Comparing prescribing and dispensing databases to study antibiotic use: a validation study of the Electronic Medical Record Administrative data Linked Database (EMRALD)
Bibliographic record
Abstract
BACKGROUND: Monitoring and studying community antibiotic use is a critical component in combating rising antimicrobial resistance. OBJECTIVES: To validate an electronic medical record dataset containing antibiotic prescriptions and to quantify some important differences between prescribing and dispensing databases. METHODS: We evaluated antibiotics prescribed and dispensed to patients ≥65 years of age during 2011-15. We compared the EMRALD prescribing database with the validated Ontario Drug Benefit (ODB) dispensing database. Using ODB as the gold standard and limiting to EMRALD physicians, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) with 95% CIs. We also compared the relative change in antibiotic use prescribed by all physicians to this population over time between the databases using Poisson regression models. RESULTS: In this population, 74% of all antibiotics dispensed were from non-EMRALD physicians. Trends in use were discordant over time. When we limited ODB to EMRALD prescribers only to assess the validity of EMRALD data, we observed good sensitivity and excellent specificity for correctly identifying antibiotics at 85% (95% CI 84%-85%) and 98% (95% CI 98%-98%), respectively. The PPV was 78% (95% CI 78%-78%) and the NPV was 99% (95% CI 99%-99%). All performance measures were higher among the highest prescribing physicians. CONCLUSIONS: We demonstrated EMRALD is well suited for studying antibiotic prescribing by EMRALD physicians. However, due to the frequency with which patients receive antibiotic prescriptions from their non-primary care physicians, we caution against the use of non-population-based prescribing databases to infer antibiotic use rates or trends over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".