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RESEARCH AND PRACTICE |
George Davey Smith is with the Department of Social Medicine, University of Bristol, England. Carole Hart is with the Department of Public Health, University of Glasgow, Scotland.
Correspondence: Requests for reprints should be sent to George Davey Smith, MD, MSc, DSc, FFPHM, Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Rd, Bristol BS8 2PR, United Kingdom (e-mail: zetkin{at}bristol.ac.uk).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. This study sought to demonstrate life-course influences on cardiovascular disease (CVD).
Methods. Data were derived from a prospective observational study in which the main outcome measure was death resulting from CVD.
Results. Combining 4 socioeconomic and behavioral risk indicators into a measure of life-course exposure produced 5 groups whose relative risks of CVD mortality ranged from 1.00 (the group with the most favorable life-course exposures) to 4.55 (the group with the least favorable life-course exposures). If the entire study population had had the CVD mortality risk of the subsample with the most favorable risk factor profile, approximately two thirds of cardiovascular deaths would not have occurred.
Conclusions. CVD risk is influenced in a cumulative fashion by socioeconomic and behavioral factors acting throughout the life course.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Cardiovascular disease (CVD) is in many ways the paradigmatic adulthood health problem for the life-course perspective. Genomic and nongenomic intergenerational factors2,4; intrauterine environment5; growth, nutrition, health, and social circumstances in childhood6–9; and a variety of behavioral and socioeconomic factors in adulthood may all contribute to the development of CVD. Most studies to date have focused on only 1 stage of the life course, on only 1 risk factor category, or on cross-sectional measures of CVD risk. Here we demonstrate the importance of life-course influences on CVD in a large cohort of men from western Scotland.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Data on sociodemographic characteristics (age, father’s occupation, age at leaving full-time education, occupation at time of screening) and health-related behaviors (tobacco and alcohol use) were collected during baseline examinations, and these data were included in the present study. Alcohol consumption was dichotomized as 15 units or more per week and fewer than 15 units per week. According to previous reports involving this cohort, increases in CVD mortality rates have been associated with alcohol consumption levels above 15 weekly units.11
The home addresses of the men at the time of screening were retrospectively postcoded, enabling us to calculate the Carstairs index—an area-based deprivation measure12—from 1981 Census of Scotland data). Deprivation categories varied from 1 (least deprived) to 7 (most deprived); these values were dichotomized as 1 to 5 and 6 to 7. Participants with missing data on any of the variables just described were excluded from the present analyses, which were based on 5628 participants.
Deaths among study participants are flagged via the National Health Service central registry in Edinburgh; we ascertained mortality rates over a 25-year follow-up period. Here we analyze CVD mortality (International Classification of Diseases, Ninth Revision, codes 390–459).13
Logistic regression was used in calculating age-adjusted odds ratios for associations between variables. Proportional hazards coefficients and their standard errors were calculated after adjustments for age. Exponentiated coefficients were taken as indicators of relative rates of death. Populationattributable-risk fractions were calculated via standard methods.14
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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). People who were disadvantaged with respect to childhood social circumstances (as indexed by father’s social class) were more likely to quit school early, to have manual jobs in later adulthood, to live in deprived areas as adults, to be cigarette smokers, and to have high levels of alcohol consumption.
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Table 2 presents relative rates of CVD mortality according to father’s social class and later-life risk factors. Behavioral factors (smoking and heavy drinking), age at leaving full-time education, and later-life socioeconomic factors (social class, deprivation category) all provided additional discrimination of CVD mortality risk when they were combined with father’s social class. In all cases, the separate contributions of father’s social class and the other risk indicator were significant at conventional (P < .05) levels. In no cases were there substantial or conventionally significant interactions between father’s social class and the later-life risk indicator.
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). At the extreme ends of the index were participants with no unfavorable indicators (i.e., those whose fathers were employed in nonmanual occupations, who themselves had nonmanual jobs in adulthood, and who neither smoked nor were heavy alcohol drinkers at the time of screening) and those with 4 unfavorable indicators (i.e., those whose fathers were employed in manual occupations, who themselves had manual jobs in adulthood, and who were smokers and heavy drinkers at the time of screening). The intermediate categories simply summed the number of unfavorable risk indicators.
