Risk of Mortality in Immigrants with Multiple Sclerosis in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Little is known about how mortality in multiple sclerosis (MS) may differ based on sociodemographic factors, such as immigrant status. We compared mortality in immigrants versus long-term residents with MS in Ontario, Canada. METHODS: In this retrospective cohort study, we applied a validated algorithm to linked, population-based immigration and health administrative data to identify incident MS cases in Ontario between 1994 and 2014. We identified date of death, if it occurred. We used a Cox model adjusting for age, sex, income, and comorbidity, to compare survival in immigrants versus long-term residents. RESULTS: There were 23,603 incident MS cases of whom 1,410 (6.0%) were immigrants. After adjusting for covariates, risk of death was higher in immigrants in the first year after diagnosis (hazard ratio [HR] 1.66; 95% CI 1.05-2.63, p = 0.031). However, in years 1-5 (HR 0.63; 95% CI 0.40-0.98, p = 0.041) and 5-10 (HR 0.42; 95% CI 0.24-0.75, p = 0.003) after diagnosis, risk of death was lower in immigrants. Older age at onset and comorbidity were associated with higher mortality; female sex and higher socioeconomic status were associated with lower mortality. CONCLUSIONS: In this large population with universal access to health care, immigrants with MS had higher mortality compared to long-term residents in the first year after onset and lower mortality thereafter. Lower mortality in immigrants to Canada is well described and thought to be due to the healthy immigrant effect. Higher mortality in the first year after MS onset warrants further investigation as some early deaths may be preventable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".