle behaviors more so than for those who reside in areas where
there is a lower prevalence of cumulative risk. Individuals living in the “poor health”
areas may have less personal choice in lifestyle due to their place of residence than
persons without such pernicious social influences, holding other factors constant.
The study findings broadly indicate that community context should be
incorporated into public health interventions. Public health policy, traditionally affected
by the methodological individualism of the biomedical model, can benefit from a
multilevel approach which places the individual back in the social milieu as an active
participant. The purposive action of individuals is constrained and shaped by their
institutional setting, which is a macro-micro connection that is also recursive. Although
the parameters of a civic community were not demonstrated by the study to be
significant for a better quality of life in Moscow due to the cultural specificity of the
construct, social connections still provide the context within which individuals are able
to influence public policy.
A second macro-micro problem in public health is to specify the causal
pathways by which health policy can influence health outcomes of individuals and
communities. The outline of the WHO research agenda as set out in the Healthy Cities
program provides a broad outline within which to couch the empirical macro-micro-
macro elements of a multilevel health profile. This is especially critical if individual
preferences are to be considered in formulating collective choices and public policy.
A further macro-micro problem centers on the issue of who is responsible for
individual level and community level health given that individuals influence the
246
communities in which they live, as well as the relative position of other individuals
within the existing context of their neighborhoods. Both of these multilevel issues were
addressed by the Moscow health profile in defining relative inequality in terms of social
status (education, occupation, income/apartment size) and average inequality in terms of
access to material resources. Inequality was defined as having average access to
material resources in comparison to the available distribution of resources between
areas. Both measures are related to the position of individuals in the social structure of a
community rather than to isolated characteristics of individual residents themselves.
The definitions of average and relative inequality in this study sought to operationalize
several of the criteria which the World Health Organization used in distinguishing
between the inequality and inequity of health gaps.
The existence of these health gaps in themselves point to the fact that they are
avoidable and preventable; that persons living under the social conditions specific to
Russia, for example, are more at risk for poor health than persons living in western
Europe. These greater risks are not due to individual behaviors but to the inequality in
nationally and culturally distributed conditions within and between countries.
Broad social strategies to increase equity and decrease inequality in public
health include the quality of living and working conditions; education about healthy
lifestyles; community participation in local, decentralized public health decision-
making; and assessments of policy and program effectiveness.
Although cross-national surveys often indiscriminately compare items across
time and countries, it is necessary to maintain the distinctiveness of periods and
cultures. It is likely that the prevalence of anomie reported by this study was a true
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cultural effect specific to the structure of the urban community of Moscow, as well as a
reflection of the political crisis occurring at the time the survey was conducted.
STUDY LIMITATIONSThe prevalence of health-related quality of life in Moscow was based upon self-
reported perceptions, derived exclusively upon the survey responses of individuals
themselves. No medical diagnoses of illness or disease were obtained, no physiological
measurements of parameters were taken, no linking with polyclinic medical records was
possible at the time due to political instability. It was therefore not possible to
distinguish between the “subjective” perceptions of illness and “objective” medical
diagnoses in order to obtain a more balanced and rounded profile of Moscow’s public
health.
It was argued that nonprofessional, lay assessments are crucial for the evaluation
of health-related quality of life. However, the relationship of lay assessment to expert
assessment is in itself an important part of public health policy which would have been
included had it not been for the chaotic atmosphere and pervasive angst. The
consistency of responses could have been analyzed, as well as the possible reasons for
inconsistencies between independent sources of health data. The health profile is thus a
picture which the community residents gave of themselves, balanced by data from the
city census on social status and access to material resources in urban areas.
The limitations of the data are affected not only by the sampling frame and
survey errors, as outlined in chapter 4, but by the response error due to the tumultuous
period of the August 18-21, 1991 Coup, which effectively closed the Soviet era in
Moscow. The impact of the Coup on responses could have been substantial in relation
to the self-assessment of health-related quality of life, in particular upon the dimensions
248
of a civic community. However, this cross-sectional picture in time was obtained at a
significant period for future assessments. It will be invaluable to validate health
outcomes with follow-up research, linking individuals to medical records and the city
death register.
The stability of multilevel models are sensitive to the number of variables
included, the number of random parameters, and the number of macro groups. Larger
samples of groups provide greater stability and reliability. Several changes in the
variables tested in the model produced different results. The final model was based on
testing the conceptual dimensions as defined in Figure 1. Although this was a complete
universe of urban areas in Moscow, a better fit for the model may be had with a larger
sample within groups, as well as greater attention to random selection with known
probability of selection. The probability of selection was unknown for this dataset,
which was clearly biased by a large response and interviewer error (Appendix 7).
Another major limiting factor which might have affected responses was the use
of an instrument developed in the United States, a society based on different political,
economic, and social principles than Soviet Russia. Although concrete questions and
the physical health questions were the easiest to translate, any psychosocial items and
the social cohesion dimension presented a different set of concerns. The responses for
most subjective assessments were in the negative direction. In cumulating responses,
life happiness or life satisfaction were insignificantly small as compared to life
unhappiness and life dissatisfaction. This was also true for poor/fair rather than
good/excellent self-rated health, and disability/chronic conditions in contrast to acute
symptoms/no health problems of the Alameda Physical Health Profile. This negative
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picture of Moscow residents may be a cultural predisposition to attribute a darker hue to
reality than exists, in contrast to other cultures, like the United States, where it is taboo
to casually disclose in public how bad things really are in one’s life. In any case, it is
more likely that responses reflected the social context, given the regularity of subjective
perceptions between other dimensions like social cohesion.
FUTURE RESEARCHNot sufficient progress has been made in the problem of measuring the
difference between concepts of inequity and inequality as relative and average
inequality, respectively. The monitoring of inequity in health has been part of WHO
policy every six years. Inequity has been made an explicit policy component in
formulating community health profiles and deriving community health policies.
However little advances have been made in refining the definition and development of
measures of inequity and their application in either health surveys or health profiles.
In those countries which have been regular participants in the WHO Healthy
City strategy, periodic health profiles in the 1990’s were progress reports which rarely
included measures of macro inequality effects on health. In Russia and other states of
the Former Soviet Union, health profiles have been drawn for the first time as baseline
assessments. A comprehensive national health profile of Russia has still not been
published at the end of the millennium. At the present time, this study remains the only
baseline profile of the city of Moscow which addresses contextual effects of urban
inequality on individual health status outcomes.
