Título: A Bayesian network-based approach for the Brazillian birth care system
Palestrante: Thais Cristina de Oliveira da Fonseca (DME - UFRJ)
Data: 27/10/20
Horário: 15:30h
Local: Transmissão online
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Resumo: This work investigates the causes of high rates (up to 88%) of the cesarean section (CS) in hospitals in Brazil. Evidence indicates that rates over 10-15% are correlated with maternal death, morbidity and near death. The usual approach to relate factors and outcome in the birth network is based on regression that do not allow for cause-effect inference. I propose a novel approach based on Bayesian networks to capture both non-linearities and complex cause-effect relations. The proposed network integrate both the knowledge from experts to elicit the graph structure and data of 12 hospitals (7200 women) to estimate model parameters. The theoretical birth network, even though described in papers in the area of public health, has not been mathematically constructed and confirmed by data. In particular, a quality improvement intervention called “Adequate Birth” (PPA) will be analyzed. The PPA was a pioneer project to reshape the birth care system in Brazil. The main results presented are (i) comprehensive guidelines to decrease CS rates depending on the estimated Bayesian network, (ii) integration of factors in a full model which will be tested using data obtained from the PPA intervention, (iii) query analysis based on changes in the system, (iv) a tool for policymakers aiming to optimize the cost-effectiveness of future interventions.
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