Association of Medication Adherence With HbA1c Control Among American Indian Adults With Type 2 Diabetes Using Tribal Health Services.

OBJECTIVE: To examine HbA1c levels and adherence to oral glucose-lowering medications and their association with future HbA1c levels among American Indian adults with type 2 diabetes (T2D) receiving medications at no cost from a tribal health care system. RESEARCH DESIGN AND METHODS: Tribal citizens with T2D who used Choctaw Nation Health Services Authority (CNHSA) and Pharmacies and had HbA1c data during 2017-2018 were included in this study. Medication adherence (proportion of days covered [PDC] ≥0.80) was calculated using 2017 CNHSA electronic health record data. RESULTS: Of the 74,000 tribal citizens living on tribal lands, 4,560 were eligible; 32% had HbA1c at or below target (≤7%), 36% were above target (>7 to ≤9%), and 32% were uncontrolled (>9%) in 2017. The percentage of patients with PDC ≥0.80 was 66% for those using biguanides, 72% for sulfonylureas, 75% for dipeptidyl peptidase 4 inhibitors, and 83% for sodium-glucose cotransporter 2 inhibitors. The proportion of patients with HbA1c at or below target increased slightly from 32% in 2017 to 42% in 2018. Higher average PDC in 2017 was associated with lower HbA1c levels in 2018 (ß = -1.143; P < 0.001). CONCLUSIONS: Medication adherence was higher than that found in previous studies using self-report methods in American Indian populations, although a smaller proportion of patients had HbA1c at or below target relative to U.S. adults with T2D. Medication adherence was associated with improved HbA1c levels for most oral glucose-lowering medication classes. Future studies of American Indians should use both longitudinal prescription data from both electronic health records and pharmacy refills.

Medication Adherence and Cardiometabolic Control Indicators Among American Indian Adults Receiving Tribal Health Services: Protocol for a Longitudinal Electronic Health Records Study.

BACKGROUND: American Indian adults have the highest prevalence of type 2 diabetes (T2D) in any racial or ethnic group and experience high rates of comorbidities. Uncontrolled cardiometabolic risk factors-insulin resistance, resulting in impaired glucose tolerance, dyslipidemia, and hypertension-increase the risk of mortality. Mortality is significantly reduced by glucose- and lipid-lowering and antihypertensive medication adherence. Medication adherence is low among American Indian adults living in non-Indian Health Service health care settings. Virtually nothing is known about the nature and extent of medication adherence among reservation-dwelling American Indian adults who primarily receive their medications without cost from Indian Health Service or tribal facilities. Electronic health records (EHRs) offer a rich but underused data source regarding medication adherence and its potential to predict cardiometabolic control indicators (C-MCIs). With the support of the Choctaw Nation of Oklahoma (CNO), we address this oversight by using EHR data generated by this large, state-of-the-art tribal health care system to investigate C-MCIs. OBJECTIVE: Our specific aims are to determine, using 2018 EHR data, the bivariate relationships between medication adherence and C-MCIs, demographics, and comorbidities and each C-MCI and demographics and comorbidities; develop machine learning models for predicting future C-MCIs from the previous year's medication adherence, demographics, comorbidities, and common laboratory tests; and identify facilitators of and barriers to medication adherence within the context of social determinants of health (SDOH), EHR-derived medication adherence, and C-MCIs. METHODS: Drawing on the tribe's EHR (2018-2021) data for CNO patients with T2D, we will characterize the relationships among medication adherence (to glucose- and lipid-lowering and antihypertensive drugs) and C-MCIs (hemoglobin A1c ≤7%, low-density lipoprotein cholesterol <100 mg/dL, and systolic blood pressure <130 mm Hg); patient demographics (eg, age, sex, SDOH, and residence location); and comorbidities (eg, BMI ≥30, cardiovascular disease, and chronic kidney disease). We will also characterize the association of each C-MCI with demographics and comorbidities. Prescription and pharmacy refill data will be used to calculate the proportion of days covered with medications, a typical measure of medication adherence. Using machine learning techniques, we will develop prediction models for future (2019-2021) C-MCIs based on medication adherence, patient demographics, comorbidities, and common laboratory tests (eg, lipid panel) from the previous year. Finally, key informant interviews (N=90) will explore facilitators of and barriers to medication adherence within the context of local SDOH. RESULTS: Funding was obtained in early 2022. The University of Florida and CNO approved the institutional review board protocols and executed the data use agreements. Data extraction is in process. We expect to obtain results from aims 1 and 2 in 2024. CONCLUSIONS: Our findings will yield insights into improving medication adherence and C-MCIs among American Indian adults, consistent with CNO's State of the Nation's Health Report 2017 goal of reducing T2D and its complications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39193.