Factors Influencing the Severity of Urinary and Defecatory Dysfunction Among the Middle-Aged and Older Adult Chinese Population: Longitudinal Study of a 5-Wave Survey Cohort
Bibliographic record
Abstract
Background: Urinary and defecatory dysfunction (UDD) is a significant concern among the aging population in China. However, there is a lack of longitudinal research exploring the risk factors of UDD severity in Chinese older adults. Objective: This study uses data from the China Health and Retirement Longitudinal Study spanning 2011 to 2020 to explore UDD risk factors in the middle-aged and older adult Chinese population, focusing on epidemiological characteristics and potential influences on severity. Methods: A longitudinal cohort of over 10,000 participants from the China Health and Retirement Longitudinal Study was analyzed across 5 waves using Bayesian logistic regression. This analysis examined associations between UDD severity and factors including demographic, lifestyle, and health-related factors, including comorbidities, BMI, and handgrip strength. Results: Higher UDD prevalence was observed among female population, older adults, those with low education levels, and rural residents. Depression, arthritis, and low handgrip strength emerged as critical predictors of severe UDD. Additionally, abnormal BMI, both underweight (odds ratio [OR] 3.019, 95% CI 1.484-5.951; P=0.002) and obesity (OR 2.697, 95% CI 1.338-5.217; P=0.005), was strongly linked to increased severity and persistence of UDD. Participants aged 66 years and older exhibited the highest UDD prevalence, with both underweight and obese individuals facing the greatest risk of persistent and worsening symptoms. Conclusions: This study is the first to longitudinally examine the risk factors of UDD severity in China's middle-aging and aging population. The findings underscore the need for targeted interventions focusing on muscle strength rehabilitation and comorbidity management to mitigate UDD progression, contributing to improved quality of life for older individuals.
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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".