Quantifying the Strain: A Global Burden of Disease (GBD) Perspective on Musculoskeletal Disorders in the United States Over Three Decades: 1990–2019
Bibliographic record
Abstract
Background: Musculoskeletal (MSK) disorders significantly contribute to global disability, especially in high-income countries. Yet, comprehensive studies on their epidemiological burden in the United States (US) are limited. Our study aims to fill this gap by characterizing the MSK disease burden in the US using Global Burden of Disease (GBD) data from 1990 to 2019. Methods: We conducted an ecological study using descriptive statistical analyses to examine age-standardized prevalence and disability-adjusted life years (DALY) rates of MSK disorders across different demographics and states. The study also assessed the impact of risk factors segmented by age and sex. Results: From 1990 to 2019, the burden of MSK disorders in the US increased significantly. Low back pain was the most prevalent condition. Age-standardized prevalence and DALY rates increased by 6.7% and 17.6%, respectively. Gout and other MSK disorders saw the most significant rise in DALY rates. Females experienced higher rates than males, and there were notable geographic disparities, with the District of Columbia having the lowest and North Dakota and Iowa the highest DALY rates. Smoking, high BMI, and occupational risks emerged as primary risk factors. Conclusions: Our study highlights the escalating burden of MSK disorders in the US, revealing significant geographic and sex disparities. These findings highlight the urgent need for targeted health interventions, policy formulation, and public health initiatives focusing on lifestyle and workplace modifications. Region- and sex-specific strategies are crucial in effectively managing MSK conditions, considering the influence of various risk factors.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".