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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Application of Low-Cost and Sustainable Solar Oxidation Treatment to Prevent Microbial Resistance in Effluents in Brazil

Camila AmaralUniversidade Federal de Minas GeraisBelo Horizonte, Minas Gerais, Brazil
Grand Challenges Brazil
Drug Resistance Burden
1 Nov 2018

This project will test a sustainable solar oxidation system as a way to remove antibiotic resistant bacteria from wastewater. The hypothesis is that this technology can enable the inactivation of antibiotic resistant bacteria and the elimination of antibiotic resistant genes from effluents in Brazil.

Monitor AMR in Community Uropathogens and Correlate Them with the Determinants of Resistance in Animal Enterobacteria Isolates

Eliana Carolina VesperoUniversidade Estadual de LondrinaLondrina, Paraná, Brazil
Grand Challenges Brazil
Drug Resistance Burden
1 Nov 2018

The project aims to monitor AMR in microorganisms of the urinary tract and correlate it with the genetic determinants of resistance in animal enterobacteria. The study results will be disseminated in order to inform potential changes to guidelines regarding selection of the appropriate antimicrobials first-line treatment for urinary tract infections (UTI).

Engineers, Pharmacists and Chemists Collaborating on the Development of an Aerobic Granular Sludge (AGS) to Remove Antibiotics from Hospital Wastewater

Leonardo MouraUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil
Grand Challenges Brazil
Drug Resistance Burden
1 Nov 2018

The project proposes to use an aerobic granular sludge (AGS) - a technology based on microbial community - to remove antibiotics and antimicrobial resistant genes from hospital wastewater. AGS is one of the latest innovations and it has not yet been applied for the treatment of hospital wastewater.

New Gestational Weight Gain Recommendations for the Brazilian Unified Health System (SUS)

Gilberto KacUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

Aims to validate the International Fetal and Newborn Growth Consortium for the 21st century (Intergrowth-21st) standards for gestational weight gain (GWG) and create new recommendations of GWG based on those standards for first trimester normal and overweight women to be used in the Brazilian Unified Health System (SUS). GWG recommendations currently used in SUS have not been properly tested or validated, thus the project might improve prenatal nutritional care and reduce post gestational weight retention.

Potential Pregnancy Days Lost (PPDL): An Innovative Gestational Age Measure to Assess Maternal and Child Health Interventions and Outcomes

Carmen DinizUniversidade de São PauloSão Paulo, São Paulo, Brazil
Grand Challenges Brazil
Data Science Approaches
1 Nov 2018

The main goal of the project is to develop and explore an innovative measure of gestational age - "potential pregnancy days lost" (PPDL) - to produce evidence of its association with maternal and child health, morbidity and mortality in the short, medium and long term. The indicator also aims to convince women and policy makers about the need to promote less interventions and "harm-free care" during pregnancy.

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.

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.

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