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(62.6%). |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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In our previous studies of the association between cumulative social disadvantage and all-cause and CVD mortality risks,9 health-related behaviors were used as control variables rather than additional risk indicators. In such presentations, the influence of smoking on socioeconomic gradients can be detected, but the additional contribution of smoking to health outcomes cannot be determined. A few previous studies have described the joint effects on mortality risk of socioeconomic position in adulthood and smoking,23,24 and one study addressed the joint effects of these indicators on carotid intima-media thickness.25
An important issue relates to whether adulthood disease risk results from interactions between early-life and later-life risk indicators or is more straightforwardly influenced by the accumulation of risk across the life course. We previously demonstrated that, in the present cohort, accumulation rather than interaction characterized the effects of childhood and adulthood social circumstances on CVD mortality risk.7 In the present study, we have shown that there are no important interactions between early-life socioeconomic circumstances and behavioral risk factors in adulthood, in keeping with evidence that there are no important interactions between smoking and adulthood social position.23,24 The influence on CVD risk of socioeconomic factors acting across the life course seems to accumulate along with that of 2 important healthrelated behaviors—smoking and heavy drinking—in adulthood.
In previous investigations involving this cohort, we demonstrated that heavy drinking is associated with increased CVD risk (in particular, increased risk of stroke).11 Here we have shown that this association is not dependent on socioeconomic confounding, either by childhood or adulthood social circumstances. In our cohort, alcohol consumption patterns probably took the form of binge drinking (rather than sustained low-level consumption), and such patterns have been associated with increased CVD risk in other studies.26
Favorable exposures and adverse, health-damaging exposures are not randomly distributed across individuals; they generally cluster within particular groups. People who are disadvantaged with respect to a given exposure tend to be disadvantaged with respect to others, as can be seen in Table 1. There are clear causal chains acting in this regard. Unfavorable childhood social circumstances increase the risk of finishing education with few credentials, which in turn leads to an unfavorable occupational trajectory in adulthood and to membership in social groups that encourage the development and maintenance of certain patterns of health-damaging behaviors.27–28 Interventions that simply select 1 item from this chain—almost invariably 1 of the health-related behaviors—and fail to recognize the societal basis for distribution of risk are unlikely to be successful, as illustrated by the frequent failure of behavioral programs targeted to individuals to reduce CVD risk.30
As mentioned earlier, if our entire study population had exhibited the CVD mortality risk of the subsample with favorable life-course socioeconomic and behavioral factor profiles, nearly two thirds of cardiovascular deaths would not have occurred during the follow-up period. This was the case despite the loss of information entailed by dichotomizing our exposure variables; if more categories had been used, an even greater proportion of cardiovascular deaths could have been attributed to these simple indicators.
We have previously shown that healthrelated selection between childhood and adulthood social positions does not account for CVD mortality differentials31; thus, these risk indicators appear to be exogenous causes (at the distal level, in the case of the socioeconomic measures) of increased cardiovascular mortality. Improving social circumstances and reducing harmful health behaviors could lead to substantial decreases in the population burden of CVD.
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Acknowledgments |
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Human Participant Protection
No protocol approval was needed for this study.
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Footnotes |
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Accepted for publication August 29, 2001.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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2. Kuh D, Ben-Shlomo Y. A Life Course Approach to Chronic Disease Epidemiology. Oxford, England: Oxford University Press Inc; 1997.
3. Austin MA. Genetic epidemiology of dyslipidaemia and atherosclerosis. Ann Med. 1996;28:459–463.[Medline]
4. Davey Smith G, Hart C, Ferrell C, et al. Birth weight of offspring and mortality in the Renfrew and Paisley Study: prospective observational study. BMJ. 1997;315:1189–1193.
5. Barker DJP. Mothers, Babies and Health in Later Life. Edinburgh, Scotland: Churchill Livingstone; 1998.
6. Gunnell DJ, Davey Smith G, Frankel S, et al. Childhood leg length and adult mortality: follow up of the Carnegie (Boyd Orr) Survey of Diet and Health in Pre-war Britain. J Epidemiol Community Health. 1998;52:142–152.[Abstract]
7. Davey Smith G, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause-specific adult mortality: prospective observational study. BMJ. 1998;316:1631–1635.
8. Frankel S, Davey Smith G, Gunnell D. Childhood socioeconomic position and adult cardiovascular mortality: the Boyd Orr cohort. Am J Epidemiol. 1999;150:1081–1084.
9. Davey Smith G, Hart C, Blane D, Gillis C, Hawthorne V. Lifetime socioeconomic position and mortality: prospective observational study. BMJ. 1997;314:547–552.
