Eliminating Medication Copayments for Low-Income Older Adults at High Cardiovascular Risk: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: One in eight people with heart disease has poor medication adherence that, in part, is related to copayment costs. This study tested whether eliminating copayments for high-value medications among low-income older adults at high cardiovascular risk would improve clinical outcomes. Methods: This randomized 2×2 factorial trial studied 2 distinct interventions in Alberta, Canada: eliminating copayments for high-value preventive medications and a self-management education and support program (reported separately). The findings for the first intervention, which waived the usual 30% copayment on 15 medication classes commonly used to reduce cardiovascular events, compared with usual copayment, is reported here. The primary outcome was the composite of death, myocardial infarction, stroke, coronary revascularization, and cardiovascular-related hospitalizations over a 3-year follow-up. Rates of the primary outcome and its components were compared using negative binomial regression. Secondary outcomes included quality of life (Euroqol 5-dimension index score), medication adherence, and overall health care costs. Results: A total of 4761 individuals were randomized and followed for a median of 36 months. There was no evidence of statistical interaction ( P =0.99) or of a synergistic effect between the 2 interventions in the factorial trial with respect to the primary outcome, which allowed us to evaluate the effect of each intervention separately. The rate of the primary outcome was not reduced by copayment elimination, (521 versus 533 events, incidence rate ratio 0.84 [95% CI, 0.66–1.07], P =0.162). The incidence rate ratio for nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death (0.97 [95% CI, 0.67–1.39]), death (0.94 [95% CI, 0.80 to 1.11]), and cardiovascular-related hospitalizations (0.78 [95% CI, 0.57 to 1.06]) did not differ between groups. No significant between-group changes in quality of life over time were observed (mean difference, 0.012 [95% CI, –0.006 to 0.030], P =0.19). The proportion of participants who were adherent to statins was 0.72 versus 0.69 for the copayment elimination versus usual copayment groups, respectively (mean difference, 0.03 [95% CI, 0.006–0.06], P =0.016). Overall adjusted health care costs did not differ ($3575 [95% CI, –605 to 7168], P =0.098). Conclusions: In low-income adults at high cardiovascular risk, eliminating copayments (average, $35/mo) did not improve clinical outcomes or reduce health care costs, despite a modest improvement in adherence to medications. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02579655.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".