Introduction The 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). Methods We 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. Results 39,923 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 (β interaction = 0.99, 95% CI: 0.99-1.00). Conclusions Over-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.

Disparities by race/ethnicity and socioeconomic status (SES) exist in rehospitalization rates and inpatient mortality rates. Few studies have examined how length of stay (LOS, a measure of hospital efficiency/quality) differs by race/ethnicity and SES. This study's objective was to determine whether differences in risk-adjusted LOS exist by race/ethnicity and SES Using a retrospective cohort of 1,432,683 medical and surgical discharges, we compared risk-adjusted LOS, in days, by race/ ethnicity and SES (median household income by patient ZIP code in quartiles), using generalized linear models controlling for demographic and clinical factors, and differences between hospitals and between diagnoses. White patients were on average older than both Black and Hispanic patients, had more chronic conditions, and had a higher inpatient mortality risk. In adjusted analyses, Black patients had a significantly longer LOS than White patients (0.25-day difference when discharged to home and 0.23-day difference when discharged to non-home destinations, both P<.001); there was no difference between Hispanic and White patients. Wealthier patients had a shorter LOS than poorer patients (0.16-day difference when discharged to home and 0.06-day difference when discharged to nonhome destinations, both P<.001). These differences by race/ethnicity reversed for Medicaid patients. Disparities in LOS exist based on a patient's race/ethnicity and SES. Black and poorer patients, but not Hispanic patients, have longer LOS compared to White and wealthier patients. In aggregate, these differences may be related to trust and implicit bias and have implications for use of LOS as a quality metric. Future research should examine the drivers of these disparities.

Introduction: The 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). Methods: We 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. Results: 39,923 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 (βinteraction = 0.99, 95% CI: 0.99-1.00). Conclusions: Over-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.

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