Overcrowded Housing Increases Risk for COVID-19 Mortality: An Ecological Study

Overcrowded Housing Increases Risk for COVID-19 Mortality: An Ecological Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Overcrowded Housing Increases Risk for COVID-19 Mortality: An Ecological Study Karan Varshney, Talia Glodjo, Jenna Adalbert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1323646/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Apr, 2022 Read the published version in BMC Research Notes → Version 1 posted 3 You are reading this latest preprint version Abstract Objectives Overcrowded housing is a sociodemographic variable associated with increased infection and mortality rates from communicable diseases. It is not well understood if this association exists for COVID-19. Our objective was to hence determine the association between household overcrowding and risk of mortality from COVID-19, and this was done by performing bivariable and multivariable analyses using COVID-19 data from cities in Los Angeles County. Results Bivariate regression revealed that overcrowded households were positively associated with COVID-19 deaths (standardized β=0.863, p<0.001). COVID-19 case totals, people aged 60+, and the number of overcrowded households met conditions for inclusion in the backwards stepwise linear regression model. Analysis revealed all independent variables were positively associated with mortality rates, primarily for individuals 60+ (standardized β ­1­­ =0.375, p=0.001), followed by overcrowded households (standardized β ­2­­ =0.346, p=0.014), and total COVID-19 cases (standardized β ­3 =0.311, p<0.001). Our findings highlight that residing in overcrowded households may be an important risk factor for COVID-19 mortality. Public health entities should consider this population when allocating resources for prevention and control of COVID-19 mortality and future disease outbreaks. COVID-19 mortality housing inequities prevention Introduction As of November 1, 2021, over 750,000 deaths in the United States (US) have been attributed to COVID-19 infection [ 1 ]. In the US, mortality among individuals with communicable and non-communicable disease is disproportionately higher for those with poor socioeconomic circumstances [ 2 ], [ 3 ]. Household size, defined as the number of individuals occupying one household, is a key sociodemographic variable related to the spread of disease. “Household overcrowding” is a term applied to households in which the number of occupants surpasses the number of rooms, and is disproportionately prevalent among Hispanic persons, persons living in rented homes, persons not born in the US, households earning less than $ 25,000 per year, in the Western US, and urban areas [ 5 ]. In particular, household overcrowding has been historically associated with an increased incidence in infectious pathogens, such as helminths and tuberculosis [ 4 ]. Although larger household size and overcrowding have been associated with a greater incidence of COVID-19 infection, there has been limited research has been conducted on the effect that household overcrowding has on COVID-19 mortality rates [ 6 ], [ 7 ]. Critically, however, one study has demonstrated a possible link between COVID-19 mortality and total number of overcrowded households, though the evidence was limited and hence the authors suggested a need to study this association in more detail [ 8 ]. Given the paucity of research in this area and its potential impact on future research and data acquisition in a pandemic setting, the purpose of this ecological study was to analyze the association between household overcrowding and mortality from COVID-19. Methods Los Angeles (LA) County has the greatest population density in the US [ 9 ] and has recorded the highest number of COVID-19 cases (>1,240,000) and deaths (>24,000) in the nation [ 10 ]. COVID-19 data was acquired for all cities in LA County [ 11 ], along with data on housing and demographics up until July 28, 2021 [ 9 ]. Institutional Review Board approval was not required, as all data used for this study is publicly available. Overcrowded households were defined as having 1.0+ persons per room. Bivariate regression was performed between the number of overcrowded households and the number of COVID-19 deaths. Backwards stepwise linear regression was then conducted with risk factors for COVID-19 mortality, such as race, sex, level of income, and age as eligible input variables. Collinearity was assessed by considering the variance inflation factors (VIF); variables with high collinearity (VIF > 8) were removed from the model. Results Data were fully available for 85 of the 88 cities in LA County. Of these 85 cities, there were a total of 540,155 COVID-19 cases, 10,947 COVID-19 deaths, and 6,784 overcrowded households. Full descriptive statistics of variables considered for analysis are listed in Table 1 . Table 1 Descriptive statistics for cities of LA County Variable Total across 85 cities Median (range) COVID-19 cases 540,155 991 (19 – 25,582) COVID-19 deaths 10,947 93 (0 – 633) Overcrowded households 138,755 987 (0 – 6,784) Males 2,193,265 19,212 (42 – 103,918) 0-19 years of age 1,146,966 9,722 (23 – 59,833) 20-59 years of age 2,467,903 22,311 (49 --114,242) 60+ years of age 862,657 8,316 (18 – 47,832) Black race 306,691 840 (0 – 46,326) Hispanic race 2,106,564 14,613 (60 – 109,103) Median household income 7,159,521 71,948 (39,738 – 239,375) Unemployed (above 16 years of age) 147,380 1,302 (2 – 7566) Bivariate regression indicated that the number of overcrowded households was positively associated with the number of COVID-19 deaths (standardized β = 0.863, p < 0.001). A stronger association was seen between COVID-19 cases, and deaths (standardized β = 0.892, p < 0.001). Of the eligible variables, three met the conditions for inclusion in the backwards stepwise linear regression model: total COVID-19 cases, the number of individuals aged 60+, and total overcrowded households. The analysis revealed that all three of these independent variables were positively associated with the number of COVID-19 deaths. The largest effect was seen in individuals aged 60+ (standardized β 1 = 0.375, p = 0.001), followed by overcrowded households (standardized β 2 = 0.346, p = 0.014), and total COVID-19 cases (standardized β 3 = 0.311, p < 0.001). For each of the three variables, results of the analyses are listed in Table 2 . Table 2 Association with COVID-19 mortality* for bivariate and multivariable analysis of eligible variables Bivariate analysis Multivariable analysis Unstandardized β (95% CI) Standardized β P-value Unstandardized β (95% CI) Standardized β P-value Overcrowded households 0.063 (0.54, 0.071) 0.863 p<0.001 0.025 (0.013, 0.037) 0.346 p<0.001 COVID-19 cases* 0.017 (0.015, 0.019) 0.892 p<0.001 0.006 (0.003, 0.009) 0.311 p=0.001 Individuals age 60+ 0.012 (0.010, 0.014) 0.825 p<0.001 0.005 (0.004, 0.007) 0.375 p<0.001 *COVID-19 case and death data from as of July 28, 2021 Discussion Per the results of our analysis, household overcrowding is a significant risk factor for COVID-19 mortality. Importantly, the results of our study revealed that in LA County, household overcrowding was an even stronger predictor of increased mortality rates than the total number of COVID-19 cases. Additionally, our findings emphasize that elderly citizens residing in overcrowded households are at a particularly increased risk of mortality from COVID-19. These findings suggest key implications for addressing the COVID-19 pandemic and future outbreaks of communicable disease. These findings are consistent with studies investigating COVID-19 transmissibility which found transmission to be greater in indoor congregate settings, such as jails and buses [ 13 ]. These settings share similar characteristics with overcrowded housing, including prolonged time spent with the same group of individuals, minimal ventilation, and multiple individuals occupying a limited space. While age, level of income, ethnic background, and medical co-morbidities have been frequently described as risk factors for poor outcomes associated with COVID-19 infection [ 12 ], our analyses suggest that public health measures designed to reduce mortality among persons with COVID-19 ought to make special consideration of persons living in overcrowded housing. The Centers for Disease Control and Prevention (CDC) has suggested that infected individuals maintain and six-foot distance between themselves and other household members to reduce transmission through the air by droplets and aerosols [ 14 ], however for persons living in overcrowded housing, complying with this recommendation may be difficult or impossible. Developing recommendations that aim to specifically address the unique needs of persons living in overcrowded housing may improve the health outcomes for this group. In addition, public health entities and healthcare providers should assess the prevalence of household overcrowding in the populations that they serve to inform interventions and more effectively allocate resources for COVID-19 prevention and control. More broadly, this study underlines how this pandemic has exacerbated the detrimental effects of the housing crisis in the US on the health of the population, and the urgent need to increase access to affordable housing to reduce morbidity and mortality from COVID-19. Household overcrowding may increase the risk of COVID-19 mortality. Public health agencies should recognize the importance of effectively allocating resources to areas with overcrowded housing during the COVID-19 pandemic and future disease outbreaks. Our findings emphasize an imperative for further studies exploring the association between overcrowded housing and COVID-19 mortality, as well as mortality attributed to other communicable pathogens. LIMITATIONS Limitations of our work include that our ecological analysis can only provide partial insights regarding the additional barriers experienced by populations in overcrowded housing, such as discrimination or social exclusion. Furthermore, we were unable to account for undocumented or homeless individuals, which are equally important populations to consider when addressing infection and mortality rates. Finally, while the cities in LA County encompass a large portion of the County’s population, they do not account for unincorporated areas (regions not governed by municipal corporations), which comprise a sizeable proportion of the County. Regardless of these limitations, our study emphasizes the imperative for further research and data acquisition on the association between household overcrowding and mortality due to COVID-19 infection. Abbreviations US United States LA Los Angeles VIF variance inflation factors CDC Centers for Disease Control and Prevention Declarations Ethics approval and consent to participate: This project utilized publicly available data, and ethics approval was hence not required. Consent for publication: As ethics approval was not required, participant consent for publication was not required. Conflict of Interest : The authors have no conflicts of interest associated with the material presented in this paper. Funding : No funding was provided to any of the authors for this work. Acknowledgements: The authors have no acknowledgements to be made as there were not any other contributors to this work. