Socioeconomic gradients in all-cause, premature and avoidable mortality among immigrants and long-term residents using linked death records in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Immigrants have been shown to possess a health advantage, yet are also more likely to reside in arduous economic conditions. Little is known about if and how the socioeconomic gradient for all-cause, premature and avoidable mortality differs according to immigration status. METHODS: Using several linked population-based vital and demographic databases from Ontario, we examined a cohort of all deaths in the province between 2002 and 2012. We constructed count models, adjusted for relevant covariates, to attain age-adjusted mortality rates and rate ratios for all-cause, premature and avoidable mortality across income quintile in immigrants and long-term residents, stratified by sex. RESULTS: A downward gradient in age-adjusted all-cause mortality was observed with increasing income quintile, in immigrants (males: Q5: 13.32, Q1: 20.18; females: Q5: 9.88, Q1: 12.51) and long-term residents (males: Q5: 33.25, Q1: 57.67; females: Q5: 22.31, Q1: 36.76). Comparing the lowest and highest income quintiles, male and female immigrants had a 56% and 28% lower all-cause mortality rate, respectively. Similar trends were observed for premature and avoidable mortality. Although immigrants had consistently lower mortality rates compared with long-term residents, trends only differed statistically across immigration status for females (p<0.05). CONCLUSIONS: This study illustrated the presence of income disparities as it pertains to all-cause, premature, and avoidable mortality, irrespective of immigration status. Additionally, the immigrant health advantage was observed and income disparities were less pronounced in immigrants compared with long-term residents. These findings support the need to examine the factors that drive inequalities in mortality within and across immigration status.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".