Analysis of the mortality trend in the indigenous population of Brazil, 2000-2016.

OBJECTIVES: The objective of the study is to analyze the mortality trend in indigenous peoples in Brazil. STUDY DESIGN: The study design used is an ecological study. METHODS: A study carried out with data from the indigenous deaths recorded on the Mortality Information System in Brazil between 2000 and 2016. The Prais Winsten regression was used to analyze the trend of overall mortality in the country and in its geopolitical regions, in addition to the trend by sex, age groups, and groups of basic causes of death. RESULTS: Of all deaths, 47,806 indigenous deaths were recorded between 2000 and 2016. The majority was male (56.5%) and 27.2% in children aged younger than 5 years. The major groups of causes of death were the circulatory diseases (15.8%) and external causes (14.9%). The affections of the perinatal period (23.7%) and infectious and parasitic diseases (18.5%) were the main causes in children aged younger than 5 years. The indigenous mortality rates showed an increasing trend in the country (annual percent change [APC] = 2.37%). There was growth in both sexes and in most age ranges. Mortality from external causes (APC = 4.71%), neoplasms (APC = 4.67%), and respiratory causes (APC = 4.14%) also presented growth. The infectious and parasitic causes and genitourinary causes showed a stable trend in the studied period. CONCLUSIONS: The increasing trend of mortality in the analyzed variables showed disparities between indigenous and non-indigenous peoples in Brazil, pointing to the need for greater investment in basic social sectors, such as health, education, housing, and sanitation.

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting.

INTRODUCTION: This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O(3)) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons. METHODS: Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O(3) concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO(2)), and O(3) (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004. RESULTS AND DISCUSSION: Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O(3) regimes were temperature, CO and NO(2) concentrations, due to their importance in O(3) chemistry in an urban atmosphere. CONCLUSION: In the prediction of O(3) concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.