Measures of SES for Electronic Health Record-based Research

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 S...

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Veröffentlicht in:American journal of preventive medicine Jg. 54; H. 3; S. 430 - 439
Hauptverfasser: Casey, Joan A., Pollak, Jonathan, Glymour, M. Maria, Mayeda, Elizabeth R., Hirsch, Annemarie G., Schwartz, Brian S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.03.2018
Elsevier Science Ltd
Schlagworte:
Academic achievement
Adult
Attainment
College graduates
Computerized medical records
Diabetes
Educational attainment
Electronic health records
Electronic Health Records - statistics & numerical data
Epidemiologic Research Design
Epidemiology
Fatigue
Female
Health
Health disparities
Health records
Health status
Health Status Disparities
High school graduates
Humans
Hypertension
Inequality
Logistic Models
Male
Marital status
Medicaid
Medical Assistance - statistics & numerical data
Medical records
Medical research
Middle Aged
Migraine
Obesity
Poverty
Risk Factors
Social Class
Socioeconomic factors
Socioeconomic status
Surveys and Questionnaires - statistics & numerical data
Type 2 diabetes mellitus
United States > US
ISSN:0749-3797, 1873-2607, 1873-2607
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Zusammenfassung: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. 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. 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. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0749-3797
1873-2607
1873-2607
DOI:10.1016/j.amepre.2017.10.004