Improving COVID-19 disease severity surveillance measures: The case for implementing accessible screening to protect high-risk populations

Background: The COVID-19 pandemic disproportionately affected minority groups and people of low socioeconomic status, necessitating early identification of high-risk populations. Objectives: We sought to investigate the varying effects of COVID-19 on low-income and high-diversity areas of Buffalo, NY. Our goal was to demonstrate the feasibility of real-time surveillance in identifying COVID-19-infected high-risk populations during an active pandemic, to indicate how this approach may be used to reduce morbidity among all high-risk populations, including children with heart disease. Methods: We assessed rates of poverty and diversity for the 17 ZIP Codes in Buffalo, NY, and characterized each as high- or low-ethnic diversity and high- or low-income. Independent sample t-tests were conducted to compare COVID-19 positive tests and hospitalizations for high- and low-diversity and high- and low-income categories. Results: The percentage of positive COVID-19 tests did not differ significantly between the high- and low-diversity ZIP Codes (4.1 % and 2.8 % respectively; difference, 466 positive tests; P = 0.22) or between the high- and low-income ZIP Codes (4.3 % and 2.8 % respectively; difference, 539 tests; P = 0.14). Among the 3161 patients hospitalized for COVID-19 between May 2nd and November 19th, 2020, the percentage differed significantly between the high- and low-diversity ZIP Codes (1.4 % and 0.9 % respectively; difference, 154 patients; P = 0.04) and between the high- and low-income ZIP Codes (1.5 % and 0.9 % respectively, difference, 1633 patients; P = 0.01). Conclusion: Despite similar rates of positive COVID-19 tests, residents of low-income neighborhoods were at higher risk (67 % higher) for COVID-19 hospitalizations than were residents of high-income neighborhoods. Likewise, residents of high diversity neighborhoods were at a 56 % greater risk of COVID-19 hospitalization when compared with residents of low diversity neighborhoods. This study demonstrates that real-time data collection and analyses are available for identifying high-risk populations for COVID-19 hospitalizations. These findings could aid in optimizing early and intensive COVID-19 testing for high-risk groups, including pediatric cardiology patients, during both the current pandemic and future infectious disease outbreaks with the aim of reducing morbidity.