Proportion of Life Spent in Canada and the Incidence of Multiple Sclerosis in Permanent Immigrants
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: While immigrants to high-income countries have a lower risk of multiple sclerosis (MS) compared with host populations, it is unknown whether this lower risk among immigrants increases over time. Our objective was to evaluate the association between proportion of life spent in Canada and the hazard of incident MS in Canadian immigrants. METHODS: We conducted a population-based retrospective cohort study in Ontario, using linked health administrative databases. We followed immigrants, who arrived in Ontario between 1985 and 2003, from January 1, 2003, to December 31, 2016, to record incident MS using a validated algorithm based on hospital admission or outpatient visits. We derived proportion of life spent in Canada based on age at arrival and time since immigration obtained from linked immigration records. We used multivariable proportional hazard models, adjusting for demographics and comorbidities, to evaluate the association between proportion of life in Canada and the incidence of MS, where proportion of life was modelled using restricted cubic spline terms. We further evaluated the role of age at migration (15 or younger vs older than 15 years), sex, and immigration class in sensitivity analyses. RESULTS: (immigration class × proportion of life) = 0.13). The results did not vary by age at migration but were statistically significant only at higher values of proportion of life for immigrants aged 15 years or younger at arrival. DISCUSSION: The risk of incident MS in immigrants varied with the proportion of life spent in Canada, suggesting an acculturation effect on MS risk. Further work is required to understand environmental and sociocultural factors driving the observed association.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".