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request Author Contributions: Conceptualization: KV. Data curation: KV, TG. Formal analysis: KV, TG, JA. Funding acquisition: N/A. Methodology: KV, TG, JA. Project administration: N/A. Visualization: KV, JA. Writing – original draft: KV, JA. Writing – reviewing & editing: KV, TG, JA. References Centers for Disease Control and Prevention [CDC]. COVID Data Tracker. U.S. Department of Health and Human Services . 2021. Retrieved from https://covid.cdc.gov/covid-data-tracker/#datatracker-home. Zonderman AB, Mode NA, Ejiogu N, Evans MK. Race and Poverty Status as a Risk for Overall Mortality in Community-Dwelling Middle-Aged Adults. JAMA Intern Med. 2016;176(9):1394-1395. doi:10.1001/jamainternmed.2016.3649 Karmakar M, Lantz PM, Tipirneni R. Association of Social and Demographic Factors With COVID-19 Incidence and Death Rates in the US. JAMA Netw Open. 2021;4(1):e2036462. Published 2021 Jan 4. doi:10.1001/jamanetworkopen.2020.36462 Neiderud CJ. How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol. 2015;5:27060. Published 2015 Jun 24. doi:10.3402/iee.v5.27060 Blake, K.S, Kellerson, R L., Simic A. Measuring overcrowding in housing. Econometrica, Inc. Bethesda, MD. 2007. Retrieved from https://www.census.gov/content/dam/Census/programs-surveys/ahs/publications/Measuring_Overcrowding_in_Hsg.pdf Mendez AD, Escobar M, Romero M, Wojcicki JM. Overcrowding and exposure to secondhand smoke increase risk for COVID-19 infection among Latinx families in the greater San Francisco Bay Area. Tob Induc Dis. 2021;19:79. Published 2021 Oct 13. doi:10.18332/tid/140827 Raisi-Estabragh Z, McCracken C, Bethell MS, et al. Greater risk of severe COVID-19 in Black, Asian and Minority Ethnic populations is not explained by cardiometabolic, socioeconomic or behavioural factors, or by 25(OH)-vitamin D status: study of 1326 cases from the UK Biobank. J Public Health (Oxf). 2020;42(3):451-460. Kamis C, Stolte A, West JS, Fishman SH, Brown T, Brown T, Farmer HR. Overcrowding and COVID-19 mortality across US counties: Are disparities growing over time?. SSM-Population Health. 2021 Jun 12:100845. U.S. Census Bureau. Datasets. United States Census Bureau. 2021. Retrieved from: https://www.census.gov/data/datasets.html Coronavirus Resource Center. COVID-19 United States Cases by County. Johns Hopkins University. 2021. Retrieved from https://coronavirus.jhu.edu/us-map. County of Los Angeles Public Health [County of LA Public Health]. LA County Daily COVID-19 Data. 2021. Retrieved from: http://publichealth.lacounty.gov/media/coronavirus/data/index.htm Rozenfeld Y, Beam J, Maier H, et al. A model of disparities: risk factors associated with COVID-19 infection. Int J Equity Health . 2020;19(1):126. Published 2020 Jul 29. doi:10.1186/s12939-020-01242-z Ge Y, Martinez L, Sun S, et al. COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19: A Population-Based Cohort Study in Zhejiang Province, China. JAMA Intern Med. 2021;181(10):1343–1350. doi:10.1001/jamainternmed.2021.4686 Centers for Disease Control and Prevention. (n.d.). How to protect yourself & others . Centers for Disease Control and Prevention. Retrieved January 14, 2022, from https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2022 Read the published version in BMC Research Notes → Version 1 posted Reviews received at journal 26 Feb, 2022 Reviewers invited by journal 25 Feb, 2022 First submitted to journal 04 Feb, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1323646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":86667209,"identity":"5168f62c-8194-4cf0-9c98-af38d1b6eed9","order_by":0,"name":"Karan Varshney","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACCRDB2AAk2BsMGBgOgPkGRGrhOUCyFokEkBYGwlokZyQ/+/hzxx05g5uPNz74ccYij4G9eZsEQ81hnFqkJdKMZ/OeeWZscDut2LDnhkQxA8+xMgmGY7i1yPEcMGZmbDucuOF2jpkEzweJxAYJIIOBDZ+W458Zf7Ydrt9w84z5zz8gLfJvgFr+4XEYe48xA2/b4QSDGzxmzDw3QLbwmEkA7cXt/faeYmagFsOZZ9KKpWXOSCS28aQVWyT2pePUInGYfTPIYfJ8xw9v/PjmWF1iP/vhjTc+fLPGqQUOFA5AGWwgIoGwBgYG+QZiVI2CUTAKRsGIBAD641hOIsaxNAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3605-9824","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Karan","middleName":"","lastName":"Varshney","suffix":""},{"id":86667210,"identity":"7b02c9be-5ab8-4232-b245-3e0634f21011","order_by":1,"name":"Talia Glodjo","email":"","orcid":"","institution":"Thomas Jefferson University","correspondingAuthor":false,"prefix":"","firstName":"Talia","middleName":"","lastName":"Glodjo","suffix":""},{"id":86667211,"identity":"2067e57c-fbd0-4b08-80cd-556d22b4ac06","order_by":2,"name":"Jenna Adalbert","email":"","orcid":"","institution":"Thomas Jefferson University","correspondingAuthor":false,"prefix":"","firstName":"Jenna","middleName":"","lastName":"Adalbert","suffix":""}],"badges":[],"createdAt":"2022-02-03 