Utilizing big data without domain knowledge impacts public health decision-making

Miao Zhang · Vishwali Mhasawade · Rumi Chunara

2 citations0/0 files storedProceedings of the National Academy of Sciences

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Urban Transport and Accessibility Urban Design and Spatial Analysis Urban Green Space and Health

Abstract

New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84-4.55) or 17.2 (95% CI 14.4-21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates.

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References

2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2015Neighborhood walkability and health outcomes among older adults: The mediating role of physical activity 2020Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment 2020Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes 2022Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes 2020Neighborhood sidewalk access and childhood obesity 2022Exploring the Mediating Effect of Physical Activities on Built Environment and Obesity for Elderly People: Evidence From Shanghai, China

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