Continence Index: a new screening questionnaire to predict the probability of future incontinence in older women in the community.

PURPOSE: Urinary incontinence (UI) is a chronic, costly condition that impairs quality of life. To identify older women most at risk, the Medical Epidemiologic and Social Aspects of Aging (MESA) datasets were mined to create a set of questions that can reliably predict future UI. METHODS: MESA data were collected during four household interviews at approximately 1 year intervals. Factors associated with becoming incontinent at the second interview (HH2) were identified using logistic regression (construction datasets). Based on p values and odds ratios, eight potential predictive factors with their 256 combinations and corresponding prediction probabilities formed the Continence Index. Its predictive and discriminatory capability was tested against the same cohort's outcome in the fourth survey (HH4 validation datasets). Sensitivity analysis, area under receiver operating characteristic (ROC) curve, predicted probabilities and confidence intervals were used to statistically validate the Continence Index. RESULTS: Body mass index, sneezing, post-partum UI, urinary frequency, mild UI, belief of developing UI in the future, difficulty stopping urinary stream and remembering names emerged as the strongest predictors of UI. The confidence intervals for prediction probabilities strongly agreed between construction and validation datasets. Calculated sensitivity, specificity, false-positive and false-negative values revealed that the areas under the ROCs (0.802 and 0.799) for the construction and validation datasets, respectively, indicated good discriminatory capabilities of the index as a predictor. CONCLUSION: The Continence Index will help identify older women most at risk of UI in order to apply targeted prevention strategies in women that are most likely to benefit.

Novel application of statistical methods to identify new urinary incontinence risk factors.

Longitudinal data for studying urinary incontinence (UI) risk factors are rare. Data from one study, the hallmark Medical, Epidemiological, and Social Aspects of Aging (MESA), have been analyzed in the past; however, repeated measures analyses that are crucial for analyzing longitudinal data have not been applied. We tested a novel application of statistical methods to identify UI risk factors in older women. MESA data were collected at baseline and yearly from a sample of 1955 men and women in the community. Only women responding to the 762 baseline and 559 follow-up questions at one year in each respective survey were examined. To test their utility in mining large data sets, and as a preliminary step to creating a predictive index for developing UI, logistic regression, generalized estimating equations (GEEs), and proportional hazard regression (PHREG) methods were used on the existing MESA data. The GEE and PHREG combination identified 15 significant risk factors associated with developing UI out of which six of them, namely, urinary frequency, urgency, any urine loss, urine loss after emptying, subject's anticipation, and doctor's proactivity, are found most highly significant by both methods. These six factors are potential candidates for constructing a future UI predictive index.