Adherence at 2 years with distribution of essential medicines at no charge: The CLEAN Meds randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Adherence to medicines is low for a variety of reasons, including the cost borne by patients. Some jurisdictions publicly fund medicines for the general population, but many jurisdictions do not, and such policies are contentious. To our knowledge, no trials studying free access to a wide range of medicines have been conducted. METHODS AND FINDINGS: We randomly assigned 786 primary care patients who reported not taking medicines due to cost between June 1, 2016 and April 28, 2017 to either free distribution of essential medicines (n = 395) or to usual medicine access (n = 391). The trial was conducted in Ontario, Canada, where hospital care and physician services are publicly funded for the general population but medicines are not. The trial population was mostly female (56%), younger than 65 years (83%), white (66%), and had a low income from wages as the primary source (56%). The primary outcome was medicine adherence after 2 years. Secondary outcomes included control of diabetes, blood pressure, and low-density lipoprotein (LDL) cholesterol in patients taking relevant treatments and healthcare costs over 2 years. Adherence to all appropriate prescribed medicines was 38.7% in the free distribution group and 28.6% in the usual access group after 2 years (absolute difference 10.1%; 95% confidence interval (CI) 3.3 to 16.9, p = 0.004). There were no statistically significant differences in control of diabetes (hemoglobin A1c 0.27; 95% CI -0.25 to 0.79, p = 0.302), systolic blood pressure (-3.9; 95% CI -9.9 to 2.2, p = 0.210), or LDL cholesterol (0.26; 95% CI -0.08 to 0.60, p = 0.130) based on available data. Total healthcare costs over 2 years were lower with free distribution (difference in median CAN$1,117; 95% CI CAN$445 to CAN$1,778, p = 0.006). In the free distribution group, 51 participants experienced a serious adverse event, while 68 participants in the usual access group experienced a serious adverse event (p = 0.091). Participants were not blinded, and some outcomes depended on participant reports. CONCLUSIONS: In this study, we observed that free distribution of essential medicines to patients with cost-related nonadherence substantially increased adherence, did not affect surrogate health outcomes, and reduced total healthcare costs over 2 years. TRIAL REGISTRATION: ClinicalTrials.gov NCT02744963.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".