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Measures of SES for Electronic Health Record-based Research

  • Joan A. Casey
    Correspondence
    Address correspondence to: Joan A. Casey, PhD, University of California, Berkeley, 2199 Addison St., Room 13B, Berkeley CA 94720
    Affiliations
    Robert Wood Johnson Foundation Health and Society Scholars Program, University of California, San Francisco, California

    Department of Environmental Science, Policy, and Management, University of California, Berkeley, California
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  • Jonathan Pollak
    Affiliations
    Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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  • M. Maria Glymour
    Affiliations
    Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, San Francisco, California
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  • Elizabeth R. Mayeda
    Affiliations
    Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, San Francisco, California

    Department of Epidemiology, University of California, Los Angeles Fielding School of Public Health, Los Angeles, California
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  • Annemarie G. Hirsch
    Affiliations
    Department of Epidemiology and Health Services Research, Geisinger Health System, Danville, Pennsylvania
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  • Brian S. Schwartz
    Affiliations
    Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

    Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland

    Center for Health Research, Geisinger Health System, Danville, Pennsylvania
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Published:December 11, 2017DOI:https://doi.org/10.1016/j.amepre.2017.10.004

      Introduction

      Although infrequently recorded in electronic health records (EHRs), measures of SES are essential to describe health inequalities and account for confounding in epidemiologic research. Medical Assistance (i.e., Medicaid) is often used as a surrogate for SES, but correspondence between conventional SES and Medical Assistance has been insufficiently studied.

      Methods

      Geisinger Clinic EHR data from 2001 to 2014 and a 2014 questionnaire were used to create six SES measures: EHR-derived Medical Assistance and proportion of time under observation on Medical Assistance; educational attainment, income, and marital status; and area-level poverty. Analyzed in 2016–2017, associations of SES measures with obesity, hypertension, type 2 diabetes, chronic rhinosinusitis, fatigue, and migraine headache were assessed using weighted age- and sex-adjusted logistic regression.

      Results

      Among 5,550 participants (interquartile range, 39.6–57.5 years, 65.9% female), 83% never used Medical Assistance. All SES measures were correlated (Spearman’s p≤0.4). Medical Assistance was significantly associated with all six health outcomes in adjusted models. For example, the OR for prevalent type 2 diabetes associated with Medical Assistance was 1.7 (95% CI=1.3, 2.2); the OR for high school versus college graduates was 1.7 (95% CI=1.2, 2.5). Medical Assistance was an imperfect proxy for SES: associations between conventional SES measures and health were attenuated <20% after adjustment for Medical Assistance.

      Conclusions

      Because systematically collected SES measures are rarely available in EHRs and are unlikely to appear soon, researchers can use EHR-based Medical Assistance to describe inequalities. As SES has many domains, researchers who use Medical Assistance to evaluate the association of SES with health should expect substantial unmeasured confounding.
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