ArticleCommunications medicine2026
Systematic evaluation of medication adherence determinants across 137 active substances on population-level real-world health data.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Extracting and Classifying Drug Discontinuations From Estonian Electronic Health Records: Development and Validation Study.Journal of medical Internet research · 2026Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe current knowledge about medication adherence is based on studies focusing only on few health conditions and little is known about how strongly adherence is shaped by person-specific behaviour. The aim of the cohort study is to 1) evaluate the effect of multiple factors affecting medication adherence in a consistent manner across 137 active substances, and 2) calculate individual medication adherence score (IMAS), evaluate its predictive power, stability over time, and impact on health outcomes. In essence, IMAS describes persons' medication-taking "baseline".
methodsWe utilised a representative dataset with electronic health records, claims, and dispensed medications across 137 active substances and applied continuous multiple interval measures of medication availability (CMA). To assess the effect of various demographic, health, and medication-related variables on CMA, we employed linear mixed models.
resultsHere we show that the medication adherence 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 22.0% 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.
conclusionsThus, IMAS represents a person-level metric that captures baseline medication-taking behaviour across therapeutic classes and predicts both medication adherence as well as health outcomes. Our analysis suggests that 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.
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