JMIR Publications

ABSTRACT

Background:

American Indian peoples (AIs) have the highest prevalence of type 2 diabetes (T2D) of any racial or ethnic group and experience high rates of co-morbidities such as obesity, cardiovascular disease (CVD), and chronic kidney disease (CKD). Uncontrolled cardio-metabolic risk factors -- insulin resistance resulting in impaired glucose tolerance, dyslipidemia, and hypertension (HTN) -- increase mortality risk. Mortality is significantly reduced by glucose- and lipid-lowering, and antihypertensive medication adherence. Medication adherence is low among AIs living in non-Indian Health Services (IHS) healthcare settings. Virtually nothing is known about the nature and extent of medication adherence among reservation-dwelling AIs who primarily receive their medications without cost from IHS/tribal facilities. Electronic health records (EHR) offer a rich but underused data source about medication adherence and its potential to predict cardio-metabolic control indicators (C-MCI) such as HbA1c, LDL-C (low density lipoprotein), SBP (systolic blood pressure). With the support of Choctaw Nation of Oklahoma (CNO), we address this oversight by using EHR data generated by this large, state-of-the-art tribal healthcare system to investigate C-MCI.

Objective:

Our specific aims are to 1) determine, using 2018 EHR data, the bivariate relationships between (a) medication adherence and C-MCIs, demographics, and co-morbidities and (b) each C-MCI and demographics and co-morbidities; 2) develop machine-learning models for predicting future C-MCI from the previous year medication adherence, demographics, co-morbidities, and common labs; and 3) identify facilitators of and barriers to medication adherence within the context of SDOH, EHR-derived medication adherence, and C-MCI.

Methods:

Drawing on the tribe’s EHR (2018-2021) data for CNO patients with T2D, we will characterize relationships among medication adherence (to glucose- and lipid-lowering, and antihypertensive drugs) and C-MCI (HbA1c ≤7%, LDL-C <100 mg/dL, and SBP <130 mm Hg), patient demographics (e.g., age, sex, SDOH, residence location) and co-morbidities (e.g., BMI>30, CVD, CKD). We will also characterize the association of each C-MCI with demographics and co-morbidities. We will use prescription and pharmacy refill data to calculate the proportion of days covered (PDC) with medications, a typical measure of medication adherence. Employing machine learning techniques, we will develop prediction models for future (2019-2021) C-MCIs based on medication adherence, patient demographics, co-morbidities, and common labs (e.g., lipid panel) from the previous year. Lastly, 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. UF and CNO have approved the IRB protocols and executed the data use agreements. Data extraction is in process. We expect to have results from Aims 1 and 2 in 2024.

Conclusions:

Our findings will yield insights to improve medication adherence and C-MCI among AIs, consistent with CNO’s State of the Nation’s Health Report 2017 goal of reducing T2D and its complications.