Associations of Race, Insurance, and Zip Code-Level Income with Nonadherence Diagnoses in Primary and Specialty Diabetes Care

Abstract

Introduction: 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.Methods: Participants were identified from 5 primary care and 4 endocrinology sites in the

Predictors of lack of glycemic control in persons with type 2 diabetes

“…Our study adds to existing data that showed an association between socioeconomic factors and HbA1c in people with diabetes [ 8 , 12 ]. However, most of the other studies were cross-sectional.…”

“…Thus, neighborhood may be a more important factor in receiving adequate diabetes care. There is a difference in adherence and healthcare utilization depending on the residential ZIP codes of patients [ 8 , 18 ]. We think there is an opportunity to improve access to diabetes care and improve glycemic control in low-income areas despite many factors being beyond the control of medical professionals.…”

“…A better understanding of these factors may help in designing better treatment strtegies. Most of the previous studies on the association between socioeconomic factors and HbA1c were crossectional and provided limited information [ 8 , 9 ]. We conducted a longitudinal data analysis to understand factors associated with lack of glycemic control in patients receiving clinical care within a major academic healthcare system.…”

Dual Incontinence and Risk of Fall: A Retrospective Cohort Study

“…Household income was estimated by primary residential zip code, a commonly used area-level proxy for socioeconomic status variables. 17,18 Data on markers of frailty, ability to perform activities of daily living (ADLs), abuse, and fall were extracted from intake questionnaires. In our practice, during a new patient ambulatory encounter, the fol-lowing questions on frailty and ADLs are asked: "Have you had an unintentional weight loss of more than 10 pounds over the past 3 months?"…”

“…The following demographics data were extracted from the medical record: age, race, ethnicity, body mass index, and primary residential zip code. Household income was estimated by primary residential zip code, a commonly used area-level proxy for socioeconomic status variables 17,18 …”