Patterns of cost-related medication underuse among Canadian adults with cancer: a cross-sectional study using survey data
Bibliographic record
Abstract
BACKGROUND: Cost-related medication underuse (CRMU) has been reported within the general population in Canada. In this study, we assessed patterns of CRMU among Canadian adults with cancer. METHODS: This is a cross-sectional study using survey data. We accessed data sets from the 2015/16 Canadian Community Health Survey (CCHS) and reviewed the records of adults (≥ 18 yr) with a history of cancer who were prescribed medication in the previous 12 months. We collected information about sociodemographic features, health behaviours and CRMU, and conducted a multivariable logistic regression analysis for factors associated with CRMU. RESULTS: A total of 8581 participants were eligible for the current study. In the weighted multivariable logistic regression analysis, the following factors were associated with CRMU: younger age (odds ratio [OR] 2.55, 95% confidence interval [CI] 1.79-3.63), female sex (male sex v. female sex OR 0.62, 95% CI 0.44-0.88), Indigenous racial background (Indigenous v. White OR 2.37, 95% CI 1.49- 3.77), unmarried status (OR 1.59, 95% CI 1.09-2.30), poor self-perceived health (excellent v. poor self-perceived health OR 0.36, 95% CI 0.17-0.77), lower annual income (< $20 000 v. income ≥ $80 000 OR 3.08, 95% CI 1.75-5.41) and lack of insurance for prescription medications (OR 2.49, 95% CI 1.77-3.50). INTERPRETATION: The toll of CRMU among adults seems to be unequally carried by women, racial minorities, and younger (< 65 yr) and uninsured patients with cancer. Discussion about a national pharmacare program for people without private insurance is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".