Maternal characteristics and the risk of neonatal mortality in Brazil between 2006 and 2016

Abstract

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.

SPNeoDeath: A demographic and epidemiological dataset having infant, mother, prenatal care and childbirth data related to births and neonatal deaths in São Paulo city Brazil – 2012–2018

“…As mentioned, in the context of Brazilian public health data, occurrence of missing or inconsistent data is common and it mostly happens due to the incorrect filling of handwritten forms. Rows having fields with inconsistent values were removed, and a general approach for demographic studies to deal with missing values were used based on approaches of similar studies [ [3] , [4] , [5] ]. All the features had less than 12% of missing values and basically, two different techniques were applied: for non-categorical features, with continuous numerical values, the mean value for this feature in the dataset is calculated, and the feature is filled with this value; whereas features with categorical values (discrete values) are filled using the most frequent value for this feature in the dataset (the mode value).…”

The maternal profile associated with the underlying root cause of neonatal mortality using machine learning and administrative health data

“…Neonatal mortality involves various biological, socioeconomic, and health-care factors and has become the biggest challenge in fighting infant mortality. The increasing 1 importance of neonatal deaths on infant mortality aside from the greater availability and quality of health data in Brazil has enabled more precise analyses and led to a significant number of studies covering different factors, regions, and methods regarding this issue [2].…”

“…In Brazil, the IMR has undergone a continual decrease in recent decades, mainly due to improvements in sanitary conditions and the reduction in Postneonatal which has declined from 51% in 1990 to 38% in 2015 [3]. Nowadays the NM which is the main component of the IMR has also been following a declining trend, mainly due to favorable changes in factors related to pregnancy and childbirth, despite the increase in its shared proportion [2].…”

“…In the last 65 years, the global mortality rate has declined 5 times, however, disparities between developed and developing countries demonstrate that there is work to do. The fact that mortality rates can be 10 times higher in developing countries indicates that a high percentage of these deaths can be avoidable [2].…”

“…Evaluating determinants of infant mortality can favor the better targeting of public policy funding, which is increasingly possible through online platforms that make data available, such as the Department of Brazilian Health Ministry (DATASUS) responsible for collecting, processing, and disseminating public health data [2].…”