Comparison of measures of medication adherence from pharmacy dispensing and insurer claims data
Bibliographic record
Abstract
OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.
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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 | 0.052 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".