Outpatient purchasing patterns of hydroxychloroquine and ivermectin in the USA and Canada during the COVID-19 pandemic: an interrupted time series analysis from 2016 to 2021
Bibliographic record
Abstract
BACKGROUND: Hydroxychloroquine and ivermectin received widespread attention after initial studies suggested that they were effective against COVID-19. However, several of these studies were later discredited. OBJECTIVES: We explored the impact of scientific articles, public announcements and social media posts on hydroxychloroquine and ivermectin purchases in the USA and Canada during the COVID-19 pandemic. METHODS: We conducted a retrospective, population-based time series analysis of retail hydroxychloroquine and ivermectin purchases in the USA and Canada from February 2016 through to December 2021, using IQVIA's Multinational Integrated Data Analysis database. We fitted the purchasing rates with interventional autoregressive integrated moving average models. We used Google Trends to identify the most influential interventions to include in the models. RESULTS: There were significant pulse increases in hydroxychloroquine purchases in March 2020 in both the USA (P < 0.0001) and Canada (P < 0.0001). For ivermectin, there were no significant changes in April 2020 in either the USA (P = 0.41) or Canada (P = 0.16); however, significant pulse increases occurred from December 2020 to January 2021 in both the USA (P = 0.0006) and Canada (P < 0.0001), as well as significant ramp increases from April to August 2021 in both the USA (P < 0.0001) and Canada (P = 0.02). The increases in ivermectin purchases were larger in the USA than in Canada. CONCLUSIONS: Increases in hydroxychloroquine and ivermectin purchasing rates aligned with controversial scientific articles and social media posts. This highlights the importance of scientific integrity and disseminating accurate epidemiologic information during pandemics.
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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".