Drug utilization patterns before and during COVID-19 pandemic in Manitoba, Canada: A population-based study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led the Canadian provincial governments to take unprecedented measures, including restrictions to healthcare services and pharmacists. Limited evidence exists on changes in prescription trends in Canada during the pandemic period. OBJECTIVES: To examine the trend of prescription medications' utilization before and during COVID-19, among incident and prevalent users in the general population. We examined 18 major classes of medications. METHODS: We used the administrative health databases from the province of Manitoba, Canada, to conduct a province-wide cross-sectional study. Incident and prevalent use was compared between two time periods; pre-COVID-19: July 2016-March 2020 and during COVID-19: April 2020-March 2021. Interrupted time series analysis using autoregressive models was used to quantify the change in level and slope in quarterly medication use among incident and prevalent users. RESULTS: The quarterly study population ranged from 1,353,485 to 1,411,630 Manitobans. The most common comorbidities were asthma (26.67%), hypertension (20.64%), and diabetes (8.31%). On average, the pandemic restrictions resulted in a 45.55% and 12.17% relative decline in the aggregated utilization of all drugs among both incident and prevalent users, respectively. Subclass analysis showed a 46.83%, 23.05%, and 30.98% relative drop among incident users of antibiotics, cardiovascular drugs and opioids use, respectively. We observed a significant slope increase during COVID-19 among the quarterly cardiovascular, antidiabetics, alpha-1 blockers, and statins incident users compared to the pre-COVID-19 period. We noted a significant decrease in level among NSAIDs, opioids, and antibiotic prevalent users, however, no significant changes in slope were observed. CONCLUSION: Our findings show a significant impact of COVID-19 measures on prescription trends in the general population. The observed decline among several medication classes was temporary. Further research is needed to monitor prescription trends and better understand if those changes were associated with increased health services and worsened outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".