Prescription fill rates for acute and chronic medications in claims-EMR linked data

Nonadherence to prescribed medications poses a significant public health problem. Prescription data in electronic medical records (EMRs) linked with pharmacy claims data provides an opportunity to examine the prescription fill rates and factors associated with it. Using a claims-EMR linked data, patients who had a prescription for either an antibiotic, antihypertensive, or antidiabetic in EMR were identified (index prescription). Prescription fill was defined as a pharmacy claim found within the 90 days following the EMR prescription. For each medication group, patient characteristics and fill rates were examined using descriptive statistics. Multivariate logistic regression was used to evaluate the association between fill rates and factors such as age, race, brand vs generic, and prior treatment during 365 days before the index date. Among 77,996 patients with index antibiotic prescription, 78,462 with index antihypertensive prescription, and 24,013 with index antidiabetic prescription, the prescription fill rate was 73%, 74%, and 76%, respectively. Overall, African American race was negatively associated with fill rates (odds ratio [OR] 0.8 for all 3 groups). Prior treatment history was positively associated with antihypertensives (OR 5.6, 95% confidence interval [CI] 5.4–5.7) or antidiabetics (OR 4.1, CI 3.8–4.4) but negatively with antibiotics (OR 0.6, CI 0.6–0.6). Older age was an additional factor that was negatively associated with first time fill rate among patients without prior treatment. Significant proportions of patients, especially patients with no prior treatment history, did not fill prescriptions for antibiotics, antihypertensives, or antidiabetics. The association between patient factors and medication fill rates varied across different medication groups.