ABSTRACT
BackgroundCity-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households. MethodsWe obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown. FindingsAmong individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22 - 96%); Q3 vs. Q1: 48%, (17 - 89%); Q4 vs. Q1: 66%, (30 - 211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7 -76%). InterpretationBy increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households. FundingNational Center for Advancing Translational Sciences; Clinical and Translational Science Center at Weill Cornell Medical College.
Subject(s)
COVID-19 , Substance-Related DisordersABSTRACT
IntroductionThe role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households, and COVID-19 in New York City (NYC). MethodsWe conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as proportion of estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering. Results39,923 suspected COVID-19 cases presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant ({beta}interaction = 0.99, 95% CI: 0.99-1.00). ConclusionsOver-crowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.