Impact of the COVID-19 pandemic on hospital antimicrobial purchasing in Canada (2018–2021): An exploratory analysis
Bibliographic record
Abstract
Background: The impact of the COVID-19 pandemic on antimicrobial use in Canadian hospitals is not well characterized. We explored the relationship between the COVID-19 pandemic and Canadian hospital antimicrobial purchasing (AMP)—a proxy for consumption. Methods: Hospital-level AMP data were obtained from IQVIA, a health analytics company, and matched with inpatient patient-day denominator data from 28 hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Monthly AMP was measured using defined daily doses (DDDs) per 1,000 patient-days. Segmented linear regression with hospital-level clustering assessed for step and slope changes in AMP between pre-pandemic (January 1, 2018 – February 29, 2020) and pandemic (March 1, 2020 – December 31, 2021) periods. Results: Although we found an initial increase in AMP with the onset of the pandemic (+42 DDDs/1,000 patient-days [pd]) followed by a decreasing trend in AMP during the pandemic (−5 DDDs/1,000 pd per month), neither was statistically significant. Changes in trends varied across antimicrobial classes/subclasses, with decreases in broad-spectrum penicillins (−2 DDDs/1,000 pd per month, p < .001) and macrolides/lincosamides (−2 DDDs/1,000 pd per month, p < .001) and an increase in carbapenems (1 DDD/1,000 pd per month, p < .001). These results coincided with decreases in piperacillin/tazobactam ( p = .003) and azithromycin ( p = .001) and an increase in meropenem ( p < .001). Conclusions: We observed a transient increase in overall AMP with the onset of the pandemic (March 2020) in this exploratory analysis of a sample of 28 hospitals. Changes in trends varied by antimicrobial class/subclass and individual agent. Further work is needed to discern contributors to these trends, such as changes in inpatient characteristics and treatment guidelines.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".