The COVID-19 Pandemic Did Not Negatively Impact Adherence to Cardiovascular Medications Among 548,601 Chronically Treated Patients in Alberta
Bibliographic record
Abstract
Background: Studies have suggested that the COVID-19 pandemic negatively impacted patient adherence with chronic medications. We explored whether adherence patterns changed in patients chronically treated with cardiovascular drugs after onset of the COVID-19 pandemic. Methods: In this retrospective cohort study we examined drug dispensation data for all adult Albertans who were chronic users of at least 1 cardiovascular drug class between 2017 and 2023. We calculated each patient's proportion of days covered (PDC) for each drug class in the prepandemic phase (March 15, 2018 to March 14, 2020) and the pandemic phase (March 15, 2020 to March 14, 2022), and used generalized estimating equation logistic regression to estimate the effect of time period on achievement of good adherence (PDC >0.8) after adjusting for age, sex, socioeconomic status, and comorbidities. Results: Of 548,601 chronic users of at least 1 cardiovascular drug class between March 15, 2018 and March 14, 2022, 47.2% were women, the mean age was 62.3 years, and 55.4% had Charlson Comorbidity Index (CCI) scores of 0. The most frequently dispensed cardiovascular drugs were angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (67.6%) and statins (53.8%); the most frequent diagnoses were hypertension (77.2%), diabetes mellitus (30.6%), and ischemic heart disease (19.6%). Chronic users of cardiovascular drugs were more likely to have PDC >0.8 during the pandemic than in the prepandemic period: 75.4% vs 72.8%, with adjusted odds ratios ranging from 1.05 (95% confidence interval 1.00-1.11) for mineralocorticoid receptor antagonists to 1.16 (95% confidence interval 1.15-1.17) for statins. Conclusions: Chronic users of cardiovascular drugs exhibited better adherence during the COVID-19 pandemic than before the 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".