Incidence and prevalence of multiple sclerosis in Europe: a systematic review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is the most common cause of neurological disability in young adults worldwide and approximately half of those affected are in Europe. The assessment of differential incidence and prevalence across populations can reveal spatial, temporal and demographic patterns which are important for identifying genetic and environmental factors contributing to MS. However, study methodologies vary and the quality of the methods can influence the estimates. This study aimed to systematically review European studies of incidence and prevalence of MS and to provide a quantitative assessment of their methodological quality. METHODS: A comprehensive literature search was performed to obtain all original population-based studies of MS incidence and prevalence in European populations conducted and published between January 1985 and January 2011. Only peer-reviewed full-text articles published in English or French were included. All abstracts were screened for eligibility and two trained reviewers abstracted the data and graded the quality of each study using a tool specifically designed for this study. RESULTS: There were 123 studies that met the inclusion criteria. The study estimates were highly heterogeneous, even within regions or countries. Quality was generally higher in the more recent studies, which also tended to use current diagnostic criteria. Prevalence and incidence estimates tended to be higher in the more recent studies and were higher in the Nordic countries and in northern regions of the British Isles. With rare exceptions, prevalence and incidence estimates were higher in women with ratios as high as 3:1. Few studies examined ethnicity. Epidemiological data at the national level was uncommon and there were marked geographical disparities in available data, with large areas of Europe unrepresented and other regions well-represented in the literature. Only 37% of the studies provided standardized estimates. CONCLUSIONS: Despite the breadth of the literature on the epidemiology of MS in Europe, inter-study comparisons are hampered by the lack of standardization. Further research should focus on regions not yet studied and the evaluation of ethnic differences in MS prevalence and incidence. National-level studies using current diagnostic criteria, validated case definitions and similar age- and sex-standardization would allow better geographical comparisons.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".