ABSTRACT
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.
ABSTRACT
IntroductionThe COVID-19 Healthcare Personnel Study (CHPS) was designed to assess and mitigate adverse short and long-term physical and mental health impacts of the COVID-19 pandemic on New Yorks health care workforce. Here we report selected baseline results. MethodsOnline survey of New York State physicians, nurse practitioners and physician assistants registered with the New York State Department of Health. Survey-weighted descriptive results were analyzed using frequencies, proportions, and means, with 95% confidence intervals. Odds ratios were calculated for association using survey-weighted logistic regression. ResultsApproximately 51.5% (95% CI 49.1, 54.0) of the survey-weighted respondents reported having worked directly or in close physical contact with COVID-19 patients. Of those tested for COVID-19, 27.3% (95% CI 22.5, 32.2) were positive. Having symptoms consistent with COVID-19 was associated with reporting a subsequent positive COVID-19 test (OR=14.0, 95% CI 5.7, 34.7). Over half of the respondents, (57.6%) reported a negative impact of the COVID-19 efforts on their mental health. Respondents who indicated that they were redeployed or required to do different functions than usual in response to COVID-19 were more likely to report negative mental health impacts (OR=1.3, 95% CI 1.1, 1.6). ConclusionsAt the height of the COVID-19 pandemic in New York State in Spring 2020, more than half of physicians, nurse practitioners and physician assistants included in this study responded to the crisis, often at a cost to their physical and mental health and disruption to their lives.
ABSTRACT
IntroductionThe population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic and racial characteristics play an important role. MethodsWe analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas (ZCTA) with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. ResultsSpatial clustering accounted for approximately 32% of the variation in the data. For every one unit increase in a scaled standardized measure of Chronic Obstructive Pulmonary Disease (COPD) in a community, there was an approximate 8-fold increase in the risk of a positive COVID-19 test in a ZCTA (Incidence Density Ratio = 8.2, 95% Credible Interval 3.7, 18.3). There was a nearly five-fold increase in the risk of a positive COVID-19 test. (IDR = 4.8, 95% Cr I 2.4, 9.7) associated with the proportion of Black / African American residents. Increases in the proportion of residents older than 65, housing density and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, COPD, heart disease, housing density and Black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of Black/African American residents and proportion of older persons. ConclusionsAreas with large proportions of Black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.
