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Restructuring and redesigning the Maternal and Perinatal Care Network in the State of São Paulo to reduce the maternal, fetal, and neonatal mortality ratio (ReMaP)

Grant number: 23/10075-0
Support Opportunities:Research Grants - Research in Public Policies
Duration: March 01, 2024 - February 29, 2028
Field of knowledge:Health Sciences - Medicine - Maternal and Child Health
Principal Investigator:Rossana Pulcineli Vieira Francisco
Grantee:Rossana Pulcineli Vieira Francisco
Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated researchers: Agatha Sacramento Rodrigues ; Alexandra Valéria Maria Brentani ; Catia Martinez Minto ; Cristiane de Freitas Paganoti ; Daniel Lorber Rolnik ; Edmund Chada Baracat ; Fabricio da Silva Costa ; Fernanda Spadotto Baptista ; Marco Aurelio Knippel Galletta ; Maria de Lourdes Brizot ; Mario Henrique Burlacchini de Carvalho ; Rafaela Alkmin da Costa

Abstract

High values of maternal mortality ratio (MMR) and infant mortality are indicators of precarious socioeconomic conditions, low schooling and difficulty in accessing quality health services, constituting important goals for achieving sustainable development among countries. In 2015, the Sustainable Development Goals (SDGs) agenda was consolidated. This agenda has in its Objective 3 "to ensure a healthy life and promote well-being for all, at all ages". In order to adapt the global indicators of the 2030 Agenda to the Brazilian reality, the Federal Government, in partnership with the Institute of Applied Economic Research (Ipea), proposed for target 3.1, to reduce the MMR to a maximum of 30 deaths per 100,000 live births and how target 3.2. reduce neonatal mortality to a maximum of 5 per thousand live births by 2030. Achieving these goals requires political, scientific, and strategic effort. One of the strategies is to allow access to public data in a structured and responsible way so that society has access to information, that public managers can make decisions based on evidence and that discussions about public policies are based on reliable data, a need that in the Obstetrics becomes evident by the difficulty we have faced in reducing mortality in this population. The main objective of this project is to achieve a reduction in maternal, fetal and neonatal mortality, through analysis, redesign and monitoring of the maternal and perinatal care network (scientific contribution to the management of public policies) carried out by researchers specialized in the field of health sciences. Data, epidemiology and maternal-infant care in joint work with managers from the state health department. This project has as its predominant characteristic of the public management process, the public policies in execution (PEX) within the scope of the maternal and perinatal care network of the State of São Paulo, a government program established by the Ministry of Health and by the Secretary of State for São Paulo Health. Despite being a consolidated program, the difficulties observed for the reduction of maternal, fetal and neonatal mortality in the State of São Paulo, make this project of paramount importance since we see opportunities for improvement in this line of care, with the construction of instruments for monitoring and analysis of the different initiatives, as well as the possibility of redesigning, adapting and qualifying the network, based on data analysis. The following databases will be used: SINASC (National System on Live Births), SIM (Information System on Mortality), CNES (National Register of Health Establishments), SIHSUS (SUS Hospital Information System), IBGE data (Census, PNAD and PNADC), in addition to data on regulation and plans of Rede Cegonha for each of the RRAS. These bases will be treated and loaded using the ETL flow (extract, transform, load) and the analyses will be performed using the open programs R and Python. To integrate the information, similarity algorithms will be applied between the identified data, either by some key variable or by means of a probabilistic model. In the association and prediction analyzes, supervised and unsupervised machine learning models and algorithms will be considered. Monitoring panels with real-time updates will also be built, available to managers and the entire population. Free software will be used and the documentation of each analysis will be disclosed, allowing transparency of reproducibility. In the FAPESP PPPP 2023 call, we found an ideal scenario to present this project (ReMaP), which will give us a unique opportunity to bring together researchers with experience in this type of data analysis and public management to modify these indicators in the state of São Paulo. (AU)

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