Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-27T15:06:30.416Z Has data issue: false hasContentIssue false
  • English
  • Français

Social stratification and allostatic load: shapes of health differences in the MIDUS study in the United States

Published online by Cambridge University Press:  28 January 2019

Javier M. Rodriguez ,
Arun S. Karlamangla ,
Tara L. Gruenewald ,
Dana Miller-Martinez ,
Sharon S. Merkin  and
Teresa E. Seeman
Javier M. Rodriguez*
Affiliation:
Department of Politics and Government and the Inequality and Policy Research Center, Claremont Graduate University, Claremont, USA
Arun S. Karlamangla
Affiliation:
Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, USA
Tara L. Gruenewald
Affiliation:
Davis School of Gerontology, University of Southern California, USA
Dana Miller-Martinez
Affiliation:
Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, USA
Sharon S. Merkin
Affiliation:
Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, USA
Teresa E. Seeman
Affiliation:
Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, USA
*
*Corresponding author. Email: javier.rodriguez@cgu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Social stratification is an important mechanism of human organization that helps to explain health differences between demographic groups commonly associated with socioeconomic gradients. Individuals, or group of individuals, with similar health profiles may have had different stratification experiences. This is particularly true as social stratification is a significant non-measurable source of systematic unobservable differences in both SES indicators and health statuses of disadvantage. The goal of the present study was to expand the bulk of research that has traditionally treated socioeconomic and demographic characteristics as independent, additive influences on health by examining data from the United States. It is hypothesized that variation in an index of multi-system physiological dysregulation – allostatic load – is associated with social differentiation factors, sorting individuals with similar demographic and socioeconomic characteristics into mutually exclusive econo-demographic classes. The data were from the Longitudinal and Biomarker samples of the national Study of Midlife Development in the US (MIDUS) conducted in 1995 and 2004/2006. Latent class analyses and regression analyses revealed that physiological dysregulation linked to socioeconomic variation among black people, females and older adults are associated with forces of stratification that confound socioeconomic and demographic indicators. In the United States, racial stratification of health is intrinsically related to the degree to which black people in general, and black females in particular, as a group, share an isolated status in society. Findings present evidence that disparities in health emerge from group-differentiation processes to the degree that individuals are distinctly exposed to the ecological, political, social, economic and historical contexts in which social stratification is ingrained. Given that health policies and programmes emanate from said legal and political environments, interventions should target the structural conditions that expose different subgroups to different stress risks in the first place.

Keywords

Social StratificationAllostatic loadHealth Disparities
Type
Research Article
Information
Journal of Biosocial Science , Volume 51 , Issue 5 , September 2019 , pp. 627 - 644
Copyright
© Cambridge University Press, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adler, NE, Boyce, T, Chesney, MA, Cohen, S, Folkman, S, Kahn, RL and Syme, SL (1994) Socioeconomic status and health: the challenge of the gradient. American Psychologist 49(1), 15–24.Google Scholar
Annandale, E (2009) Women’s Health and Social Change, 1st Edition. Routledge, London.Google Scholar
