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In JMIR public health and surveillance

BACKGROUND : While it is well-known that older individuals with certain comorbidities are at highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE : Development of a COVID-19 Community Risk Score that summarizes complex disease prevalence together with age and sex, and compare the score to different social determinants of health indicators and built environment measures derived from satellite images using deep-learning.

METHODS : We develop a robust COVID-19 Community Risk Score (COVID-19 Risk Score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications, such as death. We mapped the COVID-19 Risk Score to corresponding zip codes in New York City and associated the score with COVID-19 related death. We further model the variance of the COVID-19 Risk Score using satellite imagery and social determinants of health.

RESULTS : Using 2019 chronic disease data, the COVID-19 Risk Score describes 85% of variation in co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28K census tract neighborhoods (median population size of tracts: 4,091). The COVID-19 Risk Score is associated with a 40% greater risk for COVID-19 related death across New York City (April and September 2020) for a 1 standard deviation (SD) change in the score (risk ratio for 1 SD change in COVID-19 Risk Score: 1.4, P < .001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 Risk Score in the United States in census tracts (r2 = 0.87).

CONCLUSIONS : The COVID-19 Risk Score localizes risk at the census tract level and was able to predict COVID-19 related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Deonarine Andrew, Lyons Genevieve, Lakhani Chirag