Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Gender-based violence and socioeconomic inequalities: Does living in more deprived neighbourhoods increase women's risk of intimate partner violence?

This study investigates the influence of neighbourhood socioeconomic conditions on women's likelihood of experiencing intimate partner violence (IPV) in Sao Paulo, Brazil. Data from 940 women who were interviewed as part of the WHO multi-country study on women's health and domestic violence against women, and census data for Sao Paulo City, were analyzed using multilevel regression techniques. A neighbourhood socioeconomic-level scale was created, and proxies for the socioeconomic positions of the couple were included. Other individual level variables included factors related to partner's behaviour and women's experiences and attitudes. Women's risk of IPV did not vary across neighbourhoods in Sao Paulo nor was it influenced by her individual socioeconomic characteristics. However, women in the middle range of the socioeconomic scale were significantly more likely to report having experienced violence by a partner. Partner behaviours such as excessive alcohol use, controlling behaviour and multiple sexual partnerships were important predictors of IPV. A women's likelihood of IPV also increased if either her mother had experienced IPV or if she used alcohol excessively. These findings suggest that although the characteristics of people living in deprived neighbourhoods may influence the probability that a woman will experience IPV, higher-order contextual dynamics do not seem to affect this risk. While poverty reduction will improve the lives of individuals in many ways, strategies to reduce IPV should prioritize shifting norms that reinforce certain negative male behaviours. (C) 2012 Elsevier Ltd. All rights reserved.