Effect of Free Medicine Distribution on Health Care Costs in Canada Over 3 Years
Bibliographic record
Abstract
Importance: Few interventions are proven to reduce total health care costs, and addressing cost-related nonadherence has the potential to do so. Objective: To determine the effect of eliminating out-of-pocket medication fees on total health care costs. Design, Setting, and Participants: This secondary analysis of a multicenter randomized clinical trial using a prespecified outcome took place across 9 primary care sites in Ontario, Canada (6 in Toronto and 3 in rural areas), where health care services are generally publicly funded. Adult patients (≥18 years old) reporting cost-related nonadherence to medicines in the past 12 months were recruited between June 1, 2016, and April 28, 2017, and followed up until April 28, 2020. Data analysis was completed in 2021. Interventions: Access to a comprehensive list of 128 medicines commonly prescribed in ambulatory care with no out-of-pocket costs for 3 years vs usual medicine access. Main Outcome and Measures: Total publicly funded health care costs over 3 years, including costs of hospitalizations. Health care costs were determined using administrative data from Ontario's single-payer health care system, and all costs are reported in Canadian dollars with adjustments for inflation. Results: A total of 747 participants from 9 primary care sites were included in the analysis (mean [SD] age, 51 [14] years; 421 [56.4%] female). Free medicine distribution was associated with a lower median total health care spending over 3 years of $1641 (95% CI, $454-$2792; P = .006). Mean total spending was $4465 (95% CI, -$944 to $9874) lower over the 3-year period. Conclusions and Relevance: In this secondary analysis of a randomized clinical trial, eliminating out-of-pocket medication expenses for patients with cost-related nonadherence in primary care was associated with lower health care spending over 3 years. These findings suggest that eliminating out-of-pocket medication costs for patients could reduce overall costs of health care. Trial Registration: ClinicalTrials.gov Identifier: 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".