Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Impact of the COVID-19 Pandemic on Physical Activity among Mostly Older, Overweight Black Women Living in the Rural Alabama Black Belt

Despite well-documented global declines in physical activity (PA) during the COVID-19 pandemic, little is known regarding the specific impact among underserved, rural Alabama counties. This is concerning as this region was already disproportionately burdened by inactivity and related chronic diseases and was among the hardest hit by COVID-19. Thus, the current study examined the effect of COVID-19 on PA in four rural Alabama counties. An ancillary survey was administered between March 2020 and August 2021 to the first cohort (N = 171) of participants enrolled in a larger PA trial. Main outcomes of this survey included the perceived impact of COVID-19 on PA, leisure-time PA, and social cognitive theory (SCT) constructs at 3 months. Almost half of the participants reported being less active during the pandemic (49.7%) and endorsed that COVID-19 made PA more difficult (47.4%), citing concerns such as getting sick from exercising outside of the home (70.4%) and discomfort wearing a face mask while exercising (58%). Perceived COVID-19 impact on PA was significantly associated with education, household dependents, and gender (p's < 0.05). More women, parents, and college graduates reported that the COVID-19 pandemic made PA more difficult. Overall, there were no significant associations between PA, SCT constructs, or perceived COVID-19 impact on PA scores at 3 months. While the pandemic made PA difficult for many participants, these barriers were not associated with leisure-time PA levels or related theoretical mechanisms of action, which bodes well for the success of our ongoing intervention efforts and the resiliency of these communities.

SARS-CoV-2 Infection in Cities from the Southern Region of Bahia State, Brazil: Analysis of Variables Associated in Both Individual and Community Level

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), challenged public health systems worldwide. Individuals in low-income countries/regions are still at individual and community risk concerning inequality, sanitation, and economic conditions. Besides, during the pandemic, the transmission in municipalities and communities in the countryside and less developed regions kept viral spread and required structured and strengthened clinical and laboratory surveillance. Here, we present an observational, analytic, cross-sectional study conducted using secondary data from the Laboratório de Farmacogenômica e Epidemiologia Molecular (LAFEM)-Universidade Estadual de Santa Cruz (UESC), to evaluate individual and community factors associated to SARS-CoV-2 infection in outpatients from different cities from Southern Region of Bahia State, in Brazil. The data were collected between June 2021 and May 2022. The SARS-CoV-2 positivity by RT-qPCR was correlated with low socio-economic indicators, including the Human development index (HDI_c) and Average worker salary (AWSc). Besides, in general, females were less likely to test positive for SARS-CoV-2 (OR = 0.752; CI 95% 0.663-0.853; p < 0.0001), while brown individuals had more positivity for infection (p < 0.0001). In addition, those who had clinical symptoms were more likely to test positive for SARS-CoV-2 (OR = 6.000; CI 95% 4.932-7.299; p < 0.0001). Although dry cough, headache, and fever were the most frequent, loss of taste (OR = 5.574; CI 95% 4.334-7.186) and loss of smell (OR = 6.327; CI 95% 4.899-8.144) presented higher odds ratio to be positive to SARS-CoV-2 by RT-qPCR. Nonetheless, the distribution of these characteristics was not homogenous among the different cities, especially for age and gender. The dynamic of SARS-CoV-2 positivity differed between cities and the total population and reinforces the hypothesis that control strategies for prevention needed to be developed based on both individual and community risk levels to mitigate harm to individuals and the health system.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis

IMPORTANCE

COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear.

OBJECTIVE

To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants.

DATA SOURCES

A systematic search of PubMed, medRxiv, bioRxiv, Embase, and the World Health Organization COVID-19 databases was performed for studies published from January 1, 2020, to January 6, 2021.

STUDY SELECTION

Studies that reported data on associations between race and ethnicity and COVID-19 positivity, disease severity, and socioeconomic status were included and screened by 2 independent reviewers. Studies that did not have a satisfactory quality score were excluded. Overall, less than 1% (0.47%) of initially identified studies met selection criteria.

DATA EXTRACTION AND SYNTHESIS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Associations were assessed using adjusted and unadjusted risk ratios (RRs) and odds ratios (ORs), combined prevalence, and metaregression. Data were pooled using a random-effects model.

MAIN OUTCOMES AND MEASURES

The main measures were RRs, ORs, and combined prevalence values.

RESULTS

A total of 4 318 929 patients from 68 studies were included in this meta-analysis. Overall, 370 933 patients (8.6%) were African American, 9082 (0.2%) were American Indian or Alaska Native, 101 793 (2.4%) were Asian American, 851 392 identified as Hispanic/Latino (19.7%), 7417 (0.2%) were Pacific Islander, 1 037 996 (24.0%) were White, and 269 040 (6.2%) identified as multiracial and another race or ethnicity. In age- and sex-adjusted analyses, African American individuals (RR, 3.54; 95% CI, 1.38-9.07; P = .008) and Hispanic individuals (RR, 4.68; 95% CI, 1.28-17.20; P = .02) were the most likely to test positive for COVID-19. Asian American individuals had the highest risk of intensive care unit admission (RR, 1.93; 95% CI, 1.60-2.34, P < .001). The area deprivation index was positively correlated with mortality rates in Asian American and Hispanic individuals (P < .001). Decreased access to clinical care was positively correlated with COVID-19 positivity in Hispanic individuals (P < .001) and African American individuals (P < .001).