10. Davey Smith G, Hart C, Hole D, et al. Education and occupational social class: which is the more important indicator of mortality risk? J Epidemiol Community Health. 1998;52:153–160.[Abstract]
11. Hart CL, Davey Smith G, Hole DJ, Hawthorne VM. Alcohol consumption and mortality from all causes, coronary heart disease, and stroke: results from a prospective cohort study of Scottish men with 21 years of follow up. BMJ. 1999;318:1725–1729.
12. Carstairs V, Morris R. Deprivation and Health in Scotland. Aberdeen, Scotland: Aberdeen University Press; 1991.
13. International Classification of Diseases, 9th Revision. Geneva, Switzerland: World Health Organization; 1977.
14. Hanley JA. A heuristic approach to the formulas for population attributable fraction. J Epidemiol Community Health. 2001;55:508–514.
15. Wannamethee SG, Whincup PH, Shaper G, Walker M. Influence of fathers’ social class on cardiovascular disease in middle-aged men. Lancet. 1996;348:1259–1263.[Medline]
16. Blane D, Hart CL, Davey Smith G, Gillis CR, Hole DJ, Hawthorne VM. The association of cardiovascular risk factors with socioeconomic position during childhood and adulthood. BMJ. 1996;313:1434–1438.
17. Lynch JW. Socioeconomic factors in the behavioral and psychosocial epidemiology of cardiovascular disease. In: Schneiderman N, Gentry J, da Silva JM, Speers M, Tomes H, eds. Integrating Behavioral and Social Sciences With Public Health. Washington, DC: American Psychological Association. In press.
18. Mare RD. Socio-economic careers and differential mortality among older men in the United States. In: Vallin J, D’Souza S, Palloni A, eds. Measurement and Analysis of Mortality: New Approaches. Oxford, England: Clarendon Press; 1990:362–387.
19. Salhi M, Caselli G, Duchène J, et al. Assessing mortality differentials using life histories: a method and applications. In: Lopez A, Caselli G, Valkonen T, eds. Adult Mortality in Developed Countries: From Description to Explanation. Oxford, England: Clarendon Press; 1995:57–79.
20. Wunsch G, Duchène J, Thiltgès E, Salhi M. Socioeconomic differences in mortality: a life course approach. Eur J Popul. 1996;12:167–185.[Medline]
21. Power C, Manor O, Matthews S. The duration and timing of exposure: effects of socioeconomic environment on adult health. Am J Public Health. 1999;89:1059–1065.
22. Lynch JW, Kaplan GA, Shema SJ. Cumulative impact of sustained economic hardship on physical, cognitive, psychological and social functioning. N Engl J Med. 1997;337:1889–1895.
23. Davey Smith G, Shipley MJ. Confounding of occupation and smoking: its magnitude and consequences. Soc Sci Med. 1991;32:1297–1300.
24. Marang–van de Mheen PJ, Davey Smith G, Hart CL. The health impact of smoking in manual and non-manual social class men and women: a test of the Blaxter hypothesis. Soc Sci Med. 1999;49:1851–1856.
25. Lamont D, Parker L, White M, et al. Risk of cardiovascular disease measured by carotid intima-media thickness at age 49–51: lifecourse study. BMJ. 1999;320:273–278.
26. Britton A, McKee M. The relation between alcohol and cardiovascular disease in Eastern Europe: explaining the paradox. J Epidemiol Community Health. 2000;54:328–332.
27. Davey Smith G, Bartley M, Blane D. Explanations for socio-economic differentials in mortality: evidence from Britain and elsewhere. Eur J Public Health. 1994;4:131–144.
28. Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variations in adult health behaviour and psychosocial characteristics, by stage of the socioeconomic lifecourse. Soc Sci Med. 1997;44:809–820.
29. Davey Smith G, Gunnell D, Ben-Shlomo Y. Life-course approaches to socio-economic differentials in cause-specific adult mortality. In: Leon D, Walt G, eds. Poverty, Inequality and Health. Oxford, England: Oxford University Press; 2001:88–124.
30. Ebrahim S, Davey Smith G. Systematic review of randomised controlled trials of multiple risk factor interventions for preventing coronary heart disease. BMJ. 1997;314:1666–1674.
31. Hart CL, Davey Smith G, Blane D. Social mobility and 21 year mortality in a cohort of Scottish men. Soc Sci Med. 1998;47:1121–1130.
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