Epidemiology of Multiple Sclerosis: Global, Regional, National and Sub-National-Level Estimates and Future Projections
Bibliographic record
Abstract
BACKGROUND: The epidemiology of multiple sclerosis (MS) is complex due to the interaction of various risk factors. This study assesses the global, regional, national and sub-national burden of MS and predicts future trends. METHODS: Data from the Global Burden of Disease Study 2021 was analyzed to assess the epidemiology of MS from 1990 to 2021. Data from the World Bank was used to determine the socio-demographic predictors of MS prevalence using multivariate analysis. RESULTS: Globally, 1.89 million people live with MS, with over 62,000 new cases diagnosed in 2021. The global prevalence of MS is 23.9 cases per 100,000 population, with a continuous increase over the past three decades. North America and Western Europe had the highest prevalence, incidence, disability-adjusted life-years (DALYs), and mortality rates. Countries with the highest prevalence were Sweden (219 /100,000 population), Canada (182), Norway (176), Ireland (163), and the UK (158). Analysis of subnational level data from US revealed that northern states such as Utah, Montana, and Rhode Island had incidence rates double those of southern states such as Hawaii, Mississippi and Louisiana. CONCLUSIONS: The burden of MS is rising worldwide, especially in developed countries. To reduce this burden, it is essential to understand the distribution and risk factors of MS, and to address healthcare disparities in early diagnosis, access to treatment and social services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".