The effect of cost on adherence to prescription medications in Canada
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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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 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".