Incidence and prevalence of paediatric-onset multiple sclerosis in two Canadian provinces: a population-based study representing over half of Canada’s population
Bibliographic record
Abstract
BACKGROUND: Population-based studies estimating the epidemiology of paediatric-onset multiple sclerosis (PoMS) are scarce. METHODS: We accessed population-based health administrative data from two provinces in Canada, Ontario and British Columbia (BC). Individuals with PoMS were identified via a validated case definition. The index date ('MS onset') was the first demyelinating or MS specific claim recorded ≤18 years of age. We estimated the age-standardised annual incidence and prevalence of PoMS, and 95% CIs between 2003 and 2019. We used negative binomial regression models to assess the temporal changes in the annual crude incidence and prevalence of PoMS, and the ratios comparing sex groups. RESULTS: From 2003 to 2019, a total of 148 incident PoMS cases were identified in BC, and 672 in Ontario. The age-standardised annual incidence of PoMS was stable in both provinces, averaging 0.95 (95% CI 0.79 to 1.13) in BC and 0.98 (95%CI 0.84 to 1.12) in Ontario per 100 000 person-years. The incidence ratio by sex (female vs male) was also stable over the study period, averaging 1.5:1 (95% CI 1.06 to 2.08, BC) and 2.0:1 (95% CI 1.61 to 2.59, Ontario). The age-standardised prevalence per 100 000 people rose from 4.75 (2003) to 5.52 (2019) in BC and from 2.93 (2003) to 4.07 (2019) in Ontario, and the increase was statistically significant in Ontario (p=0.002). There were more female prevalent PoMS cases than males in both provinces. CONCLUSIONS: Canada has one of the highest rates of PoMS globally, and the prevalence, but not incidence, has increased over time. Allocation of resources to support the growing youth population with MS should be a priority.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".