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Objectives To examine racial/ethnic and regional differences in medication adherence in patients with diabetes taking oral anti-diabetic, anti-hypertensive, and cholesterol lowering medications and to identify the pharmacies and prescribers who serve these communities. Methods Administrative claims data was analyzed for members enrolled in a large health plan in Hawaii (2008-2010) with diabetes mellitus who were taking three types of medications: 1) oral anti-diabetic medications; 2) anti-hypertensive medications; 3) cholesterol lowering medications (n=5136). The primary outcome was medication adherence based on medication possession ratios. Multivariable logistic regression models were estimated to examine the association between race/ethnicity and region to adherence to each drug class separately, followed by non-adherence to all three. Covariates included age, gender, education level, chronic conditions, copayment level, and number of prescribers and pharmacies from which the patients received their medications. Key Findings After adjustment for other factors, Filipinos [OR=0.58, 95%CI(0.45,0.74)], Native Hawaiians [OR=0.74, 95%CI(0.56,0.98)], and people of other race [OR=0.67, 95%CI(0.55,0.82)] were significantly less adherent to anti-diabetic and anti-hypertensive medications than Japanese. For cholesterol-lowering medications, all racial and ethnic groups were significantly less adherent than Japanese, except mixed race. We also found that different racial/ethnic groups tended to use different pharmacies and prescribers, particularly in rural areas Conclusion Adherence differed by race/ethnicity as well as age and region. Qualitative research involving subgroups (e.g. Filipinos, Native Hawaiians, people under age 50) is needed to identify how to adapt and enhance the effects of interventions shown to be efficacious in prior studies.

Introduction The true extent of racial and ethnic disparities in COVID-19 hospitalizations may be hidden by misclassification of race and ethnicity. This study aimed to quantify this inaccuracy in a hospital’s electronic medical record (EMR) against the gold standard of self-identification and then project data onto state-level COVID-19 hospitalizations by self-identified race and ethnicity. Methods To identify misclassification of race and ethnicity in the EMRs of a hospital in Honolulu, Hawaii, research and quality improvement staff members surveyed all available patients (N = 847) in 5 cohorts in 2007, 2008, 2010, 2013, and 2020 at randomly selected hospital and ambulatory units. The survey asked patients to self-identify up to 12 races and ethnicities. We compared these data with data from EMRs. We then estimated the number of COVID-19 hospitalizations by projecting racial misclassifications onto publicly available data. We determined significant differences via simulation-constructed medians and 95% CIs. Results EMR–based and self-identified race and ethnicity were the same in 86.5% of the sample. Native Hawaiians (79.2%) were significantly less likely than non–Native Hawaiians (89.4%) to be correctly classified on initial analysis; this difference was driven by Native Hawaiians being more likely than non–Native Hawaiians to be multiracial (93.4% vs 30.3%). When restricted to multiracial patients only, we found no significant difference in accuracy ( P = .32). The number of COVID-19–related hospitalizations was 8.7% higher among Native Hawaiians and 3.9% higher among Pacific Islanders when we projected self-identified race and ethnicity rather than using EMR data. Conclusion Using self-identified rather than hospital EMR data on race and ethnicity may uncover further disparities in COVID-19 hospitalizations.