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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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Initiatives: Grand Challenges Brazil

The Role of Social Media, Bolsa Família Program and Primary Health Care in Vaccination Coverage for Children Under Five in Brazil

Patricia Boccolini, Centro Universitário Arthur Sá Earp Neto (Petrópolis, Rio de Janeiro, Brazil)
Feb 4, 2021
Grand Challenges Brazil> Data Science Approaches

This project aims to understand and analyze the determinants of vaccination coverage in the Brazilian territory by assessing its association with socio-economic factors, public health spending, coverage of primary health care and the Bolsa Família Program and the influence of patterns of content dissemination on immunization on social media. The results will be disseminated through virtual games, podcasts, interactive panels, infographics, an e-book for municipal managers, a webinar for undergraduate students in the health field and a seminar on World Immunization Day.

Accessibility to Services: Proximity to Health Facilities and the Importance of Adequate Care in Reducing Maternal and Child Mortality from Preventable Causes

Valdemar Neto, Fundação Getúlio Vargas (Rio de Janeiro, Rio de Janeiro, Brazil)
Jan 5, 2021
Grand Challenges Brazil> Data Science Approaches

This project aims to demonstrate the importance of adequate health care for pregnant women in health facilities and its effects on maternal and child mortality. The project will also measure the causal effect of the distances taken for pregnant women to access health facilities and the health care provided, which will be useful to identify the gaps in maternal health care and to drive decisions on resources allocation and prioritized actions. A new database will be created for monitoring of access to public health facilities, considering distances and real-world accessibility conditions.

Evaluation of the Use of Long-Lasting Insecticide-Impregnated Nets (LLINs) as a Strategy to Control Malaria Transmission and Its Implications, in Five Brazilian Amazon States

Iziz Monica Sucupira, Instituto Evandro Chagas (IEC) (Belém, Pará, Brazil)
Dec 21, 2020
Grand Challenges Brazil> Malaria Elimination

This study intends to evaluate the use of long- lasting insecticide-impregnated nets (LLINs) and their implications in a municipality of five Brazilian Amazonian states. Brazil has been using LLINs as a supplementary control tool for over 10 years and during this period many questions regarding its effectiveness were raised. Therefore, the present project aims to verify if the distribution strategy was accompanied by specific information, such as individual or group orientations, calendar provision, explanation about using, washing and caring in order to assess if there is a need to adapt this strategy to the population’s habits, culture and education, to the local dynamics of malaria transmission and environmental factors.

Make Childbirth Interventions More Visible Through Information Systems

Carmen Simone Diniz, Universidade de São Paulo (São Paulo, São Paulo, Brazil)
Dec 18, 2020
Grand Challenges Brazil> Data Science Approaches

This project will link the SINASC, SIM, SIGA and SIH databases to assess the role of interventions in childbirth and will also analyze the avoidability of neonatal and maternal outcomes. It will develop guidelines and training for the classification of “onset of childbirth”. The group will use the Robson classification, map the disruptions of the pandemic to perinatal care, and will explore the role of maternal nutritional status on intervention rates in childbirth and on maternal and neonatal results, making the databases available for research and for training purposes.

Use of Real-World Data and Machine Learning to Monitor Maternal Mortality and Severe Maternal Morbidity

Rosa Maria Domingues, Instituto de Pesquisa Clínica Evandro Chagas (Rio de Janeiro, Rio de Janeiro, Brazil)
Dec 15, 2020
Grand Challenges Brazil> Data Science Approaches

In Brazil, the only information system that provides data on maternal morbidity is the Hospital Information System (SIH), but there are difficulties in implementing the criteria recommended by WHO and doubts about the quality of information. The project will validate the SIH against the Maternal Near Miss criteria and will build and validate an algorithm to identify severe maternal morbidity. As a product, it will develop an online indicator panel for the surveillance of maternal health to be used by SUS managers.

Breastfeeding in Brazil in the MATRECI Model: Mapping, Trend, Clustering and Impact

Cristiano Boccolini, Fiocruz (Rio de Janeiro, Rio de Janeiro, Brazil)
Dec 11, 2020
Grand Challenges Brazil> Data Science Approaches

The objective of this project is to map the implementation and evolution of breastfeeding initiatives in the scope of primary health care (PHC), assessing their spatial and temporal distribution patterns and their correlation with the evolution of the indicators. The team will map the successful cases of pro-breastfeeding programs and identify their impact. A single dataset containing information on PHC infrastructure and breastfeeding rates and programs will be generated, as well as the qualification and validation of SISVAN breastfeeding information.

"Mind The Gap" - Identifying Family Poverty Profiles and Designing Scenarios to Overcome Child Poverty in Post Pandemic Years

Dandara Ramos, Universidade Federal da Bahia (Salvador, Bahia, Brazil)
Dec 8, 2020
Grand Challenges Brazil> Data Science Approaches

The project will identify the poverty profiles of families with children under the age of five enrolled in the Single Registry considering data from the Family Development Index, the Brazilian Deprivation Index and the Municipal Human Development Index. It will assess the amount needed to overcome the families' poverty gap and also estimate the mortality rates in each of these profiles. The results will be disseminated through dashboards, forecasting scenarios for stakeholders and a workshop with stakeholders from the Ministry of Citizenship and stakeholders. The project will also provide forecast scenarios for COVID-19 associated crises with a high level of granularity.

Predictive Factors of Response to Treatment Offered by SUS in Women Crack Cocaine Users: Epigenomic Analyses and Supervised Learning Algorithms

Thiago Viola, Pontifícia Universidade Católica do Rio Grande do Sul (Porto Alegre, Rio Grande do Sul, Brazil)
Dec 4, 2020
Grand Challenges Brazil> Data Science Approaches

This project will identify clinical, sociodemographic, psychosocial, neurocognitive and epigenomic factors to assist in the identification of the most effective response to the treatment offered by SUS to detoxify the use of crack and cocaine by women. The project will use the Random Forest algorithm in a database developed by the research group itself in order to predict the factors that impact adherence and maintenance of abstinence among users.

AMPLIA SAÚDE - Pre- and Perinatal Period Observatory

Doris Kosminsky, Universidade Federal do Rio de Janeiro (Rio de Janeiro, Rio de Janeiro, Brazil)
Dec 3, 2020
Grand Challenges Brazil> Data Science Approaches

This project will produce a consolidated database, which aggregates available data on emerging diseases (Zika and COVID-19); external climatic conditions (droughts and floods) and environmental problems (disasters, fires, pollution) as risk factors for unfavorable obstetric and neonatal outcomes. The database will enable the production of information on maternal and early neonatal morbidity and mortality in an accessible way.

Brazilian Obstetric Observatory

Agatha Rodrigues, Universidade Federal do Espírito Santo (Vitória, Espírito Santo, Brazil)
Dec 3, 2020
Grand Challenges Brazil> Data Science Approaches

The objective of this project is to create an obstetric observatory through an interactive platform for monitoring, analyzing public data and disseminating information in the area of ​​Obstetrics in Brazil. It will provide exploratory data analysis with the purpose of assessing the impacts of the H1N1 (2009) and COVID-19 (2020) pandemics on maternal, fetal and neonatal health. A book entitled on the subject will also be produced and made available free of charge.

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.