Global Prevalence and Incidence of Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder affecting upper and lower motor neurons. Due to its rarity and rapidly progressive nature, studying the epidemiology of ALS is challenging, and a comprehensive picture of the global burden of this disease is lacking. The objective of this systematic review was to describe the global incidence and prevalence of ALS. METHODS: We searched MEDLINE, Embase, Global Health, PsycInfo, Cochrane Library, and CINAHL to identify articles published between January 1, 2010, and May 6, 2021. Studies that were population based and reported estimates of prevalence, incidence, and/or mortality of ALS were eligible for inclusion. This study focuses on the incidence and prevalence. Quality assessment was performed using a tool developed to evaluate methodology relevant to prevalence and incidence studies. This review was registered with PROSPERO, CRD42021250559. RESULTS: This search generated 6,238 articles, of which 140 were selected for data extraction and quality assessment. Of these, 85 articles reported on the incidence and 61 on the prevalence of ALS. Incidence ranged from 0.26 per 100,000 person-years in Ecuador to 23.46 per 100,000 person-years in Japan. Point prevalence ranged from 1.57 per 100,000 in Iran to 11.80 per 100,000 in the United States. Many articles identified cases with ALS from multiple data sources. DISCUSSION: There is variation in reported incidence and prevalence estimates of ALS across the world. While registries are an important and powerful tool to quantify disease burden, such resources are not available everywhere. This results in gaps in reporting of the global epidemiology of ALS, as highlighted by the degree of variation (and quality) in estimates of incidence and prevalence reported in this review.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".