Spatial analysis of COVID-19 clusters and contextual factors in New York City

• Published in: Spatial and spatio-temporal epidemiology Vol. 34; p. 100355
• Main Authors: Cordes, Jack, Castro, Marcia C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.08.2020
• Subjects: Adult; Aged; Aged, 80 and over; Clinical Laboratory Techniques - statistics & numerical data; Cluster Analysis; Communicable Diseases, Emerging - epidemiology; Coronavirus Infections - diagnosis; Coronavirus Infections - epidemiology; COVID-19; COVID-19 Testing; Disease Outbreaks - statistics & numerical data; Female; Health inequalities; Health Status Disparities; Healthcare Disparities - economics; Healthcare Disparities - ethnology; Humans; Infectious disease; Male; Middle Aged; New York City - epidemiology; Pandemics - statistics & numerical data; Pneumonia, Viral - diagnosis; Pneumonia, Viral - epidemiology; Risk Assessment; Spatial Analysis; Urban health; Urban Health - economics; Urban Health - ethnology; Urban Population; New York City; Cluster analysis; Infectious disease; Urban health; Health inequalities
• ISSN: 1877-5845; 1877-5853
• DOI: 10.1016/j.sste.2020.100355

•Proportion positive tests were positively associated with marginalized statuses.•Low testing and high positivity were associated with public transportation use.•We recommend testing and health care resources be directed to eastern Brooklyn. Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.