{"id":{"repo_id":"brazil-ufpb","oai_identifier":"oai:repositorio.ufpb.br:123456789/823"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-ufpb/oai:repositorio.ufpb.br:123456789/823","repository":{"repo_id":"brazil-ufpb","name":"Brazil UFPB","base_url":"https://repositorio.ufpb.br/oai/request"},"display":{"title":"Uso de regressão logística para identificar os fatores de risco associados à ocorrência de anomalias congênitas em recém-nascidos","abstract":"The logistic regression models have been extensively applied in several areas of knowledge, particularly in the area of health. The condition of studying binary response variables in terms of a set of explanatory factors have become increasingly common in epidemiological studies. Thus, this study aims to use a logistic regression model to investigate the risk factors associated with the occurrence of congenital malformation in children from a hospital in João Pessoa - PB. The results showed that age, mother's education, the use of corticosteroids during pregnancy, type of childbirth and APGAR measures 1 and 5 minutes were associated with the probability of birth of children with congenital abnormality. Furthermore, it was found that the adjusted model could correctly classify over 93% of the cases examined.","abstract_html":"The logistic regression models have been extensively applied in several areas of knowledge, particularly in the area of health. The condition of studying binary response variables in terms of a set of explanatory factors have become increasingly common in epidemiological studies. Thus, this study aims to use a logistic regression model to investigate the risk factors associated with the occurrence of congenital malformation in children from a hospital in João Pessoa - PB. The results showed that age, mother's education, the use of corticosteroids during pregnancy, type of childbirth and APGAR measures 1 and 5 minutes were associated with the probability of birth of children with congenital abnormality. Furthermore, it was found that the adjusted model could correctly classify over 93% of the cases examined.","abstract_has_math":false,"creators":["Souza, Lidia Dayse Araujo de"],"institution":"Estatística","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-11-03","date_published":"2014-11-03","updated_at":"2026-07-24T01:18:03Z","subjects":["Regressão logística","Modelo de regressão","Logistic regression","Regression model","Malformação congênita","Congenital malformation"],"languages":["pt"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repositorio.ufpb.br/jspui/handle/123456789/823","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Souza, Lidia Dayse Araujo de"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-11-03T20:17:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-03T20:17:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-11-03"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Estatística"]},{"key":"dc:type","label":"Dc Type","values":["TCC"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Regressão logística","Modelo de regressão","Logistic regression","Regression model","Malformação congênita","Congenital malformation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["pt"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repositorio.ufpb.br/jspui/handle/123456789/823"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The logistic regression models have been extensively applied in several areas of knowledge, particularly in the area of health. The condition of studying binary response variables in terms of a set of explanatory factors have become increasingly common in epidemiological studies. Thus, this study aims to use a logistic regression model to investigate the risk factors associated with the occurrence of congenital malformation in children from a hospital in João Pessoa - PB. The results showed that age, mother's education, the use of corticosteroids during pregnancy, type of childbirth and APGAR measures 1 and 5 minutes were associated with the probability of birth of children with congenital abnormality. Furthermore, it was found that the adjusted model could correctly classify over 93% of the cases examined."]},{"key":"dc:title","label":"Title","values":["Uso de regressão logística para identificar os fatores de risco associados à ocorrência de anomalias congênitas em recém-nascidos"]}]}],"canonical_facts":{"dc:creator":["Souza, Lidia Dayse Araujo de"],"dc:date.accessioned":["2014-11-03T20:17:36Z"],"dc:date.available":["2014-11-03T20:17:36Z"],"dc:date.issued":["2014-11-03"],"dc:description.abstract":["The logistic regression models have been extensively applied in several areas of knowledge, particularly in the area of health. The condition of studying binary response variables in terms of a set of explanatory factors have become increasingly common in epidemiological studies. Thus, this study aims to use a logistic regression model to investigate the risk factors associated with the occurrence of congenital malformation in children from a hospital in João Pessoa - PB. The results showed that age, mother's education, the use of corticosteroids during pregnancy, type of childbirth and APGAR measures 1 and 5 minutes were associated with the probability of birth of children with congenital abnormality. Furthermore, it was found that the adjusted model could correctly classify over 93% of the cases examined."],"dc:identifier.uri":["https://repositorio.ufpb.br/jspui/handle/123456789/823"],"dc:language.iso":["pt"],"dc:publisher.department":["Estatística"],"dc:subject":["Regressão logística","Modelo de regressão","Logistic regression","Regression model","Malformação congênita","Congenital malformation"],"dc:title":["Uso de regressão logística para identificar os fatores de risco associados à ocorrência de anomalias congênitas em recém-nascidos"],"dc:type":["TCC"]},"updated_at":"2026-07-24T01:18:03Z"}