Prevalence and Factors Related to Urinary Incontinence in the Elderly: A Systematic Review
Bibliographic record
Abstract
Urinary incontinence (UI) is a prevalent condition among the elderly, affecting approximately 30%-50% of individuals over 65 years old. Understanding the prevalence of UI and its associated risk factors is crucial for improving management and treatment strategies. This systematic review aims to synthesize existing literature on the prevalence of UI and identify significant risk factors influencing its occurrence in diverse populations. A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, the Cochrane Library, and Web of Science, for studies published after 2014. Studies were included if they reported the prevalence of UI and associated risk factors in elderly populations. Data extraction focused on overall prevalence rates, demographic information, and specific risk factors along with their respective odds ratios or risk ratios, or prevalence ratios, confidence intervals, and p-values. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale tool. A total of 12 studies met the inclusion criteria, revealing a wide prevalence range of UI from 14.2% to 82.9%. Gender-specific prevalence shows that among men, rates can be as low as 20% (monthly UI) and as high as 35.96%, while women exhibit prevalence rates from 15% to 66.1%. Significant risk factors identified include female sex, increased body mass index, history of cancer, diabetes, cognitive impairment, and mobility limitations. The odds ratios for these factors varied, indicating a robust association with the occurrence of UI across different populations. The findings of this systematic review underscore the high prevalence of UI incontinence and its multifactorial nature, emphasizing the need for targeted screening and intervention strategies. Increased awareness among healthcare professionals about the significant risk factors associated with UI can facilitate early identification and improve patient outcomes, ultimately enhancing the quality of life for those affected.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".