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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.

While psychosocial care approaches such as assertive community treatment or partial hospitalization can help prevent psychiatric inpatient stay, the ability of specific services to prevent admission is less clear (e.g., recognizing signs of impending relapse, promoting daily structure). Therefore, within 3 months of psychiatric hospital discharge, this study examined the extent to which inpatient readmission among 264 persons with schizophrenia was averted by interventions addressing medication education, symptom education, service continuity, social skills, daily living, daily structure, and family issues. After accounting for demographic characteristics in logistic regression equations, findings suggested that interventions addressing symptom education, service continuity, and daily structure were most effective in preventing inpatient stay among individuals with four or more prior hospitalizations. However, these services became statistically insignificant in preventing readmission among counterparts with fewer previous inpatient stays. While protective effects may differ among persons with varying hospitalization histories, results indicate that resource-poor outpatient centers could focus on these three interventions when care must be limited to rehospitalization prevention.