Global prevalence of musculoskeletal pain in rural and urban populations. A systematic review with meta‐analysis. Musculoskeletal pain in rural and urban populations
Bibliographic record
Abstract
INTRODUCTION: To systematically compare the global prevalence of musculoskeletal pain and care-seeking in rural and urban populations. METHODS: A systematic review with meta-analysis of observational studies reporting a direct comparison of rural and urban populations was conducted worldwide and included back, knee, hip, shoulder, neck pain and a broad diagnosis of 'musculoskeletal pain'. A search strategy combining terms related to 'prevalence', 'musculoskeletal pain' and 'rural' was used on the following databases: MEDLINE, Embase, CINAHL, Scopus, and rural and remote health from their inception to 1 June 2022. Random-effects meta-analysis was used to pool the data. Results were presented as odds ratios (OR) along with 95% confidence intervals (95% CI). RESULTS: A total of 42 studies from 24 countries were included with a total population of 489 439 participants. The quality scores for the included studies, using the modified Newcastle Ottawa Scale tool, showed an average score of 0.78/1, which represents an overall good quality. The pooled analysis showed statistically greater odds of hip (OR = 1.62, 95% CI = 1.23-2.15), shoulder (OR = 1.42, 95% CI = 1.06-1.90) and overall musculoskeletal pain (OR = 1.26, 95% CI = 1.08-1.47) in rural populations compared to urban populations. Although the odds of seeking treatment were higher in rural populations this relationship was not statistically significant (OR = 0.76, 95% CI = 0.55-1.03). CONCLUSION: Very low-certainty evidence suggests that musculoskeletal, hip and shoulder pain are more prevalent in rural than urban areas, although neck, back and knee pain, along with care-seeking, showed no significant difference between these populations. Strategies aimed to reduce the burden of musculoskeletal pain should consider the specific needs and limited access to quality evidence-based care for musculoskeletal pain of rural populations.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".