Sexual function and help seeking for urinary incontinence in postmenopausal women

ABSTRACT

Urinary incontinence (UI) is a common condition, especially in middle-aged and older women. UI is known to affect sexual function. Many women with UI do not consult a doctor about their condition. The aim of this study was to determine the relationship of sexual function and help seeking in postmenopausal women with urinary incontinence. This cross-sectional correlation study took place from March to May 2012. The subjects were selected by a clustered sampling method from various zones of Rasht (North of Iran). The data were collected using personal data forms, Questionnaire for Urinary Incontinence Diagnosis, Incontinence Severity Index, and Incontinence Quality of Life questionnaire. Data were analyzed by SPSS17 at the significant level of P < .05 and then were compared by parametric and nonparametric tests. A total of 313 menopausal women aged 45 to 60 years (mean 52.9) were recruited for the study. The mean sexual function score was 31.07 ± 7.52. Only 27.3% of subjects seek care for urinary incontinence. There was a significant correlation between sexual function and help seeking. The results of this study indicate that there is a significant correlation between sexual function and help seeking in postmenopausal women who participated in the present study. Health-care professionals should pay more attention to sexual symptoms of UI and make patients aware of available treatments.     

A latent variable regression model for asymmetric bivariate ordered categorical data

In many areas of medical research, especially in studies that involve paired organs, a bivariate ordered categorical response should be analyzed. Using a bivariate continuous distribution as the latent variable is an interesting strategy for analyzing these data sets. In this context, the bivariate standard normal distribution, which leads to the bivariate cumulative probit regression model, is the most common choice. In this paper, we introduce another latent variable regression model for modeling bivariate ordered categorical responses. This model may be an appropriate alternative for the bivariate cumulative probit regression model, when postulating a symmetric form for marginal or joint distribution of response data does not appear to be a valid assumption. We also develop the necessary numerical procedure to obtain the maximum likelihood estimates of the model parameters. To illustrate the proposed model, we analyze data from an epidemiologic study to identify some of the most important risk indicators of periodontal disease among students 15-19 years in Tehran, Iran.