Combined impact of the COVID-19 pandemic and the number of comorbidities on community antibiotic utilisation rates: a population-based retrospective cohort study using linked health administrative data in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: To describe community antibiotic utilisation in Quebec from 2018 to 2022 and to measure the combined impact of the COVID-19 pandemic and of the number of comorbidities on utilisation rates. METHODS: Data from the Quebec Integrated Chronic Disease Surveillance System were used to describe monthly (for the overall antibiotics use) and annual (for the main antibiotic classes) changes in antibiotic utilisation rates from 2018 to 2022, stratified by the number of comorbidities (0, 1, 2 and ≥3) and age group (0-17, 18-64 and ≥65 years old). Poisson regression was used to measure the impact of the pandemic and of comorbidities on antibiotic utilisation rates. RESULTS: The study included an annual average of 424 792 children, 1 761 582 adults 18-64 years of age and 1 490 081 adults at least 65 years old. For each number of comorbidities within each age group, the utilisation rates of overall antibiotics decreased with the arrival of the pandemic and remained low despite the return of respiratory viruses in late summer 2021. This reduction was observed for all major antibiotic classes, except for fosfomycin/nitrofurantoin in adults. The pandemic and respiratory viruses' resurgence periods in adults (≥18 years) without comorbidities were associated with decreases of 25% (95% CI 25% to 25%) and 19% (18% to 19%) (children: 63% (62% to 63%) and 37% (36% to 38%)) in antibiotic utilisation compared with the prepandemic period. In adults with three or more comorbidities, utilisation decreased less, by 13% (12% to 14%) and 7% (6% to 8%) (children: 33% (21% to 43%) and 23% (8% to 35%)), respectively. Children with two comorbidities during the pandemic period also experienced a smaller decrease in antibiotic utilisation than children without comorbidities for the same period. CONCLUSION: In Quebec, antibiotic utilisation decreased during the pandemic and remained low despite the resurgence of respiratory viruses in 2021. However, this decrease was less pronounced in individuals with multiple comorbidities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".