Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: a study using individual-level GPS-based real-time sensing data
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Keywords: COVID-19 risk, individual-level data, neighborhood effect averaging problem, multiple environmental exposures, housing conditions
Abstract Type: Paper Abstract
Authors:
Jianwei Huang, The Chinese University of Hong Kong
Mei-Po Kwan, The Chinese University of Hong Kong
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Abstract
Recent studies have examined the associations between greenspace, air pollution, noise, and housing conditions with COVID-19 transmission risk using spatially aggregated data. Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using survey data collected with portable GPS-based real-time sensors of air pollutants and noise and an activity-travel diary from two typical neighborhoods in Hong Kong, the study seeks to examine 1) the associations between individuals’ COVID-19 risk with multiple environmental exposures (e.g., greenspace, PM2.5, and noise) and housing conditions (e.g., housing types) in both residential neighborhoods and along people’s daily mobility trajectories; 2) which social groups were disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple regression models, we found a significant negative association between individuals’ COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along daily mobility trajectories. We also found that high open-space and recreational-land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with workplaces in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.
Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: a study using individual-level GPS-based real-time sensing data
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Paper Abstract