Cost-related non-adherence to prescribed medicines among older adults: a cross-sectional analysis of a survey in 11 developed countries
Bibliographic record
Abstract
OBJECTIVES: To assess the effects of costs on access to medicines in 11 developed countries offering different levels of prescription drug coverage for their populations. DESIGN: Cross-sectional study of data from the Commonwealth Fund 2014 International Health Policy Survey of Older Adults. SETTING: Telephone survey conducted in 11 high-income countries: Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the UK and the USA. PARTICIPANTS: 22 532 adults aged 55 and older and living in the community in studied countries. PRIMARY OUTCOME MEASURE: Self-reported cost-related non-adherence (CRNA) in the form of either not filling a prescription or skipping doses within the last 12 months because of out-of-pocket costs. RESULTS: Estimated prevalence of CRNA among all older adults varied from <3% in the France, Norway, Sweden, Switzerland and the UK to 16.8% in the USA. Canada had the second highest national prevalence of CRNA (8.3%), followed by Australia (6.8%). Older adults in the USA were approximately six times more likely to report CRNA than older adults in the UK (adjusted OR=6.09; 95% CI 3.60 to 10.20). Older adults in Australia and Canada were also statistically significantly more likely to report CRNA than older adults in the UK. Across most countries, the prevalence of CRNA was higher among lower income residents and lower among residents over age 65. CONCLUSIONS: Observed differences in national prevalence of CRNA appear to follow lines of availability of prescription drug coverage and the extent of direct patient charges for prescriptions under available drug plans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".