Prevalence, Deaths, and Disability‐Adjusted Life Years Due to Musculoskeletal Disorders for 195 Countries and Territories 1990–2017
Bibliographic record
Abstract
OBJECTIVE: To report the levels and trends of prevalence, deaths, and disability-adjusted life years (DALYs) due to musculoskeletal disorders, categorized as low back pain, neck pain, osteoarthritis (OA), rheumatoid arthritis (RA), gout, and other musculoskeletal disorders, across 195 countries and territories from 1990 to 2017 according to age, sex, and Sociodemographic Index (SDI; a composite of sociodemographic factors). METHODS: Data were obtained from the Global Burden of Disease (GBD) Study 2017. The fatal and nonfatal burdens of musculoskeletal disorders were estimated using the Cause of Death Ensemble model and Bayesian meta-regression tool, respectively. Estimates were provided for all musculoskeletal disorders and the corresponding 6 categories at global, regional, and national levels from 1990 to 2017. Counts and age-standardized rates per 100,000 population along with 95% uncertainty intervals (95% UIs) were reported for prevalence, deaths, and DALYs. RESULTS: Globally, there were ~1.3 billion prevalent cases (95% UI 1.2 billion, 1.4 billion), 121.3 thousand deaths (95% UI 105.6 thousand, 126.2 thousand), and 138.7 million DALYs (95% UI 101.9 million, 182.6 million) due to musculoskeletal disorders in 2017. Age-standardized prevalence, death, and DALY rates per 100,000 population were 16,276.2 (95% UI 15,495.5, 17,145.8), 1.6 (95% UI 1.4, 1.6), and 1,720 (95% UI 1,264.4, 2,259.2), respectively. Age-standardized prevalence (-1.6% [95% UI -2.4, -0.8]) and DALY rates (-3.5% [95% UI -4.7, -2.3]) decreased slightly from 1990. The global point prevalence rate of musculoskeletal disorders in 2017 was higher in women than in men and increased with age up to the oldest age group. Globally, the proportion of prevalent cases according to category of musculoskeletal disorders in 2017 was greatest for low back pain (36.8%), followed by other musculoskeletal disorders (21.5%), OA (19.3%), neck pain (18.4%), gout (2.6%), and RA (1.3%). These proportions did not change appreciably compared with 1990. The burden due to musculoskeletal conditions was higher in developed countries. The countries with the highest age-standardized prevalence rates of musculoskeletal disorders in 2017 were Switzerland (23,346.0 [95% UI 22,392.6, 24,329.8]), Chile (23,007.9 [95% UI 21,746.5, 24,165.8]), and Denmark (22,166.1 [95% UI 20,817.2, 23,542.1]). The greatest increases from 1990 were found in Chile (10.8% [95% UI 6.6, 15.4]), Benin (8.8% [95% UI 6.7, 11.1]), and El Salvador (8.5% [95% UI 5.5, 11.9]). CONCLUSION: There is a large burden of musculoskeletal disorders globally, with some notable inter-country variation. Some countries have twice the burden of other countries. Increasing population awareness regarding risk factors, consequences, and evidence-informed treatment strategies for musculoskeletal disorders with a focus on the older female population in developed countries is needed, particularly for low back and neck pain and OA, which contribute a large burden among this cohort.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".