ArticleScientific reports2025
Trends in medication use during the COVID-19 pandemic in Quebec, Canada.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Patterns of prescription stimulant initiation before and during the COVID-19 pandemic: a population-based time-series analysis.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2026Article
- Trends in Medication Use Among Young Adults and the Covid-19 Pandemic Effect.Pharmacoepidemiology and drug safety · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.