Measuring the impacts of the <i>Using Antibiotics Wisely</i> campaign on Canadian community utilization of oral antibiotics for respiratory tract infections: a time-series analysis from 2015 to 2019
Bibliographic record
Abstract
BACKGROUND: On 1 November 2018, Choosing Wisely Canada launched their Using Antibiotics Wisely primary care campaign, which aimed to reduce unnecessary antibiotic prescriptions for respiratory tract infections (RTIs) through educational tools for patients and providers. OBJECTIVES: We explored the impact of this campaign on antibiotic utilization in Canada. METHODS: We conducted a population-based study in Canada between January 2015 and December 2019. We used interventional autoregressive integrated moving average models to study the impact of the Using Antibiotics Wisely campaign on the prescribing rate (prescriptions per 1000 population) of RTI-indicated antibiotics. We analysed prescription rates overall and stratified by age group, drug class and province, in each month over the study period. RESULTS: There was a 1.5% reduction in the annual prescribing rate of RTI-indicated antibiotics over the study period, which was generally consistent across age groups and provinces. Following the 2018 Using Antibiotics Wisely clinician toolkit release, we observed no significant change in RTI-indicated antibiotic prescribing rates nationally (P = 0.13). This was consistent by age group (children, P = 0.91; adults, P = 0.58; and older adults, P = 0.67) and drug class (narrow-spectrum penicillins, P = 0.88; macrolides, P = 0.85; broad-spectrum penicillins, P = 0.60; cephalosporins, P = 0.45; tetracyclines, P = 0.55; and fluoroquinolones, P = 0.98). In our secondary analysis of prescription rates in provinces that self-identified as prioritizing Using Antibiotics Wisely, we observed no significant change following the launch of the campaign. CONCLUSIONS: The introduction of the Using Antibiotics Wisely campaign in Canada has not caused a significant change in short-term antibiotic prescribing patterns. Community antibiotic stewardship campaigns that include components beyond education may be more impactful.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".