Incident prescriptions for common cardiovascular medications: comparison of recent versus pre-2020 medication adherence and discontinuation in three universal health care systems
Bibliographic record
Abstract
BACKGROUND: Health system disruptions since onset of the COVID-19 pandemic may have adversely impacted adherence to medications for common cardiovascular risk factors. METHODS: We examined adherence to and discontinuation of incident prescriptions for medications treating hypertension, dyslipidemia, diabetes, and atrial fibrillation in Ontario, Alberta, and Nova Scotia, Canada. We compared the recent period (April 1, 2020 through most recently available follow-up: September 30, 2021 for Ontario; March 31, 2021 for Alberta; and March 31, 2022 for Nova Scotia) to the baseline, pre-pandemic period (April 1, 2014 through March 31, 2019). In each province, people aged ≥66 years with a valid health number and corresponding incident prescription were included. For each medication class, adherence in the recent period, defined as ≥ 0.80 proportion-of-days-covered (PDC), was compared to the pre-pandemic period using modified Poisson regression with robust error variance, adjusted for patient characteristics. Similarly adjusted Cox proportional hazards models compared hazard of discontinuation over one year of follow-up between the two time periods. RESULTS: In the recent period, PDC ranged from 48.9% for dyslipidemia medications in Alberta to 82.2% for anticoagulants in Nova Scotia. Adherence was not different between periods, with the following exceptions: higher adherence in the recent period for antihypertensives (adjusted risk ratios [aRR] 1.08, 95% CI 1.06-1.10) and dyslipidemics (aRR 1.07, 95% CI 1.04-1.09) in Nova Scotia, and for antihyperglycemics (aRR 1.10, 95% CI 1.08-1.14) and anticoagulants (1.15, 95% CI 1.12, 1.18) in Alberta. Adherence was lower in the recent period only for antihypertensives in Alberta (aRR 0.95, 95% CI 0.93, 0.97). One-year rates of discontinuation ranged from 20.9% for anticoagulants in the Alberta recent period to 56.7% for antihypertensives in the Ontario baseline period. The adjusted hazard of discontinuation was lower or unchanged in the recent period for all medication classes. CONCLUSIONS: Despite significant health system disruptions since 2020, recent adherence to incident cardiovascular prescriptions was similar or better than before and rates of medication discontinuation were lower. However, interventions are still needed to improve existing, suboptimal adherence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".