Knowledge Generation

Benjamin Fels and Suvrit Sra of Macro-Eyes, Inc. in the U.S. will engage with frontline health workers in immunization centers and combine their knowledge with existing supply chain and immunization data using machine learning to better predict vaccine demand and thereby improve immunization coverage. Vaccine supply levels in Ethiopia are predicted using data that may be inaccurate or outdated.

Samantha Dolan and Peter Rabinowitz of the University of Washington in the U.S., and Ian Njeru of I-TECH Kenya, will improve digital data collection and monitoring of childhood immunizations at Kenyan health facilities by optimizing workflows. Using electronic tools to track immunizations has the potential to improve the accuracy of data collection and reporting, identify children who have not been vaccinated, and free up time for health care workers. To fully realize this potential, workflow patterns need optimizing for different types of health facilities.

Sailendra Appanah of EnerGaia Bangladesh Ltd in Bangladesh will teach low-income women in rural Bangladesh to farm Spirulina, which is an edible protein- and nutrient-rich microalgae, to provide better nutrition and an income for them and their families. They have developed a low-cost Spirulina production system comprising closed tanks with filtered air and water inputs, and a business model that provides the farmers with a lease-to-own financing solution and guaranteed buyers of excess product.

This research aims to analyze the relationship between a conditional cash transfer program and the child's health, considering two generations of the families and using two different approaches: econometric analysis and data mining algorithms. By analyzing the long term impacts of Bolsa Familia program on future generations' health performance, the project will investigate if a child who was born in a family whose grandparents received the cash transfer is in better health conditions than a similar child born in a family whose grandparents did not receive the same benefit.

Infectious diseases may have only transitory impacts on pregnant mothers, but they can have lasting impacts on children. Can public interventions mitigate these impacts? This project aims to identify how exposure to localized epidemiological risk factors in the fetal period influences developmental outcomes for children through the early years of life. The researchers propose to evaluate in what extent the access to primary health care and social welfare programs mitigate negative impacts in child development.

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.

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.

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.

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.

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.