Impact of the COVID-19 pandemic on prescription drug use and costs in British Columbia: a retrospective interrupted time series study
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of the COVID-19 pandemic on prescription drug use and costs. DESIGN: Interrupted time series analysis of comprehensive administrative health data linkages in British Columbia, Canada, from 1 January 2018 to 28 March 2021. SETTING: Retrospective population-based analysis of all prescription drugs dispensed in community pharmacies and outpatient hospital pharmacies and irrespective of the drug insurance payer. PARTICIPANTS: Between 4.30 and 4.37 million individuals (52% women) actively registered with the publicly funded medical services plan. INTERVENTION: COVID-19 pandemic and associated mitigation measures. MAIN OUTCOME MEASURES: Weekly dispensing rates and costs, both overall and stratified by therapeutic groups and pharmacological subgroups, before and after the declaration of the public health emergency related to the COVID-19 pandemic. Relative changes in post-COVID-19 outcomes were expressed as ratios of observed to expected rates. RESULTS: After the onset of the pandemic and subsequent COVID-19 mitigation measures, overall medication dispensing rates dropped by 2.4% (p<0.01), followed by a sustained weekly increase to return to predicted levels by the end of January 2021. We observed abrupt level decreases in antibacterials (30.3%, p<0.01) and antivirals (22.4%, p<0.01) that remained below counterfactuals over the first year of the pandemic. In contrast, there was a week-to-week trend increase in nervous system drugs, yielding an overall increase of 7.3% (p<0.01). No trend changes in the dispensing of respiratory system agents, ACE inhibitors, antidiabetic drugs and antidepressants were detected. CONCLUSION: The COVID-19 pandemic impact on prescription drug dispensing was heterogeneous across medication subgroups. As data become available, dispensing trends in nervous system agents, antibiotics and antivirals warrant further monitoring and investigation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".