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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Published online: December 11, 2017
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© 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