CONCLUSIONS AND RELEVANCE

In this study, members of racial and ethnic minority groups had higher risks of COVID-19 positivity and disease severity. Furthermore, socioeconomic determinants were strongly associated with COVID-19 outcomes in racial and ethnic minority populations.

Education Attainment, Intelligence and COVID-19: A Mendelian Randomization Study

Background: Evidence of socioeconomic inequality in COVID-19-related outcomes is emerging, with a higher risk of infection and mortality observed among individuals with lower education attainment. We aimed to evaluate the potential interventions against COVID-19 from the socioeconomic perspective, including improvement in education and intelligence. Methods: With a two-sample Mendelian randomization approach using summary statistics from the largest genome-wide association meta-analysis, univariable analysis was adopted to evaluate the total causal effects of genetically determined education attainment and intelligence on COVID-19 outcomes. Multivariable analysis was performed to dissect the potential mechanisms. Results: Genetic predisposition to higher education attainment by 1 SD (4.2 years) was independently associated with reduced risk of COVID-19 severity (OR = 0.508 [95% CI: 0.417–0.617]; p < 0.001). Genetically higher education attainment also lowered the risk of COVID-19 hospitalization (0.685 [0.593–0.791]; p < 0.001), but the association was attenuated after adjustment for beta estimates of intelligence in multivariable analysis. Genetically higher intelligence was associated with reduced risk of COVID-19 hospitalization (0.780 [0.655–0.930]; p = 0.006), with attenuation of association after adjustment for education attainment. Null association was observed for genetically determined education attainment and intelligence with SARS-CoV-2 infection. Conclusion: Education may act independently and jointly with intelligence in improving the COVID-19 outcomes. Improving education may potentially alleviate the COVID-19-related health inequality.

The Relationship Between COVID-19 Infection Rates and Social Determinants of Health in Broward and Miami-Dade Counties, Florida

Objective

To determine the relationship between per capita income and COVID-19 cases in Broward and Miami-Dade Counties of Florida, USA. 

Background

Low socioeconomic status predisposes individuals to worse health outcomes. For example, during the 2003 SARS-CoV pandemic and the 2009 H1N1 influenza pandemic disadvantaged individuals were more likely to become infected. More recently, a study found that deaths due to COVID-19 were associated with disadvantaged areas across the United States. South Florida, in particular Broward and Miami-Dade Counties, has experienced a significant burden of coronavirus cases. Investigating the association of income on coronavirus cases in Broward and Miami-Dade Counties may aid in identifying and treating those individuals at increased risk. 

Methods

This retrospective cross-sectional study used data gathered by the Florida Department of Health and 2018 U.S. Census. COVID-19 cases from March 2 - November 1, 2020 were tallied by ZIP code in Florida’s Broward and Miami-Dade Counties and scaled per housing unit. An exhaustive regression analysis using County “Miami-Dade” or “Broward,” sex, race, ethnicity, median age, and estimated per capita income was performed for each combination of independent variables in MATLAB (MathWorks, Natick, USA). Regression models were evaluated using both adjusted R-squared and the Akaike Information Criterion, along with the number of significant predictors. The most optimal model with the highest number of significant predictors was selected.

Results

Among all other variables, sex, race, and ethnicity as the variables that best predicted COVID-19 cases per housing unit within a certain ZIP code. The adjusted R-squared of this optimal model was 0.5062, indicating that within each ZIP code in Broward and Miami-Dade Counties 50.62% of the variance in COVID-19 cases per housing unit can be explained by these variables. A significant relationship was found between the number of COVID-19 cases and individuals who were Black or African American (p < 0.001), individuals who were Hispanic or Latino (p < 0.001), and male to female ratio (p = 0.016). Per capita income, age, and county were not statistically significant predictors in any model tested.

Conclusions

Racial and gender disparities may be more significant contributors to COVID-19 cases than per capita income in housing units. Based on the results of this study, investigators may consider applying this model to similar variables in order to inform the management and prevention of cases in the present and future pandemics.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BACKGROUND

Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE

This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.

METHODS

We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.

RESULTS

Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r²=0.87).

CONCLUSIONS

The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BACKGROUND

Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE

This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.

METHODS

We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.

RESULTS

Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87).