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APPENDICESAPPENDIX 1: Total permanent Moscow population, 1989, unweighted andweighted sampling distributions, Moscow, 1991, by sex and urban area_______________________________________________________________________________________URBAN CENSUS* UNWEIGHTED WEIGHTED** AREA Total(N) M(%) F(%) SAMPLE(N) M(%) F(%) M(%) F(%)_______________________________________________________________________________________MOSCOW 8875579 44.9 55.1 1629 30.3 69.7 44.2 55.8______________________________________________________________________01-Babushkinski 339184 1.7 2.1 56 .9 2.5 1.3 2.002-Baumanski 83550 .4 .5 17 .3 .7 0.4 0.603-Volgogradski 334056 1.7 2.1 64 1.1 2.8 1.7 2.404-Gagarinski 286961 1.5 1.7 46 .7 2.1 0.9 1.905-Dzerzhinski 143528 .7 .9 22 .5 .9 1.0 0.506-Zhelezhno. 157692 .8 1.0 33 .5 1.5 0.8 1.207-Kalininski 98916 .5 .6 18 .5 .6 0.7 0.508-Kievski 158129 .8 1.0 33 .4 1.6 0.6 1.509-Kirovski 582524 3.0 3.6 123 2.5 5.1 4.0 3.910-Krasnopres. 134560 .7 .8 41 .8 1.7 1.1 1.411-Krasnogvard. 655941 3.4 4.0 116 3.1 4.1 4.7 3.312-Kuibishevsk. 344241 1.7 2.2 65 1.2 2.8 1.6 2.113-Kuncevski 384643 1.9 2.4 69 1.2 3.0 1.7 2.314-Leningradsk. 320526 1.6 2.0 66 1.0 3.0 1.4 2.515-Leninski 106703 .5 .7 31 .6 1.2 0.9 0.916-Lyublinski 311749 1.6 1.9 59 .8 2.7 1.1 2.117-Moskvoretsk. 103059 .5 .7 17 .4 .6 0.8 0.518-Octiabrski 218456 1.1 1.4 35 .7 1.3 0.9 0.919-Pervomaiski 363926 1.8 2.3 62 1.0 2.8 1.3 2.220-Perovski 458525 2.3 2.9 77 1.2 3.5 1.7 3.021-Proletarski 239873 1.2 1.5 40 .7 1.7 1.0 1.522-Sverdlovski 115588 .6 .7 19 .4 .7 0.6 0.623-Sevastopol. 286146 1.4 1.8 53 1.1 2.2 1.7 1.724-Sovetski 452568 2.3 2.8 98 1.9 4.1 2.6 3.325-Sokolnicheski 97437 .5 .6 9 .1 .4 0.2 0.426-Solncevski 113460 .6 .7 18 .4 .7 0.6 0.627-Taganski 146822 .7 .9 25 .5 .8 0.6 0.728-Timiriazev. 413296 2.1 2.6 61 .9 2.8 1.2 2.229-Tushinski 260501 1.3 1.6 52 1.3 1.9 2.0 1.530-Frunzenski 161267 .8 1.0 40 .6 1.8 0.9 1.531-Horoshevski 339238 1.7 2.1 59 .9 2.7 1.2 2.132-Cheremushk. 503699 2.6 3.1 78 1.5 3.3 2.2 2.633-Zelenograd 158815 .9 .9 27 .4 1.3 0.6 1.0______________________________________________________________________
Notes to Appendix 2 :*The distribution of the permanent 1989 Moscow population of working age, 20 years andolder (n=6673984), was not available for each urban area, therefore these frequencies bydistrict also include children of all ages;** Because the sampling distribution was based only on adults aged 18 years and older,the referent Moscow population for weighting was calculated from all those age 15years(N=7213121) and was therefore more than the permanent working age Moscowpopulation; age-sex proportions were derived by dividing the number in age-sex groupsby the base referent population 15 years;+Weights=the proportion referent population in age-sex cells divided by proportion of
sample distribution in age-sex cells.
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APPENDIX 4: Ordered Logit Regression of Physical Health Profile by LifeChances, Health Choices, and Civic Community
(logit b; odds ratio e^b; % change in OR))
------------------------------------------------------------------------------------- Odds of: poor/fair>m vs <=m Physical Health (scored from high 7 to low 1) b z P>|z| e^b/ %-------------+------------------------------------------------------------------------HRQOL lohealt2 | 1.229 11.006 0.000 3.421 242.1 lifehap2 | 0.234 2.193 0.028 1.263 26.4DEMOGRAPHICS i_age | 0.036 8.559 0.000 1.037 3.8 agenewdv | 0.001 1.626 0.104 1.001 0.2Educational Level educ6 | -0.916 -2.800 0.005 0.400 -60.0 educ5 | -0.698 -1.666 0.096 0.497 -50.3 educ4 | -0.998 -2.482 0.013 0.368 -63.2 educ3 | -0.970 -2.653 0.008 0.378 -62.1 educ2 | -1.253 -3.517 0.000 0.285 -71.4Female * age * educational level Fageed5 | 0.047 3.413 0.001 1.049 4.9 Fageed4 | 0.011 2.081 0.037 1.012 1.2Occupational Status profess2 | -1.809 -2.877 0.004 0.163 -83.6Education * occupation Xed6prof | 1.271 1.943 0.052 3.567 256.7 Xed5whit | -1.234 -2.159 0.031 0.291 -70.9 Xed5prof | 1.782 1.834 0.067 5.946 494.7 Xed4prof | 1.929 2.576 0.010 6.888 588.8 Xed3pens | 1.591 1.925 0.054 4.908 390.9Female * occupational status femprof | 1.684 2.316 0.021 5.391 439.2 femmanua | 0.489 1.732 0.083 1.631 63.1female * educational level * occupational status Fed6prof | -1.258 -1.703 0.089 0.284 -71.6 Fed5prof | -3.451 -2.843 0.004 0.031 -96.8 Fed5pens | -3.103 -2.637 0.008 0.044 -95.5 Fed4prof | -1.824 -2.116 0.034 0.161 -83.9 Fed3pens | -1.383 -1.631 0.103 0.250 -74.9Mobility i_naddr2 | -0.178 -1.762 0.078 0.836 -16.3
HEALTH CHOICES femfat | -0.332 -1.661 0.097 0.717 -28.3 femetohn | 0.536 2.951 0.003 1.710 71.0 fat2 | 0.412 2.419 0.016 1.510 51.1 etohn2 | -0.525 -3.311 0.001 0.591 -40.9CIVIC COMMUNITYInformal Networks i_npalC | 0.010 1.887 0.059 1.010 1.1Formal Networks/ group memberships uniongr2 | -0.684 -2.140 0.032 0.504 -49.6 kidgrp2 | 0.594 3.065 0.002 1.812 81.3 i_protr2 | 1.373 2.959 0.003 3.947 294.7occupation * formal networks/group memberships Xwhitpro | -1.561 -2.769 0.006 0.209 -79.0 Xprofpro | -1.368 -2.578 0.010 0.254 -74.6 Xpenspro | -1.350 -1.863 0.062 0.259 -74.1 Xmanuuni | -0.676 -2.899 0.004 0.508 -49.2education * formal network/group membership ed6union | 0.699 1.862 0.063 2.013 101.3 ed6relig | 0.477 2.643 0.008 1.612 61.2 ed4union | 0.676 1.748 0.080 1.966 96.6 ed3union | 0.888 2.237 0.025 2.430 143.0 ed2union | 1.756 3.871 0.000 5.790 479.0female * formal networks/group memberships femrelig | 0.281 2.239 0.025 1.324 32.5Social Cohesion
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femstres | -0.103 -3.006 0.003 0.901 -9.8 distress | 0.225 6.775 0.000 1.252 25.3Social Support femnegsp | -0.049 -2.477 0.013 0.951 -4.8________________________________________________________________________________ e^b = exp(b) = factor change in odds for unit increase in X % = percent change in odds for unit increase in X b = raw coefficient z = z-score for test of b=0 P>|z| = p-value for z-test % = percent change in odds for unit increase in X
