Modelagem dos registros de nascimentos com as condições de vida no semiárido brasileiro
The Brazilian semiarid region is the most populous on the planet and presents deficit of development indicators as well as maternal and child care. In the feld of vital statistics there are problems with births underreporting, whose the studies, are useful for monitoring and improving the quality of data. The current work aimed modeling the variable birth record with the living conditions of the Brazilian Semiarid, by logistic regression. Therefore, it made use of micro data from the Census 2010 taking into consideration to the item investigated , whether people aged up to 10 years old have had documents relating to their births. As a model, the variable record birth was considered a dependent, and nineteen socio-economic and demographic models as independent. Two data bases banks were built: Bank 1- that considered being bene ciary or having income monthly from the Social Program Bolsa Família or Child Labor Eradication Program, and Bank 2, without considering this variable. For both banks, the level of signi cance was p<0.05. For the Bank 1, the signi cant variables were housing (sanitary sewage and water), respondent education level (read), and purchasing power (machine, refrigerator, cell phone). For the Bank 2, the signi cant variables were, where the respondent lives (water), education level (read), monetary aspect (computer, bike, TV, fridge, phone) and demographic (age). It has been concluded that the birth registration can be modeled from the socio-economic variables and demographic speci ed. That is, in broader terms, people with less favorable conditions for the birth registration are those respondents with no education, with a low income and poor household conditions. With these results, it is expected to contribute for the strategies de nition and the promotion of public policies that reduce or eliminate the underreporting in semiarid States.
Registro de nascimento, Regressão quantílica, Birth Registration, Logistic Regression
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