Basic sanitation and self-reported health in Brazilian capitals: a multilevel analysis.

Objective: This study aimed to analyze the association between the contextual determinants related to basic sanitation and self-reported health in Brazilian capitals.

Methods: The sample consisted of 27,017 adults (≥18 years) residing in the 27 Brazilian capitals in 2013, from the National Health Survey (PNS). The association between self-reported health and sanitation (sewage system, water supply and garbage collection) was analyzed using Bayesian multilevel models, controlling for individual factors (first level of the model) and area-level socioeconomic characteristics (second level).

Income Inequality and Self-Reported Health Among Older Adults in Brazil.

This study analyzes the association between income inequality and self-reported health (SRH) in older adults, and separately for the young-old and very-old groups, residing in each of the 27 Brazilian capitals. The sample consisted of 4,912 individuals aged 60 or older residing in Brazilian capitals in 2013. Bayesian multilevel models were applied to the whole sample and separately for individuals aged 60 to 79 (young-old), and 80 or more (very-old).

[Analysis of the prevalence of cardiovascular diseases and associated factors among the elderly, 2000-2010].

The scope of this study was to analyze the difference in the prevalence of cardiovascular disease (CVD) between 2000 and 2010 and its association with socio-economic and risk factors among the elderly. Bayesian multilevel models were applied to analyze the difference in the prevalence of CVD, and the association between individual characteristics and the presence of CVD was analyzed by logistic regression models with complex samples separately for 2000, 2006 and 2010. This research is part of the Health, Welfare and Aging (SABE) study, performed in Sao Paulo, Brazil, and used the samples collected in 2000, 2006 and 2010.

Overachieving Municipalities in Public Health: A Machine-learning Approach.

Background: Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict life expectancy at birth, and then compare health-related characteristics of the under- and overachievers (i.e.

Factors associated with the use of antihypertensives among seniors.

Objective: Analyze the use of antihypertensives among seniors and the association with socioeconomic and behavioral characteristics.

Methods: In this seriate cross-sectional study, we used data from the Saúde, Bem Estar e Envelhecimento study (SABE - Health, Well-being, and Aging), conducted in 2000, 2006, and 2010 in the city of São Paulo. Association between the use of antihypertensives and the demographic, behavioral, and socioeconomic characteristics and risk factors was analyzed by using multilevel logistic regression models.

Environmental factors and cardiovascular diseases: the association of income inequality and green spaces in elderly residents of São Paulo, Brazil.

Objective: We aimed to analyse the individual and contextual determinants associated with cardiovascular diseases (CVDs) morbidity among the elderly.

Methods: The sample consisted of 1333 individuals aged 60 or older residing in the city of São Paulo, from the Health, Welfare and Aging (SABE) study survey performed in 2010. The association between CVD with both income inequality and green spaces was analysed using Bayesian multilevel models, controlling for individual and contextual factors.