Can your neighborhood affect your health? Data science says yes
Faculty Interview: Dustin Duncan
Dustin Duncan is an Affiliated Faculty member at the Center for Data Science, an Assistant Professor at NYU’s Department of Population Health, and the Principal Leader at the Spatial Epidemiology Lab. His work focuses on the intersection of of public space and personal health, backed by a fundamental understanding of data science.
What did you study in school? How did you get to what you study now?
As an undergraduate student at Morehouse College, I majored in Psychology and minored in Public Health Sciences. I completed my master’s work at the Harvard T.H. Chan School of Public Health (HSPH), and went on to complete my doctorate in Social Epidemiology at HSPH, in addition to being an Alonzo Smythe Yerby Postdoctoral Fellow at HSPH from 2011 to 2013.
How did you become interested in social epidemiology?
I became interested in studying the social epidemiology of neighborhoods during my master’s program, when I was a research assistant at Dana-Farber Cancer Institute’s Center for Community-Based Research (DFCI). At DFCI, I co-authored a paper demonstrating that perceiving one’s neighborhood as unsafe is associated with reduced walking among urban predominantly racial/ethnic minority low-income adults. This was my first published peer-reviewed paper, and was published in PLoS Medicine.
In spatial epidemiology, are you using data science to look at the ways in which health outcomes are affected on a micro-level (such as disease mapping and clustering), or is it more of a macro view (which areas are most likely to be affected)?
My research embraces a social ecological perspective, interrogating the impact of different “levels” on people’s health. However, the majority of our work is at the community level, where we investigate how neighborhood characteristics can influence population health and health disparities in vulnerable populations predominantly in urban environments.
Can you talk about the history of the field of spatial epidemiology and how it has evolved over time with advancements in statistics and data collection?
Social scientists have long recognized the salience of context in health. For instance, Louis René Villermé, a noted French physician and statistician, studied neighborhood effects on health in Paris. In 1830, he published a paper examining mortality patterns in different Parisian neighborhoods, and found that observed differences in death rates were highly correlated with the degree of poverty in a given neighborhood. Sadly though, over the years, research has been primarily focused on biomedical individualism (i.e. individual-level factors). But there has been a resurgence of interest in context as it relates to health, which stems from an increasing appreciation for recognizing that a myriad macro-social factors are important to health.
Recently, the field of spatial epidemiology has seen data collection shifts stemming from technological advancements and the use of newer statistical methods. My colleagues and I are using GPS technology to define more realistic views of neighborhood contexts called “activity space neighborhoods.” My work has argued that, compared to static administrative boundaries — such as ZIP codes and census tracts — egocentric and GPS-defined neighborhoods are the best methods in defining neighborhood contexts.
What sorts of public health problems are you trying to study?
My lab, the Spatial Epidemiology Lab (www.spatialepilab.org), employs a geospatial lens in studying health behaviors and outcomes, especially obesity, hypertension, type 2 diabetes, drug abuse, and HIV/AIDS.
Can you give a couple of examples of specific projects?
With funding from NYU’s Center for Drug Use and HIV Research, and My Brother’s Keeper, my colleague Dr. DeMarc Hickson — from the Jackson State University School of Public Health — and I are currently conducting a study to examine the feasibility of obtaining GPS spatial behavior data among a sample of approximately 100 black men who have sex with men in metropolitan centers in the Southern United States.
Also, I recently received a National Institute for the Humanities award for a project that uses advanced GPS methods to understand how certain neighborhoods influence HIV outcomes in New York City. The study will use real-time geospatial methods to investigate mobility across neighborhoods and how this affects HIV risk among young men who have sex with men.
When did you start to incorporate data science into your research?
Data science has been an increasingly important element to my research for some time now. In my studies of neighborhoods and health, for example, we have utilized novel data sources such as Walk Score, Grindr and electronic health records.
How are you using these data sources?
Walk Score’s web-based algorithm calculates a score of walkability based on distance to various categories of amenities (e.g. schools, stores, parks, and libraries). We’ve documented associations between Walk Score and cardiometabolic outcomes. That is to say, living in a more walkable neighborhoods is associated with increased walking in neighborhoods, less body mass index, less waist circumference, less systolic blood pressure, less diastolic blood pressure and a lower resting heart rate.
Electronic health records can be utilized to collect objectively measured clinical health data, as a way of correcting errors and biases associated with self-reported survey measures. Because electronic health records have address data, researchers can geocode that information to estimate neighborhood-level factors to link to clinical health outcomes. In a recent study, we examined the association of walkable built environment characteristics with body mass index (BMI) among a large sample of children and adolescents. We found that built environment characteristics that increase walkability were associated with a lower BMI.
Additionally, my work uses GPS devices and smartphones to examine social networks in neighborhoods. Geosocial-networking applications, such as the dating application Grindr, utilize GPS technologies to allow users to browse user profiles and facilitate connections between users based on physical proximity. Grindr is a commonly and widely used geosocial-networking smartphone application for sexual minority men to meet anonymous sexual partners, creating a new digital environment worthy of further investigation in studies of sexual risk behavior and substance use in sexual minority men. My recent work has been some of the first of its kind to utilize broadcast advertisements on Grindr to recruit participants and deliver surveys to assess risk behaviors of application users.
What drew you to the CDS program at NYU?
I was drawn to the CDS program at NYU in part because of its interdisciplinary commitment and perspective. The key to making advances in the world of public health really depends on different sectors and disciplines working together. For example, in my own research, I engage with colleagues trained in a wide range of disciplines — economics, engineering, statistics, medicine, geography, sociology and psychology — because each can bring unique knowledge and perspectives to the study, making the work stronger and more relevant.
Originally published at cds.nyu.edu on April 22, 2016.
