Associations between social determinants of health and mental health disorders among U.S. population: a cross-sectional study.

S Tanarsuwongkul, J Liu, M Spaulding, K Perea-Schmittle, M Lohman, Q Wang
Author Information
  1. S Tanarsuwongkul: Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, USA.
  2. J Liu: Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA. ORCID
  3. M Spaulding: Department of Biomedical Engineering, Molinaroli College of Engineering and Computing, University of South Carolina, Columbia, SC, USA.
  4. K Perea-Schmittle: Department of Biology, New Mexico Institute of Mining and Technology, Socorro, NM, USA.
  5. M Lohman: Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  6. Q Wang: Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, USA.

Abstract

AIMS: The impact of social determinants of health (SDOH) on mental health is increasingly realized. A comprehensive study examining the associations of SDOH with mental health disorders has yet to be accomplished. This study evaluated the associations between five domains of SDOH and the SDOH summary score and mental health disorders in the United States.
METHODS: We analyzed data from a diverse group of participants enrolled in the All of Us research programme, a research programme to gather data from one million people living in the United States, in a cross-sectional design. The primary exposure was SDOH based on Healthy People 2030: education access and quality, economic stability, healthcare access and quality, social and community context, and neighbourhood and built environment. A summary SDOH score was calculated by adding each adverse SDOH risk (any SDOH vs. no SDOH). Our primary outcomes were diagnoses of major depression (MD) (i.e., major depressive disorder, recurrent MD or MD in remission) and anxiety disorders (AD) (i.e., generalized AD and other anxiety-related disorders). Multiple logistic regression models were used to determine adjusted odd ratios (aORs) for MD and/or ADs after controlling for covariates.
RESULTS: A total of 63,162 participants with MD were identified (22,277 [35.3%] age 50-64 years old; 41,876 [66.3%] female). A total of 77,624 participants with AD were identified (25,268 [32.6%] age 50-64 years old; 52,224 [67.3%] female). Factors associated with greater odds of MD and AD included having less than a college degree, annual household income less than 200% of federal poverty level, housing concerns, lack of transportation, food insecurity, and unsafe neighbourhoods. Having no health insurance was associated with lower odds of both MD and AD (aOR, 0.48; 95% confidence interval [CI], 0.46-0.51 and aOR, 0.44; 95% CI, 0.42-0.47, respectively). SDOH summary score was strongly associated with the likelihood of having MD and AD (aOR, 1.97; 95% CI, 1.89-2.06 and aOR, 1.69; 95% CI, 1.63-1.75, respectively).
CONCLUSIONS: This study found associations between all five domains of SDOH and the higher odds of having MD and/or AD. The strong correlations between the SDOH summary score and mental health disorders indicate a possible use of the summary score as a measure of risk of developing mental health disorders.

Keywords

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MeSH Term

Humans
Cross-Sectional Studies
Social Determinants of Health
United States
Female
Male
Middle Aged
Adult
Depressive Disorder, Major
Mental Disorders
Mental Health
Anxiety Disorders
Aged
Socioeconomic Factors
Young Adult
Adolescent
Health Services Accessibility
Risk Factors

Word Cloud

Created with Highcharts 10.0.0SDOHhealthMDdisordersADmentalsummaryscoresocialstudyaOR095%1determinantsassociationsparticipantsmajor3%]associatedoddsCIfivedomainsUnitedStatesdataUsresearchprogrammecross-sectionalprimaryaccessqualityriskdepressioniedisorderanxietyand/ortotalidentifiedage50-64 yearsoldfemalelessrespectivelyAIMS:impactincreasinglyrealizedcomprehensiveexaminingyetaccomplishedevaluatedMETHODS:analyzeddiversegroupenrolledgatheronemillionpeoplelivingdesignexposurebasedHealthyPeople2030:educationeconomicstabilityhealthcarecommunitycontextneighbourhoodbuiltenvironmentcalculatedaddingadversevsoutcomesdiagnosesdepressiverecurrentremissiongeneralizedanxiety-relatedMultiplelogisticregressionmodelsuseddetermineadjustedoddratiosaORsADscontrollingcovariatesRESULTS:6316222277[3541876[667762425268[326%]52224[67Factorsgreaterincludedcollegedegreeannualhouseholdincome200%federalpovertylevelhousingconcernslacktransportationfoodinsecurityunsafeneighbourhoodsinsurancelower48confidenceinterval[CI]46-0514442-047stronglylikelihood9789-2066963-175CONCLUSIONS:foundhigherstrongcorrelationsindicatepossibleusemeasuredevelopingAssociationsamongUSpopulation:All

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