Access to healthcare is an important dimension of inequalities between urban and rural areas. Access is lower in rural areas due to the population's greater social vulnerability and greater difficulties in access among its social groups. Based on data from the health supplement of the Brazilian National Household Sample Survey, we analyzed the determinants of access and differences between urban and rural areas from 1998 to 2008. The analysis of determinants of access to health services used binary logistic regression. Differences between urban and rural areas were disaggregated as observable factors (enabling, need, and predisposing) and non-observable factors (supply and difficulty in access). The results highlight that inequality in access is higher in rural areas. Need factors are fundamental determinants of access to health, while enabling factor are more important for explaining the differences between urban and rural areas. The slight reduction in differences during the period was due mainly to changes in the rural population's composition.

The objective of this study was to estimate life expectancy with and without a specific chronic disease among the Brazilian elderly population, by sex and socioeconomic factors, for the years 1998 and 2008. Life expectancy with and without hypertension, diabetes, bronchitis/asthma, and heart disease were calculated using the Sullivan method and prevalence estimates from data collected in the two years through the Brazilian National Household Survey (PNAD). Hypertension was the chronic disease with the largest effect on life expectancy. Among socioeconomic determinants, education proved more relevant than income. Having more years of education increased the average healthy time. Socioeconomic inequality negatively affected the health of women more than men. Despite the social changes in Brazil in recent decades with a reduction in inequality and poverty, the effect of socioeconomic inequality in the country on the health status of the elderly remains evident.

Neonatal deaths account for more than 60% of infant deaths and are a major concern in Brazil. The reduction of the occurrence of these events appears to be more challenging than post-neonatal deaths, as such a reduction depends more on factors related to the pregnancy and childbirth than sanitary and health conditions. The aim of the present study was to evaluate the influence of maternal factors (schooling, marital status, and age) on the risk of neonatal mortality in Brazil between 2006 and 2016. Data were collected from the Brazilian Institute of Geography and Statistics as well as two information systems of the public health-care system: Mortality Information System and Live Birth Information System. The total valid sample size was 28,362,359 children. Visualization and classification methods were performed. The results revealed a considerably higher risk of neonatal deaths when the mothers were unmarried, had a low level of schooling, and were outside the 20-34-year-old age group. Different demographic profiles in Brazil exert an influence on neonatal health. The identification of the risk factors of neonatal mortality can assist in ensuring pregnancy, delivery, and a neonatal period of greater quality.

Infant mortality is an important health measure in a population as a crude indicator of the poverty and socioeconomic level. It also shows the availability and quality of health services and medical technology in a specific region. Although improvements have been observed in the last decades, the implementation of actions to reduce infant mortality is still a concern in many countries. To address such an important problem, this paper proposes a new support decision approach to classify newborns according to their neonatal mortality risk. Using features related to mother, newborn, and socio-demographic, we model the problem using a data-driven classification model able to provide the probability of a newborn dying until days of life. More than a theoretical study, decision support tools as the one proposed here is relevant in countries in development as Brazil, because it aims at identifying risky neonates that may die to raise the attention of medical practitioners so that they can work harder to reduce the overall neonatal mortality. Overcoming an AUC of 96%, the proposed method is able to provide not just the probability of death risk but also an explicable interpretation of most important features for model decision, which is paramount in public health applications. Furthermore, we provide an extensive analysis across different rounds of experiments, including an analysis of pre and post partum features influence over data-driven model. Finally, different from previously conducted studies which rely on databases with less than 100,000 samples, our model takes advantage from a new proposed database, constructed using more than 1,400,000 samples comprising births and deaths extracted from public records in São Paulo-Brazil from 2012 to 2018.

SPNeodeath dataset includes births and deaths of infants during the neonatal period from São Paulo city between 2012 and 2018, containing more than 1.4 million records. The dataset was created from SINASC and SIM Brazilian information systems for births and deaths respectively. SINASC comprises information about demographic and epidemiological data for the infant, mother, prenatal care and childbirth. SIM collects information about mortality, and it is used as the basis for the calculation of vital statistics, such as neonatal mortality rate. SIM was only used to label records from SINASC, when the death happened until 28 days of life. SPNeodeath has 23 variables with socioeconomic maternal condition features, maternal obstetrics features, newborn related features and previous care related features, besides a label feature describing if the subject survived, or not, after 28 days of life. In order to build the dataset, DBF files were downloaded from DATASUS ftp repository and converted to CSV format, the R programming language, and then the CSV files were processed using Python programming language. Features with incorrect values and unknowing information were removed.