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When one family member looks out the window at sidewalks and green space and another sees a multilane highway and power lines, the differences may contribute to more than just sibling rivalry. A new study by University of Maryland public health researchers reveals that these neighborhood characteristics correlate with different health outcomes.
Quynh and Thu Nguyen, twin sisters and UMD associate professors of epidemiology and biostatistics, spearheaded the research. Utilizing Google Street View, they combined neighborhood walkability and aesthetic data with health information from the Utah Population Database, a unique resource linking family histories with demographic and medical statistics.
Collaborating with experts from the University of Utah and the University of Alabama, the team analyzed how the built environment affects health among siblings and twins. Their findings, published in the June issue of SSM—Population Health, show a significant correlation between positive neighborhood characteristics and reduced rates of obesity and diabetes.
The study examined records from nearly 2 million people, including 1 million siblings and 14,000 identical and fraternal twins. Across all samples, the presence of walkable environments with greenery and mixed land use was associated with 15%-20% reductions in obesity and diabetes rates.
“Neighborhoods research is especially complicated because it’s often observational. You can only observe where people live; you often don’t get a chance to randomize them to their residential neighborhoods,” said Quynh Nguyen, the study’s first author. “Using siblings, we can try to control for shared family characteristics and even shared genetics.”
The researchers collected nearly 1.4 million Google Street View images from Utah’s main streets, using computer vision models to identify neighborhood features such as greenery, sidewalks, and non-single-family homes. They then integrated this data with health statistics, including diabetes diagnoses and obesity rates, from the state’s population database.
This study builds on previous research by the Nguyens and others, which similarly found that neighborhood features like green streets, sidewalks, and crosswalks promote physical activity. According to Nguyen, additional studies with different designs could strengthen the case for causality.
“People will counter by saying, ‘It’s not the neighborhoods that matter; it’s actually the composition of neighborhood residents,'” she explained. “It’s a complex mixture of things, so to kind of tease out which contributions are place-based and which are individual-level effects or even family-level effects is important.”
Future studies could further explore how factors like income inequality and unequal access to desirable neighborhoods impact health, along with modifiable built environment features.
“An even larger picture would be, how do people get sorted into different neighborhoods?” Thu Nguyen said. “What are the different historical policies, current policies, discrimination biases that constrain people’s movements to different neighborhoods?”
Next, the Nguyens plan to analyze Google Street View images from 2007–22 in Washington, D.C., to examine how environmental changes, including gentrification and segregation, correspond with shifts in neighborhood health over time. They hope the longitudinal study will expand on the correlations found in their current research.
More information can be found in the study by Quynh C. Nguyen et al, “Neighborhood built environment, obesity, and diabetes: A Utah siblings study,” published in SSM – Population Health (2024).
