Price elasticity of demand for ready-to-drink sugar-sweetened beverages in Brazil.

The taxation of sugar-sweetened beverages is a policy that has been adopted in many countries worldwide, including Latin American, to reduce sugar consumption. However, little is known about how taxation on these products may affect their demand. The present study aims to estimate the price elasticity of demand for sugar-sweetened beverages in Brazil. This study advances the literature by proposing a breakdown between ready-to-drink sugar-sweetened beverages and sugar-sweetened beverages that require some preparation before being consumed. With this disaggregation, it is possible to obtain more accurate elasticities for the group of products that will be effectively taxed. We estimated a Quadratic Almost Ideal Demand System (QUAIDS) model using the Household Budget Survey 2017-2018 microdata. The results show that ready-to-drink beverages is more consumed but less sensitive to changes in price than prepared beverages. The price elasticity of demand for ready-to-drink and prepared sugar-sweetened beverages was -1.19 and -3.38. Additionally, we observe heterogeneity in these price elasticities across household incomes, with a more elastic demand among lower-income households for ready to drink beverages. The findings suggest that taxing ready-to-drink sweetened beverages could potentially reduce sugar consumption directly through a decrease in the consumption of sugary drinks and this effect could be reinforced by reducing the consumption of other sugar-rich products. Therefore, the taxation police should effective contribute to minimize health risks associated to the sugar consumption.

Symptoms for early diagnosis of chronic kidney disease in children - a machine learning-based score.

The objective of this study was to reveal the signs and symptoms for the classification of pediatric patients at risk of CKD using decision trees and extreme gradient boost models for predicting outcomes. A case-control study was carried out involving children with 376 chronic kidney disease (cases) and a control group of healthy children (n = 376). A family member responsible for the children answered a questionnaire with variables potentially associated with the disease. Decision tree and extreme gradient boost models were developed to test signs and symptoms for the classification of children. As a result, the decision tree model revealed 6 variables associated with CKD, whereas twelve variables that distinguish CKD from healthy children were found in the "XGBoost". The accuracy of the "XGBoost" model (ROC AUC = 0.939, 95%CI: 0.911 to 0.977) was the highest, while the decision tree model was a little lower (ROC AUC = 0.896, 95%CI: 0.850 to 0.942). The cross-validation of results showed that the accuracy of the evaluation database model was like that of the training. CONCLUSION: In conclusion, a dozen symptoms that are easy to be clinically verified emerged as risk indicators for chronic kidney disease. This information can contribute to increasing awareness of the diagnosis, mainly in primary care settings. Therefore, healthcare professionals can select patients for more detailed investigation, which will reduce the chance of wasting time and improve early disease detection. WHAT IS KNOWN: • Late diagnosis of chronic kidney disease in children is common, increasing morbidity. • Mass screening of the whole population is not cost-effective. WHAT IS NEW: • With two machine-learning methods, this study revealed 12 symptoms to aid early CKD diagnosis. • These symptoms are easily obtainable and can be useful mainly in primary care settings.

Cognitive Dissonance in the Self-assessed Health in Brazil: A CUB Model Analysis Using 2013 National Health Survey Data.

This study ai ms to verify and analyze the existence of cognitive dissonance in the self-assessment of health by individuals in Brazil, that is, the difference between self-rated health and the health status of individuals. To accomplish this, we use data from the 2013 National Health Survey, which collected the self-assessments that individuals made of their health and information about their health status. This information was used to build indices that seek to represent a person's health status in relation to chronic illnesses, physical and mental well-being, eating habits and lifestyle. To identify the presence of cognitive dissonance, the CUB (Combination of a discrete Uniform and shifted Binomial distributions) model was used, which relates self-assessed health with the developed indices. Cognitive dissonance was identified in self-assessed health in relation to eating habits and lifestyle, and this dissonance may be associated with a present bias in the self-assessment of health in Brazil.