Pain as a risk factor for common mental disorders. Results from the Netherlands Mental Health Survey and Incidence Study-2: a longitudinal, populationbased study - PAIN 159 (2018) 712–718
Bibliographic record
Abstract
Pain might be an important risk factor for common mental disorders. Insight into the longitudinal association between pain and common mental disorders in the general adult population could help improve prevention and treatment strategies. Data were used from the first 2 waves of the Netherlands Mental Health Survey and Incidence Study-2, a psychiatric epidemiological cohort study among the Dutch general population aged 18 to 64 years at baseline (N 5 5303). Persons without a mental disorder 12 months before baseline were selected as the at-risk group (n54974 for any mood disorder; n54979 for any anxiety disorder; and n55073 for any substance use disorder). Pain severity and interference due to pain in the past month were measured at baseline using the Short Form Health Survey. DSM-IV mental disorders were assessed at both waves using the Composite International Diagnostic Interview version 3.0. Moderate to very severe pain was associated with a higher risk of mood (odds ratio [OR] 5 2.10, 95% confidence interval [CI] 5 1.33-3.29) or anxiety disorders (OR 5 2.12, 95% CI 5 1.27-3.55). Moderate to very severe interference due to pain was also associated with a higher risk of mood (OR52.14, 95% CI51.30-3.54) or anxiety disorders (OR51.92, 95% CI 5 1.05-3.52). Pain was not significantly associated with substance use disorders. No interaction effects were found between pain severity or interference due to pain and a previous history of mental disorders. Moderate to severe pain and interference due to pain are strong risk factors for first-incident or recurrent mood and anxiety disorders, independent of other mental disorders. Pain management programs could therefore possibly also serve as a preventative program for mental disorders.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".