Abstract

Evidence suggests that clinicians may view or label patients as nonadherent in a biased manner. Therefore, we performed a retrospective cohort analysis exploring associations between patient demographics and zip code-level income with the International Classification of Diseases, Tenth Version (ICD-10) diagnoses for nonadherence among type 2 diabetes mellitus (T2DM) patients, comparing primary and specialty care settings. Providers in the primary care group included internal medicine and family medicine physicians. In the specialty care group, providers included endocrinologists and diabetologists only. Participants were identified from 5 primary care and 4 endocrinology sites in the University of Pennsylvania Health System between January 1, 2015, and January 1, 2019. Demographics, hemoglobin A1c (HbA1c), and ICD-10 codes for T2DM and nonadherence were extracted from the electronic health record and analyzed in October 2019. Log-binomial regression models were used to estimate patients' risk of nonadherence labeling by race, insurance, and zip code-level median household income, controlling for patient characteristics and HbA1c as a proxy for diabetes self-management. Results were compared between primary and specialty care sites. A total of 6072 patients aged 18-70 years were included in this study. Black race, Medicare, and Medicaid were associated with increased nonadherence labeling while controlling for patient characteristics ([ARR = 2.48, 95% CI: 2.01, 3.04], [ARR = 1.82, 95% CI: 1.50, 2.18], [ARR = 1.61, 95% CI: 1.32, 1.93], respectively). The results remained significant on adjustment with zip code-level income and showed no differences between primary and specialty sites. Lower-income zip codes showed a significant association with increased rates of nonadherence labeling. Black race, non-private insurance, and lower-income zip codes were associated with disproportionately high rates of nonadherence labeling in both primary and specialty management of T2DM, possibly suggestive of racial or class bias.

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