Misreporting of coverage and cost-related non-adherence to prescription drugs: an analysis using the Canadian Community Health Survey
Bibliographic record
Abstract
Background: Canada is the only developed country with universal healthcare but no universal prescription drug coverage. Prescription drug coverage in Canada is often described as a “patchwork” system; eligibility for coverage varies by province and influenced by circumstance. Subsets of the population are eligible for partial or full provincial coverage for their prescription medications through public and/or private coverage. Methods: The extent and factors associated with misreporting of drug insurance and cost-related non-adherence (CRNA) to prescribed medicines were investigated in three study populations: Ontario seniors 65 and over, Quebec seniors 65 and over, and Quebec adults 25-64 using pooled data from the 2015/2016 Canadian Community Health Survey (CCHS). The rationale for these study cohorts was that the vast majority had partial or full coverage for prescription medications from a public and/or private source. The factors associated with CRNA to prescribed medicines were also explored in these three subgroups. Results: There is a degree of misreporting of drug insurance among Ontario seniors (17%), Quebec seniors (18%) and Quebec adults (9%). Quebec adults who declared CRNA to prescribed drugs had twice the odds of misreporting prescription drug coverage (OR 2.1 95% CI 1.3-3.4). Lower-income earners among Ontario seniors (OR 1.8, 95% CI 1.3-2.6), Quebec seniors (OR 1.7 95% CI 1.2-2.6), and Quebec adults (OR 3.4, 95% CI 2.3-5.1) were more likely to misreport coverage. Quebec seniors were more likely to misreport if they had less than a secondary school education (OR 1.4, 95% CI 1.1-1.8). Ontario seniors who were immigrants were more likely to misreport coverage (OR 1.5, 95% CI 1.2-1.8), as were Quebec seniors who were immigrants (OR 2.2, 95% CI 1.4-3.5). Ontario seniors who had a flu shot in the past 12 months (OR 0.7, 95% CI 0.5-9.9) and Quebec adults who had visited a GP in the past 12 months (OR 0.6, 95% CI 0.45,0.77) were less likely to misreport coverage. CRNA to prescribed drugs was reported by Ontario seniors (3.3%), Quebec seniors (2.5%), and Quebec adults (5.3%). Low-income Ontario seniors (OR 2.9, 95% CI 1.5-5.7) and Quebec adults (2.5, 95% CI 1.6-3.8) were more likely to report CRNA to prescribed medicines. Quebec adults with chronic conditions (OR 1.7, 95% CI 1.2-2.4) and those in self-reported poor health (OR 2.4, 95% CI 1.3-4.4) were also more likely to report CRNA to prescribed drugs. Conclusions: There appears to be a socio-economic gradient in misreporting and CRNA among Ontario seniors, Quebec seniors, and Quebec adults. Given most of these subgroups will have coverage, we hypothesize a degree of measurement error among responses. More specifically, respondents who report CRNA to prescribed medicines may reflect measurement error.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".