CONCLUSIONS

The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

The Impact of the COVID-19 Pandemic on Social, Health, and Economy

In late December 2019, a series of acute atypical respiratory disease occurred in Wuhan, China, which rapidly spread to other areas worldwide. It was soon discovered that a novel coronavirus was responsible, named the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2, 2019-nCoV). The impact of the COVID-19 pandemic on the population’s health is unprecedented in recent years and the impact on a social level even more so. The COVID-19 pandemic is the most large-scale pandemic on earth this century, and the impact in all life sectors is devasting and directly affected human activity in the first wave. The impact on the economy, social care systems, and human relationships is causing an unprecedented global crisis. SARS-CoV-2 has a strong direct acute impact on population health, not only at the physiological level but also at the psychological level for those who suffer it, those close to them, and the general population, who suffer from the social consequences of the pandemic. In this line, the economic recession increased, even more, the social imbalance and inequity, hitting the most vulnerable families, and creating a difficult context for public institutions to address. We are facing one of the greatest challenges of social intervention, which requires fast, effective, and well-coordinated responses from public institutions, the private sector, and non-governmental organizations to serve an increasingly hopeless population with increasingly urgent needs. Long-term legislation is necessary to reduce the vulnerability of the less fortunate, as well as to analyze the societal response to improve the social organization management of available resources. Therefore, in this scoping review, a consensus and critical review were performed using both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases. The main search engines were PubMed, SciELO, and Google Scholar. The method was a narrative literature review of the available literature. The aim was to assess the effects of the COVID-19 pandemic on population health, where the possible interventions at the health level are discussed, the impact in economic and social areas, and the government and health systems interventions in the pandemic, and finally, possible economic models for the recovery of the crisis are proposed.

COVID-19 and children's health in the United States: Consideration of physical and social environments during the pandemic

Public health measures necessary to counteract the coronavirus disease 2019 (COVID-19) pandemic have resulted in dramatic changes in the physical and social environments within which children grow and develop. As our understanding of the pathways for viral exposure and associated health outcomes in children evolves, it is critical to consider how changes in the social, cultural, economic, and physical environments resulting from the pandemic could affect the development of children. This review article considers the environments and settings that create the backdrop for children's health in the United States during the COVID-19 pandemic, including current threats to child development that stem from: A) change in exposures to environmental contaminants such as heavy metals, pesticides, disinfectants, air pollution and the built environment; B) changes in food environments resulting from adverse economic repercussion of the pandemic and limited reach of existing safety nets; C) limited access to children's educational and developmental resources; D) changes in the social environments at the individual and household levels, and their interplay with family stressors and mental health; E) social injustice and racism. The environmental changes due to COVID-19 are overlaid onto existing environmental and social disparities. This results in disproportionate effects among children in low-income settings and among populations experiencing the effects of structural racism. This article draws attention to many environments that should be considered in current and future policy responses to protect children's health amid pandemics.

Actual Racial/Ethnic Disparities in COVID-19 Mortality for the Non-Hispanic Black Compared to Non-Hispanic White Population in 35 US States and Their Association with Structural Racism

Introduction

While the increased burden of COVID-19 among the Black population has been recognized, most attempts to quantify the extent of this racial disparity have not taken the age distribution of the population into account. In this paper, we determine the Black–White disparity in COVID-19 mortality rates across 35 states using direct age standardization. We then explore the relationship between structural racism and differences in the magnitude of this disparity across states.

Methods

Using data from the Centers for Disease Control and Prevention, we calculated both crude and age-adjusted COVID-19 mortality rates for the non-Hispanic White and non-Hispanic Black populations in each state. We explored the relationship between a state-level structural racism index and the observed differences in the racial disparities in COVID-19 mortality across states. We explored the potential mediating effects of disparities in exposure based on occupation, underlying medical conditions, and health care access.

Results

Relying upon crude death rate ratios resulted in a substantial underestimation of the true magnitude of the Black–White disparity in COVID-19 mortality rates. The structural racism index was a robust predictor of the observed racial disparities. Each standard deviation increase in the racism index was associated with an increase of 0.26 in the ratio of COVID-19 mortality rates among the Black compared to the White population.

Conclusions

Structural racism should be considered a root cause of the Black–White disparity in COVID-19 mortality. Dismantling the long-standing systems of racial oppression is critical to adequately address both the downstream and upstream causes of racial inequities in the disease burden of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40615-021-01028-1.

Household factors and the risk of severe COVID-like illness early in the US pandemic

OBJECTIVE

To investigate the role of children in the home and household crowding as risk factors for severe COVID-19 disease.

METHODS

We used interview data from 6,831 U.S. adults screened for the Communities, Households and SARS/CoV-2 Epidemiology (CHASING) COVID Cohort Study in April 2020.

RESULTS

In logistic regression models, the adjusted odds ratio [aOR] of hospitalization due to COVID-19 for having (versus not having) children in the home was 10.5 (95% CI:5.7-19.1) among study participants living in multi-unit dwellings and 2.2 (95% CI:1.2-6.5) among those living in single unit dwellings. Among participants living in multi-unit dwellings, the aOR for COVID-19 hospitalization among participants with more than 4 persons in their household (versus 1 person) was 2.5 (95% CI:1.0-6.1), and 0.8 (95% CI:0.15-4.1) among those living in single unit dwellings.

CONCLUSION

Early in the US SARS-CoV-2 pandemic, certain household exposures likely increased the risk of both SARS-CoV-2 acquisition and the risk of severe COVID-19 disease.