ABSTRACT
BACKGROUND: City-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. METHODS: We 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. FINDINGS: Among 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%). INTERPRETATION: By 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.
Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Child , Communicable Disease Control , Hospitalization , Humans , New York City/epidemiology , SARS-CoV-2ABSTRACT
IMPACT: Patients living in overcrowded zip codes were at increased risk of contracting severe COVID-19 after controlling for confounding disease and socioeconomic factors OBJECTIVES/GOALS: This study sought to examine whether residences in over-crowded zip codes with higher reported over-crowding represented an independent risk factor for severe COVID-19 infection, defined by presentation to an emergency department. METHODS/STUDY POPULATION: In this zip code tabulated area (ZCTA)-level analysis, we used NYC Department of Health disease surveillance data in March 2020 merged with data from the CDC and ACS to model suspected COVID-19 case rates by zip code over-crowdedness (households with greater than 1 occupant per room, in quartiles). We defined suspected COVID-19 cases as emergency department reported cases of pneumonia and influenza-like illness. Our final model employed a multivariate Poisson regression models with controls for known COVID-19 clinical (prevalence of obesity, coronary artery disease, and smoking) and related socioeconomic risk factors (percentage below federal poverty line, median income by zip-code, percentage White, and proportion of multigenerational households) after accounting for multicollinearity. RESULTS/ANTICIPATED RESULTS: Our analysis examined 39,923 suspected COVID-19 cases across 173 ZCTAs in NYC between March 1 and March 30 2020. We found that, after adjusted analysis, for every quartile increase in defined over-crowdedness, case rates increased by 32.8% (95% CI: 22.7%% to 34.0%, P < 0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: Over-crowdedness by zip code may be an independent risk factor for severe COVID-19. Social distancing measures such as school closures that increase house-bound populations may inadvertently worsen the risk of COVID-19 contraction in this setting.