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APPENDIX 5: Type of Inequality in urban area by Physical Health Profile andQOL: Low-High development of new urban resources by Low-High access to urbanresources in residence areas of Moscow, 1991;(n=1629) percent-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
AREA INEQUALITY 1=LODEV-HIACCESS 2=HIDEV-HIACC 3=LODEV-LOACC 4=HIDEV-LOACC
UNHAPPY WITH LIFENPHYSLOW |other yes |other yes |other yes |other yes-----------+----------------------+----------------------------------------------------- energy | 25 4 | 12 4 | 62 15 | 46 22 | 13.37 5.33 | 14.46 8.51 | 12.86 7.65 | 13.57 10.00-----------+----------------------+------------------------------------------------------ acute< | 34 16 | 14 4 |103 31 | 73 30 | 18.18 21.33 | 16.87 8.51 | 21.37 15.82 | 21.53 13.64-----------+----------------------+------------------------------------------------------ acute> | 49 10 | 26 8 |113 47 | 90 50 | 26.20 13.33 | 31.33 17.02 | 23.44 23.98 | 26.55 22.73-----------+----------------------+----------------------------------------------------- chronic | 61 32 | 22 23 |159 74 | 94 85 | 32.62 42.67 | 26.51 48.94 | 32.99 37.76 | 27.73 38.64-----------+----------------------+------------------------------------------------------ impair | 18 13 | 9 8 | 45 29 | 36 33 | 9.63 17.33 | 10.84 17.02 | 9.34 14.80 | 10.62 15.00-----------+----------------------+------------------------------------------------------ Total |187 75 | 83 47 |482 196 |339 220 % Percent |100 100 |100 100 |100 100 |100 100----------------------------------------------------------------------------------------- chi2(4) = 11.55 (P=.021)| chi2(4) = 9.96 (P=.04)| chi2(4) = 10.25 (P=.036)| chi2(4)=13.73 (P=.008)-----------+----------------------+------------------------------------------------------UNSATISFIED WITH LIFE-----------+----------------------------------------------------------------------------- energy | 20 9 | 12 4 | 60 17 | 57 11 | 11.98 9.47 | 14.81 8.16 | 13.07 7.76 | 14.88 6.25-----------+----------------------+---------------------------------------------------- acute< | 32 18 | 15 3 | 97 37 | 80 23 | 19.16 18.95 | 18.52 6.12 | 21.13 16.89 | 20.89 13.07-----------+----------------------+----------------------------------------------------- acute> | 43 16 | 23 11 |113 47 | 99 41 | 25.75 16.84 | 28.40 22.45 | 24.62 21.46 | 25.85 23.30-----------+----------------------+------------------------------------------------------ chronic | 49 44 | 24 21 |139 94 |105 74
| 29.34 46.32 | 29.63 42.86 | 30.28 42.92 | 27.42 42.05-----------+---------------------+------------------------------------------------------- impair | 23 8 | 7 10 | 50 24 | 42 27 | 13.77 8.42 | 8.64 20.41 | 10.89 10.96 | 10.97 15.34-----------+----------------------+----------------------------------------------------- Total |167 95 | 81 49 |459 219 |383 176 |100 100 |100 100 |100 100 |100 100----------------------------------------------------------------------------------------- chi2(4) = 8.86 (P=.06) | chi2(4) = 9.67 (P=.04) | chi2(4) = 12.55 (P=.01) | chi2(4) = 21.63 (P<.000)-----------+----------------------+-----------------------------------------------------DISSATISFIED WITH JOB-----------+----------------------+------------------------------------------------------ energy | 12 17 | 3 13 | 22 55 | 17 51 | | 12.50 10.24 | 7.89 14.13 | 11.34 11.36 | 11.97 12.23 |-----------+----------------------+------------------------------------------------------ acute< | 15 35 | 2 16 | 30 104 | 22 81 |
| 15.62 21.08 | 5.26 17.39 | 15.46 21.49 | 15.49 19.42 |-----------+----------------------+----------------------------------------------------- acute> | 23 36 | 15 19 | 28 132 | 41 99 | | 23.96 21.69 | 39.47 20.65 | 14.43 27.27 | 28.87 23.74 |-----------+----------------------+------------------------------------------------------ chronic | 38 55 | 10 35 | 89 144 | 45 134 | | 39.58 33.13 | 26.32 38.04 | 45.88 29.75 | 31.69 32.13 |-----------+----------------------+----------------------------------------------------- impair | 8 23 | 8 9 | 25 49 | 17 52 | | 8.33 13.86 |21.05 9.78 | 12.89 10.12 | 11.97 12.47 |-----------+----------------------+----------------------------------------------------- Total | 96 166 | 38 92 |194 484 |142 417 |
254
|100 100 |100 100 |100 100 |100 100 |----------------------------------------------------------------------------------------- chi2(4) = 3.65 (P=.45) | chi2(4) = 11.03 (P=.02) | chi2(4) = 23.66 (P=.000) | chi2(4) = 2.04 (P=.73)----------------------------------------------------------------------------------------------------+----------------------+----------
POOR OR FAIR SELF-RATED HEALTH |other yes |other yes |other yes |other yes |-----------+----------------------------------------------------------------------------- energy | 25 4 | 13 3 | 65 12 | 61 7 | | 21.37 2.76 | 24.53 3.90 | 22.89 3.05 | 24.70 2.24 |-----------+----------------------+------------------------------------------------------
acute< | 29 21 | 12 6 | 92 42 | 66 37 | | 24.79 14.48 | 22.64 7.79 | 32.39 10.66 | 26.72 11.86 |-----------+----------------------+------------------------------------------------------ acute> | 34 25 | 18 16 | 77 83 | 72 68 | | 29.06 17.24 | 33.96 20.78 | 27.11 21.07 | 29.15 21.79 |-----------+----------------------+------------------------------------------------------ chronic | 25 68 | 8 37 | 37 196 | 36 143 | | 21.37 46.90 | 15.09 48.05 | 13.03 49.75 | 14.57 45.83 |-----------+----------------------+------------------------------------------------------ impair | 4 27 | 2 15 | 13 61 | 12 57 | | 3.42 18.62 | 3.77 19.48 | 4.58 15.48 | 4.86 18.27 |-----------+----------------------+------------------------------------------------------ Total |117 145 | 53 77 |284 394 |247 312 | |100 100 |100 100 |100 100.00 |100 100 |------------------------------------------------------------------------------------------ chi2(4) = 52.41 (P=.000) | chi2(4) = 33.72 (P=.000) | chi2(4) = 181.94 (P=.000) | chi2(4) = 138.79 (P=.000)-----------+----------------------+------------------------------------------------------