Bird, CE, Seeman, T, Escarce, JJ, Basurto-Dávila, R, Finch, BK, Dubowitz, T et al. (2010) Neighbourhood socioeconomic status and biological ‘wear and tear’in a nationally representative sample of US adults. Journal of Epidemiology and Community Health 64(10), 860–865.Google Scholar
Black, D (1982) Inequalities in Health: The Black Report. Penguin Books, New York.Google Scholar
Bound, J, Geronimus, AT, Rodriguez, JM and Waidmann, T (2014) The implications of differential trends in mortality for social security policy. Michigan Retirement Reserch Center Research Paper (MRRC) No. 2014-314. Ann Arbor, MI, doi: 10.2139/ssrn.2549216.Google Scholar
Bound, J, Geronimus, AT, Rodriguez, JM and Waidmann, TA (2015) Measuring recent apparent declines in longevity: the role of increasing educational attainment. Health Affairs 34(12), 2167–2173.Google Scholar
Brooks, KP, Gruenewald, T, Karlamangla, A, Hu, P, Koretz, B and Seeman, TE (2014) Social relationships and allostatic load in the MIDUS study. Health Psychology 33(11), 1373–1381.Google Scholar
Clougherty, JE, Souza, K and Cullen, MR (2010) Work and its role in shaping the social gradient in health. Annals of the New York Academy of Sciences 1186(1), 102–124.Google Scholar
Collins, CA and Williams, DR (1999) Segregation and mortality: the deadly effects of racism? Sociological Forum 14(3), 495–523.Google Scholar
Cottrell, D, Herron, MC, Rodriguez, JM and Smith, DA (2018) Mortality, incarceration, and African American disenfranchisement in the contemporary United States. American Politics Research, 1532673X18754555.Google Scholar
Crenshaw, K (1991) Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Review, 1241–1299.Google Scholar
Denton, M, Prus, S and Walters, V (2004) Gender differences in health: a Canadian study of the psychosocial, structural and behavioural determinants of health. Social Science & Medicine 58(12), 2585–2600.Google Scholar
Fried, LP, Tangen, CM, Walston, J, Newman, AB, Hirsch, C, Gottdiener, J et al. (2001) Frailty in older adults. Journals of Gerontology Series A: Biological Sciences and Medical Sciences 56(3), 146–157.Google Scholar
Friedman, EM, Karlamangla, AS, Gruenewald, T, Koretz, B and Seeman, TE (2015) Early life adversity and adult biological risk profiles. Psychosomatic Medicine 77(2), 176–185.Google Scholar
Geronimus, AT (1992) The weathering hypothesis and the health of African-American women and infants: evidence and speculations. Ethnicity & Disease 2(3), 207–221.Google Scholar
Geronimus, AT, Hicken, M, Keene, D and Bound, J (2006) “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. American Journal of Public Health 96(5), 826–833.Google Scholar
Geronimus, AT, Hicken, MT, Pearson, JA, Seashols, SJ, Brown, KL and Cruz, TD (2010) Do US black women experience stress-related accelerated biological aging? Human Nature 21(1), 19–38.Google Scholar
Geronimus, AT, Pearson, JA, Linnenbringer, E, Schulz, AJ, Reyes, AG, Epel, ES et al. (2015) Race–ethnicity, poverty, urban stressors, and telomere length in a Detroit community-based sample. Journal of Health and Social Behavior 56(2), 199–224.Google Scholar
Gruenewald, TL, Karlamangla, AS, Hu, P, Stein-Merkin, S, Crandall, C, Koretz, B and Seeman, TE (2012) History of socioeconomic disadvantage and allostatic load in later life. Social Science & Medicine 74(1), 75–83.Google Scholar
Gruenewald, TL, Seeman, TE, Karlamangla, AS and Sarkisian, CA (2009) Allostatic load and frailty in older adults. Journal of the American Geriatrics Society 57(9), 1525–1531.Google Scholar
Haas, SA (2006) Health selection and the process of social stratification: the effect of childhood health on socioeconomic attainment. Journal of Health and Social Behavior 47(4), 339–354.Google Scholar
Hagenaars, JA and McCutcheon, AL (2002) Applied Latent Class Analysis. Cambridge University Press, Cambridge.Google Scholar
