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Costa et al.

recently published (Veloso, Kassar, Oliveira, et al., 2019) and show that mother’s age above 35 years and the absence
of a partner are the most significant predictors of neonatal death. This confirms the results found in the present study,
but the study by Veloso, Kassar, Oliveira et al. did not consider the role of education in reducing mortality rates.
The inadequate quality and availability of data persist as a significant barrier to a better understanding of neonatal
mortality in Brazil, as the most vulnerable places are also those with the weakest data reporting (Maia, Souza and Mendes,
2015; Morais and Costa, 2017; Szwarcwald et al., 2019). The inadequate quality of the data affects the accuracy of the
results, such that this extensive database must be analyzed as a sample.

5. Conclusion
In the present study, we found that the risk of neonatal deaths was considerably higher among unmarried mothers with
a low level of schooling as well as those outside the 20-34 years old age group, demonstrating that these features are
relevant to the outcome. The investigation of maternal characteristics is crucial to accurate monitoring and ensuring
continuity in the reduction of NMR in Brazil.
Throughout this work, we performed an exploratory analysis of data from the SIM and SINASC databases, resulting
in graphical visualizations that enabled the evaluation of maternal characteristics from this quantitative perspective, along
with the demographic, biological, and cultural elements developed in the discussion. However, this study has some
limitations that should be considered. Although the dataset had nearly 30 million entries, problems related to the quality
and consistency of the data impeded a perfect dataset linkage. Indeed, inadequate data quality and availability constitute
a significant barrier. There were missing data on mother’s race and the most vulnerable places have weak data coverage.
Working with higher quality data and greater coverage is an open issue for further research that would enable more
precision and the use of other technologies that require data on a specific condition or quantity, such as machine learning
algorithms.
Infant and NMR are among the most important indices for gauging the overall level of public health as well as the
social and economic development of a country or region (WHO, 1981). As Brazil has already achieved the Sustainable
Development Goals (SDGs) for the reduction of infant and neonatal mortality established by the UN, new targets have
been determined, such as an estimated NMR of 5.3 per 1,000 live births. The results of the present study confirm a
trend in this direction and underscore the need to continue investing in actions aimed at combating preventable deaths
through policies designed at reducing inequalities, expanding education, as well as the accessibility of effective health-
care services to ensure a safe pregnancy for all ages and marital statuses. It is also important for Brazil to continue
investing in public access platforms and the quality of information through a broadening of coverage for better guided
decision-making.
Availability of Data and Materials Section

The dataset used in this paper is available at https://drive.google.com/drive/folders/19dFhQ8XEYzUBVYkqV9NKafxr
KgWf5iOB?usp=sharing.

Authors’ Contributions

PHC designed the study, performed the analysis, interpreted the data, and drafted the manuscript. LCA designed the study,
interpreted the data, drafted, and revised the manuscript. CEB performed the analysis and revised the manuscript. NMA
and RCB performed the analysis. TC designed the study, interpreted the data, and revised the manuscript. All authors read
and approved the final manuscript.
Ethics

This paper uses publicly available data (SIM and SINASC) that has been de-identified and was deemed exempt from
approval from a human research ethics committee.
Conflicts of Interest and Funding

All authors report no conflicts of interest. This research was supported by the Brazilian Ministry of Health through
the National Council for Scientific and Technological Development (CNPq) (Process n: 443774/2018-8). It was also
supported by NVIDIA, which donated a GPU XP Titan used by the research team.

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