BackgroundObesity is growing at an alarming rate in Latin America. Lifestyle behaviours such as physical activity and dietary intake have been largely associated with obesity in many countries; however studies that combine nutrition and physical activity assessment in representative samples of Latin American countries are lacking. The aim of this study is to present the design rationale of the Latin American Study of Nutrition and Health/Estudio Latinoamericano de Nutrición y Salud (ELANS) with a particular focus on its quality control procedures and recruitment processes.Methods/DesignThe ELANS is a multicenter cross-sectional nutrition and health surveillance study of a nationally representative sample of urban populations from eight Latin American countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Perú and Venezuela). A standard study protocol was designed to evaluate the nutritional intakes, physical activity levels, and anthropometric measurements of 9000 enrolled participants. The study was based on a complex, multistage sample design and the sample was stratified by gender, age (15 to 65 years old) and socioeconomic level. A small-scale pilot study was performed in each country to test the procedures and tools.DiscussionThis study will provide valuable information and a unique dataset regarding Latin America that will enable cross-country comparisons of nutritional statuses that focus on energy and macro- and micronutrient intakes, food patterns, and energy expenditure.Trial RegistrationClinical Trials NCT02226627

ObjectivesThe study aims to evaluate the magnitude of multimorbidity in Brazilian adults, as well to measure their association with individual and contextual factors stratified by Brazilian states and regions.MethodsA national-based cross-sectional study was carried out in 2013 with Brazilian adults. Multimorbidity was evaluated by a list of 22 physical and mental morbidities (based on self-reported medical diagnosis and Patient Health Questionnaire-9 for depression). The outcome was analysed taking ≥2 and ≥3 diseases as cut-off points. Factor analysis (FA) was used to identify disease patterns and multilevel models were used to test association with individual and contextual variables.ResultsThe sample comprised 60 202 individuals. Multimorbidity frequency was 22.2% (95% CI 21.5 to 22.9) for ≥2 morbidities and 10.2% (95% CI 9.7 to 10.7) for ≥3 morbidities. In the multilevel adjusted models, females, older people, those living with a partner and having less schooling presented more multiple diseases. No linear association was found according to wealth index but greater outcome frequency was found in individuals with midrange wealth index. Living in states with higher levels of education and wealthier states was associated with greater multimorbidity. Two patterns of morbidities (cardiometabolic problems and respiratory/mental/muscle–skeletal disorders) explained 92% of total variance. The relationship of disease patterns with individual and contextual variables was similar to the overall multimorbidity, with differences among Brazilian regions.ConclusionsIn Brazil, at least 19 million adults had multimorbidity. Frequency is similar to that found in other Low and and Middle Income Countries. Contextual and individual social inequalities were observed.

Resumo O objetivo do estudo foi analisar a mudança na prevalência de doença cardiovascular (DCV) entre 2000 e 2010 e sua associação com os fatores socioeconômicos e fatores de risco em idosos. A diferença da prevalência de DCV ao longo do período foi analisada por meio de modelos multinível bayesianos e a análise da associação entre a presença de DCV e os fatores individuais utilizou modelos de regressão logística para amostras complexas nos três períodos separadamente (2000, 2006 e 2010). A presente pesquisa utilizou os dados do Estudo de Saúde, Bem-Estar e Envelhecimento (SABE), realizada no município de São Paulo, referente às amostras de 2000, 2006 e 2010. Foi observado um aumento geral na prevalência de DCV em idosos no município de São Paulo na última década, apresentando prevalências iguais a 17,9% em 2000, 22,2% em 2006 e 22,9% em 2010. Em relação à prevalência em 2000, foi observado o aumento estatisticamente significativo da presença de DCV em 2006 (OR = 3,20 IC95% = 1,93-5,31) e 2010 (OR = 2,98 IC95% = 1,51-5,89), mesmo após o ajuste estatístico para características individuais. A presença de DCV apresentou associação com maior faixa etária, histórico de tabagismo e presença de diabetes e hipertensão arterial, sendo observada uma associação inversa entre a presença de DCV e a ingestão de álcool.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.

In general, our findings were consistent with the income inequality theory, that is, people living in places with higher income inequality had an overall higher odd of mental disorders, albeit not always statistically significant. The fact that depression, but not anxiety, was statistically significant could indicate a pathway by which inequality influences health.