Validity of the days supply field in pharmacy administrative claims data for the identification of blister packaging of medications
Bibliographic record
Abstract
PURPOSE: Pharmacy claims data is often used in pharmacoepidemiology studies, but no studies to date have examined whether it was possible to identify the use of blister packs in these databases. We aimed to determine whether medications dispensed in days divisible by 7 are more likely to be blister packed than medications dispensed in other quantities. METHODS: Community pharmacies in Manitoba were invited to participate in a mail-out survey to identify the use of blister packaging for up to 25 patients who had a solid oral medication dispensed from April 1, 2012 to March 31, 2014. Eligible medications were identified using the population-based province-wide retail pharmacy network. Algorithms for identifying the use of blister packaging were determined by comparing the proportion of fills that confirmed blister pack use between different days supply quantities. RESULTS: Twenty-seven out of 32 pharmacies that agreed to participate completed the survey. The total number of prescriptions in the analysis was 2045 of which 131 (6.4%) were dispensed in blister packaging. Overall, prescriptions dispensed in days divisible by 7 yielded a 72.5% sensitivity, 86.6% specificity, 30.3% PPV, and 97.9% NPV compared with prescriptions dispensed in other quantities. A 28-day to 30-day comparison yielded an 87.9% sensitivity, 96.1% specificity, 64.6% PPV, and 99.0% NPV. CONCLUSION: While the NPV was high, the PPV for identifying blister packaging using the days supply field in pharmacy claims data was modest given the low prevalence in blister pack use. The best predictor occurred when 28 days was compared with 30 days. KEY POINTS Blister packs are arranged in 4 × 7 compartments and are often used to improve adherence, but no studies have examined whether it was possible to identify the use of blister packs using the days supply field in pharmacy claims data. Findings show that a 28-day supply yielded a high sensitivity and specificity for identifying the use of blister packaging compared with a 30-day supply, but there is potential for misclassification. Future studies directed at examining subgroups that are more likely to use blister packs and replication of findings using other data sources in other jurisdictions are encouraged.
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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 | 0.051 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".