Trends in medication use during the COVID-19 pandemic in Quebec, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted health and services worldwide. We aimed to describe the changes in medication use during the COVID-19 pandemic in Quebec, Canada. Using a large healthcare database, we created weekly cohorts of all individuals ≥ 1 year old covered by the public drug plan from January 2016 to March 2022. We calculated the weekly number of prevalent and new users of different medications, including both chronic and short-term medications. We integrated the 2016-2019 weekly numbers in Quasi-Poisson regressions, with each gender and age group fitted separately. From these models, we estimated the weekly proportions of prevalent and new users expected for 2020-2021 and their 99% prediction interval [99% PI]. Results were analyzed using the ratio of the overall weekly proportion of users (observed/expected) across four periods, selected according to the different waves of the pandemic: Period 1: 1st wave (February 2020-August 2020), Period 2: 2nd wave (August 2020-March 2021), Period 3: 3rd and 4th waves (March 2021-December 2021), and Period 4: 5th and 6th waves (December 2021-March 2022). Each cohort included over 3,000,000 individuals (53% female). The proportion of new users of most medications dropped in Period 1, with exceptions like antipsychotics (ratio of adjusted overall weekly proportion observed/expected [99% PI] 1.02 [1.00-1.04]). From Period 2 onwards, the initiation of antidiabetics, lipid-lowering medications and attention deficit hyperactivity disorder (ADHD) medications, among others, exceeded expected trends, but remained below expectations notably for systemic antibiotics (Period 4: 0.71 [0.69-0.72]), nasal/oral corticosteroids (Period 4: 0.69 [0.67-0.70]/Period 4: 0.69 [0.67-0.70]) and medications for obstructive lung diseases (Period 4: 0.69 [0.68-0.71]). While the prevalent use of most chronic medications remained relatively close to expectations, observed immediate and long-term variations in medication use should be considered in studies including pandemic years and anticipated in public health planning in case of future pandemics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.004 | 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".