The National Institutes of Health (NIH) announced it will award up to $400,000 to individuals or groups who design an effective method for analyzing a large data set of first-time pregnancies and identifying risk factors for adverse outcomes, such as hypertensive disorders, diabetes and infection.
A total of $50,000 will be awarded to each of seven winners designing the most effective means to analyze the data. An additional $10,000 will be awarded to the top five winners whose methods identify risk factors in disadvantaged populations.
The Decoding Maternal Morbidity Data Challenge will be administered by NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Using computational analysis, data mining, artificial intelligence and other methods, winning entrants will need to devise ways for analyzing the vast store of participant data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), a racially, ethnically and geographically diverse sample of people beginning in the sixth week of pregnancy and continuing through delivery. NuMoM2b was established in 2010 and has compiled data on more than 10,000 pregnant women. The data were collected from interviews, questionnaires, clinical measurements, patient charts and biological specimens. NuMoM2b aims to identify pregnancy risks for women who have not given birth previously.
“Without a prior pregnancy for comparison, it is difficult to identify risks for adverse pregnancy outcomes,” said Maurice Davis, D.H.A., of the NICHD Pregnancy and Perinatology Branch, who is overseeing the challenge. “NuMoM2b has provided important information on the health needs of this unique population, and we look to the Decoding Maternal Morbidity Data Challenge to identify even more effective ways to harness these powerful data.”
Pregnancy complications or morbidity may result from conditions women have before pregnancy or develop during pregnancy. It is difficult to estimate the effects of pregnancy complications on maternal and newborn outcomes because they encompass a broad range of conditions that vary in severity. According to the U.S. Centers for Disease Control and Prevention, severe maternal morbidity includes unexpected outcomes of labor and delivery that result in significant short- or long-term consequences to a woman’s health.
The Decoding Maternal Morbidity Data Challenge seeks to address maternal mortality and morbidity by focusing on its underlying causes, such as obesity, mental health issues and substance abuse disorders. Limited access to health insurance and healthcare are also contributing factors.
Because maternal mortality and morbidity affect Black and Indigenous/Alaskan Native women at a much higher rate than other groups, applicants are encouraged to develop methods addressing the needs of these communities.