Global, regional, and national burden of other musculoskeletal disorders 1990–2017: results from the Global Burden of Disease Study 2017
Bibliographic record
Abstract
OBJECTIVES: To describe the level and trends of point prevalence, deaths and disability-adjusted life years (DALYs) for other musculoskeletal (MSK) disorders, i.e. those not covered by specific estimates generated for RA, OA, low back pain, neck pain and gout, from 1990 to 2017 by age, sex and sociodemographic index. METHODS: Publicly available modelled estimates from the Global Burden of Disease (GBD) 2017 study were extracted and reported as counts and age-standardized rates per 100 000 population for 195 countries and territories between 1990 and 2017. RESULTS: Globally, the age-standardized point prevalence estimates and deaths rates of other MSK disorders in 2017 were 4151.1 and 1.0 per 100 000. This was an increase of 3.4% and 7.2%, respectively. The age-standardized DALY rate in 2017 was 380.2, an increase of 3.4%. The point prevalence estimate was higher among females and increased with age. This peaked in the 65-69 year age group for both females and males in 2017, followed by a decreasing trend for both sexes. At the national level, the highest age-standardized point prevalence estimates in 2017 were seen in Bangladesh, India and Nepal. The largest increases in age-standardized point prevalence estimates were observed in Romania, Croatia and Armenia. CONCLUSION: The burden of other MSK disorders is proven to be substantial and increasing worldwide, with a notable intercountry variation. Data pertaining to specific diseases within this overarching category are required for future GBD MSK estimates. This would enable policymakers to better allocate resources and provide interventions appropriately.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".