Association between end-of-life cancer care and immigrant status: a retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To compare recent immigrants and long-term residents in Ontario, Canada, on established health service quality indicators of end-of-life cancer care. DESIGN: Retrospective, population-based cohort study of cancer decedents between 2004 and 2015. SETTING: Ontario, Canada. PARTICIPANTS: We grouped 13 085 immigrants who arrived in Ontario in 1985 or later into eight major ethnic groups based on birth country, mother tongue and surname, and compared them to 229 471 long-term residents who were ≥18 years at the time of death. PRIMARY AND SECONDARY OUTCOME MEASURES: Aggressive care, defined as a composite of ≥2 emergency department visits, ≥2 new hospitalisations or an intensive care unit admission within 30 days of death; and supportive care, defined as a physician house call within 2 weeks, or palliative nursing or personal support worker home visit within 6 months of death. Multivariable logistic regression was used to examine the association between immigration status and the odds of each main outcome. RESULTS: Compared with long-term residents, immigrants overall and by ethnic group had higher rates of aggressive care (13.7% vs 17.5%, respectively; p<0.001). Among immigrants, Southeast Asians had the highest use while White-Eastern and Western Europeans had the lowest. Supportive care use was similar between long-term residents and immigrants (50.0% vs 50.5%, respectively; p=0.36), though lower among Southeast Asians (46.6%) and higher among White-Western Europeans (55.6%). After adjusting for sociodemographic characteristics and comorbidities, immigrants remained more likely than long-term residents to receive aggressive care (OR: 1.15, 95% CI 1.09 to 1.21), yet were less likely to receive supportive care (OR: 0.95, 95% CI 0.91 to 0.98). CONCLUSIONS: Among cancer decedents in Ontario, immigrants are more likely to use aggressive healthcare services at the end of life than long-term residents, while supportive care varies by ethnicity. Contributors to variation in end-of-life care require further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".