APPENDIX 6: Kish Tables
APPENDIX 7: Overall Design EffectIn a rapidly changing sociopolitical climate, it is especially important to provide
an accurate image of Russia. Surveys are a major source of information for popular
consumption, published by the media and professional journals, often used by business
and government agencies for foreign and economic policy development (McKeehan,
1993a). It is thus important to see to what extent the picture we have of Russia is
accurate, and to what extent the validity and reliability of reported statistics describing
public opinion and attitudes are affected by components of survey error, such as mode
and interviewer effects.
Although international comparisons between survey modes and error indicate
differential effects upon population parameters, there is a dearth of published data
255
concerning survey modes, survey quality, and survey error in Russia (McKeehan,
1993b). The importance of attending to the quality of survey data by estimating
systematic bias in data collection is particularly acute in nations new to the free flow of
information, such as the Newly Independent States of the Former Soviet Union. A
systematic effect on survey results may be estimated by measurement bias (interviewer,
mode, instrument, and response effects); and by bias of nonobservation (frame
noncoverage, sampling error of subgroup statistic, unit and item nonresponse).
To sift through the differential effect of error sources with any accuracy in
relation to interviewer error and a particular mode of data collection requires an
experimental survey. If multiple survey results are not accessible for a more accurate
meta-analysis of systematic bias, the measurement of specific bias in one survey, may
still be reported as an indicator of the more complex underlying issue of survey quality.
Kish (1962) proposed the (int), the between interviewer variability, as a partial
measure of survey quality. The (int) is the unit free effect on the total variance of a
measure, where (int) depends upon between and within mean square interviewer error
and interviewer workloads (the number of cases assigned to each interviewer), as well
as sample size and total number of interviewers. The model assumes a random
assignment of interviewer workload. The (int) also defines the Design Effect which
reflects the effect of interviewer error on the variance of survey statistics.
The following section discusses two types of measurement bias which affected
the results of the 1991 Moscow Quality of Life survey: interviewer effect, as measured
by the Kish between-interviewer variability, and telephone mode effects. Moscow
University’s research firm, ‘OPINIO’, provided interviewer staff, data collection,
256
supervision, and the telephone sampling frame.
INTERVIEWER EFFECTInterviewer error was calculated according to the Kish additive model of int ,
in a oneway analysis of variance where interviewers are the single factor and
int = {(Va - Vb) m} {[Va - Vb) m] + Vb};
where Va = between interviewer error; Vb = within interviewer error; and m is
workload, the number of interviews assigned to each interviewer. The design effect of
interviewer error on the sample mean was calculated as: deffint = 1+int (m-1).
Interviewer bias has most often been studied as the effect of individual
interviewer attitudes or ideology upon obtained responses. The (int) has been shown
to be a useful indicator of several components of interviewer effect other than
interviewer ideology: workload size; number of interviewers; selection bias due to
interviewers; outliers among interviewers with respect to some questions and with
respect to other interviewers. (int), has been suggested as a measure of survey quality
indicating how specific components of interviewer error may be reduced by survey
design and other components by interviewer training. (int) has often interpreted as
reflecting the professional quality of interviewer performance. Interviewers affect
sampling variance by selecting respondents properly; response error by delivering the
instrument properly; missing values and nonresponse error through appropriate coding
and follow-up procedures, etc. High mean values of (int) may indicate the use of
poorly prepared staff as interviewers, as well as those factors unrelated to individual
subjective behavior, such as random assignment and workload size.
Kish (1987) illustrates that increased sample validity is equivalent to decreased
257
bias: survey quality is increased when variance is decreased by designing larger sample
sizes. Insofar as values of (int) are also related to sample size, it is a measure of
survey quality independent of interviewer effect. Because (int) and deff are calculated
based on the number of interviewers and size of workload, these two factors are useful
in monitoring interviewer effect in survey designs, in addition to sample size and
interviewer training.
The expected values of a cumulative distribution of (int), obtained by Groves
and Magilavy (1986) for selected variables over replicated samples, were found to
decrease when number of interviewers increased; decrease when number of cases
increased; decrease when workload size decreased. The cumulative distribution of (int)
may be seen as a normative range for the values of (int). Any.values outside this range
may be due to outliers: extreme questions or interviewers. About 70% of surveys,
analyzed by Groves, had mean values of (int) <0.04; about 85% of surveys had mean
values <0.06; and about 98% had mean values of (int) <0.08.
