Prevalence and Risk Factors of Urinary Incontinence Among Elderly Adults in Rural China
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore the prevalence of urinary incontinence (UI) and several subtypes: (stress, urge, and mixed UI) and the influence of multiple factors on the likelihood of UI. DESIGN: Epidemiological study based on cross-sectional data collection. SUBJECTS AND SETTING: The sample comprised 1279 inhabitants 65 years and older residing in 10 villages randomly selected from the Shanxi province, located in North China. METHODS: The presence and types of UI were assessed using the International Consultation of Incontinence Questionnaire-Short Form. Sociodemographic parameters were also recorded, along with data on lifestyle, bowel function, and medical conditions. The Activity of Daily Living Scale and Mini-Mental State Examination instruments were used to evaluate physical and cognitive functions, respectively. A multivariate logistic regression model with the backward method was employed to identify factors associated with UI. RESULTS: The prevalence of any UI among the rural Chinese elderly 65 years and older was 46.8%, with a female predominance (56.3% in females vs 35.0% in males). The most common incontinence subtype in women was mixed UI (n = 170, 24.0%), followed by stress UI (n = 131, 18.5%) and urge UI (n = 97, 13.7%). The most prevalent form of UI in males was urge UI (n = 190, 33.2%), followed by stress UI (n = 5, 0.9%) and mixed UI (n = 5, 0.9%). Less than one quarter of respondents (17%, n = 102) of participants with UI had consulted a doctor. Multivariate analysis found that poorer physical function, poor quality of sleep, and fecal incontinence were common factors associated with UI in both women and men. In women, higher body mass index and constipation were also independent correlates, as were poor vision and heart disease in men. Poorer physical function was associated with all UI subtypes. For female stress UI, poorer cognitive status, tea drinking, and hypertension also emerged as independent risk factors. Heart disease was an independent risk factor in both female and male urge UI; as was consumption of a non-plant-based diet for female mixed and urge UI; nonfarmer and traumatic brain injury for female urge UI; and poor vision and fecal incontinence in male urge UI. CONCLUSIONS: Chinese rural citizens showed a high UI prevalence, but only a small proportion had consulted a health care provider. Physical function decline was the most important contributor to UI among participants. Individualized intervention programs targeting modifiable risk factors among high-risk populations should be developed.
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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 | 0.000 | 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".