MS risk in immigrants in the McDonald era
Bibliographic record
Abstract
Objective To determine risk factors for multiple sclerosis (MS) in immigrants and to compare MS risk in immigrants and long-term residents in Ontario, Canada. Methods We applied a validated algorithm to linked, population-based immigration and health claims data to identify incident cases of MS in immigrants and long-term residents between 1994 and 2016. We conducted 2 multivariable Cox proportional hazards regression analyses: 1 analysis limited to the immigrant cohort assessing potential risk factors for developing MS, and 1 analysis comparing MS risk between immigrants and matched long-term residents (1:3 match). Results We identified 2,304,302 immigrants for the immigrant-only analysis, of whom 1,526 (0.066%) developed MS. Risk was greatest in those <15 years old at landing (referent <15 years; 16–30 years: hazard ratio [HR] 0.73, 95% confidence interval [CI] 0.63–0.85; 31–45 years: HR 0.55, 95% CI 0.47–0.64). Immigrants from the Middle East (HR 1.22, 95% CI 1.06–1.40) were at greater MS risk than immigrants from Western nations; all other regions had lower risk (p < 0.0001). The matched analysis included 2,207,751 immigrants and 6,362,169 long-term residents. Immigrants were less likely to develop MS than long-term residents (p < 0.0001), although this lower risk was attenuated with longer residence in Canada. Conclusions MS incidence in immigrants to Ontario, Canada, varied widely by region of origin, with greatest risk seen in those from the Middle East. Longer residence in Canada was associated with increased risk, even with migration in adulthood, suggesting that environmental exposures into adulthood contribute to MS risk.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".