Do women of reproductive age presenting with pelvic floor dysfunction have undisclosed anal incontinence: A retrospective cohort study

BackgroundIndirect and direct trauma following vaginal birth can negatively impact on the pelvic floor function increasing the risk of anal incontinence. It is often difficult for women to openly disclose that they have anal incontinence and there are limited data collection tools available for the identification of these women in a clinical setting.AimThis study aims to describe the prevalence of undisclosed anal incontinence in antenatal and postnatal women with pelvic floor dysfunction.MethodsRetrospective cohort study of 230 antenatal and postnatal women referred to a Continence Nursing Service in a large tertiary hospital in South Australia, Australia, with pelvic floor dysfunction. A criteria list was utilised to access the primary reason for referral, anal incontinence assessments and attendance to an appointment.ResultsAnal incontinence was identified in 26% of women (n = 59). Anal incontinence was the primary reason for referral amongst 8 women, with the remaining 51 women identified as having anal incontinence following clinical screening via phone consultation. Eighty six percent of women stated they had not previously disclosed anal incontinence to health professionals. Overall, 71% of symptomatic women (n = 28 antenatal and n = 14 postnatal women) attended appointments to a service specialising in pelvic floor dysfunction.ConclusionWomen presenting with urinary incontinence or other markers of pelvic floor dysfunction should be actively screened for anal incontinence as the prevalence of this condition is high amongst childbearing women.     

Gay Abuse Screening Protocol (GASP):Screening for Abuse in Gay Male Relationships

ABSTRACT

Thirty-two male patients in gay relationships and eight family physicians were recruited from a family practice in order to determine comfort with an eight-question Gay Abuse Screening Protocol (GASP). The GASP was administered during a typical clinical encounter. After the encounter, physicians and patients each completed a 5-point Likert Scale questionnaire to assess their comfort levels with each of the 8 GASP questions (Likert Scale: 1?=?not at all comfortable to 5?=?very comfortable). The mean comfort score was high (Likert?>4) for both patients (4.16 ± 0.18) and physicians (4.71 ± 0.18). However, mean comfort scores were significantly lower for abused patients (3.26 ± 0.75) than nonabused patients (4.57 ± 0.26). Patients were comfortable (Likert?>3) with 76.2% of GASP items while physicians were comfortable with all GASP items.     

Conflict and Aggression as Stressors in the Work Environment of Nursing Assistants:

Elder abuse and mistreatment occuring in institutional settings is a phenomenon that is as yet poorly understood. In considering the context in which elder abuse and mistreatment may arise, understanding the caregivers' work environment is an important variable. As front line staff, nursing assistants are subject to multiple stressors that underscore the highly interpersonal nature of caregiving. This paper describes the results of a Canadian study that examined conflict, aggression, and burn-out in one group of nursing assistants. Nursing assistant burn-out scores were similar to scores reported for other health-care workers. Subjects reported that conflict with residents most commonly related to the resident wanting to go outside the facility or personal hygiene. An analysis of incident reports indicated that less than 0.3% of the physical and verbal aggression nursing assistants endure from residents is formally acknowledged. On average, a nursing assistant in this health care facility may expect to be physically assaulted by residents 9.3 times per month and verbally assaulted 11.3 times per month. Findings indicate a slight correlation between burn-out and conflict and also between burn-out and reported aggression from residents. A statistically significant relationship was noted between conflicts with residents and resident aggression from residents.     

Using information on realized effects to determine prospective causal effects

The potential outcomes approach to causal inference postulates that each individual has a number of possibly latent outcomes, each of which would be observed under a different treatment. For any individual, some of these outcomes will be unobservable or counterfactual. Information about post-treatment characteristics sometimes allows statements about what would have happened if an individual or group with these characteristics had received a different treatment. These are statements about the realized effects of the treatment. Determining the likely effect of an intervention before making a decision involves inference about effects in populations defined only by characteristics observed before decisions about treatment are made. Information on realized effects can tighten bounds on these prospectively defined measures of the intervention effect. We derive formulae for the bounds and their sampling variances and illustrate these points with data from a hypothetical study of the efficacy of screening mammography.     