The Moscow QOL survey had a mean value of (int)=0.03 for 33 selected
variables. While the range of (int) in the Moscow survey was within the average range
reported by Groves, suggesting the applicability of (int) in cross-cultural contexts, the
design effect indicated an increase in the variance of survey statistics by an average
factor of 2.2. Some implications of this inflated deff follow.
In examining the interaction between interviewer effect and other variables upon
the magnitude of (int), past research has investigated the relevance of several
structural factors in questionnaires and respondents: a.) question form and content:
258
whether open/closed; whether factual/attitudinal and b.) respondent traits: gender,
education, age, region of residence (Groves and Magilavy, 1980).
There have been inconsistent findings with respect to question type. Freeman
and Butler (1976) report increased variance in the mean (int) due to interaction
between interviewer, respondent, and question type: open-ended or emotive questions
(O’Muircheartaigh, 1976,1979), and cognitively ambiguous or difficult questions,
concerning income or employment (Hansen, et al, 1961). Kish (1962) reports no
difference in the mean values of (int) between factual and attitudinal questions or
between open and closed questions. Groves and Magilavy (1986) looked at nine surveys
and also found little effect of question type upon between interviewer variance.
Respondent traits, such as sex and age, have been reported as having equivocal
influence on mean values of (int). Groves found respondent education did not increase
(int) variability as expected for respondents with less than 12 years of education.
However, Freeman and Butler found that younger interviewers had smaller values for
(int) than older interviewers. The largest (int) held for old interviewers paired with old
respondents, whereas (int) values decreased for younger interviewers (31 years) who
had younger respondents. Tucker, as well as Groves, concluded that (int) increased for
older respondents, due mostly to greater nonresponse rates among the elderly in
telephone surveys rather than interviewer ideology. Tucker (1983) also found increased
variance between interviewers by geographic regions when questions were related to
that region. Tucker found that (int) size was the product of interaction between
interviewer, respondent, and item, as well as due to the nonrandom assignment of
259
workload by region.
Kish (1962) postulates an additive model of (int) across subclasses of
respondent traits such as sex and age, assuming random assignment of respondents to
interviewers. If the (int) is significant only for the total sample but not within
respondent subclasses, the value of the total (int) is due to the nonrandom distribution
of subclasses across interviewers and not to subjective interviewer behavior, like
ideology. Kish shows that if interviewer bias is the same for respondent subclasses, the
interviewer bias cancels out across subclasses, and (int) tends towards zero. Thus
(int) is additive across subclasses if bias is constant across subclasses: there is no
interaction between respondent trait and interviewer bias. Interaction occurs when
interviewer effects vary across subclasses. If both the total (int) and subclass (int)
are significantly large, then interviewer bias is related to that subclass for a particular
item. If interviewer bias is significant in subclasses but not in the total, it is too weak to
appear in the total.
The specific distribution of (int) was investigated initially by respondent sex
and age, given a lack of prior published data for comparison on (int) in Moscow
surveys and only tangentially for question form and content.
Table 34 shows (int) and deff for selected variables. The magnitudes of (int)
obtained for the total sample and subclasses lie below the range of “maximum” values
(above which 10% of (int) values are greater) derived by Groves from past research
for similar items. The magnitude of total interviewer variance (int) was reduced when
respondent gender was controlled. However, the persisting large difference of (int)
260
between respondent gender groups indicates an interaction between interviewers and
women respondents for specific items.
Two items, the frequency of changing place of employment (nwork) and the
number of days hospitalized (nhosp), had a larger interviewer bias among women
respondents than in the total sample. The (int) for only two items (age and height)
was greater among men than in the total sample. Interviewer bias was not greatly
reduced in several items for women reporting divorces (ndivorce), chronic conditions
(nchronic), symptoms (nsymptom), or social group memberships (ngroup).
The (int) of three sensitive, attitudinal scales were within the ranges for factual
items, except for a large interviewer bias among women responding to an anomie scale
consisting of 7 items. As a whole, the open-ended items did not have larger total (int)
than closed items.
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TABLE 39: INTERVIEWER EFFECTS FOR SELECT VARIABLES BY SEX, MOSCOW HRQOLSURVEY, SEPTEMBER, 1991
___________________________________________________________________VARIABLE TOTAL MEN WOMENNAME TYPE * pint deff pint deff pint deff
___________________________________________________________________DEMOGRAPHICSAGE F-O -.0089 0.66 .0027 1.10 -.0099 0.62EDUC F-C .0179 1.69 .0060 1.23 .0108 1.42MARRIAGE F-C -.0035 0.86 -.0034 0.87 .0007 1.03NDIVORCE F-O .0188 1.72 -.0014 0.95 .0155 1.59NWORK F-O .0294 2.13 .0015 1.06 .0363 2.39HEALTH CONDITIONSNCHRONIC F-C .0351 2.35 .0059 1.23 .0323 2.24NSYMPTOM F-C .0477 2.84 .0119 1.45 .0426 2.64HT F-O -.0095 0.63 .0099 1.38 -.0003 0.99WT F-O .0063 1.24 .0085 1.33 .0100 1.39HEALTH A-C .0101 1.39 .0029 1.12 .0075 1.29MEDICAL UTILIZATIONDAYMD F-O .0072 1.28 .0076 1.29 .0081 1.31NHOSP F-O .0293 2.13 -.0078 0.70 .0336 2.29MDSATISF A-C .0447 2.72 .0299 2.16 .0264 2.02ATTITUDESNANOMIE A-C .0866 4.34 .0175 1.68 .0819 4.16NSATISF A-C .0184 1.71 .0038 1.14 .0135 1.52NSPOUSE A-C .0295 2.14 .0151 1.58 .0258 1.99NUMBER OF SOCIAL CONTACTSNGROUP F-C .0412 2.59 .0073 1.28 .0365 2.41NFRIENDS F-O .0209 1.80 .0120 1.46 .0125 1.48NSEENMOS F-O .0398 2.53 .0285 2.09 .0240 1.93___________________________________________________________________*a=attitude; f=factual; o=open; c=closed
262
In looking at the (int) among respondent age groups, Table 35 indicated that,
as compared to the gender groups, many total (int) values were reduced within age
groups often by almost half. This is consistent with the interviewer effects found in
other western surveys. Several (int) disappear completely. The interviewer effect
among men for height (ht) was eliminated when age was controlled. Interviewer bias
among women for specific items (nhosp, nwork, nanomie) persisted within age groups,
and were often concentrated in one specific age group.
