Immigration, ethnicity, and avoidable mortality in Canada, 1991–2006
Bibliographic record
Abstract
OBJECTIVE: Avoidable mortality is a well-recognized, but less studied indicator of the performance of the health system. First, the study seeks to establish whether immigrants overall and selected foreign-born ethnic groups (Western Europeans, South Asians, Chinese, and Filipinos) have an advantage over nonimmigrants in avoidable mortality. Second, it assesses the effect of sociodemographic and socioeconomic factors on any observed differences by duration of residence. DESIGN: Deaths grouped by cause of death and by behavioral risk factors, namely smoking-related and alcohol-related, were derived from the 1991 Canadian Census Cohort: Mortality and Cancer Follow-up. The analysis estimated age-standardized mortality rates (ASMRs), rate ratios, and rate differences and also fitted hazard regression models for the overall Canadian-born population and for selected foreign-born ethnicities by sex. Predictors were assessed at baseline. RESULTS: Compared to the Canadian-born persons, foreign-born men and women had lower ASMRs for overall avoidable mortality and also for selected causes of avoidable mortality. The only exception to this overall trend was for ischemic heart disease among South Asian women. Except for the order of prominence, the three leading causes of death for nonimmigrant and immigrant men and women overall were ischemic heart diseases, smoking-related diseases, and neoplasms. A similar pattern was observed among the ethnic groups, except for circulatory heart diseases replacing ischemic heart diseases and smoking-related diseases among Chinese and Filipino women, respectively. In the hazard regression analysis, the risk of avoidable mortality was lower for immigrants overall and selected ethnicities irrespective of the duration in Canada compared to nonimmigrants. These differences persisted even with adjustment for sociodemographic and socioeconomic factors. CONCLUSION: Immigrants overall and the selected ethnicities enjoy an advantage over nonimmigrants in avoidable mortality. However, for certain causes of death especially ischemic heart disease mortality among South Asian women, immigrants appeared worse-off than nonimmigrants. The results suggest differential access to and use of health services, differences in protective health-related behavior, and the healthy immigrant effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".