Chronic pain in mental disorders: An umbrella review of the prevalence, risk factors, and treatments across 957,168 people with mental disorders and 16,606,910 controls
Bibliographic record
Abstract
BACKGROUND: Chronic pain (CP) and mental disorders often coexist, yet their relationship lacks comprehensive synthesis. This first hierarchical umbrella review examined systematic reviews and meta-analyses, also observational studies and randomized controlled trials (where reviews are currently lacking) to report CP prevalence, risk factors, and treatment across mental disorders. METHODS: We searched MEDLINE, PsycINFO, Embase, Web of Science, and CINAHL, identifying 20 studies on anxiety, depression, bipolar disorder, schizophrenia, ADHD, autism, or dementia, and CP. Quality was assessed using AMSTAR and Newcastle-Ottawa Scale. RESULTS: Prevalence varied widely-23.7% (95% CI 13.1-36.3) in bipolar disorder to 96% in PTSD-consistently exceeding general population rates (20-25%). Risks were elevated, with bidirectional links in depression (OR = 1.26-1.88). Risk factors included female gender, symptom severity, and socioeconomic disadvantage, though data were limited beyond PTSD and depression. Treatment evidence was sparse: cognitive behavioral therapy showed small effects on pain (SMD = 0.27, 95% CI -0.08-0.61), acupuncture with medication improved pain (MD = -1.06, 95% CI -1.65--0.47), and transcranial direct current stimulation reduced pain in dementia (d = 0.69-1.12). Methodological issues were evident, including heterogeneous designs and inconsistent pain definitions. CONCLUSIONS: This review confirms CP as a significant comorbidity in mental disorders. Clinicians should prioritize routine pain screening and multimodal treatments. Researchers need longitudinal studies with standardized assessments to clarify causality and improve interventions. Taken together, this work highlights an urgent need for integrated psychiatric care approaches, emphasizing that addressing CP could enhance mental health outcomes and overall patient well-being.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".