Prevalence of chronic non-cancer pain among military veterans: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
INTRODUCTION: Chronic non-cancer pain is common among military veterans; however, the prevalence is uncertain. This information gap complicates policy decisions and resource planning to ensure veterans have access to healthcare services that align with their needs. METHODS: Following Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols, we searched MEDLINE, EMBASE, PsycINFO, CINAHL and Web of Science from inception to 9 February 2023 for observational studies reporting the prevalence of chronic non-cancer pain among military veterans. We performed random-effects meta-analysis to pool pain prevalence data across studies and used the Grading of Recommendations, Assessment, Development and Evaluation approach to evaluate the certainty of evidence. RESULTS: Forty-two studies that included 14 305 129 veterans were eligible for review, of which 28 studies (n=5 011 634) contributed to our meta-analysis. Most studies (90%; 38 of 42) enrolled US veterans, the median of the mean age among study participants was 55 years (IQR 45-62) and 85% were male. The pooled prevalence of chronic non-cancer pain was 45%; however, we found evidence of a credible subgroup effect based on representativeness of the study population. Moderate certainty evidence found the prevalence of chronic pain among studies enrolling military veterans from the general population was 30% (95% CI 23% to 37%) compared with 51% (95% CI 38% to 64%) among military veterans sampled from populations with high rates of conditions associated with chronic pain (p=0.005). CONCLUSION: We found moderate certainty evidence that 3 in every 10 military veterans from the general population live with chronic non-cancer pain. These findings underscore the importance of ensuring access to evidence-based care for chronic pain for veterans, and the need for prevention and early management to reduce transition from acute to chronic pain. Further research, employing a standardised assessment of chronic pain, is needed to disaggregate meaningful subgroups; for example, the proportion of veterans living with moderate to severe pain compared with mild pain.
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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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".