2277. Variability in Changes in Physician Outpatient Antibiotic Prescribing from 2019 to 2021 during the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Outpatient antibiotic prescribing decreased during the COVID-19 pandemic. Understanding how antibiotic prescribing habits changed differentially based on physician and practice characteristics presents an opportunity to inform antibiotic stewardship. Our objective was to evaluate inter-physician variability and predictors of changes in antibiotic prescribing before (2019) and during (2020/2021) the COVID-19 pandemic. Methods We conducted a retrospective cohort analysis of physicians in Ontario, Canada prescribing oral antibiotics in the outpatient setting between 1 January 2019 and 31 December 2021 using the IQVIA Xponent dataset. The primary outcome was the change in the number of antibiotic prescriptions between the pre-pandemic and pandemic period. Secondary outcomes were changes in the selection of broad-spectrum agents and long-duration ( >7 days) antibiotic use. We used multivariable linear regression models to evaluate physician- and practice-level predictors of change. Results There were 17,288 physicians included in the study with substantial inter-physician variability in changes in antibiotic prescribing (median change of -43.5 antibiotics per physician, IQR -136.5 to -5.0). In the multivariable model, later career stage (adjusted mean difference [aMD] -45.3, 95% confidence interval [CI] -52.9 to -37.8, p< .001), family medicine (aMD -46.0, 95% CI -62.5 to -29.4, p< .001), male patient sex (aMD -52.4, 95% CI -71.1 to -33.7, p< .001), low patient comorbidity (aMD -42.5, 95% CI -50.3 to -34.8, p< .001), and high prescribing to new patients (aMD -216.5, 95% CI -223.5 to -209.5, p< .001) were associated with decreases in antibiotic initiation. Family medicine and high prescribing to new patients were associated with significant decreases in selection of broad-spectrum agents and prolonged antibiotic use. Conclusion Antibiotic prescribing changed throughout the COVID-19 pandemic with overall decreases in antibiotic initiation, broad-spectrum agents, and prolonged antibiotic courses with inter-physician variability. These findings present opportunities for targeted community antibiotic stewardship interventions. Disclosures All Authors: No reported disclosures
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".