Association Between Socioeconomic Inequalities in Pain and All-Cause Mortality in the China Health and Retirement Longitudinal Study: Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Few studies focus on the equality of pain, and the relationship between pain and death is inconclusive. Investigating the distribution of pain and potential mortality risks is crucial for ameliorating painful conditions and devising targeted intervention measures. OBJECTIVE: Our study aimed to investigate the association between inequalities in pain and all-cause mortality in China. METHODS: Longitudinal cohort data from waves 1 and 2 of the China Health and Retirement Longitudinal Study (2011-2013) were used in this study. Pain was self-reported at baseline, and death information was obtained from the 2013 follow-up survey. The concentration index and its decomposition were used to explain the inequality of pain, and the association between pain and death was analyzed with a Cox proportional risk model. RESULTS: A total of 16,747 participants were included, with an average age of 59.57 (SD 9.82) years. The prevalence of pain was 32.54% (8196/16,747). Among participants with pain, the main pain type was moderate pain (1973/5426, 36.36%), and the common pain locations were the waist (3232/16,747, 19.3%), legs (2476/16,747, 14.78%) and head (2250/16,747, 13.44%). We found that the prevalence of pain was concentrated in participants with low economic status (concentration index -0.066, 95% CI -0.078 to -0.054). Educational level (36.49%), location (36.87%), and economic status (25.05%) contributed significantly to the inequality of pain. In addition, Cox regression showed that pain was associated with an increased risk of all-cause mortality (hazard ratio 1.30, 95% CI 1.06-1.61). CONCLUSIONS: The prevalence of pain in Chinese adults is concentrated among participants with low economic status, and pain increases the risk of all-cause death. Our results highlight the importance of socioeconomic factors in reducing deaths due to pain inequalities by implementing targeted interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".