Identifying Virtual and In-Person Antibiotic Prescribing Behaviors Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
Context: The majority of antibiotic use in healthcare (90% by volume) occurs in the primary care setting, where, on average, 25% of antibiotic prescriptions are avoidable. Virtual care may lead to a reduction in the number of inappropriate antibiotic prescriptions. Objective: To identify how antibiotic prescribing behavior changed over time during the COVID-19 pandemic in virtual versus in-person primary care visits. Study Design and Analysis: Cross sectional cohort. We examined the proportion of visits that were virtual. For visits where an antibiotic was received, sorted by the following antibiotic indication groups: respiratory tract infections (RTI), skin/soft tissue (SSI), urinary tract infections (UTI) and other infections. Dataset: Canadian Primary Care Sentinel Surveillance Network electronic medical record data from sites across Canada in British Columbia, Alberta, Manitoba, Ontario, Quebec, Nova Scotia and Newfoundland. Population Studied: The cohort was defined as any patient with a healthcare encounter between January 2019 and December 2020. Outcome measure: Percent change in visits in 2020 compared to 2019, for all encounters, and for encounters with an antibiotic prescription, sorted by visit type (virtual versus in-person), and stratified by sex, age group and rurality. Results: There were 901,649 patients with a visit during the 2019 study period, and 839,839 patients with a visit during the 2020 study period. Evaluating visits for these patients, we found that the there was a significant reduction in visits associated with an antibiotic in all indication groups: relative reduction of -38% for RTI, -3.9% for SSI, -2.6% for UTI, and -15.8% for other infections. Looking more closely at the type of visit reveals that in 2019, 2.5% of visits were virtual, compared to 33% in 2020. While the increase in virtual visits was consistent by sex, we found that there were significantly less virtual visits in children (0-18 years), compared to other age groups: 25.33% of all visits were virtual for 0-18 years, compared to 33.58% in 19-39 years, 34.27% in 40-64 years, and 32.75% in 65+ years. In addition, we found urban patients had more virtual visits in 2020 than rural locations (33.34% versus 29.88%, respectively). Conclusions: Virtual visits across Canada in primary care increased tremendously during the COVID-19 pandemic. There was a corresponding reduction in visits where an antibiotic was prescribed.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".