The study published last month in the Journal of Urban Health shows that the child populations in some neighborhoods are not being tested as completely as they should be, said Khalifa Afane, a student in the M.S. program in data science who wrote the study with his advisor, Juntao Chen, Ph.D., an assistant professor in the computer and information science department.

For the study, they used the city’s publicly available lead testing data, which he said “nobody has analyzed before” at the neighborhood level.

A Toxic Heavy Metal

Lead is a toxic heavy metal that can cause learning disabilities and behavior problems. Children pick it up from lead-based paint or contaminated dust, soil, and water. Lead exposure risk “remains persistent” among vulnerable groups including low-income and non-Hispanic Black children, the study says.

The city promotes blood lead level testing and awareness of lead poisoning in high-risk communities through a variety of educational efforts and partnerships.

But some high-risk neighborhoods still don’t get enough testing, Afane said.  A case in point is Greenpoint in Brooklyn vs. South Beach in Staten Island. The study says that despite similar numbers of children and similar rates of lead testing, Greenpoint has consistently averaged eight times more cases—97 out of 3,760 tests conducted in 2021, compared to just 12 in South Beach that year (out of 3,720 tests).

There should actually be more testing of children in Greenpoint, Afane said, because their risk is clearly higher. While testing efforts have expanded in the city, he said, “it matters much more where these extra tests were actually conducted,” since lead is more prevalent in some neighborhoods than in others, he said.

More than 400 Cases May Have Been Missed

For the study, he analyzed test result data from 2005 to 2021, focusing on children under 6 years old who were found to have blood lead levels of 5 micrograms per deciliter. Afane applied a machine learning algorithm to the testing data and projected that another 410 children with elevated blood lead levels might be identified per year citywide, mostly in vulnerable areas, by expanding testing in neighborhoods that tend to have higher case rates.

The highest-risk neighborhoods are in Brooklyn, Queens, and the north shore of Staten Island, and average about 12 cases per 1,000 tests, compared to less than four in low-risk neighborhoods, Afane said.

The city helps coordinate care for children with elevated levels and also works to reduce lead hazards. Since 2005, the number of New York City children under 6 years old with elevated blood lead levels has dropped 93%, a city report says.

Using a Data-Informed Strategy

But the study recommends a better, data-informed, strategy to focus more lead testing on high-need areas. “What we wanted to highlight here is that this needs to be done and reported at the neighborhood level, not at the city level,” Afane said.

The study also recommends awareness campaigns in high-risk areas emphasizing early detection, and it calls on local authorities to step up monitoring of water quality and blood lead levels in pregnant women.

“Our main goal was to use data science and machine learning tools to genuinely improve the city,” Afane said. “Data analysis is a powerful skill that could be used much more often to make a positive impact in our communities.”

Their method for introducing machine learning in chemistry classes has been honored with the inaugural James C. McGroddy Award for Innovation in Education, named for a donor who funded the award’s cash prize. (See related story.)

The recipients are Elizabeth Thrall, Ph.D., assistant professor of chemistry; Yijun Zhao, Ph.D., assistant professor of computer and information science; and Joshua Schrier, Ph.D., the Kim B. and Stephen E. Bepler Chair in Chemistry. They will share the $10,000 prize, awarded in April.

Chemistry and Computation Come Together

The three awardees’ project shows how to reduce the barriers to learning about programming and computation by integrating them into chemistry lessons. The project came together during the COVID pandemic—since chemistry students were working from their computers, far from the labs on campus, it made sense to give them some computational projects, in addition to experiments they could conduct at home, Thrall said.

Because little had been published about teaching machine learning to chemistry students, she got together with Schrier and Zhao to design an activity. Zhao, director of the Master of Science in Data Science program at Fordham, involved a student in the program, Seung Eun Lee, GSAS ’22, who had studied chemistry as an undergraduate.

Their first classroom project—published in the Journal of Chemical Education in 2021—involves vibrational spectroscopy, used to identify the chemical properties of something by shining a light on it and recording which wavelengths it absorbs. Students built models that analyzed the resulting data and “learned” the features of different molecular structures, automating a process that they had learned in an earlier course.

For another project, the professors taught students about machine-learning tools for identifying possible hypotheses about collections of molecules. Machine learning lets the students winnow down the molecular data and, in Schrier’s words, “make that big haystack into a smaller haystack” that is easier for a scientist to manage. The professors designed the project with help from Fernando Martinez, GSAS ’23, and Thomas Egg, FCRH ’23, and Thrall presented it at an American Chemical Society meeting in the spring.

Thumbs-Up from Students

How did students react to the machine learning lessons? According to a survey following the first project, 63% enjoyed applying machine learning, and 74% wanted to learn more about it.

“I think that students recognize that these are useful skills … that are only going to become more important throughout their lives,” Thrall said. Schrier noted that students have helped develop additional machine learning exercises in chemistry over the past two years.

Machine Learning in Education and Medicine

Zhao noted the growing applications of machine learning and data science. She has applied them to other fields through collaborations with Fordham’s Graduate School of Education and the medical schools at New York University and Harvard, among other entities.

The McGroddy Award came as a surprise. “I don’t think that we expected to win,” Schrier said, “just because there’s so many other excellent pedagogical innovations throughout Fordham.”

Eva Badowska, Ph.D., dean of the Faculty of Arts and Sciences at the time the award was granted, said the professors’ “path-breaking interdisciplinary work has transformed lab courses in chemistry.”

There were 20 nominations, and faculty members reviewing them “were humbled by the creativity, innovation, and generative energy of the faculty’s pedagogical work,” she said.

In addition to the McGroddy Award, the Office of the Dean of Faculty of Arts and Sciences is providing two $1,000 honorable mention prizes recognizing the pedagogy of Samir Haddad, Ph.D., and Stephen Holler, Ph.D., associate professors of philosophy and physics, respectively.