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1.
Inform Med Unlocked ; 20: 100398, 2020.
Article in English | MEDLINE | ID: mdl-33102685

ABSTRACT

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 28 t h 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.

2.
Data Brief ; 32: 106093, 2020 Oct.
Article in English | MEDLINE | ID: mdl-32802921

ABSTRACT

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.

3.
Cad Saude Publica ; 34(6): e00213816, 2018 06 21.
Article in Portuguese | MEDLINE | ID: mdl-29947662

ABSTRACT

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.


O acesso à saúde é uma importante dimensão das desigualdades entre áreas urbanas e rurais. O acesso é menor nas áreas rurais em função da maior vulnerabilidade social de sua população e das maiores dificuldades de acesso que seus grupos sociais estão submetidos. A partir de dados do suplemento de saúde da Pesquisa Nacional por Amostra de Domicílios, foram analisados os determinantes do acesso e das diferenças entre áreas urbanas e rurais nos anos de 1998 a 2008. A análise dos determinantes do acesso aos serviços de saúde foi realizada pelo modelo de regressão logística binária. As diferenças entre áreas urbanas e rurais foram decompostas em fatores observáveis (fatores de capacitação, necessidade e predisposição) e não observáveis (oferta e dificuldade de acesso). Os resultados destacam que a desigualdade de acesso é elevada e maior nas áreas rurais. Os fatores de necessidade são determinantes fundamentais do acesso à saúde, enquanto que os fatores de capacitação são mais importantes para explicar as diferenças entre as áreas urbanas e rurais. A tênue redução das diferenças no período se deveu fundamentalmente a mudanças na composição da população rural.


El acceso a la salud es una importante dimensión de las desigualdades entre áreas urbanas y rurales. El acceso es menor en las áreas rurales, en función de una mayor vulnerabilidad social de su población y de las mayores dificultades de acceso a la que están sometidos sus grupos sociales. A partir de los datos del suplemento de salud de la Encuesta Nacional por Muestra de Domicilios, se analizaron los determinantes de acceso y diferencias entre áreas urbanas y rurales, desde el año 1998 a 2008. El análisis de los determinantes de acceso a los servicios de salud se realizó mediante un modelo de regresión logística binaria. Las diferencias entre áreas urbanas y rurales se dividieron en factores observables (factores de capacitación, necesidad y predisposición) y no observables (oferta y dificultad de acceso). Los resultados destacan que la desigualdad de acceso es elevada y superior en las áreas rurales. Los factores de necesidad son determinantes fundamentales del acceso a la salud, mientras que los factores de capacitación son más importantes para explicar las diferencias entre áreas urbanas y rurales. La tenue reducción de las diferencias en el período se debió fundamentalmente a cambios en la composición de la población rural.


Subject(s)
Health Services Accessibility/statistics & numerical data , Healthcare Disparities/statistics & numerical data , Rural Health Services/statistics & numerical data , Urban Health Services/statistics & numerical data , Adult , Age Distribution , Brazil , Female , Humans , Logistic Models , Male , Middle Aged , Rural Population/statistics & numerical data , Rural Population/trends , Sex Distribution , Socioeconomic Factors , Time Factors , Urban Health Services/trends , Urban Population/statistics & numerical data , Urban Population/trends , Vulnerable Populations/statistics & numerical data , Young Adult
4.
Cad. Saúde Pública (Online) ; 34(6): e00213816, 2018. tab
Article in Portuguese | LILACS | ID: biblio-952397

ABSTRACT

O acesso à saúde é uma importante dimensão das desigualdades entre áreas urbanas e rurais. O acesso é menor nas áreas rurais em função da maior vulnerabilidade social de sua população e das maiores dificuldades de acesso que seus grupos sociais estão submetidos. A partir de dados do suplemento de saúde da Pesquisa Nacional por Amostra de Domicílios, foram analisados os determinantes do acesso e das diferenças entre áreas urbanas e rurais nos anos de 1998 a 2008. A análise dos determinantes do acesso aos serviços de saúde foi realizada pelo modelo de regressão logística binária. As diferenças entre áreas urbanas e rurais foram decompostas em fatores observáveis (fatores de capacitação, necessidade e predisposição) e não observáveis (oferta e dificuldade de acesso). Os resultados destacam que a desigualdade de acesso é elevada e maior nas áreas rurais. Os fatores de necessidade são determinantes fundamentais do acesso à saúde, enquanto que os fatores de capacitação são mais importantes para explicar as diferenças entre as áreas urbanas e rurais. A tênue redução das diferenças no período se deveu fundamentalmente a mudanças na composição da população rural.


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.


El acceso a la salud es una importante dimensión de las desigualdades entre áreas urbanas y rurales. El acceso es menor en las áreas rurales, en función de una mayor vulnerabilidad social de su población y de las mayores dificultades de acceso a la que están sometidos sus grupos sociales. A partir de los datos del suplemento de salud de la Encuesta Nacional por Muestra de Domicilios, se analizaron los determinantes de acceso y diferencias entre áreas urbanas y rurales, desde el año 1998 a 2008. El análisis de los determinantes de acceso a los servicios de salud se realizó mediante un modelo de regresión logística binaria. Las diferencias entre áreas urbanas y rurales se dividieron en factores observables (factores de capacitación, necesidad y predisposición) y no observables (oferta y dificultad de acceso). Los resultados destacan que la desigualdad de acceso es elevada y superior en las áreas rurales. Los factores de necesidad son determinantes fundamentales del acceso a la salud, mientras que los factores de capacitación son más importantes para explicar las diferencias entre áreas urbanas y rurales. La tenue reducción de las diferencias en el período se debió fundamentalmente a cambios en la composición de la población rural.


Subject(s)
Humans , Male , Female , Adult , Middle Aged , Young Adult , Urban Health Services/statistics & numerical data , Rural Health Services/statistics & numerical data , Healthcare Disparities/statistics & numerical data , Health Services Accessibility/statistics & numerical data , Rural Population/trends , Rural Population/statistics & numerical data , Socioeconomic Factors , Time Factors , Urban Population/trends , Urban Population/statistics & numerical data , Brazil , Logistic Models , Sex Distribution , Age Distribution , Urban Health Services/trends , Vulnerable Populations/statistics & numerical data
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