Systematic evaluation of medication adherence determinants across 137 ingredients on population-level real-world health data

The current knowledge about medication adherence (MA) is based on studies focusing only on few health conditions. We utilised a representative dataset with electronic health records, claims, and prescribed medications to 1) evaluate the effect of multiple factors affecting MA in a consistent manner across 137 ingredients, and 2) calculate individual medication adherence score (IMAS), evaluate its predictive power, stability over time, and impact on health outcomes. The MA ranged from 0.423 (albuterol, 95% CI 0.414–0.432) to 0.922 (warfarin, 95% CI 0.917–0.926). The demographic, health- and medication-related factors explained 11.6% and IMAS 22% of the variation in adherence. IMAS predicted adherence across medication classes, reduced the risk of overall hospitalisation (Hazard ratio = 0.76, 95% CI 0.60–0.97, p<0.05) and cause-specific incidence for 17 conditions. Thus, medication-taking behaviour represents a broader patient-level phenomenon manifesting consistently across medications, suggesting its potential for personalised interventions in clinical practice and more efficient public health strategies and policies.