Is the relationship between chronic pain and mortality causal? A propensity score analysis
Bibliographic record
Abstract
ABSTRACT: Chronic pain is a serious and prevalent condition that can affect many facets of life. However, uncertainty remains regarding the strength of the association between chronic pain and death and whether the association is causal. We investigate the pain-mortality relationship using data from 19,971 participants aged 51+ years in the 1998 wave of the U.S. Health and Retirement Study. Propensity score matching and inverse probability weighting are combined with Cox proportional hazards models to investigate whether exposure to chronic pain (moderate or severe) has a causal effect on mortality over a 20-year follow-up period. Hazard ratios (HRs) with 95% confidence intervals (CIs) are reported. Before adjusting for confounding, we find a strong association between chronic pain and mortality (HR: 1.32, 95% CI: 1.26-1.38). After adjusting for confounding by sociodemographic and health variables using a range of propensity score methods, the estimated increase in mortality hazard caused by pain is more modest (5%-9%) and the results are often also compatible with no causal effect (95% CIs for HRs narrowly contain 1.0). This attenuation highlights the role of confounders of the pain-mortality relationship as potentially modifiable upstream risk factors for mortality. Posing the depressive symptoms variable as a mediator rather than a confounder of the pain-mortality relationship resulted in stronger evidence of a modest causal effect of pain on mortality (eg, HR: 1.08, 95% CI: 1.01-1.15). Future work is required to model exposure-confounder feedback loops and investigate the potentially cumulative causal effect of chronic pain at multiple time points on mortality.
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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 | 0.113 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".