Validating hospital antibiotic purchasing data as a metric of inpatient antibiotic use
Bibliographic record
Abstract
OBJECTIVES: Antibiotic purchasing data are a widely used, but unsubstantiated, measure of antibiotic consumption. To validate this source, we compared purchasing data from hospitals and external medical databases with patient-level dispensing data. METHODS: Antibiotic purchasing and dispensing data from internal hospital records and purchasing data from IMS Health were obtained for two hospitals between May 2013 and April 2015. Internal purchasing data were validated against dispensing data, and IMS data were compared with both internal metrics. Scatterplots of individual antimicrobial data points were generated; Pearson's correlation and linear regression coefficients were computed. A secondary analysis re-examined these correlations over shorter calendar periods. RESULTS: Internal purchasing data were strongly correlated with dispensing data, with correlation coefficients of 0.90 (95% CI = 0.83-0.95) and 0.98 (95% CI = 0.95-0.99) at hospitals A and B, respectively. Although dispensing data were consistently lower than purchasing data, this was attributed to a single antibiotic at both hospitals. IMS data were favourably correlated with, but underestimated, internal purchasing and dispensing data. This difference was accounted for by eight antibiotics for which direct sales from some manufacturers were not included in the IMS database. The correlation between purchasing and dispensing data was consistent across periods as short as 3 months, but not at monthly intervals. CONCLUSIONS: Both internal and external antibiotic purchasing data are strongly correlated with dispensing data. If outliers are accounted for appropriately, internal purchasing data could be used for cost-effective evaluation of antimicrobial stewardship programmes, and external data sets could be used for surveillance and research across geographical regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".