Maternal, Newborn, and Adolescent Health

Lucas Malla of the Kemri-Wellcome Trust Research Program in Kenya will apply a novel statistical method to determine how the timing and rates of gestational weight gain during pregnancy affect maternal and child health in Africa to identify risk factors and those at highest risk. Excessive or insufficient gestational weight gain can predict adverse maternal and child health outcomes such as gestational diabetes, preterm birth, and infant mortality.

Geoffrey Arunga of BroadReach in Kenya will develop a digital assessment tool to identify women with the highest risk of maternal morbidity and adverse pregnancy outcomes, and their causes, and to inform clinicians and health policies to improve maternal health and survival. They will apply advanced statistical analyses and machine learning techniques to clinical, social, and economic data from an existing longitudinal study of pregnant women in West Africa to identify the data that can best predict risk.

Christopher Seebregts of Jembi Health Systems NPC in South Africa will use a data science approach to improve maternal, newborn, and child health by developing algorithms that integrate diverse personal and clinical data taken from disparate sources to make them more informative. To test their approach, they will apply it to existing data taken in the Tshwane health district in South Africa for a program looking to prevent mother-to-child transmission of HIV.

Naeemah Abrahams of the South African Medical Research Council will study the impact of gender-based violence on the health of mothers and children in South Africa to better inform prevention and health strategies. Gender-based violence affects one in three women globally, and rates are high in Africa, particularly for women of reproductive age. However, the effects on their health remain largely unknown, in part because of the lack of follow-up studies.

Said Mohammed Ali from the Ministry of Health in Tanzania will use machine learning to develop a model for predicting gestational age based on routinely collected data and use it to better define when a birth should be classified as premature in Africa and thereby at higher risk of neonatal death. Premature births in Africa are currently defined as those occurring before 37 weeks. However, data suggests that births occurring just after this cut-off are also at higher risk of neonatal death, suggesting that the definition needs reevaluating.

Joseph Akuze Waiswa of Makerere University College of Health Sciences, School of Public Health in Uganda will leverage their dataset on pregnancy outcomes collected by household survey in four African sites to improve the quality of data on stillbirth and neonatal death rates to optimize interventions and investments. Around half the global burden of stillbirths and neonatal deaths occurs in Africa. However, the total numbers are derived from household surveys, and the quality of the data is relatively poor, with missing events and misclassifications of the cause of death.

Sikolia Wanyonyi of Aga Khan University in Kenya will analyze datasets on maternal mortality during hospital deliveries to determine the causes and to develop prediction models to help identify effective interventions in specific settings. Maternal mortality rates in Kenya are only slowly reducing, despite the increase in hospital deliveries, which may be due to a combination of different factors such as the quality of care and clinical characteristics.

Angela Dramowski of Stellenbosch University in South Africa will determine the most effective antibiotics to use and when to use them for treating bloodstream infections in neonates to reduce mortality rates. In Sub-Saharan Africa, a quarter of a million deaths in children under five are caused by bacterial infections during the first 28 days of life. The causal bacteria and their susceptibility to antibiotics change over time and across different regions, thus standard treatment guidelines are likely outdated.

Adeladza Kofi Amegah of the University of Cape Coast in Ghana will investigate how diet, the environment and low birth weight lead to child undernutrition in socially-disadvantaged communities in West Africa. Studies have shown strong associations between socio-economic status and child undernutrition, but they have not identified the actual causes, which is critical for developing effective interventions.

Anthony Ngugi of Aga Khan University in Kenya will use a modeling approach to determine the optimal allocation of limited child nutrition budgets that will most effectively reduce mortality and morbidities, like stunting and anemia, caused by malnutrition. They will use the Optima Nutrition modeling tool, which combines cost functions with an epidemiological model, to make predictions about the cost-efficacy of different funding allocations, for example on food fortification or education.