Prevalence, Geographic Variations, and Determinants of Pain Among Older Adults in China: Findings from the National Urban and Rural Elderly Population (UREP) Survey
Bibliographic record
Abstract
Objectives: This study aimed to reveal the prevalence, geographic variations, and determinants of pain among the Chinese older adult population and provide empirical strategies for pain management in older adults in China. Methods: A total of 21,346 Chinese residents aged ≥ 60 years from 31 provinces in mainland China participated in our survey. Standardized questionnaires were used to collect data on socioeconomic characteristics, lifestyle factors, and self-reported pain experiences. Multivariate logistic regression models were used to estimate the associations between individual socioeconomic status, chronic diseases, and pain. Results: The national prevalence of pain was 56.5% (95% CI: 55.9–57.1%), representing approximately 140 million Chinese older adults. The prevalence increased with aging and peaked at 80 years and older (61.00%, 95% CI: 59.30–62.70%). Women (62.36%, 95% CI: 61.47–63.25%), rural residents (61.27%, 95% CI: 60.34–62.20%), and those with no formal education (65.08%, 63.90–66.26%) had a higher prevalence than men (50.27%, 95% CI: 49.32–51.22%), urban residents (52.19%, 95% CI: 51.28–53.10%), and those with higher education levels, respectively. Provincial prevalence ranged from 38.98% in Shanghai to 72.75% in Gansu Province. The presence of chronic diseases significantly increased the odds of pain, with multimorbidity (three or more chronic diseases) showing the strongest association (OR = 11.380, 95% CI: 10.257–12.627). Conclusions and Implications: Pain was highly prevalent among older adults in China and varied geographically. Socioeconomic status, chronic diseases, and multimorbidity were strongly associated with pain prevalence. Our findings support prioritizing the reduction in gender and geographic disparities in China’s pain management strategies. An integrated approach addressing both pain and chronic diseases should be urgently established in China’s healthcare system for older adults.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".