Healthcare costs at the end-of-life among immigrant and non-immigrant groups in Manitoba, Canada
Bibliographic record
Abstract
ObjectivesIt is known that healthcare costs tend to increase during the last year of life. Recognizing the importance of efficient resource distribution for end-of-life care, this study compares healthcare costs incurred for migrants and long-term Manitobans and identifies the factors that impact healthcare costs during the last year of life. MethodsThis retrospective matched-cohort study used 15 databases linked at the individual-level, including immigration records, medical claims, hospital abstracts, drug prescriptions, emergency department visits, home care, long-term care, vital statistics mortality, housing and employment/income assistance, for those who died between January 2005 and December 2022 in Manitoba. Conditional zero-inflated gamma hurdle (ZIG) and quantile regression models were used. ResultsThe average end-of-life healthcare costs for international migrants (2469) and Long-term Manitobans (2362) were CA$44,909 and CA$16,593, respectively. According to the adjusted ZIG model, international migrants had 17% higher costs. Among international migrants, Government Assisted and Blended Visa Office-Referred Refugees (GAR/BVOR) had 36% higher costs than Long-term Manitobans. Additionally, costs were higher for those without a partner (13%), receiving employment/income assistance (30%), having higher comorbidity (309% for 4+ comorbidities vs. 0 comorbidities), death at the hospital (171%), and long-term care (169%). Adjusted Quantile regression analyses revealed that only GAR/BVOR had higher costs across all levels of the cost distribution than long-term Manitobans. ConclusionIn the last year of life, international migrants incurred greater healthcare costs than non-immigrants. However, the differences with non-immigrants varied depending on international migrants’ characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".