How ISE students engineered a better way to get breast milk to babies in need
In North Carolina, there are more than 10,000 premature births each year. These fragile newborns need the best possible nutrition to grow and develop into healthy infants. Breast milk has life-saving antibodies that protect these preemies against disease and illness.
Ill premature babies sometimes cannot breastfeed and/or their mothers are unable to pump enough milk. For a baby in the neonatal intensive care unit (NICU), milk donations can be essential to life. This is where WakeMed’s Mothers’ Milk Bank steps in. It is a non-profit milk bank that provides 200,000 ounces of safe, pasteurized donor breast milk a year to babies in birthing centers, NICU’s, and hospitals in North Carolina and all along the East Coast.
One of those hungry babies was the child of ISE faculty member Dr. Natalia Summerville. “I have a personal interest and commitment to the organization since my baby was one of the lucky beneficiaries,” shared Summerville.
So when Summerville’s Data Analytics for Engineering class selected their projects that support a local non-profit through data analytics, she knew exactly whom to call. “I reached out and discussed some challenges that could be approached through data analytics with their director, Montana Wagner-Gillespie,” said Summerville. Wagner-Gillespie, a 2013 NC State graduate, jumped at the chance to work with the ISE Department. The class identified two problem areas, facility locations and culture testing.
The Facility Locations Project
Currently, Mothers’ Milk Bank has limited drop-off locations available to donors. If a drop-off site is not located near a donor, the milk bank pays for the shipping of the breast milk to their facility. This is quite an expensive process.
To increase the milk bank’s reach and to cut costs, the student team — Jennifer Breese, Diego Hernandez, Sean Murray, Drew Schell, and Conner Walker — developed a facility location mathematical model to determine the effectiveness of more drop-off locations.
The model used data collected by the milk bank that included donation history, donor location (Zip codes), and delivery method (shipped, dropped-off, etc.). With the use of SAS/OR Software, the team determined that the milk bank could open as many as 15 more drop-off locations and save more than $10,000 by doing so. An added benefit to the new locations is that they would increase awareness of the program and encourage others to take part.
“For a nonprofit with limited staff, the research the students did would never have been possible to do internally,” said Wagner-Gillespie. “They created a clean, easy to understand presentation that could be used to justify donor growth initiatives and new projects to senior leadership. Way to make a difference team.”
The Culture Testing Project
Before accepting any milk, WakeMed screens each donor for many factors including alcohol use, travel to Europe, and use of certain prescription medicines. Once they have received the milk, it goes through two different testing phases. The first is a pre-culture test in which WakeMed selects random samples and tests them for Bacillus — a pathogenic bacteria that affects premature, non-immunized or low-birth infants.
After the first test, they combine two to four donations into a batch. Then, all batches are tested for Bacillus during the second testing phase. If they find Bacillus, they must discard a large amount of milk. The goal of the student team was to streamline the process to save time and maximize the amount of milk distributed to those in need.
A positive result occurs when either a positive donation slipped through the initial pre-culture testing or a handler contaminated the milk during the pasteurization process. The student team — Jacob Green, Brycen Moser, Liesl Miranda, and Abhimanyu Shah — set out to determine the frequency of each and whether there was any correlation between particular donors and handlers result in a positive test.
Through the student team’s analysis, it was determined that the milk bank could save over $130,000 worth of discarded milk by pre culture testing every donation during phase one. The cost of the extra tests was only $7,828.50 compared to the $140,000 worth of discarded milk.
The student teams encouraged the milk bank to continue to update its models over the next couple of years to see if new trends and conclusions emerge.