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Abstract
BackgroundAlthough it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the 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.ObjectiveThis study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.MethodsWe developed 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 modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.ResultsUsing 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 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).ConclusionsThe 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.
Highlights
The COVID-19 pandemic has disrupted major world economies and overwhelmed hospital intensive care units worldwide [1].In the United States alone, the virus has spread throughout urban and rural communities and killed over 300,000 Americans to date [2]
From the 500 Cities data, we chose 13 population-level health indicators that correspond to individual-level chronic disease risk factors associated with COVID-19–related hospitalization and death based on reports from China, Italy, and the United States
Prevalence and Heterogeneity of COVID-19–Associated Comorbidities and Risk Factors Across 500 Cities of the United States We present summary statistics of the prevalence of the 15 COVID-19 comorbidities and risk factors for 27,648 census tracts across the United States using the 2019 release of the Centers for Disease Control and Prevention (CDC) 500 Cities data and American Community Survey (ACS) data collected between 2013 and 2017 (Figure 1A, 1C)
Summary
The COVID-19 pandemic has disrupted major world economies and overwhelmed hospital intensive care units worldwide [1].In the United States alone, the virus has spread throughout urban and rural communities and killed over 300,000 Americans to date [2]. Maharana and Nsoesie [26] developed an approach to map the built environment to obesity prevalence using deep learning analysis of satellite imagery, highlighting a potentially novel method of using measurements of the built environment to quantify disease risk. It is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the 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
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