The effect of cost on adherence to prescription medications in Canada
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Many patients do not adhere to treatment because they cannot afford their prescription medications, putting them at increased risk of adverse health outcomes. We determined the prevalence of cost-related nonadherence and investigated its associated characteristics, including whether a person has drug insurance. METHODS: Using data from the 2007 Canada Community Health Survey, we analyzed the responses of 5732 people who answered questions about cost-related nonadherence to treatment. We determined the national prevalence of cost-related nonadherence and used logistic regression to evaluate the association between cost-related nonadherence and a series of demographic and socioeconomic variables, including province of residence, age, sex, household income, health status and having drug insurance. RESULTS: Cost-related nonadherence was reported by 9.6% (95% confidence interval [CI] 8.5%-10.6%) of Canadians who had received a prescription in the past year. In our adjusted model, we found that people in poor health (odds ratio [OR] 2.64, 95% CI 1.77-3.94), those with lower income (OR 3.29, 95% CI 2.03-5.33), those without drug insurance (OR 4.52, 95% CI 3.29-6.20) and those who live in British Columbia (OR 2.56, 95% CI 1.49-4.42) were more likely to report cost-related nonadherence. Predicted rates of cost-related nonadherence ranged from 3.6% (95% CI 2.4-4.5) among people with insurance and high household incomes to 35.6% (95% CI 26.1%-44.9%) among people with no insurance and low household incomes. INTERPRETATION: About 1 in 10 Canadians who receive a prescription report cost-related nonadherence. The variability in insurance coverage for prescription medications appears to be a key reason behind this phenomenon.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it