Community Antibiotic Use at the Population Level During the SARS-CoV-2 Pandemic in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background The objective of this study was to examine the aggregate rates of antibiotic use at the population level and compare these rates over time against historical averages to identify the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting control measures on community prescribing. Methods We collected antibiotic prescriptions and physician office visits from January 1, 2016, to July 21, 2020. We calculated monthly prescription rates stratified by sex, age group, profession, diagnosis type, and antibiotic class. We looked at monthly prescription rate as a moving average over time. Using the interrupted time series analysis method, we estimated the changes in prescription rates after March 2020. Results The moving average of overall monthly prescription rates during January–June 2020 was below the minimum of the historical years’ moving averages (2016–2019). We observed a >30% reduction in overall monthly prescription rates in April, May, and July of 2020 compared with the same months of 2019. We observed that overall monthly prescription rates experienced a significant level change of –12.79 (P < .001) during the coronavirus disease 2019 pandemic after March 2020, with the greatest level change being –18.02 among children 1–4 years of age (P < .001). We estimated an average –5.94 (P < .001) change in respiratory tract infection (RTI)–associated monthly prescription rates after March 2020. Overall prescription rates comparing January–July 2019 and their 2020 counterparts showed a decrease in monthly prescribing ranging from –1 to –5 for amoxicillin, amoxicillin and enzyme inhibitors, azithromycin, clarithromycin, and sulfamethoxazole. Conclusions In British Columbia, Canada, overall and RTI-specific monthly antibiotic prescription rates declined significantly during April–July 2020 compared with the same months in prepandemic years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".