The winners of the National Institutes of Health's Decoding Maternal Morbidity Data Challenge were announced today in conjunction with the White House "day of action" on maternal health. Twelve prizes were awarded to seven winners who proposed innovative solutions to identify risk factors in first-time pregnancies. Without a prior pregnancy for comparison, it is difficult to identify risks for adverse pregnancy outcomes. Early detection of these risks can help reduce pregnancy complications and prevent maternal deaths.

Any maternal death is one too many. A healthy pregnancy and childbirth should be a given, but sadly, it's not. Understanding and reducing pregnancy-related complications and deaths — or maternal morbidity and mortality — is a high priority for NIH."

Diana W. Bianchi, M.D., Director, NIH's Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

In the United States, more than 700 women die each year from pregnancy complications or giving birth, according to the Centers for Disease Control and Prevention. Another 50,000 women experience life-threatening complications that are considered "near misses" for maternal death, sometimes causing serious, long-term health problems. The CDC estimates that Black women are three times more likely to die from a pregnancy-related cause than white women. Women over age 35 years or those residing in rural areas are also at higher risk.

Last year, NIH spent an estimated $224 million in research funding to prevent maternal morbidity and mortality. While most of these funds went toward traditional funding mechanisms, challenges offer unique opportunities to stimulate ideas and solutions. The Decoding Maternal Morbidity Data Challenge prizes totaled $400,000. Seven prizes of $50,000 were awarded for innovation, and five additional prizes of $10,000 were awarded for addressing health disparities.

All the proposals analyzed participant data from NICHD's Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), a racially, ethnically and geographically diverse sample of people who are pregnant for the first time. NuMoM2b was established in 2010 and has compiled data on more than 10,000 pregnant women, including research data beginning in the sixth week of pregnancy and continuing through delivery.

"The winning teams developed methods to analyze the data, accurately flagging cases that were high-risk for complications," said NICHD's Maurice Davis, D.H.A., who managed the challenge. "These computational methods can now be used to analyze additional data from other pregnancies. These solutions have the potential to make a real difference and save lives."

The team leads for each of the winning proposals are as follows (asterisks denote winners of both prize categories):

Nicole Carlson, Ph.D.*
Emory University, Atlanta

Ali Ebrahim, Ph.D
Delfina, San Francisco

Britnee Johnston*
Johnston and Company, LLC, Salt Lake City

Monica Keith, Ph.D.*
University of Washington, Seattle

Yaping Li
Feng Ya, LLC, Watkinsville, Georgia

Ainesh Pandey*
IBM Data Science and AI Elite, San Francisco

Ansaf Salleb-Aouissi, Ph.D.
Columbia University, New York City

For more information about the challenge and a list of all team members, please visit: https://www.nichd.nih.gov/research/supported/decodingmmdatachallenge.