TABLE 40: INTERVIEWER EFFECTS FOR SELECT VARIABLES BY AGE, MOSCOW HRQOLSURVEY, SEPTEMBER, 1991
___________________________________________________________________VARIABLE <19-29yrs 30-49yrs 50-64yrs >64yrsNAME pint deff pint deff pint deff pint deff
___________________________________________________________________DEMOGRAPHICSSEX -.0127 0.51 -.0037 0.86 -.0026 0.89 -.0079 0.69EDUC -.0027 0.89 .0051 1.19 .0212 1.82 .0033 1.13MARRIAGE -.0004 0.99 -.0071 0.73 .0029 1.11 -.0039 0.85NDIVORCE .0017 1.07 .0054 1.21 .0149 1.58 .0005 1.02NWORK .0104 1.40 .0117 1.45 .0136 1.53 .0366 2.41*HEALTH CONDITIONSNCHRONIC -.0026 0.89 .0112 1.43 .0222 1.86* .0148 1.57NSYMPTOM .0049 1.19 .0224 1.87* .0209 1.81 .0059 1.23HT -.0088 0.66 .0083 1.32 -.0014 0.94 .0075 0.71WT -.0259 0.01 -.0006 0.98 -.0005 0.98 .0135 1.52HEALTH -.0076 0.71 .0146 1.56 -.0015 0.94 -.0005 0.98MEDICAL UTILIZATIONDAYMD .0071 0.73 .0059 1.23 .0096 1.37 -.0033 0.87NHOSP 0 1.0 .0732 3.82* .0315 2.21 -.0095 0.64MDSATIS .0400 2.54 .0115 1.44 .0103 1.39 .0281 2.08ATTITUDESNANOMIE .0018 1.07 .0604 3.33* .0214 1.82 .0152 1.59NSATISF -.0026 0.89 .0049 1.19 -.0009 0.96 .0042 1.16NSPOUSE -.0011 0.96 .0060 1.23 .0091 1.35 .0208 1.80NUMBER OF SOCIAL CONTACTSNGROUP .0104 1.40 .0209 1.80* .0087 1.34 .0062 1.24NFRIEND -.0039 0.85 .0011 1.04 .0018 1.07 .0126 1.49NSEENMOS .0219 1.85 .0126 1.49 .0311 2.19 .0018 1.07___________________________________________________________________* significant interviewer outlier
263
Those items which had a high interviewer bias among women respondents, also
had a higher interviewer bias among two age groups. Persistent high (int), above 0.02
with concomitant deff above 1.5, were clustered in two age groups: 30-49 years and 50-
64 years.
If the total and subclass (int) are significant (as in nhosp, nwork, nanomie) the
subsample (int) causes the increase in the total interviewer bias. Large (int) among
age and gender groups may be related to question content: ambiguity, sensitivity, and
cultural appropriateness. What may be routinely asked in one country may be looked at
askance in another. Although there is a lack of space here to understand this interaction
between interviewer, respondent subclass, and item more clearly, it is necessary to
check second and third-order interaction levels to isolate the exact components of
interviewer bias.
This Moscow data supported Kish’s additive model of interviewer error, where
(int) is reduced across subclasses if there is no interaction between (int) and
subclass traits. If subclasses, such as age and sex, and interactions between subclasses,
are not taken into account, the interviewer bias may be overestimated by the total
(int). The effect of (int) on subclass interviewer variability decreases proportionately
to a decrease in the size of the subclass, therefore oversampling women in certain age
groups will increase the (int) for women and for those age groups.
The magnitude of the interviewer bias is related to the random assignment of
workloads as well as workload size. There were a total of 51 interviewers in the
264
Moscow Quality of Life Survey: 35% had a response rate of 80% and an average
workload, m or k, of m=n/a= 39.04 (where n= sample size= 1991, a= number of
interviewers= 51); and k=39.54, adjusted for different workload sizes per interviewer
(Kish, 1962). (int) is calculated with m or k. The mean workload, however, obscured
the fact that only a quarter of the interviewers completed nearly half (48%) the sample.
Figure 24 indicates the uneven distribution of respondent workloads among
interviewers: 25% of interviewers had large workloads of more than 41 respondents.
These factors increased interviewer effect, (int).
FIGURE 24: INTERVIEWER WORKLOAD, MOSCOW HRQOL SURVEY, SEPTEMBER, 1991
Table 5: Interviewer Workload (W) by N Interviewers
4
29
5 62
5
05
101520253035
W 1-20 W 21-30 W 31-40 W 41-60 W 61-80 W >80
In nonrandomized workloads, where the interviewer has wide scope in choosing
respondents, the overestimation of (int) due to selection bias may be substantial (Kish,
1962). Since large workloads affect between interviewer variance, maintaining equal
and optimally sized workloads, as well as random assignments, is important in
265
regulating the magnitude of (int). It is likely that several persistently high values of
(int) in the Moscow data were due to specific interviewers with large workloads or
uneven distributions of respondent subclasses.
Interviewers were responsible for randomly selecting respondent within
telephone households. Incorrect procedures resulted in a sample distribution which had
twice as many women 30-49 years old as the Moscow population. The sample also had
46% more women who were 50-64 years old and 45% more women who were 65
years old than in the general Moscow population. The size of respondent subclasses is
unlikely to be related to the distribution of telephones since the oversampling is based
on sex within age groups. A smaller (int) for men in the Moscow data is thus also due
to a smaller number of men in the sample, rather than to different attitudes of
interviewers toward certain questionnaire items and gender.
A ratio I was calculated of interviewers who were significantly different from
more than 80% of interviewers, as compared to the total number of interviewers who
were significantly different from each other at the 0.05 level. The ratio I indicated that
among items with significant (int), among age groups, interviewer outliers were
responsible for much of the variance. (int) , as the between interviewer variance, tends
toward zero when the variance between interviewers equals the variance within
interviewer workload for a specific item (dep var). (int) tends toward 1 when total
variance equals variance between interviewers. In this case, the maximum variance in
the item is accounted for by the variation between interviewers and not by the variation
within workload.
The largest between interviewer variance for specific items was related to a few
266
interviewers who differed significantly from 80% of interviewers, for example, for the
item nwork among respondents over 64 years old (Table 35).
