Abstract / Summary
Purpose: This study aimed to identify the demographic and clinical predictors of statin adherence among patients prescribed statins for the primary prevention of CVD during the first two years following their first recorded statin prescription, using real-world data. Methods: The Electronic Practice Based Research Network Linked Dataset was used in this study. Statin adherence was calculated using a modified proportion of days covered (PDC), based on prescribing data from the date of the first recorded statin prescription to the to the last recorded visit within the two years observation period. Patients with a PDC [≥] 80% were classified as adherent. All analyses were performed using R software. Descriptive analyses and multivariate logistic regression were performed. Backward stepwise selection guided by the Akaike Information Criterion was used to derive a parsimonious final model. Results: Overall, 3,432 patients accounting for 57,227 visits were included. The mean PDC was 91.6% (SD 22.2%), and 72.0% of patients were classified as adherent. Higher odds of adherence was observed among patients aged over 65 years (adjusted odds ratio [AOR] 1.4, 95% confidence interval [CI] 1.1-1.8), those residing in Socio-Economic Indexes for Australia 2021 Index of Relative Socio-Economic Advantage and Disadvantage category 3 (AOR 2.1, 95% CI 1.6-2.9), those prescribed three medications, including a statin (AOR 1.8, 95% CI 1.3-2.4) and those with one selected comorbidity (AOR 1.4, 95% CI 1.1-1.7), compared with their respective reference categories. Patients prescribed two or more statin types had lower odds of adherence (AOR 0.7, 95% CI 0.5-0.9). Conclusions: Age, area level socioeconomic status, and the number of prescribed medications, selected comorbidities and statin types were associated with statin adherence. However, adherence was estimated from prescribing rather than dispensing data, and the model demonstrated modest discrimination. Further validation in independent populations is needed to validate these associations.