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 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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".