TY - JOUR
T1 - Measures of SES for Electronic Health Record-based Research
AU - Casey, Joan A.
AU - Pollak, Jonathan
AU - Glymour, M. Maria
AU - Mayeda, Elizabeth R.
AU - Hirsch, Annemarie G.
AU - Schwartz, Brian S.
N1 - Publisher Copyright:
© 2018 American Journal of Preventive Medicine
PY - 2018/3
Y1 - 2018/3
N2 - 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.
AB - 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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U2 - 10.1016/j.amepre.2017.10.004
DO - 10.1016/j.amepre.2017.10.004
M3 - Article
C2 - 29241724
AN - SCOPUS:85037578248
SN - 0749-3797
VL - 54
SP - 430
EP - 439
JO - American journal of preventive medicine
JF - American journal of preventive medicine
IS - 3
ER -