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 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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".