To what extent do biomarkers account for the large social disparities in health in Moscow?
Highlights
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and heart parameters. ► These two sets of markers appeared to be important predictors of health outcomes.
Introduction
Social disparities in Russian mortality appear to be wider than those observed in the West, they are greater for men than for women, and they are continuing to increase (Shkolnikov, Leon, Adamets, Andreev, & Deev, 1998). The least advantaged segments of the population bore the brunt of the mortality crisis, whereas highly educated Russians enjoyed modest improvements in life expectancy in the late 20th century (Shkolnikov et al., 2006). For health outcomes other than mortality, there is much less research regarding social disparities. On one hand, some evidence indicates a sizeable socioeconomic gradient in self-assessed health status (Bobak, Pikhart, Rose, Hertzman, & Marmot, 2000; Dubikaytis, Larivaara, Kuznetsova, & Hemminki, 2010; Nicholson, Bobak, Murphy, Rose, & Marmot, 2005; Perlman & Bobak, 2008). On the other hand, the results of one study suggest that material deprivation, but not education, is associated with poor physical function in Russia (Bobak, Pikhart, Hertzman, Rose, & Marmot, 1998).
Researchers have argued that, at least in part, the social gradient in health reflects differences in the burden of physiological stress (Kristenson, Eriksen, Sluiter, Starke, & Ursin, 2004; Steptoe & Marmot, 2002). The allostatic load framework proposes that repeated or prolonged exposure to environmental challenges can result in multi-system physiological dysregulation, which may ultimately lead to health decline (McEwen & Stellar, 1993). Such dysregulation is typically operationalized by examining elevated (or reduced) operating levels of biological parameters (“biomarkers”) related to cardiovascular, inflammatory, and neuroendocrine function. These measures have been shown to predict diverse health outcomes including self-assessed health status, physical function, and mortality (see review by Juster, McEwen, & Lupien, 2010). Many factors (e.g., health behaviors, access to health care, exposure to infection, and genes), which may be unrelated to stress exposure, could play a role in generating social disparities in health (see reviews by Crimmins & Seeman, 2004; Steptoe & Marmot, 2002). Nonetheless, any individual characteristic or social factor that might explain social disparities in health (except possibly for mortality from external causes) would likely operate via physiological pathways. The question is whether we can identify the biomarkers that account for the association between socioeconomic status and health.
Most prior studies that have investigated social disparities in biomarkers are based on Western samples and focus on a small number of standard cardiovascular risk factors related to hypertension, dyslipidemia, obesity, and hyperglycemia (e.g., Kanjilal et al., 2006; Winkleby, Jatulis, Frank, & Fortmann, 1992). More recently, studies have examined the association between socioeconomic status and inflammatory markers (e.g., C-reactive protein, interleukin-6). Some research has also explored the social gradient in a multi-system measure of biological risk. Most of these studies find the expected association between higher status and lower biological risk (Gustafsson, Janlert, Theorell, Westerlund, & Hammarstrom, 2011; Hu, Wagle, Goldman, Weinstein, & Seeman, 2007; Kubzansky, Kawachi, & Sparrow, 1999; Seeman et al., 2008; Singer & Ryff, 1999; Weinstein, Goldman, Hedley, Yu-Hsuan, & Seeman, 2003), although one study finds a significant relationship only for females (Dowd & Goldman, 2006) and another reports a non-significant relationship (Seeman et al., 2004).
Although several studies have investigated whether biological parameters mediate the association between socioeconomic status and mortality (Beauchamp et al., 2010; Harald et al., 2008; Khang & Kim, 2005; Lynch, Kaplan, Cohen, Tuomilehto, & Salonen, 1996; Ramsay et al., 2009; Seeman et al., 2004; Song et al., 2006), including three studies in Russia (Dennis et al., 1993; Malyutina et al., 2004; Shkolnikov, Andreev, & Maleva, 2000), few have evaluated the extent to which biomarkers account for social disparities in general measures of physical health. Three studies that explored this issue with respect to overall self-rated health found that biomarkers explained only a small share of the socioeconomic gap in Taiwan (≤11%) (Dowd & Goldman, 2006; Goldman, Turra, Rosero-Bixby, Weir, & Crimmins, 2011; Hu et al., 2007) and the U.S. (2–4%) (Goldman et al., 2011) and none of the gap in Costa Rica (Goldman et al., 2011). The same studies and one other in the U.S. (Koster et al., 2005) examined social disparities in physical function; again, the results suggested that biomarkers account for, at most, a small fraction of the gap.
