The Ontario Program To Improve AntiMIcrobial USE (OPTIMISE): A Descriptive Analysis of Dispensed Antibiotics
Bibliographic record
Abstract
Abstract Background Antimicrobial resistant infections are an emerging global public health crisis. Antibiotic use is the largest modifiable risk factor for antimicrobial resistance. Greater than 90% of antibiotic use in Canada occurs outside of the hospital setting; however, there is a lack of data describing the patterns of community antibiotic use. Our objective was to describe outpatient antibiotic prescriptions for all of Ontario, Canada and to examine variability in antibiotic prescribing across physicians. Methods We conducted a cross-sectional study of antibiotics dispensed from community pharmacies in Ontario, Canada, between March 1, 2016 and February 28, 2017. Ontario has a population of 13.9 million people and over 30,000 physicians. We analyzed data from the Xponent™ database by QuintilesIMS. Xponent™ is based on data from 79% of retail pharmacies in Ontario. QuintilesIMS uses a geospatial extrapolation algorithm to project antibiotic utilization on 100% of the population. This analysis describes physician antibiotic prescribing patterns stratified by patient age and sex. Results There were 6,995,416 antibiotics dispensed or 501/1,000 population. The highest prescribing rate was for patients aged 65 and older at 702 antibiotic scripts/1,000 population, children 0–17 years received 477 antibiotic scripts/1,000 population and adults 18–64 years 446 antibiotics/1,000 population. Females aged 65 years and older received the highest number of antibiotics. Narrow spectrum penicillins, macrolides, first-generation cephalosporins, and second-generation fluoroquinolones (ciprofloxacin and norfloxacin) were the most common classes of antibiotics overall; however, the urinary antibiotics including ciprofloxacin, norfloxacin, and nitrofurantoin were the most common in older females (Figure 1). There was significant prescriber variability with 25% of all antibiotics being prescribed by 2.2% of physicians. Family physicians comprised 91% of these high prescribers. Conclusion This population-based study quantified community antibiotic utilization and demonstrated marked prescriber variability. Future antibiotic stewardship interventions should target the minority of family physicians that prescribe the majority of antibiotics. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".