Trends in antibiotic use and treatment failure in community-acquired uncomplicated urinary tract infections treated by family physicians in British Columbia
Bibliographic record
Abstract
Introduction: In the last decade, the prevalence of antimicrobial resistance (AMR) among uropathogens has risen worldwide. Uncomplicated urinary tract infections (uUTIs) in females are one of the most common reasons for antibiotic prescribing by family physicians (FPs) in Canada. The treatment for this infection is mainly empirical. Considering the impact of inappropriate antibiotic prescription on AMR, it is important to examine the trends of antibiotic use, assess the characteristics of the population, and evaluate treatment failure (TF) while considering local resistance rates in British Columbia (BC). This thesis examined the trends of antibiotic use for uUTI by FPs and TF in community-acquired uUTIs in the female population of BC. Methods: A retrospective cohort study was conducted using linked administrative databases to identify uUTIs episodes in the female population of BC from 2012 to 2023. Antibiotic switch, kidney infection, and sepsis were used as a proxy for TF during the follow-up period of 28 days. Associations between year (change per year), therapy line, and time to TF were estimated using Cox regression analysis. Poisson regression with robust standard errors was used to assess the appropriateness of antibiotic prescription by therapy line over time. Results: Among 1,934,014 uUTI episodes in the female population, 9.4% experienced TF. The rate of physician-treated uUTI episodes showed a downward trend over the study period, with an increased use in guideline concordant first-line therapy. Patients who were prescribed alternative or non-recommended antibiotics were more likely to develop kidney infection and sepsis and those who received non-recommended antibiotics were more likely to have an antibiotic switch during the episode. Conclusion: The proportion of physician-managed uUTI episodes that led to TF has remained stable over the study period. A downward trend in physician-managed uUTI episodes was observed and may relate to a shift to pharmacy and nurse prescribing. First-line antimicrobials have been consistently the preferred choice of therapy among FPs. Further studies are needed to assess other healthcare providers’ prescription behaviour.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".