Jan Blustein is a Professor of Health Policy and Medicine at NYU’s Wagner School of Public Service, and an Affiliated faculty member at the Center for Data Science. Her work takes a data-driven approach to studying the intersection of social science & policy with early childhood development.
What did you study in school? How did you get to what you study now?
I studied medicine, but I’ve always been interested in social science and policy. After I finished medical school, I worked in government, and then eventually made it to NYU, where I got a Ph.D. at the Wagner School.
When did you start to incorporate data science into your research?
I’ve always been interested empirical work rather than theory – and that kind of work requires data.
Could you tell me about some of the projects you’re working on?
I have a couple of projects right now. One is on cesarean delivery, and its consequences for child health. With colleagues at NYU medical school and Peking University, I’ve just completed a study of cesarean delivery in China, and we found that the number of cesareans was considerably lower than previously thought, however, the geographic variations were remarkable. That study is based on over 1 million births!
How has the way in which you use data science in the field of public health changed over the years?
The size of datasets used to be a big issue, but it no longer is.
Could you talk about how you’re using data science to look at correlations between perinatal exposures and child chronic disease?
Much of my epidemiologic work draws on surveys that follow subjects over time. For instance, along with colleagues at NYU medical school (Leo Trasande, Martin Blaser and Teresa Attina), I looked at correlations between exposure to antibiotics early in life, and later life obesity. We analyzed a British study that followed ca. 12,000 children from birth to age 21 years. We found that early life (0-6 months) exposure to antibiotics was associated with a higher risk of obesity later. We did not find similar associations for exposure to antibiotics later in early childhood (6-12 months, 12-18 months).
While the epidemiological work was correlational, it is consistent with controlled evidence from Dr. Blaser’s laboratory, where mice exposed to antibiotics in the days after birth are at greater risk for adiposity in later life. Mice exposed later in early childhood do not have the same elevated risk.
This work (along with other evidence) suggests that in very early life the bacterial ecology is especially fragile. Perhaps we should be especially careful in using antibiotics with very young children. This is an area of active research among clinicians, bench scientists, and public health researchers.
Are there any areas of your field that aren’t being impacted by data science that should be?
I’m sure there are. There’s no shortage of data out there. The real challenge I think is not so much the technical challenge, as the imperative to analyze thoughtfully and knowledgeably.