Hancock, AM (2007) When multiplication doesn’t equal quick addition: examining intersectionality as a research paradigm. Perspectives on Politics 5(1), 63–79.Google Scholar
Hudson, DL, Neighbors, HW, Geronimus, AT and Jackson, JS (2016) Racial discrimination, John Henryism, and depression among African Americans. Journal of Black Psychology 42(3), 221–243.Google Scholar
Josephson, JJ and Tolleson-Rinehart, S (2000) Introduction: gender, sex, and American political life. Gender and the Political Process, 3–17.Google Scholar
Juster, RP, McEwen, BS and Lupien, SJ (2010) Allostatic load biomarkers of chronic stress and impact on health and cognition. Neuroscience & Biobehavioral Reviews 35(1), 2–16.Google Scholar
Karlamangla, AS, Merkin, SS, Crimmins, EM and Seeman, TE (2010) Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001–2006. Annals of Epidemiology 20(8), 617–628.Google Scholar
Karlamangla, AS, Miller-Martinez, D, Lachman, ME, Tun, PA, Koretz, BK and Seeman, TE (2014) Biological correlates of adult cognition: midlife in the United States (MIDUS). Neurobiology of Aging 35(2), 387–394.Google Scholar
Kwarteng, JL, Schulz, AJ, Mentz, GB, Israel, BA, Shanks, TR and Perkins, DW (2016) Neighbourhood poverty, perceived discrimination and central adiposity in the USA: independent associations in a repeated measures analysis. Journal of Biosocial Science 48(6), 709–722.Google Scholar
Lanza, ST, Collins, LM, Lemmon, DR and Schafer, JL (2007) PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling 14(4), 671–694.Google Scholar
Lazarsfeld, PF (1955) Recent developments in latent structure analysis. Sociometry 18(4), 391–403.Google Scholar
Linzer, DA and Lewis, J (2007) poLCA: Polytomous Variable Latent Class Analysis. R Package Version 1.1.Google Scholar
Linzer, DA and Lewis, JB (2011) poLCA: an R package for polytomous variable latent class analysis. Journal of Statistical Software 42(10), 1–29.Google Scholar
Lipowicz, A, Szklarska, A and Malina, RM (2013) Allostatic load and socioeconomic status in Polish adult men. Journal of Biosocial Science 46(2), 155–167.Google Scholar
Love, GD, Seeman, TE, Weinstein, M and Ryff, CD (2010) Bioindicators in the MIDUS National Study: protocol, measures, sample, and comparative context. Journal of Aging and Health 22(8), 1059–1080.Google Scholar
Marcoulides, GA and Moustaki, I (2012) Latent Variable and Latent Structure Models. Routledge, New York.Google Scholar
Marcus, AC and Seeman, TE (1981) Sex differences in reports of illness and disability: a preliminary test of the “fixed role obligations” hypothesis. Journal of Health and Social Behavior 22(2), 174–182.Google Scholar
Markides, KS and Machalek, R (1984) Selective survival, aging and society. Archives of Gerontology and Geriatrics 3(3), 207–222.Google Scholar
Matthews, DR, Hosker, JP, Rudenski, AS, Naylor, BA, Treacher, DF and Turner, RC (1985) Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man Diabetologia 28(7), 412–419.Google Scholar
McCall, L (2005) The complexity of intersectionality. Signs 30(3), 1771–1800.Google Scholar
McEwen, BS and Seeman, T (1999) Protective and damaging effects of mediators of stress: elaborating and testing the concepts of allostasis and allostatic load. Annals of the New York Academy of Sciences 896(1), 30–47.Google Scholar
Merkin, SS, Basurto-Dávila, R, Karlamangla, A, Bird, CE, Lurie, N, Escarce, J and Seeman, T (2009) Neighborhoods and cumulative biological risk profiles by race/ethnicity in a national sample of US adults: NHANES III. Annals of Epidemiology 19(3), 194–201.Google Scholar
Mori, T, Karlamangla, AS, Merkin, SS, Crandall, CJ, Binkley, N, Greendale, GA and Seeman, TE (2014) Multisystem dysregulation and bone strength: findings from the study of midlife in the United States. Journal of Clinical Endocrinology & Metabolism 99(5), 1843–1851.Google Scholar
O’Brien, KM (2012) Healthy, wealthy, wise? Psychosocial factors influencing the socioeconomic status-health gradient. Journal of Health Psychology 17(8), 1142–1151.Google Scholar
