Opioid-containing antitussives in Switzerland: a descriptive cross-sectional time-series analysis of pharmacy sales 2013–2022
Bibliographic record
Abstract
BACKGROUND: Opioid-containing antitussives are used to symptomatically treat a dry, irritative cough but bring a risk of misuse as recreational drugs. Since the update of the Swiss Therapeutic Products Act in 2019, opioid-containing antitussives are regulated more strictly, being available only on prescription and no longer over the counter. AIM: This study aimed to describe the sales trends of opioid-containing antitussives in Switzerland between 2011 and 2022 to assess the impact of the regulation change. METHODS: We descriptively analysed cross-sectional data of opioid sales from wholesalers to pharmacies and self-dispensing physicians as an indicator of community use. MAIN FINDINGS: An estimated 369 million standard units of opioid-containing antitussives were sold over the whole observation period, of which 59% contained dextromethorphan as the active ingredient. Sales decreased slowly between 2011 and 2019, then dropped substantially in 2020 (-30.4% compared to previous year) and 2021 (-15.2%), then partially recovered in 2022. The sales of codeine-containing antitussives did not recover until the end of the study period (quarter 3 of 2022) and remained 37.3% lower than before the rescheduling (quarter 4 of 2018). DISCUSSION: It is likely that repeated media attention on cases of misuse of opioid-containing antitussives led to more cautious dispensing in Switzerland leading up to the revision of the Therapeutic Products Act in 2019. The substantial decrease in sales in 2020 and 2021 was likely related to the COVID-19 pandemic rather than the rescheduling of opioid-containing antitussives. Longer data collection will be needed to assess the impact of the regulation change post-pandemic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".