Spillover From an Intervention on Antibiotic Prescribing for Family Physicians
Bibliographic record
Abstract
Importance: Antibiotic audit and feedback is effective at reducing antibiotic prescribing in primary care. Objective: To evaluate the spillover of an audit-and-feedback intervention originally targeted at patients aged 65 years or older on a broader population of all age groups. Design, Setting, and Participants: This is a post hoc secondary analysis of a randomized clinical trial that was conducted among primary care physicians in Ontario, Canada. Physicians were randomized in a 4:1 allocation, with a peer comparison feedback letter sent in January 2022. Physicians in the control group received no letter. The randomized clinical trial was conducted from January 2021 to December 2022, and this analysis was performed from March to June 2024. Exposure: A mailed antibiotic prescribing feedback letter, with peer comparison. Main Outcomes and Measures: The primary outcome was the total number of antibiotic prescriptions by physicians at 12 months after the intervention for patients of all ages. This analysis was conducted utilizing a different administrative data source than the original trial; this source contained antibiotic prescription counts for all patient age groups. Data were analyzed with Poisson regression models, adjusted for baseline prescribing and stratified by patient age and sex. Results: Overall, 4964 of 5097 randomized physicians (97.4%) were included in this analysis. There were 3967 (74.5%) in the intervention group and 997 (25.5%) in the control group; 2766 physicians (55.7%) were male, and 2549 (51.3%) had been in practice for 25 years or more. The intervention group showed a reduction in antibiotic prescriptions at 12 months after intervention compared with the control group (adjusted rate ratio [aRR], 0.93; 95% CI, 0.93-0.94). Significant reductions were seen across all age and sex groups and for antibiotics typically used for respiratory infections. Additionally, the proportion of prescriptions exceeding 7 days decreased significantly in the intervention group (aRR, 0.82; 95% CI, 0.82-0.83). Conclusions and Relevance: In this post hoc secondary analysis of a randomized clinical trial of peer comparison antibiotic audit and feedback for physicians with data from patients aged 65 and older, the intervention group had a reduction in antibiotic prescriptions across all patient ages. These findings suggest that routinely collected administrative data can be effectively used for implementing and evaluating antibiotic audit and feedback, even when limited to older patients. Trial Registration: ClinicalTrials.gov Identifier: NCT04594200.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".