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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
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 info...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7419335/ https://www.ncbi.nlm.nih.gov/pubmed/32802921 http://dx.doi.org/10.1016/j.dib.2020.106093 |
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author | Beluzo, Carlos Eduardo Silva, Everton Alves, Luciana Correia Bresan, Rodrigo Campos Arruda, Natália Martins Sovat, Ricardo Carvalho, Tiago |
author_facet | Beluzo, Carlos Eduardo Silva, Everton Alves, Luciana Correia Bresan, Rodrigo Campos Arruda, Natália Martins Sovat, Ricardo Carvalho, Tiago |
author_sort | Beluzo, Carlos Eduardo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7419335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-74193352020-08-14 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 Beluzo, Carlos Eduardo Silva, Everton Alves, Luciana Correia Bresan, Rodrigo Campos Arruda, Natália Martins Sovat, Ricardo Carvalho, Tiago Data Brief Medicine and Dentistry 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. Elsevier 2020-07-30 /pmc/articles/PMC7419335/ /pubmed/32802921 http://dx.doi.org/10.1016/j.dib.2020.106093 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Medicine and Dentistry Beluzo, Carlos Eduardo Silva, Everton Alves, Luciana Correia Bresan, Rodrigo Campos Arruda, Natália Martins Sovat, Ricardo Carvalho, Tiago 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Medicine and Dentistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7419335/ https://www.ncbi.nlm.nih.gov/pubmed/32802921 http://dx.doi.org/10.1016/j.dib.2020.106093 |
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