This paper uses data from a population-based survey of older Moscow residents to address two research questions. First, are social disparities evident across different clusters of biomarkers? Our measure of biological risk incorporates not only standard cardiovascular risk factors (hereafter referred to as “standard markers”), but also markers of inflammation and neuroendocrine activity as well as information about heart function based on a 24-h ambulatory ECG—data that are rarely collected in a population-based survey. These data could be especially important in light of the huge role that cardiovascular disease plays in excess Russian mortality.
Second, does this measure of biological risk mediate the link between socioeconomic status and health? In light of the burden of chronic stressors experienced by Russians throughout the 20th century and especially during recent decades—particularly by persons of lower social status—we anticipate that these biological parameters will account for a substantial share of the social gradient in health outcomes. At the same time, we recognize that previous studies in other countries have found very modest effects (Dowd & Goldman, 2006; Goldman et al., 2011; Hu et al., 2007; Koster et al., 2005), underscoring the importance of evaluating these relationships in the Russian context.
Section snippets
Data
The data come from the Survey on Stress, Aging, and Health in Russia (SAHR), a population-based sample of Muscovites aged 55 and older that has been described in detail elsewhere (Shkolnikova et al., 2009). The survey was fielded between December 1, 2006 and June 30, 2009. The fieldwork and data processing were conducted jointly by the National Research Center for Preventive Medicine (NRCPM) in Moscow, the Max Planck Institute for Demographic Research in Rostock (Germany) and Duke University in
Results
On average (based on weighted analyses), men were younger, better educated, and had more material resources than women (Table 1). They also scored better than women in terms of self-reported general health, physical function and bodily pain.
Discussion
To the best of our knowledge, this is the first study exploring relationships between social disparities in health and biomarkers in Russia. This analysis, made possible by the rich health interview and biomarker data collected in SAHR, identified the major links among two measures of socioeconomic position, biomarkers related to several physiological systems, and broad indicators of health among Muscovites.
The magnitude of social inequalities in biological risk varied across systems. We found
Role of the funding source
This work was supported by the National Institute on Aging (grant numbers R01AG026786, R01AG16790, R01AG16661, P01-AG020166), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant number R24HD047879), and the Dynasty Foundation (Russia). The funding agencies had no role in the study design; in the collection, analysis, or interpretation of the data; in writing the manuscript; or in the decision to submit it for publication.
Disclosure statement
None of the authors has a conflict of interest that could inappropriately influence the results of this study.
Acknowledgments
We express our gratitude to James W. Vaupel at MPIDR (Rostock, Germany) for his leadership in designing the SAHR and for encouraging investigation of the associations between socioeconomic status and biomarkers. We are also grateful to Viktoria Metelskaya at NRCPM for providing consultation on the biochemical measurements and Evgeny Andreev at the New Economic School (Moscow) and Alexander Deev at NRCPM (Moscow), who were responsible for the massive data cleaning and processing work.
References (54)
- et al.
Socioeconomic factors, perceived control and self-reported health in Russia. A cross-sectional survey
Social Science & Medicine
(1998) - et al.
Socioeconomic factors, material inequalities, and perceived control in self-rated health: cross-sectional data from seven post-communist countries
Social Science & Medicine
(2000) - et al.
Microalbuminuria and concentrations of antioxidants among US adults
American Journal of Kidney Diseases
(2005) Neuroendocrine biomarkers, social relations, and the cumulative costs of stress in Taiwan
Social Science & Medicine
(2008)- et al.
Do biological measures mediate the relationship between education and health: a comparative study
Social Science & Medicine
(2011) - et al.
Allostatic load biomarkers of chronic stress and impact on health and cognition
Neuroscience and Biobehavioral Reviews
(2010) - et al.
Psychobiological mechanisms of socioeconomic differences in health
Social Science & Medicine
(2004) - et al.
Education, marital status, and total and cardiovascular mortality in Novosibirsk, Russia: a prospective cohort study
Annals of Epidemiology
(2004) - et al.
Prevalence, components, and correlates of metabolic syndrome (MetS) among elderly Muscovites
Archives of Gerontology and Geriatrics
(2012) - et al.
Socio-economic influences on self-rated health in Russian men and women – a life course approach
Social Science & Medicine
(2005)