Impact of the COVID-19 Pandemic on Adherence to Most Costly Chronic Disease Medications in British Columbia, Canada: A Population-Based Interrupted Time Series Analysis
Bibliographic record
Abstract
Purpose: To address limited population-level data on prescription medication taking during COVID-19, we assessed the impact of the pandemic on adherence to the costliest drug classes prescribed for chronic diseases in British Columbia (BC). Patients and Methods: Of the 100 top drug classes contributing to total drug spending in 2020, we categorized those prescribed for chronic diseases into 26 drug groups; specifically, drugs for psychiatric and neurologic, cardiac and respiratory, hormone-related, and immune and musculoskeletal conditions. Using administrative health data on all dispensed medications, we quantified adherence by monthly proportion of days covered (PDC) and performed interrupted time-series analysis (ITS) to estimate changes in PDC trends 1-year before and after the implementation of pandemic mitigation measures. Results: We included 3,906,377 adults with ≥1 prescription to ≥1 included drug groups. The most common prescriptions among our study population were for antidepressants (45.0%), drugs for obstructive airway diseases (41.6%), renin-angiotensin system agents (30.5%), diuretics (28.2%), and lipid modifying agents (24.8%). ITS models for 22 of 26 drug groups showed statistically significant changes in monthly PDC trends, with the greatest change occurring among parenteral immunosuppressants, injectable insulins and analogues, and renin-angiotensin system agents. Conclusion: Findings suggest that the pandemic did not substantially impact adherence to commonly used medications; however, adherence was found to be suboptimal across all drug groups regardless of the impact of COVID-19. Medication adherence remains a critical therapeutic challenge requiring our attention irrespective of major healthcare system stressors such as COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.002 | 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 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".