Inequity in insurance coverage for prescription drugs in New Brunswick, Canada
Bibliographic record
Abstract
OBJECTIVES: To describe the extent to which New Brunswick residents reported having drug insurance coverage supplementary to Canadian Medicare; to examine associations between socioeconomic and demographic characteristics, health status, language identity, and having reported such coverage; and to document any changes in coverage associated with the introduction of the New Brunswick Drug Plan in 2014. METHODS: We used repeated cross-sectional data for New Brunswick from eight cycles of the Canadian Community Health Survey from 2007 to 2017 and undertook logistic regression analysis. RESULTS: We found statistically significant, substantial and policy-relevant socioeconomic differences in the reporting of prescription drug insurance coverage among those 25-64 years and those ≥ 65 years of age, and an increasing reliance on private drug insurance over time. We found that individuals in the second decile of household income were particularly vulnerable to reporting neither public nor private drug coverage. The introduction of the New Brunswick Drug Plan in 2014 does not appear to have led to increased public drug coverage; however, from 2014, the decreasing trend in public drug coverage appears to have ceased. Those who reported lower health status usually had lower odds of reporting private drug coverage but higher odds of reporting public drug coverage. Driven by differences in private coverage, we found that relative to anglophones, francophones were less likely to report any drug coverage. CONCLUSION: Our findings emphasize the shortcomings of drug insurance systems such as that introduced in New Brunswick and substantiate calls for a universal drug program. New Brunswick's increasing reliance on private drug insurance is of concern and warrants additional research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 teacher head, 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".