Awi Federgruen1, Sherin Naha2. 1. Graduate School of Business at Columbia University, 419 Uris Hall, 3022 Broadway, New York 10027, NY, United States. Electronic address: af7@columbia.edu. 2. Management Science & Engineering Masters Program at Columbia University, United States.
Abstract
OBJECTIVE: With an eye toward possible public policy implications, our objective is to identify the socio-economic and demographic factors that drive the large variation in COVID-19 incidence rates observed within relatively compact geographic regions, and to quantify the relative impact of each of these factors. We use international comparisons as a starting point. METHODS: New York City, consisting of some 175 zip codes, is an ideal arena to pursue the above study given the large variation in case incidence rates across zip codes. We conducted systematic regression studies employing data with zip code granularity. Our model specifications are based on a well-established epidemiologic model that explains the effects of household sizes on R0. RESULTS: Average household size emerges as the single most important driver behind the large variation in COVID-19 incidence rates. It independently explains 62% of the variation. The percentage of the population above the age of 65 and the percentage below the poverty line are also strongly positively associated with zip code incidence rates. As to ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population are significantly associated, but the magnitude of the impact is smaller. (The proportion of Asians within a zip code has a negative association.) Contrary to common belief, population density, by itself, does not have a significantly positive impact (other than when a high population is driven by large household sizes). CONCLUSION: Our findings support implemented and proposed policies to quarantine patients and separate infected individuals from families or dormitories; they also support newly revised nursing home admission policies.
OBJECTIVE: With an eye toward possible public policy implications, our objective is to identify the socio-economic and demographic factors that drive the large variation in COVID-19 incidence rates observed within relatively compact geographic regions, and to quantify the relative impact of each of these factors. We use international comparisons as a starting point. METHODS: New York City, consisting of some 175 zip codes, is an ideal arena to pursue the above study given the large variation in case incidence rates across zip codes. We conducted systematic regression studies employing data with zip code granularity. Our model specifications are based on a well-established epidemiologic model that explains the effects of household sizes on R0. RESULTS: Average household size emerges as the single most important driver behind the large variation in COVID-19 incidence rates. It independently explains 62% of the variation. The percentage of the population above the age of 65 and the percentage below the poverty line are also strongly positively associated with zip code incidence rates. As to ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population are significantly associated, but the magnitude of the impact is smaller. (The proportion of Asians within a zip code has a negative association.) Contrary to common belief, population density, by itself, does not have a significantly positive impact (other than when a high population is driven by large household sizes). CONCLUSION: Our findings support implemented and proposed policies to quarantine patients and separate infected individuals from families or dormitories; they also support newly revised nursing home admission policies.
Authors: Rebecca Cordery; Lucy Reeves; Jie Zhou; Aileen Rowan; Patricia Watber; Carolina Rosadas; Michael Crone; Marko Storch; Paul Freemont; Lucy Mosscrop; Alice Cowley; Gina Zelent; Kate Bisset; Holly Le Blond; Sadie Regmi; Christian Buckingham; Ramlah Junaideen; Nadia Abdulla; Joseph Eliahoo; Miranda Mindlin; Theresa Lamagni; Wendy Barclay; Graham P Taylor; Shiranee Sriskandan Journal: Lancet Microbe Date: 2022-08-24
Authors: Erika H Newton; Rolando G Valenzuela; Priscilla M Cruz-Menoyo; Kimberly Feliberti; Timothy D Shub; Cadence Z M Trapini; Santiago Espinosa de Los Reyes; Christina M Melian; Leslie D Peralta; Héctor E Alcalá Journal: J Racial Ethn Health Disparities Date: 2022-09-30
Authors: Clare L Gillies; Alex V Rowlands; Cameron Razieh; Vahé Nafilyan; Yogini Chudasama; Nazrul Islam; Francesco Zaccardi; Daniel Ayoubkhani; Claire Lawson; Melanie J Davies; Tom Yates; Kamlesh Khunti Journal: J R Soc Med Date: 2022-02-04 Impact factor: 5.344