Suitability of Measures of Pharmacy-Based Medication Adherence for Routine Clinical Use Among Patients with Chronic Diseases: A Systematic Review
Bibliographic record
Abstract
Purpose: To identify the suitability of pharmacy-based measures for determining medication adherence in routine clinical use. Methods: Data were obtained through PubMed and Scopus databases up to December 2023 without publication year restrictions. This review included English studies on assessing medication adherence for hypertension, hyperlipidemia, asthma, chronic obstructive pulmonary disease, and diabetes, using pharmacy databases and providing full-text access. We investigated evidence quality utilizing the Newcastle-Ottawa Scale for non-randomized studies (cohort, case-control, and cross-sectional) and the Risk of Bias Assessment Tool for Non-randomized Studies-2 and JADAD scales for quasi-experiments and randomized control trials, respectively. We determined validity characteristics (completeness, accuracy, reliability, objectivity, continuous adherence history, non-intrusiveness, sensitivity, and specificity) and applicability (cost-effectiveness, ease of use, and interpretability) to evaluate the suitability of pharmacy-based medication adherence measures in clinical settings. Results: This review retrieved 1513 studies, of which 74 met the inclusion criteria. All of the studies, which were published from 2000 to 2023 and mostly utilized a retrospective cohort design (n = 53), included 17.6 million patients. Of the 74 studies, 50 were conducted in the United States. Diabetes mellitus (n = 40) was the most prevalent disease, whereas the medication possession ratio (n = 46) and prescription days covered (n = 31) were the most prevalent pharmacy-based matrix. According to the results, 73 articles demonstrated validity characteristics, whereas 1 article lacked these characteristics. All 74 (100%) articles had applicability characteristics. Conclusion: This systematic review demonstrates that pharmacy-based measures possess valid characteristics, including comprehensive, accurate, objective, reliable, and continuously updated adherence history records. These measures are designed to minimize disruption while offering high sensitivity and specificity. Furthermore, they are characterized by their practicality, being cost-effective, easy to implement, and easy to interpret. These findings suggest that pharmacy-based measures are potentially suitable to assess medication adherence for routine clinical use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".