Association between overcrowded households, multigenerational households, and COVID-19: a cohort study

Abstract

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.

Transmission of SARS-CoV-2 in standardised First Few X cases and household transmission investigations: a systematic review and meta-analysis

“…(31,35) Furthermore, household size and structure (e.g., high-rise apartments, high-vs low-density households) varies between regions and urban-rural localities, as well as socio-economic status of different subpopulations which may all contribute to lower observed hSAR. (36) The timing of each investigation must be considered in relation to local epidemic activity and evolving PHSMs when interpreting the results reported in this meta-analysis (Supplementary Figure 1), however, these details were not sufficiently reported in included investigations. Most included investigations (n = 48) were finalised in the first six months of 2020, likely during circulation of the ancestral virus and early SARS-CoV-2 variants, i.e., prior to the designation of Alpha and Delta as Variants of Concern (37) (Appendix, Supplementary Figure 1), which are known to have increased transmissibility (38,39).…”

“…(31, 35) Furthermore, household size and structure (e.g., high-rise apartments, high- vs low-density households) varies between regions and urban-rural localities, as well as socio-economic status of different subpopulations which may all contribute to lower observed hSAR. (36)…”

Assessing the Burden of COVID-19 in Developing Countries: Systematic Review, Meta-Analysis, and Public Policy Implications

“…Evidently, it is very difficult to insulate elderly people from the virus in a slum or a rural village. This is likely also impacted by the higher proportion of multi-generational families in developing countries (28), a known risk-factor for COVID-19 infection and death (29, 30) For example, seroprevalence in slum neighbourhoods of Mumbai was about four times higher than in non-slum neighbourhoods (31). Our analysis indicates that the relatively uniform prevalence of COVID-19 in developing countries has dramatically increased the number of fatalities in these locations.…”

Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City

“…Our primary analysis therefore included, by ZCTA, the proportion of residents who are White, the proportion of residents living under the federal poverty line (both as a percentage), and the median income (in 2018 US dollars). Furthermore, as we did in prior work on the first COVID-19 wave in NYC [ 23 ], we also included as a control covariate the ZCTA-level proportion of households that are overcrowded. This proportion was defined as the estimated number of housing units with more than one occupant per room, divided by the number of occupied housing units, expressed as a percentage.…”

“…We conducted two sensitivity analyses to understand the robustness of our findings. To test whether our results could be explained by other potentially confounding socioeconomic characteristics, we expanded the number of ZCTA-level control covariates to include the proportion of essential workers who were more likely to work in the community and not at home (calculated according to methodology described in [ 23 , 27 ]), as well as the prevalence of COVID-19 related clinical risk factors such as obesity, diabetes, coronary heart disease, smoking and chronic obstructive airway disease. Second, we included the proportion of overcrowded households (defined above) as an independent variable in our model along with quartiles of multigenerational households.…”