Patterns of borrowing to finance out-of-pocket prescription drug costs in Canada: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: Out-of-pocket drug costs lead many Canadians to engage in cost-related nonadherence to prescription medications, but our understanding of other consequences such as borrowing money remains incomplete. In this descriptive study, we sought to quantify the frequency of borrowing to pay for prescription drugs in Canada and characteristics of Canadians who borrowed money for this purpose. METHODS: In partnership with Statistics Canada, we designed and administered a cross-sectional rapid-response module in the Canadian Community Health Survey administered by telephone to Canadians aged 12 years or more between January and June 2016. We restricted our analyses to participants who responded to the question regarding borrowing money to pay for prescription drugs and used logistic regression to identify characteristics associated with borrowing. RESULTS: A total of 28 091 Canadians responded to the survey (overall response rate 61.8%). The weighted proportion of respondents who reported having borrowed money to pay for prescription drugs in the previous year was 2.5% (95% confidence interval 2.2%-2.8%), an estimated 731 000 Canadians. The odds of borrowing were higher among younger adults, people in poor health and people lacking prescription drug insurance. Other factors associated with increased adjusted odds of borrowing were having 2 or more chronic conditions, low household income and higher out-of-pocket prescription drug costs. INTERPRETATION: Many Canadians reported borrowing money to pay for out-of-pocket prescription drug costs, and borrowing was more prevalent among already vulnerable groups that also report other compensatory behaviours to address challenges in paying for prescription drugs. Future research should investigate policy responses intended to increase equity in access to prescription drugs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".