Publicly subsidised smoking cessation medicines in times of <scp>COVID</scp>‐19 in Australia: An interrupted time series analysis
Bibliographic record
Abstract
INTRODUCTION: In Australia, the available published literature demonstrated a spike in dispensed prescription medicines after the onset of the COVID-19 pandemic that subsequently returned to expected levels. Smoking cessation medicines may not follow this pattern because quit attempts are influenced by a range of factors. Knowledge of whether dispensing of these medicines has changed since the pandemic is lacking. We explored the change in dispensing of publicly subsidised smoking cessation medicines since the pandemic. METHODS: Australia's universal health-care system provides access to government-subsidised medicines via the Pharmaceutical Benefits Scheme and records of dispensed medicines are publicly available on a nationally aggregated level. We retrieved Pharmaceutical Benefits Scheme data from January 2016 to January 2021. We used interrupted time series modelling to quantify the impact of COVID-19 on dispensing of nicotine replacement therapy (NRT) patches, varenicline and all smoking cessation treatments combined separately. RESULTS: After an initial spike in medicines at the onset of the pandemic, the monthly rate of prescriptions dispensed for varenicline was predominantly within predicted ranges, while that of NRT patches was predominantly below predicted ranges. DISCUSSION AND CONCLUSIONS: There has been a differential change in the number of subsidised smoking cessation medicines supplied in Australia since the COVID-19 pandemic, with varenicline prescriptions largely within, and NRT patches largely lower than, expected ranges. The reasons for the apparent change in dispensing of subsidised smoking cessation medicines are unclear.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".