Risks Associated With Tendinitis: Effects From Demographic, Socioeconomic, and Psychological Status Among Brazilian Workers

Background Self-reported tendinitis/tenosynovitis was evaluated by gender, age group, skin color, family income, and educational and psychological status. Methods The study was carried out in a representative sample of formally contracted Brazilian workers from a household survey. A total of 54,660 participants were included. Occupations were stratified according to estimated prevalences of self-reported injuries. Non-conditional logistic regression was performed, and all variables were analyzed in two occupational groups. Results The overall prevalence rate of tendinitis/tenosynovitis was 3.1%: 5.5% in high-prevalence occupations (n=10,726); and 2.5% in low-prevalence occupations (n=43,934). White female workers between the ages of 45 and 64 years and at a higher socioeconomic level were more likely to report tendinitis/tenosynovitis regardless of their occupational category. An adjusted OR = 3.59 [95% CI: 3.15-4.09] was found between tendinitis/tenosynovitis and psychological status. Conclusion Among formally contracted Brazilian workers, higher income can imply greater physical and psychological demands that, regardless of occupational stratum, increase the risk of tendinitis/tenosynovitis. Am. J. Ind. Med. 53:72-79, 2010. (C) 2009 Wiley-Liss, Inc.

Dietary predictors of serum total carotene in low-income women living in Sao Paulo, south-east Brazil

Objective Dietary intake and nutritional status of antioxidant vitamins have been reported to protect against some cancers The objective of the present study was to assess the correlations between serum levels of carotenoids (including beta-, alpha- and gamma-carotene), lycopene, retinol, alpha- and gamma-tocopherols, and dietary intakes estimated by an FFQ, among low-income women in the Brazilian Investigation into Nutrition and Cervical Cancer Prevention (BRINCA) study. Design Cross-sectional study of data for 918 women aged 21-65 years participating in the BRINCA study in Sao Paulo city. Multiple linear regression models were used with serum nutrient levels as the dependent variable and dietary intake levels as the independent variable, adjusted for confounding factors. Results In energy-adjusted analyses, the intakes of dark green and deep yellow vegetables and fruits (partial R(2) = 4.8%), total fruits and juices (partial R(2) = 1.8%), vegetables and fruits (partial R(2) = 1.8%), carrots (partial R(2) = 1.4%) and citrus fruits and juices only (partial R(2) = 0.8%) were positively correlated only with serum total carotene levels, after adjusting for serum total cholesterol concentration, age, hospital attended, smoking status. BMI and presence of cervical lesions Multiple-adjusted serum levels of carotenoids were positively correlated with intake quartiles of dark green and deep yellow vegetables and fruits and total fruits and juices independent of smoking status. Conclusions The intake of specific fruits and vegetables was an independent predictor of serum total carotene levels in low-income women living in Sao Paulo

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.