In summary, the data reported in this study support the Kish additive model of
interviewer error as a useful indicator in a cross-cultural survey for isolating several
components of interviewer bias: workload size; number of interviewers; equal
distribution of workloads; random assignment of respondents; interviewer outliers, item
type, inadequate field training and supervision. Mean values for (int) were reduced
when respondent traits, such as age and sex, were controlled. Large values for (int)
within respondent subclasses were related to differential workloads among interviewers;
nonrandom selection of respondents within subclasses by interviewers; interaction
between question type, respondent subclass and interviewer; as well as outliers among
interviewers.
267
APPENDIX 8: Russian language questionnaire
268
APPENDIX 10: Alameda Physical Health ProfileDISABILITY-SEVERE
Q1: Here is a list of activities that people sometimes have trouble with: troublefeeding themselves, trouble dressing themselves, trouble moving around. Do you havetrouble doing any of these things?
1-yes2-no
Q2: Here are two more activities that people sometimes have trouble with:trouble climbing stairs and trouble getting outdoors. Do you have trouble doing either ofthese things?
1-yes2-no
Q3: Are you now unable to work because of some illness or injury?1-yes2-no
Q4: If yes, then for how long?_______________________
DISABILITY-LESSQ5: Have you had to change the kind of work you used to do, or had to cut
down on the number of hours you used to work because of some illness or injury, forthe past 6 months?
1-yes2-no
Q6: Have you had to cut down or stop any other activity you used to do becauseof some illness or injury, for the past 6 months?
1-yes2-no
CHRONIC CONDITIONS Q7:Here is a list of medical conditions that usually last for some time. Have
you had any of these conditions during the past 12 months?7.1 high blood pressure 1-yes 2-no7.2 heart trouble 1-yes 2-no7.3 stroke 1-yes 2-no7.4 chronic bronchitis 1-yes 2-no7.5 asthma 1-yes 2-no7.6 arthritis or rheumatism 1-yes 2-no7.7 epilepsy 1-yes 2-no7.8 cancer 1-yes 2-no7.9 tuberculosis 1-yes 2-no
269
7.10 stomach ulcer or duodenal ulcer 1-yes 2-no7.11 chronic gallbladder trouble 1-yes 2-no7.12 chronic liver trouble 1-yes 2-no7.13 hernia or rupture. 1-yes 2-no
Q8: Here is a list of physical impairments. Do you have any of these?8.1 missing hand/arm 1-yes 2-no8.2 missing foot/leg 1-yes 2-no8.3 trouble seeing (even with glasses) 1-yes 2-no8.4 trouble hearing (even with hearing aid)1-yes 2-no
Q9: Do you have any other medical condition, ailment, or impairment that hasnot been listed so far? Describe it here.
_____________________________________________
SYMPTOMATICQ11: Here is a list of physical ailments. Have you had any of these during
the past 12 months?11.1 frequent cramps in the legs 1-yes 2-no11.2 pain in the heart or tightness
or heaviness in the chest 1-yes 2-no11.3 trouble breathing or shortness of breath 1-yes 2-no11.4 swollen ankles 1-yes 2-no11.5 pains in the back or spine 1-yes 2-no11.6 repeated pain in the stomach 1-yes 2-no11.7 frequent headaches 1-yes 2-no11.8 constant coughing or frequent heavy chest colds 1-yes 2-no11.9 paralysis of any kind 1-yes 2-no11.10 stiffness, swelling, or aching in
any joint or muscles 1-yes 2-no11.11 getting very tired in a short time 1-yes 2-no
NO COMPLAINTS/PHYSICAL-ENERGY Q10: How often are you completely worn out at the end of the day?
10.1 almost every day 1-yes 2-no10.2 sometimes 1-yes 2-no10.3 rarely or never 1-yes 2-no
Q12: Would you say you have more energy or less energy than most peopleyour age?
1-more2-same3-less
270
Q13: How often do you have any trouble getting to sleep or staying asleep?1-almost every day2-sometimes3-rarely or never
Q14: When you have only 4 or 5 hours of sleep during the night how tireddo you feel the next day?
1-a great deal2-somewhat3-not at all
271
APPENDIX 11: Measures of HRQOL (Self-rated Health, Life Satisfaction , LifeHappiness) and Life Choices, Life Chances, and Civic Community
HRQOL:(SELF-RATED HEALTH) Q21: How would you describe your overall
general health?1-excellent2-good3-fair4-poor
(LIFE SATISFACTION) Q122: How often are you completely satisfied withyour life?
1-rarely or never2-sometimes3-often
(LIFE HAPPINESS) Q127: All in all, how happy are you these days?1-not so happy2-pretty happy3-very happy
(DOMAIN SATISFACTION) Q128: Considered everything, how satisfied areyou with your present job?
1-not satisfied2-somewhat satisfied3-very satisfied
LIFE CHOICEHEALTH PRACTICES(CHOICE1) Q26: How many hours of sleep do you usually get a night?
1-less than 7 hours2-7-8 hours3-9 or more hours
(CHOICE2) Q27: How often do you eat breakfast?1-almost every day2-sometimes3-rarely or never
(CHOICE3) Q28: How often do you eat in between your regular meals?1-almost every day2-sometimes3-rarely or never
(CHOICE4) Q29: Here is a list of active things that people do in their freetime. How often do you do any of these things?
272
1-morning exercises 1.1-almost every day 1.2-sometimes
1.3-rarely or never2-exercises at home 2.1-almost every day 2.2-sometimes
2.3-rarely or never3-sports 3.1-almost every day
3.2-sometimes 3.3-rarely or never
4-work on dacha 4.1-almost every day 4.2-sometimes 4.3-rarely or never
5-other ______________________
(CHOICE5) Q30: How many times did you drink wine, beer, or liquor in thepast month?
_______________________
(CHOICE6) Q31: How many drinks of alcohol did you have in the pastmonth?
_______________________(in grams)
(CHOICE7) Q32: If you smoke regularly, how much do you smoke in oneday?
_______________________
(CHOICE8) Q33: How tall are you?_______________________(in meters)
(CHOICE9) Q34: How much do you weigh?_______________________(in kilograms)
PREVENTION(DDS) Q15: When was the last time you went to a dentist just for a general
checkup even though your teeth were not giving you any trouble?1-within past 6 months2-within the past year3-over a year ago4-never
(MD) Q16: When was the last time you went to see a doctor just for ageneral checkup even though you were feeling well and had not been sick?
