Global, regional, and national trends and burden of multiple sclerosis in adolescents and young adults: a data analysis from 1990 to 2021 and projections to 2040
Bibliographic record
Abstract
Background and Objectives: Multiple sclerosis (MS) significantly impacts adolescents and young adults (aged 15-39 years), causing substantial neurological disability. Despite therapeutic advances, the global burden persists due to disparities in healthcare access and lifestyle factors. This study analyzes global, regional, and national trends and burdens of MS using Global Burden of Disease (GBD) 2021 data, emphasizing the influence of the Socio-demographic Index (SDI). Methods: Using data from the GBD 2021 data, we conducted a secondary analysis. We assessed the prevalence, mortality, and disability-adjusted life years (DALYs) of MS across 204 countries/territories from 1990 to 2021, calculating age-standardized rates. Results: In 2021, global MS prevalence was 489,310 cases, associated with 1,424 deaths and 215,869 disability-adjusted life years (DALYs). The global age-standardized rates per 100,000 were: prevalence 22.2 (19.8 - 24.8), deaths 0.2 (0.2 - 0.2), and DALYs 11.4 (9.8 - 13.2). Compared to 1990, these rates decreased by 0.4%, 12.8%, and 11.0% respectively, while the number of prevalent cases increased by 52.4%. Sweden (161.6), Canada (134.2), and Norway (131.5) had the highest age-standardized prevalence. The UK (71.3) had the highest and Nauru (0.4) the lowest age-standardized DALY rate. Female MS death rates exceeded those of males across all ages, peaking at 20-24 years. Regionally, age-standardized DALY rates showed an inverse V-shaped relationship with the SDI. Discussion: Although the global MS burden among adolescents and young adults (AYAs) decreased, the substantial and rising prevalence demands attention. The burden from premature mortality and stark geographic variations in prevalence and DALYs indicate inadequate disease management, underscoring the need for enhanced awareness and effective interventions. Urgent, targeted healthcare policies are required to address geographical inequities, sex-specific pathophysiology, and modifiable risk factors such as smoking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".