Awarded Grants

Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

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Early Childhood Development Friendly Index: Assessing the Enabling Environment for Nurturing Care in Brazilian Municipalities

Muriel GubertUniversidade de BrasíliaBrasília, Distrito Federal, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

The study aims to develop an Early Childhood Development friendly index (ECD-FI) based on a core set of evidence-based nurturing care indicators to assess the factors contributing to enabling environments and promote ECD at the municipal level by monitoring and identifying opportunities to scale up ECD programs. The index will be created through machine learning and will run analytical models considering demographic information and risk factors at the municipal level. This disaggregated data is not available in Brazil.

How and When: Disentangling Cash and Care Effects of Conditional Cash Transfers on Birth Outcomes

Cecilia MachadoFundação Getúlio VargasRio de Janeiro, Rio de Janeiro, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Seeks to understand the impacts of the Bolsa Família conditional cash transfer on birth outcomes (e.g., birth weight, gestational weeks, etc). The proposed design will disentangle the measured effects into two components: one that is associated to the cash transfer; and another related to prenatal care assistance. Moreover, this strategy will allow the researchers to determine the window of opportunity where CCT interventions exhibit highest impacts on birth outcomes, recognizing heterogeneous impacts according to how early in the pregnancy the CCT intervention starts.

Decision-Making Support Platform Based on Visual Analytics and Machine Learning to Subsidize Public Politics Focused on Gestational Health

Tiago CarvalhoInstituto Federal de São PauloCampinas, São Paulo, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

The project will develop a platform to provide services for decision-making support for neonatal death preventive actions by using data from CIDACS cohort. The platform will offer three services: cohort data visualization for decision-making support by comparative human visual analysis, prediction of risk of neonatal death based on machine learning models, and simulator of public policies impact influencing on the risk of neonatal death.

Assessing the Impact of Hospital-Based Breastfeeding Interventions on Infant Health

Cristiano BoccoliniFiocruzRio de Janeiro, Rio de Janeiro, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Aims to access all 68.3 million living births certificates from Brazil, from 1994 to 2016, and compare them with breastfeeding policies in all Brazilian hospitals to assess the impact of the initiatives on infant health. The study also plans to estimate the number of avoidable deaths during this time period, if those initiatives were adopted in Brazil.

Spatial Analysis of Child Vaccination Coverage and its Relation to Socioeconomic and Health Characteristics in Brazil

Carolina BarbieriUniversidade Católica de SantosSantos, São Paulo, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

By analyzing national children vaccination coverage from spatial perspectives, the study aims to uncover insights into the traditional surveillance. This will help to identify coverage rates, regions of greater vulnerability by providing a differentiated look at the logic of equity in health. Understanding the low childhood vaccination coverage will help to guide public policies for the purpose of interventions.

Data Science to Inform the Design and Evaluation of Interventions to Improve Perinatal Outcomes: Lessons from the Mãe Coruja Program

Jailson CorreiaMunicipal Health SecretariatRecife, Pernambuco, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

The study is aimed at evaluating the effectiveness of Mãe Coruja intervention in reducing low birthweight and preterm birth. By using appropriate statistical methods, the study will use the Cidacs dataset combined with the data from Mãe Coruja program to carry out the quasi-experimental study. With the support of machine learning techniques, the project will also Identify social, economic, geographic and environmental conditions that are associated with the outcomes. The researchers will also build an index of perinatal health risk to inform improvements in targeting populations and the deployment of similar strategies and programs elsewhere in Brazil.

Influenza in Pregnancy and Birth Outcomes in the Brazilian Semi-Arid Region: the INFLUEN-SA Study

Aldo LimaUniversidade Federal do CearáFortaleza, Ceará, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Studies show that seasonal influenza in Ceará, in the Northeast region of Brazil, occurs 2 to 3 months earlier than in the South and Southeast, which guides the national calendar of vaccination. By using data science approaches, the study will test if Brazil's current national policy targeting vaccination only during the months of April and May inadequately protects against the harmful maternal-fetal effects of influenza in the Semi-Arid and northern regions of Brazil. If the hypothesis confirms, the study has the potential to change policy and modify the vaccination calendar.

Using the 100M Cohort to Establish Critical Air Pollution Thresholds for Safe Childbirth in Brazil

Alexandra BrentaniUniversidade de São PauloSão Paulo, São Paulo, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Does air pollution affect the rates of stillbirths, congenital malformations and neonatal mortality? This study aims to answer this question by merging the child health data collected within the 100 Million Brazilian Cohort from Cidacs with high-resolved satellite-derived data on air pollution to establish critical ambient air pollution thresholds for preventing adverse birth outcomes and malformations based on concentrations of fine particles, PM 2.5.

Use of Interactive Infographic in the PMCP - Analysis of Indicators to Improve the Quality of Maternal and Child Health

Judith KelnerUniversidade Federal de PernambucoCaruaru, Pernambuco, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

The proposal will develop a platform for the analysis and visualization of data that will allow managers, public servants and other stakeholders involved in the Mãe Coruja Program at Pernambuco state (PMCP) to extract strategic information to improve the intervention. The focus will be on the implementation and actual enforcement of public policies, considering the high gestational risk and sexually transmitted infections (STI). Currently, health databases are for consultations only. The innovation of this proposal is to create an intelligent cloud platform for the analysis and distribution of health information to improve health care of women enrolled in PMCP.

Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care

Erika ThomazUniversidade Federal do MaranhãoSão Luiz, Maranhão, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Identifying the preventable causes and performing early risk stratification of pregnant women are instrumental to develop strategies to prevent and reduce preterm birth (PTB). The ability to identify at-risk pregnancies and to enroll women in prevention strategies has been difficult due to complexity of associated risk factors. The study aims to combine different national level data sources to understand the main predictors of PTB and develop a machine-learning-based predictive model to conduct automated risk stratification at the point of care level, integrated with advanced data visualization for clinical decision support.

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