Cumulative biological risk and socio-economic differences in mortality: MacArthur Studies of Successful Aging
Abstract
Previous research has suggested that socio-economic status (SES) differences in mortality are only partially explained by differences in life-style, psychological and social factors. Seven year mortality data (1988-1995) from the MacArthur Study of Successful Aging, a longitudinal study of adults, aged 70-79, from New Haven, CT; East Boston, MA; and Durham, NC; were used to test the hypothesis that a cumulative measure of biological dysregulation ("allostatic load"), reflecting multiple regulatory systems, would serve as a further mediator of SES differences in mortality. Logistic regression analyses revealed that a cumulative index of biological risk explained 35.4% of the difference in mortality risk between those with higher versus lower SES (as measured by less than high school education versus high school or greater educational attainment). Importantly, the cumulative index provided independent explanatory power, over and above a measure of doctor-diagnosed disease, though the latter also contributed to education-related variation in mortality risks. The summary measure of biological risk also accounted for more variance than individual biological parameters, suggesting the potential value of a multi-systems view of biological pathways through which SES ultimately affects morbidity and mortality.
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Health disparities can be driven by certain groups' increased exposure to adverse life experiences.
“Evidence indicates that health disparities can be driven by certain groups’ increased exposure to ALEs [8,9,15].”
Adverse experiences in adulthood can negatively affect physical and mental health, and the effect of traumatic events throughout the lifetime can be cumulative and substantial.
“Adverse experiences in adulthood can also negatively affect physical and mental health, and the effect of traumatic events throughout the lifetime can be cumulative and substantial [7–9].”
Preliminary evidence suggests that links between SES and allostatic load are at least partially explained by SES-related gradients in factors such as health behaviors.
“Preliminary evidence also suggests that links between SES and AL are at least partially explained by SES-related gradients in factors such as health behaviors 85”
From Socio‐economic differentials in peripheral biology: Cumulative allostatic load (2010) · cites this paper as [85]
Several published reports provide evidence supporting the role of multiple biological systems in mediating SES differentials in mortality.
“Several published reports provide evidence supporting the role of multiple biological systems in mediating SES differentials in mortality.12,57,85,174”
From Socio‐economic differentials in peripheral biology: Cumulative allostatic load (2010) · cites this paper as [85]
In the MacArthur Study of Successful Aging, the total allostatic load index mediates about 35% of the education gradient in mortality, while none of its component items or subscales accounts for close to this, with most accounting for under 15%.
“Analyses from the MacArthur Study of Successful Aging, for example, document that the total AL index mediates some 35% of the education gradient in mortality while none of its component items or subscales accounts for anything close to this, with most accounting for under 15%.85”
From Socio‐economic differentials in peripheral biology: Cumulative allostatic load (2010) · cites this paper as [85]
A more comprehensive assessment of biological risks associated with SES, the allostatic load index, does the best job of explaining observed SES gradients in mortality risk.
“showing that a more comprehensive assessment of biological risks associated with SES (i.e., our index of AL) does the best job of explaining observed SES gradients in mortality risk.85”
From Socio‐economic differentials in peripheral biology: Cumulative allostatic load (2010) · cites this paper as [85]
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“Evidence indicates that health disparities can be driven by certain groups’ increased exposure to ALEs [8,9,15].”
Background
- Authors
- Seeman, T. E., Crimmins, E., Huang, M.-H., Singer, B., Bucur, A., Gruenewald, T., Berkman, L. F., & Reuben, D. B.
- Affiliations
- University of California, Los Angeles (US)
- University of Southern California (US)
- Princeton University (US)
- Harvard University (US)
- Corresponding author
- Seeman, Teresa E
- ORCID
- Venue
- Social Science & Medicine
- Publisher
- Elsevier BV
- Published
- 2004-05
- Volume
- 58
- Issue
- 10
- Pages
- 1985-1997
- ISSN
- 0277-9536 (ISSN-L)
- OpenAlex
- W2083274461
- Cited by
- 521
- References
- 69
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 11 · 2023: 14 · 2022: 15 · 2021: 29 · 2020: 23 · 2019: 42 · 2018: 26 · 2017: 21 · 2016: 31 · 2015: 17 · 2014: 36 · 2013: 26 · 2012: 27
- FWCI
- 17.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Social Science & Medicine (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- John D. and Catherine T. MacArthur Foundation
- University of Vermont
- National Institutes of Health (5r01ag017056-03)
- National Institute on Aging (AG17265, P30AG017265, AG 17056)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Social Sciences › Health › Public Health and Social Inequalities
- Health Sciences › Health Professions › General Health Professions › Health, psychology, and well-being
- Keywords
- socioeconomic status, mortality, allostatic load, biological dysregulation, successful aging, older adults, educational attainment, disease diagnosis
- MeSH terms
- Adaptation, Physiological, Aged, Aging, Allosteric Regulation, Boston, Connecticut, Educational Status, Female, Humans, Interviews as Topic, Life Style, Longitudinal Studies, Male, Mortality, North Carolina, Social Class, Socioeconomic Factors, Sociology, Medical, Logistic Models, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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