The Associations of Prescription Drug Insurance and Cost-Sharing With Drug Use, Health Services Use, and Health: A Systematic Review of Canadian Studies
Bibliographic record
Abstract
OBJECTIVES: In Canada, public insurance for physician and hospital services, without cost-sharing, is provided to all residents. Outpatient prescription drug coverage, however, is provided through a patchwork system of public and private plans, often with substantial cost-sharing, which leaves many underinsured or uninsured. METHODS: We conducted a systematic review to examine the association of drug insurance and cost-sharing with drug use, health services use, and health in Canada. We searched 4 electronic databases, 2 grey literature databases, 5 specialty journals, and 2 working paper repositories. At least 2 reviewers independently screened articles for inclusion, extracted characteristics, and assessed risk of bias. RESULTS: The expansion of drug insurance was associated with increases in drug use, individuals who reported drug insurance generally reported higher drug use, and increases in and higher levels of drug cost-sharing were associated with lower drug use. Although a number of studies found statistically significant associations between drug insurance or cost-sharing and health services use, the magnitudes of these associations were generally fairly small. Among 5 studies that examined the association of drug insurance and cost-sharing with health outcomes, 1 found a statistically significant and clinically meaningful association. We did not find that socioeconomic status or sex were effect modifiers; there was some evidence that health modified the association between drug insurance and cost-sharing and drug use. CONCLUSIONS: Increased cost-sharing is likely to reduce drug use. Universal pharmacare without cost-sharing may reduce inequities because it would likely increase drug use among lower-income populations relative to higher-income populations.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".