Effect of immigration on multiple sclerosis sex ratio in Canada: the Canadian Collaborative Study
Bibliographic record
Abstract
BACKGROUND: The ratio of female to male (F:M) multiple sclerosis (MS) cases varies geographically, generally being greater in areas of high prevalence. In many regions, including Canada, rising MS incidence in women has been implied by the marked increase in F:M ratio. METHODS: We examined the F:M ratio over time in MS patients in the Canadian Collaborative Study born outside Canada, with onset postmigration (n = 2531). We compared the trends to native-born Canadians, by region of origin and age at migration. RESULTS: Regression analysis showed that year of birth (YOB) was a significant predictor of sex ratio in immigrants (chi(2) = 21.4, p<0.001 correlation r = 0.61). The rate of change in sex ratio was increasing in all migrant subgroups (by a factor of 1.16 per 10-year period, p<0.001), with the steepest increase observed in those from Southern Europe (1.27/10 years, p<0.001). The overall immigrant F:M ratio was 2.17, but varied by country of origin. It was significantly lower in migrants from Southern Europe compared with Northern Europe or USA (1.89 vs 2.14 and 2.86, p = 0.023 and p = 0.0003, respectively). Increasing age at immigration was associated with decreasing sex ratio (p = 0.041). The sex ratio of individuals migrating <21 was significantly higher than those migrating > or =21 (2.79 vs 1.96, p = 0.004). CONCLUSIONS: MS sex ratio in immigrants to Canada is increasing but variable by region of origin and influenced by age at migration. The findings highlight the importance of environmental effect(s) in MS risk, which are likely gender-specific.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".