Association of Social Adversity with Comorbid Diabetes and Depression Symptoms in the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study: A Syndemic Framework
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Background: U.S. Hispanics/Latinos experience high lifetime risk for Type 2 diabetes and concurrent psychological depression. This comorbidity is associated with poorer self-management, worse disease outcomes, and higher mortality. Syndemic theory is a novel social epidemiological framework that emphasizes the role of economic and social adversity in promoting disease comorbidity and health disparities. Purpose: Informed by the syndemic framework, this study explored associations of socioeconomic and psychosocial adversity (low income/education, trauma history, adverse childhood experiences, ethnic discrimination, neighborhood problems [e.g., violence]) with comorbidity of diabetes and depression symptoms in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and Sociocultural Ancillary Study. Methods: Participants were 5,247 Latino adults, aged 18–74, enrolled in four U.S. cities from 2008 to 2011. Participants completed a baseline physical exam and measures of depression symptoms and psychosocial adversity. Multinomial logistic regression analyses were conducted to examine associations of adversity variables with comorbid diabetes and high depression symptoms. Results: Household income below $30,000/year was associated with higher odds of diabetes/depression comorbidity (odds ratio [OR] = 4.61; 95% confidence interval [CI]: 2.89, 7.33) compared to having neither condition, as was each standard deviation increase in adverse childhood experiences (OR = 1.41; 95% CI: 1.16, 1.71), ethnic discrimination (OR = 1.23; 95% CI: 1.01, 1.50), and neighborhood problems (OR = 1.53; 95% CI: 1.30, 1.80). Conclusion: Low household income, adverse childhood experiences, ethnic discrimination, and neighborhood problems are related to comorbid diabetes and depression in U.S. Latinos. Future studies should explore these relationships longitudinally.
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Some reviewed authors suggested mechanisms for interactions among diseases and social factors, such as 'multiplicative interactions', 'cumulative effect', and 'bidirectionality'.
“Some authors suggested mechanisms for interactions among diseases and social factors, such as ‘ multiplicative interactions ’ [ 27 ], ‘cumulative effect’ [ 25 ], and ‘bidirectionality’ [ 23 ].”
From Syndemic contexts: findings from a review of research on non-communicable diseases and interviews with experts (2021) · cites this paper as [23]
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- Authors
- McCurley, J. L., Gutierrez, A. P., Bravin, J. I., Schneiderman, N., Reina, S. A., Khambaty, T., Castañeda, S. F., Smoller, S., Daviglus, M. L., O’Brien, M. J., Carnethon, M. R., Isasi, C. R., Perreira, K. M., Talavera, G. A., Yang, M., & Gallo, L. C.
- Affiliations
- Harvard University (US)
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, CA, USA
- San Diego State University (US)
- University of Miami (US)
- Albert Einstein College of Medicine (US)
- University of Illinois Chicago (US)
- Northwestern University (US)
- Department of Preventive Medicine, Feinberg School of Medicine, Chicago, IL, USA
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- McCurley, Jessica L
- ORCID
- Venue
- Annals of Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-05
- Volume
- 53
- Issue
- 11
- Pages
- 975-987
- ISSN
- 0742-5554 (ISSN-L)
- 0883-6612
- 1532-4796
- PMC
- PMC6779072
- OpenAlex
- W2933342152
- Semantic Scholar
- 2ac6b0eeef5bd1065e3c04879dfde6fcfa958f04
- Cited by
- 41
- References
- 77
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 5 · 2023: 9 · 2022: 9 · 2021: 4 · 2020: 3 · 2019: 1
- FWCI
- 4.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Annals of Behavioral Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Heart, Lung, and Blood Institute
- University of North Carolina (N01-HC65233)
- University of Miami (N01-HC65234)
- Albert Einstein College of Medicine (N01-HC65235)
- Northwestern University (N01-HC65236)
- San Diego State University (N01-HC65237)
- National Center on Minority Health and Health Disparities
- National Institute on Deafness and Other Communication Disorders
- National Institute of Dental and Craniofacial Research
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Neurological Disorders and Stroke
- Office of Dietary Supplements (RC2 HL101649)
- National Institutes of Health
- University of California, San Diego (5T32HL079891–06)
- Fogarty International Center
- University of California Global Health Institute (R25 TW009343)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Social Sciences › Sociology and Political Science › Racial and Ethnic Identity Research
- Keywords
- type 2 diabetes, depression, syndemic theory, social adversity, socioeconomic adversity, psychosocial adversity, Hispanic adults, household income, adverse childhood experiences, ethnic discrimination, neighborhood violence, trauma history, health disparities
- MeSH terms
- Syndemic, Adolescent, Adult, Aged, Depressive Disorder, Diabetes Mellitus, Type 2, Educational Status, Female, Hispanic or Latino, Humans, Life Change Events, Male, Middle Aged, Poverty, Psychosocial Deprivation, Risk Factors, United States, Comorbidity, Health Status Disparities, Young Adult
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.
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