Cost-related nonadherence to prescribed medicines among older Canadians in 2014: a cross-sectional analysis of a telephone survey
Bibliographic record
Abstract
BACKGROUND: Canadians receive universal coverage for medically necessary hospital and physician services, but pharmaceutical coverage is incomplete. We sought to assess the effects of cost on access to medicines among older Canadians using data from a large survey conducted in 2014. METHODS: This is a cross-sectional analysis of data from the Commonwealth Fund's 2014 International Health Policy Survey of Older Adults. Our primary outcome variable was self-reported cost-related nonadherence in the form of either not filling a prescription or skipping doses within the last 12 months because of out-of-pocket costs. We computed sample-weighted estimates of the population prevalence of cost-related nonadherence and conducted logistic regression analyses to determine associated factors. RESULTS: We estimate that the prevalence of cost-related nonadherence in 2014 among Canadians aged 55 years and older was 8.3% (about 1 in 12). The population prevalence and adjusted odds of cost-related nonadherence was significantly higher among Canadians who were younger, in worse health, poorer or without private health insurance. Regional differences in population prevalence of cost-related nonadherence were not significant. The only provincial or regional difference in the adjusted odds of cost-related nonadherence was that residents of Quebec aged 55-64 years were about half as likely to report nonadherence as similarly aged residents of Ontario, our reference province (adjusted odds ratio 0.49, 95% confidence interval 0.29-0.82). INTERPRETATION: The financial accessibility of prescription medicines still is a substantial public health issue in Canada that affects 1 in 12 Canadians older than 55 years of age. Older Canadians at greatest risk of cost-related nonadherence to prescribed treatments are those with low incomes and those without private insurance to cover costs not covered by public programs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".