1-past 6 months2-within the past year3-over a year ago4-never
273
UTILIZATION(ILLMD) Q19: How many times have you seen a doctor because you were not
feeling well in the last 30 days?_______________________
(HOSP) Q20: How many times have you been to a clinic or hospital becauseyou were not feeling well in the last 30 days?
_______________________
(REGMD) Q21A:Where do you receive your regular source of medical care inthe past year?
1-polyclinic2-occupational/job clinic3-medical cooperative4-private physician
(QUALMD) Q24: Where, in your opinion, is the best quality medical careprovided?
1-polyclinic2-occupational/job clinic3-medical cooperative4-private physician
(SATISFMD) Q25: How satisfied are you with the quality of health carewhich you have regularly received in the past year?
1-very satisfied2-satisfied3-somewhat satisfied4-not at all satisfied
ECONOMIC VALUES(PAYMD) Q22: If you could see any doctor which you yourself chose to
see, would you pay to do so, if necessary?1-yes2-no
(FREEMD) Q23: Do you think the government should provide all medicalcare free of charge?
1-yes2-no
CIVIC COMMUNITY
SOCIAL COHESION(NORM1) Q113:With everything in such a state of disorder, it's hard for a
274
person to know where he stands from one day to the next.1-true2-false
(NORM2) Q114:People were better off in the old days when everyone knewjust how he was expected to act.
1-true2-false
(NORM7) Q119:Everything changes so quickly these days that I often havetrouble deciding which are the right rules to follow.
1-true2-false
(MEANING3) Q115:What is lacking in the world today is the old kind offriendship that lasted for a lifetime.
(MEANING4) Q116:The trouble with the world today is that most peoplereally don't believe in anything.
1-true2-false
(MEANING5) Q117:I often feel that many things our parents stood for are justgoing to ruin before our very eyes.
1-true2-false
(MEANING6) Q118:With everything so uncertain these days, it seems asthough anything could happen.
1-true2-false
SOCIAL SUPPORT - MARITAL QUALITY(FAMILY1) Q104:Many women(men) feel that they are not as good wives
(husbands) as they would like to be. How often have you felt this way?1-never or rarely2-sometimes3-often
(FAMILY2) Q105:Does your wife (husband) give you as much understandingas you need?
1-no, not really2-yes,but not completely3-yes, completely
(FAMILY3) Q106:Does your wife (husband) show you as much affection as
275
you would like?
1-more than I like2-as much as I like3-less than I like
(FAMILY4) Q107:Even happily married couples sometimes have problemsgetting along with each other. How often does this happen to you?
1-rarely or never2-sometimes3-often
(FAMILY6) Q109:Do you ever regret your marriage?1-rarely or never2-sometimes3-often
(FAMILY7) Q110:Have you seriously considered separation or divorcerecently?
1-yes2-no
SOCIAL CAPITAL(INFORMAL-NFAM) Q35: How many close friends do you have (people
that you feel at ease with, can talk to about private matters, and can all on for help)?_______________________
(INFORMAL-NFRIENDS) Q37: How many relatives do you have thatyou feel close to?
_______________________
(INFORMAL-NCONTACT) Q38: How many of these friends or relativesdo you see at least once a month?
_______________________
(FORMAL-RELIGION1) Q39: Do you go to any religious services (church,synagogue, evangelical meeting, muslim, etc.)?
1-yes2-no
(FORMAL-RELIGION2) Q40: If yes, how often?_______________________
(FORMAL-HOBBIES1) Q41: Do you belong to any social or recreationalgroup?
1-yes
276
2-no
(FORMAL-HOBBIES2) Q42: If yes, which ones?_______________________
(FORMAL)Q43: Do you belong to any:43.1-labor union 1-yes 2-no43.2-commercial group 1-yes 2-no43.3-professional association? 1-yes 2-no
(FORMAL-CHILDGR) Q44: Do you belong to a group concerned withchildren (such as PTA, Boy Scout group)?
1-yes2-no
(FORMAL-CHARITY) Q45: Do you belong to any group concerned withcommunity betterment, or charity, or any other group?
1-yes2-no
LIFE CHANCES and DEMOGRAPHICS(AGE) Q48: How old are you?
_______________________
(SEX) Q49: What is your sex?1-male2-female
(EDUC) Q50: What is your last educational level?1-elementary2-incomplete secondary3-general secondary4-technical secondary5-incomplete higher (college)6-college degree
(MARITAL) Q51: Have you ever been married? If so, are you now:1-single2-married3-married, but living apart4-divorced5-divorced, but living together6-widowed
(NDIVORCE) Q52: How many times have you been divorced?_______________________
277
(NCHILD) Q53: How many children do you have in your family?_______________________
(OCCUP, PRESTIGE, EMPLOY) Q54: What is your occupation?1-student in technical institute2-student in university3-worker4-amed services5-research worker; college teacher6-engineer-technician; industrial manager7-service professional: physician, lawyer, teacher, etc8-creative professional: writer, journalist, artist, performing arts, etc.9-government worker, official10-social organization worker11-trade and retail worker12-worker in cooperative, joint-venture, real estate13-service worker: registered nurse, accountant laboratory technician, etc.14-temporarily unemployed15-housework16-pensioner
(URBAN AREA) Q55: In which raiyon of Moscow do you live?1-Babushkinski2-Baumanski3-Volgogradski4-Gagarinski5-Dzerzhinski6-Zhelezhno.7-Kalininski8-Kievski9-Kirovski10-Krasnopresnenskyi11-Krasnogvardeiskyi12-Kuibishevskyi13-Kuncevski14-Leningradskyi15-Leninskyi16-Lyublinskyi17-Moskvoretskyi18-Octiabrskyi19-Pervomaiskyi20-Perovskyi21-Proletarskyi22-Sverdlovskyi23-Sevastopolskyi
278
24-Sovetskyi25-Sokolnicheskyi26-Solncevskyi27-Taganskyi28-Timiriazevskyi29-Tushinskyi30-Frunzenskyi31-Horoshevskyi32-Cheremushkinskyi33-Zelenograd
(APT) Q56: What are your living accomodations?1-government apartment2-cooperative apartment3-room in a communal apartment4-place, room in a dormitory5-rent privately
(LANG) Q57: What language do you prefer to speak at home?_______________________
279
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