Impact of community pharmacist antibiotic prescribing for minor ailments: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: On 1 January 2023, Ontario expanded pharmacists' scope of practice, allowing them to prescribe medications for 13 minor ailments, including antibiotics for uncomplicated urinary tract infections (UTIs) and Lyme disease (LD) prophylaxis. This study evaluates pharmacist billing claims and pharmacist and physician antibiotic-prescribing rates before and after policy implementation. DESIGN: An interrupted time series analysis measuring changes in prescribing trends post-implementation. SETTING: This retrospective study analysed visit claims and antibiotic prescribing for UTIs and LD prophylaxis before policy implementation (2022) and after (2023-2024) in Ontario. PARTICIPANTS: Data from Ontarians PRIMARY OUTCOME MEASURES: Prescribing rates were standardised per 1000 inhabitants, stratified by provider type, patient age and sex, and antibiotic type. RESULTS: In 2023 and 2024, pharmacists submitted over 1.47 million minor ailment claims, with UTIs making up 34.2% and LD prophylaxis making up 2.6% of total claims. UTI claims were primarily for women aged 25-64, and LD prophylaxis peaked in spring and fall. Pharmacist prescribing of eligible urinary drugs in females increased by 33.3 per 1000 person-years (95% CI 30.8 to 36.6) while physician prescribing decreased by 23.3 (95% CI -32.2 to -15.3), leading to a modest net increase of 10.1 (95% CI 0.0 to 18.7). Pharmacist prescribing of doxycycline was offset by decreased physician prescribing, resulting in no change (0.0, 95% CI -1.0 to 0.9). Pharmacist prescribing for other antibiotics was low over the study timeframe, while physician prescribing increased, which was driven by increased prescribing of penicillins and macrolides. CONCLUSION: There was a clear increase in pharmacist prescribing for eligible drugs in the eligible population post-policy implementation. Pharmacists in Ontario appear to be prescribing within policy limits for uncomplicated UTIs and LD prophylaxis.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".