The Effect of COVID-19 Vaccination on Outpatient Antibiotic Prescribing in Older Adults: A Self-Controlled Risk-Interval Study
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) vaccination has been associated with reduced outpatient antibiotic prescribing among older adults with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We assessed the impact of COVID-19 vaccination on outpatient antibiotic prescribing in the broader population of older adults, regardless of SARS-CoV-2 infection status. METHODS: We included adults aged ≥65 years who received their first, second, and/or third COVID-19 vaccine dose from December 2020 to December 2022. We used a self-controlled risk-interval design and included cases who received an antibiotic prescription 2-6 weeks before vaccination (pre-vaccination or control interval) or after vaccination (post-vaccination or risk interval). We used conditional logistic regression to estimate the odds of being prescribed (1) any antibiotic, (2) a typical "respiratory" infection antibiotic, or (3) a typical "urinary tract" infection antibiotic (negative control) in the post-vaccination interval versus the pre-vaccination interval. We accounted for temporal changes in antibiotic prescribing using background monthly antibiotic prescribing counts. RESULTS: 469 923 vaccine doses met inclusion criteria. The odds of receiving any antibiotic or a respiratory antibiotic prescription were lower in the post-vaccination versus pre-vaccination interval (aOR, .973; 95% CI, .968-.978; aOR, .961; 95% CI, .953-.968, respectively). There was no association between vaccination and urinary antibiotic prescriptions (aOR, .996; 95% CI, .987-1.006). Periods with high (>10%) versus low (<5%) SARS-CoV-2 test positivity demonstrated greater reductions in antibiotic prescribing (aOR, .875; 95% CI, .845-.905; aOR, .996; 95% CI, .989-1.003, respectively). CONCLUSIONS: COVID-19 vaccination was associated with reduced outpatient antibiotic prescribing in older adults, especially during periods of high SARS-CoV-2 circulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".