Pearlin, LI, Schieman, S, Fazio, EM and Meersman, SC (2005) Stress, health, and the life course: some conceptual perspectives. Journal of Health and Social Behavior 46(2), 205–219.Google Scholar
Rodriguez, JM (2018) Health disparities, politics, and the maintenance of the status quo: a new theory of inequality. Social Science & Medicine 200, 36–43.Google Scholar
Rodriguez, JM, Bound, J and Geronimus, AT (2013) US infant mortality and the President’s party. International Journal of Epidemiology 43(3), 818–826.Google Scholar
Rodriguez, JM, Bound, J and Geronimus, AT (2014) Rejoinder: time series analysis and US infant mortality: de-trending the empirical from the polemical in political epidemiology. International Journal of Epidemiology 43(3), 831–834.Google Scholar
Rodriguez, JM, Geronimus, AT, Bound, J and Dorling, D (2015) Black lives matter: differential mortality and the racial composition of the US electorate, 1970–2004. Social Science & Medicine 136, 193–199.Google Scholar
Sapiro, V (2003) Theorizing gender in political psychology research. In Sears DO, Huddy L & Jervis R (eds) Oxford Handbook of Political Psychology. Oxford University Press, New York, pp. 601–634.Google Scholar
Sears, DO, Citrin, J and Van Laar, C (1995) Black exceptionalism in a multicultural society. Annual Meeting of the Society for Experimental Social Psychology. Washington, DC.Google Scholar
Sears, DO and Savalei, V (2006) The political color line in America: many “peoples of color” or black exceptionalism? Political Psychology 27(6), 895–924.Google Scholar
Seeman, M, Merkin, SS, Karlamangla, A, Koretz, B and Seeman, T (2014) Social status and biological dysregulation: the “status syndrome” and allostatic load. Social Science & Medicine 118, 143–151.Google Scholar
Slopen, N, Lewis, TT, Gruenewald, TL, Mujahid, MS, Ryff, CD, Albert, MA and Williams, DR (2010) Early life adversity and inflammation in African Americans and whites in the midlife in the United States survey. Psychosomatic Medicine 72(7), 694–701.Google Scholar
Solís, CB, Kelly-Irving, M, Fantin, R, Darnaudéry, M, Torrisani, J, Lang, T and Delpierre, C (2015) Adverse childhood experiences and physiological wear-and-tear in midlife: findings from the 1958 British birth cohort. Proceedings of the National Academy of Sciences of the USA 112(7), 738–746.Google Scholar
Stouffer, SA, Guttman, L, Suchman, EA, Lazarsfeld, PF, Star, SA and Clausen, JA (1950) Measurement and Prediction. Princeton University Press, Princeton.Google Scholar
Thoits, PA (2010) Stress and health major findings and policy implications. Journal of Health and Social Behavior 51(1) (supplement), 41–53.Google Scholar
Upchurch, DM, Stein, J, Greendale, GA, Chyu, L, Tseng, C-H, Huang, M-H et al. (2015) A longitudinal investigation of race, socioeconomic status, and psychosocial mediators of allostatic load in midlife women: findings from the Study of Women’s Health Across the Nation. Psychosomatic Medicine 77(4), 402–412.Google Scholar
Verbrugge, LM (1989) Gender, aging, and health. Aging and Health: Perspectives on Gender, Race, Ethnicity, and Class, 23–78.Google Scholar
Vogeli, C, Shields, AE, Lee, TA, Gibson, TB, Marder, WD, Weiss, KB et al. (2007) Miltiple chronic conditions: Prevalence, health consequences, and implications for quality, care management, and costs Journal of General Internal Medicine 22(3), 391–395.Google Scholar
Wiley, JF, Gruenewald, TL, Karlamangla, AS and Seeman, TE (2016) Modeling multisystem physiological dysregulation. Psychosomatic Medicine 78(3), 290–301.Google Scholar
Williams, DR and Collins, C (1995) US socioeconomic and racial differences in health: patterns and explanations. Annual Review of Sociology 21, 349–386.Google Scholar
Williams, DR, Mohammed, SA, Leavell, J and Collins, C (2010) Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences 1186(1), 69–101.Google Scholar