Joan A Casey1, Jonathan Pollak2, M Maria Glymour3, Elizabeth R Mayeda4, Annemarie G Hirsch5, Brian S Schwartz6. 1. 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. Electronic address: joanacasey@berkeley.edu. 2. Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3. Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, San Francisco, California. 4. 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. 5. Department of Epidemiology and Health Services Research, Geisinger Health System, Danville, Pennsylvania. 6. 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.
Abstract
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.
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.
Authors: Anne C Moorman; Stuart C Gordon; Loralee B Rupp; Philip R Spradling; Eyasu H Teshale; Mei Lu; David R Nerenz; Cynthia C Nakasato; Joseph A Boscarino; Emily M Henkle; Nancy J Oja-Tebbe; Jian Xing; John W Ward; Scott D Holmberg Journal: Clin Infect Dis Date: 2012-09-18 Impact factor: 9.079
Authors: Nunzia B Giuse; Taneya Y Koonce; Sheila V Kusnoor; Aric A Prather; Laura M Gottlieb; Li-Ching Huang; Sharon E Phillips; Yu Shyr; Nancy E Adler; William W Stead Journal: Am J Prev Med Date: 2016-09-19 Impact factor: 5.043
Authors: Bruna Galobardes; Mary Shaw; Debbie A Lawlor; John W Lynch; George Davey Smith Journal: J Epidemiol Community Health Date: 2006-01 Impact factor: 3.710
Authors: David P J Osborn; Sarah Hardoon; Rumana Z Omar; Richard I G Holt; Michael King; John Larsen; Louise Marston; Richard W Morris; Irwin Nazareth; Kate Walters; Irene Petersen Journal: JAMA Psychiatry Date: 2015-02 Impact factor: 21.596
Authors: Shideh Majidi; R Paul Wadwa; Franziska K Bishop; Georgeanna J Klingensmith; Marian Rewers; Kim McFann; David M Maahs Journal: J Diabetes Metab Disord Date: 2014-05-22
Authors: Joan A Casey; Peter James; Kara E Rudolph; Chih-Da Wu; Brian S Schwartz Journal: Int J Environ Res Public Health Date: 2016-03-11 Impact factor: 3.390
Authors: Joan A Casey; Dana E Goin; Kara E Rudolph; Brian S Schwartz; Dione Mercer; Holly Elser; Ellen A Eisen; Rachel Morello-Frosch Journal: Environ Res Date: 2019-07-23 Impact factor: 6.498
Authors: Melissa N Poulsen; Lisa Bailey-Davis; Jonathan Pollak; Annemarie G Hirsch; Brian S Schwartz Journal: J Acad Nutr Diet Date: 2019-03-08 Impact factor: 4.910
Authors: Jessica E Ebrahimzadeh; Jessica M Long; Louise Wang; John T Nathanson; Shazia Mehmood Siddique; Anil K Rustgi; David S Goldberg; Bryson W Katona Journal: J Genet Couns Date: 2020-03-30 Impact factor: 2.537
Authors: Kirsten Koehler; J Hugh Ellis; Joan A Casey; David Manthos; Karen Bandeen-Roche; Rutherford Platt; Brian S Schwartz Journal: Environ Sci Technol Date: 2018-05-03 Impact factor: 9.028
Authors: Tara P McAlexander; Karen Bandeen-Roche; Jessie P Buckley; Jonathan Pollak; Erin D Michos; John William McEvoy; Brian S Schwartz Journal: J Am Coll Cardiol Date: 2020-12-15 Impact factor: 24.094
Authors: Adnan I Qureshi; William I Baskett; Wei Huang; Daniel Shyu; Danny Myers; Iryna Lobanova; S Hasan Naqvi; Vetta S Thompson; Chi-Ren Shyu Journal: Ethn Dis Date: 2021-07-15 Impact factor: 1.847