A quantitative assessment of access to medicines in Canada using administrative and survey data
Bibliographic record
Abstract
Background: Prescription medicines are an important component of outpatient healthcare in Canada and account for 13.6% of total health expenditures. However, access to medicines is unequal. Despite current gaps in coverage, there is of lack information on which groups cannot afford prescription medicines and the potential impacts of expanding drug coverage on medicine access. This thesis provides novel empiric contributions to both of these knowledge areas. Methods: This thesis includes two studies of medicine access in Canada. The first used Latent Class Analysis (LCA) to identify subgroups in the population of Canadians that experienced cost-related nonadherence (CRNA) to prescription medicines. With data from the Canadian Community Health Survey, LCA was used to characterize and identify predictors of membership in different subgroups. The second study used a controlled interrupted time-series study design to examine the impact of a 2019 policy that eliminated all copayment requirements for the lowest income patients in the British Columbia (BC) Fair PharmaCare program. Using population-level administrative data, I studied the impact of the change on prescription drug use and expenditures. Additionally, I conducted a pre-post analysis to examine if the impacts of this policy were broad-based or concentrated amongst specific drug classes. Results: We identified four subgroups in the population of Canadians that experienced CRNA. There are significant differences in the profiles of patients across latent classes, and 73% of patient who report CRNA belong to subgroups characterized by higher incomes and prevalent insurance coverage. The copayment elimination policy in BC led to a 16% rise in monthly prescription drug expenditures and a 13% increase in the mean number of prescriptions dispensed for the target population, after accounting for changes in the control group. We observed increases in expenditures and dispensing across most therapeutic classes with the elimination of copayments. Conclusion: While financial constraints and insurance coverage are important determinants of CRNA, this phenomenon is not confined solely to low-income and uninsured patients. Nonetheless, the elimination of copayments for low-income households in BC led to improvements in prescription drug access and may represent a model policy for advancing access to medicines for low-income Canadians.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".