Measuring Medication Adherence in a Population-Based Asthma Administrative Pharmacy Database: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Limited studies have systematically reviewed the literature to identify and compare the various database methods and optimal thresholds for measuring medication adherence specific to adolescents and adults with asthma. In the present study, we aim to identify the methods and optimal thresholds for measuring medication adherence in population-based pharmacy databases. METHODS: We searched PubMed, Embase, International Pharmaceutical Abstracts (IPA), Web of Science, Google Scholar, and grey literature from January 1, 1998, to March 16, 2021. Two independent reviewers screened the studies, extracted the data, and assessed the quality of the studies. A quantitative knowledge synthesis was employed. RESULTS: Thirty-eight (38) retrospective cohort studies were eligible. This review identified 20 methods for measuring medication adherence in adolescent and adult asthma administrative health records. Two measures namely the medication possession ratio (MPR) and proportion of days covered (PDC) were commonly reported in 87% of the literature included in this study. From the meta-analysis, asthma patients who achieved adherence threshold of "0.75-1.00" [OR: 0.56, 95% CI: 0.41 to 0.77] and ">0.5" [OR: 0.71, 95% CI: 0.54 to 0.94] were less likely to experience asthma exacerbation. CONCLUSION: Despite their limitations, the PDC and the MPR still remain the most common measures for assessing adherence in asthma pharmacy claim databases. The evidence synthesis showed that an adherence threshold of at least 0.75 is optimal for classifying adherent and non-adherent asthma patients.
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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 | 0.043 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.036 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".