The optimal allocation combination for the two-sided sequential screening procedure based on the individual misclassification error

In this paper, the expected total costs (ETCs) of three kinds of quality cost functions for the two-sided sequential screening procedure (SQSP) based on the individual misclassification error are obtained, where the ETC is the sum of the expected cost of inspection, the expected cost of rejection and the expected cost of quality. The general formulas for all the desired probabilities and three ETCs when k screening variables are allocated into r-stages are derived. The optimal allocation combination for each ETC is determined based on the criterion of minimum ETC. Finally, we give two examples to illustrate the selection of the optimal allocation combination for the SQSP.     

Bayesian Interval Estimation for the Difference in TPRs and FPRs of Two Diagnostic Tests with Unverified Negatives

We derive Bayesian interval estimators for the differences in the true positive rates and false positive rates of two dichotomous diagnostic tests applied to the members of two distinct populations. The populations have varying disease prevalences with unverified negatives. We compare the performance of the Bayesian credible interval to the Wald interval using Monte Carlo simulation for a spectrum of different TPRs, FPRs, and sample sizes. For the case of a low TPR and low FPR, we found that a Bayesian credible interval with relatively noninformative priors performed well. We obtain similar interval comparison results for the cases of a high TPR and high FPR, a high TPR and low FPR, and of a high TPR and mixed FPR after incorporating mildly informative priors.     

Sampling methods and sensitivity analysis for large parameter sets

Models with large parameter (i.e., hundreds or thousands of parameters) often behave as if they depend upon only a few parameters, with the rest having comparatively little influence. One challenge of sensitivity analysis with such models is screening parameters to identify the influential ones, and then characterizing their influences.

Large models often require significant computing resources to evaluate their output, and so a good screening mechanism should be efficient: it should minimize the number of times a model must be exercised. This paper describes an efficient procedure to perform sensitivity analysis on deterministic models with specified ranges or probability distributions for each parameter.

It is based on repeated exercising of the model, which can be treated as a black box. Statistical checks can ensure that the screening identified parameters that account for the bulk of the model variation. Subsequent sensitivity analysis can use the screening information to reduce the investment required to characterize the influence of influential and other parameters.

The procedure exploits simplifications in the dependence of a model output on model inputs. It works best where a small number of parameters are much more influential than all the rest. The method is much more sensitive to the number of influential parameters than to the total number of parameters. It is most effective when linear or quadratic effects dominate higher order effects and complex interactions.

The paper presents a set of M athematica functions that can be used to create a variety of types of experimental designs useful for sensitivity analysis, including simple random, latin hypercube and fractional factorial sampling. Each sampling method can use discretization, folding, grouping and replication to create composite designs. These techniques have beencombined in a composite approach called Iterated Fractional Factorial Design (IFFD).

The procedure is applied to model of nuclear fuel waste disposal, and to simplified example models to demonstrate the concepts involved.     

Bayesian assessment of times to diagnosis in breast cancer screening

Breast cancer is one of the diseases with the most profound impact on health in developed countries and mammography is the most popular method for detecting breast cancer at a very early stage. This paper focuses on the waiting period from a positive mammogram until a confirmatory diagnosis is carried out in hospital. Generalized linear mixed models are used to perform the statistical analysis, always within the Bayesian reasoning. Markov chain Monte Carlo algorithms are applied for estimation by simulating the posterior distribution of the parameters and hyperparameters of the model through the free software WinBUGS.     

Fast Bayesian variable screenings for binary response regressions with small sample size

Screening procedures play an important role in data analysis, especially in high-throughput biological studies where the datasets consist of more covariates than independent subjects. In this article, a Bayesian screening procedure is introduced for the binary response models with logit and probit links. In contrast to many screening rules based on marginal information involving one or a few covariates, the proposed Bayesian procedure simultaneously models all covariates and uses closed-form screening statistics. Specifically, we use the posterior means of the regression coefficients as screening statistics; by imposing a generalized g-prior on the regression coefficients, we derive the analytical form of their posterior means and compute the screening statistics without Markov chain Monte Carlo implementation. We evaluate the utility of the proposed Bayesian screening method using simulations and real data analysis. When the sample size is small, the simulation results suggest improved performance with comparable computational cost.