10:46:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1323646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1323646/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-022-06015-1","type":"published","date":"2022-04-05T11:52:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":19968965,"identity":"ecc02446-07e0-4118-b956-7b1f3500cb20","added_by":"auto","created_at":"2022-04-05 11:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":255520,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1323646/v1/2eaf942d-e3c1-47ac-aaef-04a126536b90.pdf"}],"financialInterests":"","formattedTitle":"Overcrowded Housing Increases Risk for COVID-19 Mortality: An Ecological Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs of November 1, 2021, over 750,000 deaths in the United States (US) have been attributed to COVID-19 infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the US, mortality among individuals with communicable and non-communicable disease is disproportionately higher for those with poor socioeconomic circumstances [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Household size, defined as the number of individuals occupying one household, is a key sociodemographic variable related to the spread of disease. \u0026ldquo;Household overcrowding\u0026rdquo; is a term applied to households in which the number of occupants surpasses the number of rooms, and is disproportionately prevalent among Hispanic persons, persons living in rented homes, persons not born in the US, households earning less than \u003cspan\u003e$\u003c/span\u003e25,000 per year, in the Western US, and urban areas [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In particular, household overcrowding has been historically associated with an increased incidence in infectious pathogens, such as helminths and tuberculosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough larger household size and overcrowding have been associated with a greater incidence of COVID-19 infection, there has been limited research has been conducted on the effect that household overcrowding has on COVID-19 mortality rates [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Critically, however, one study has demonstrated a possible link between COVID-19 mortality and total number of overcrowded households, though the evidence was limited and hence the authors suggested a need to study this association in more detail [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given the paucity of research in this area and its potential impact on future research and data acquisition in a pandemic setting, the purpose of this ecological study was to analyze the association between household overcrowding and mortality from COVID-19.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eLos Angeles (LA) County has the greatest population density in the US [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and has recorded the highest number of COVID-19 cases (\u0026gt;1,240,000) and deaths (\u0026gt;24,000) in the nation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. COVID-19 data was acquired for all cities in LA County [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], along with data on housing and demographics up until July 28, 2021 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Institutional Review Board approval was not required, as all data used for this study is publicly available.\u003c/p\u003e \u003cp\u003eOvercrowded households were defined as having 1.0+ persons per room. Bivariate regression was performed between the number of overcrowded households and the number of COVID-19 deaths. Backwards stepwise linear regression was then conducted with risk factors for COVID-19 mortality, such as race, sex, level of income, and age as eligible input variables. Collinearity was assessed by considering the variance inflation factors (VIF); variables with high collinearity (VIF \u0026gt; 8) were removed from the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eData were fully available for 85 of the 88 cities in LA County. Of these 85 cities, there were a total of 540,155 COVID-19 cases, 10,947 COVID-19 deaths, and 6,784 overcrowded households. Full descriptive statistics of variables considered for analysis are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for cities of LA County\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal across 85 cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e540,155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e991 (19 \u0026ndash; 25,582)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (0 \u0026ndash; 633)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvercrowded households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138,755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e987 (0 \u0026ndash; 6,784)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,193,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,212 (42 \u0026ndash; 103,918)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0-19 years of age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,146,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,722 (23 \u0026ndash; 59,833)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20-59 years of age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,467,903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,311 (49 --114,242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60+ years of age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e862,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,316 (18 \u0026ndash; 47,832)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e840 (0 \u0026ndash; 46,326)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,106,564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,613 (60 \u0026ndash; 109,103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,159,521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71,948 (39,738 \u0026ndash; 239,375)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed (above 16 years of age)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,302 (2 \u0026ndash; 7566)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBivariate regression indicated that the number of overcrowded households was positively associated with the number of COVID-19 deaths (standardized β\u0026thinsp;=\u0026thinsp;0.863, p \u0026lt; 0.001). A stronger association was seen between COVID-19 cases, and deaths (standardized β\u0026thinsp;=\u0026thinsp;0.892, p \u0026lt; 0.001).\u003c/p\u003e \u003cp\u003eOf the eligible variables, three met the conditions for inclusion in the backwards stepwise linear regression model: total COVID-19 cases, the number of individuals aged 60+, and total overcrowded households. The analysis revealed that all three of these independent variables were positively associated with the number of COVID-19 deaths. The largest effect was seen in individuals aged 60+ (standardized β\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.375, p = 0.001), followed by overcrowded households (standardized β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.346, p = 0.014), and total COVID-19 cases (standardized β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.311, p \u0026lt; 0.001). For each of the three variables, results of the analyses are listed in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation with COVID-19 mortality* for bivariate and multivariable analysis of eligible variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnstandardized\u0026nbsp;β\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStandardized β\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUnstandardized\u0026nbsp;β\u003c/b\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eStandardized\u003c/b\u003e \u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOvercrowded households\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063 (0.54, 0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025 (0.013, 0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOVID-19 cases*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017 (0.015, 0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006 (0.003, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep=0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividuals age 60+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012 (0.010, 0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005 (0.004, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*COVID-19 case and death data from as of July 28, 2021\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePer the results of our analysis, household overcrowding is a significant risk factor for COVID-19 mortality. Importantly, the results of our study revealed that in LA County, household overcrowding was an even stronger predictor of increased mortality rates than the total number of COVID-19 cases. Additionally, our findings emphasize that elderly citizens residing in overcrowded households are at a particularly increased risk of mortality from COVID-19.\u003c/p\u003e \u003cp\u003eThese findings suggest key implications for addressing the COVID-19 pandemic and future outbreaks of communicable disease. These findings are consistent with studies investigating COVID-19 transmissibility which found transmission to be greater in indoor congregate settings, such as jails and buses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These settings share similar characteristics with overcrowded housing, including prolonged time spent with the same group of individuals, minimal ventilation, and multiple individuals occupying a limited space. While age, level of income, ethnic background, and medical co-morbidities have been frequently described as risk factors for poor outcomes associated with COVID-19 infection [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], our analyses suggest that public health measures designed to reduce mortality among persons with COVID-19 ought to make special consideration of persons living in overcrowded housing. The Centers for Disease Control and Prevention (CDC) has suggested that infected individuals maintain and six-foot distance between themselves and other household members to reduce transmission through the air by droplets and aerosols [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], however for persons living in overcrowded housing, complying with this recommendation may be difficult or impossible. Developing recommendations that aim to specifically address the unique needs of persons living in overcrowded housing may improve the health outcomes for this group. In addition, public health entities and healthcare providers should assess the prevalence of household overcrowding in the populations that they serve to inform interventions and more effectively allocate resources for COVID-19 prevention and control. More broadly, this study underlines how this pandemic has exacerbated the detrimental effects of the housing crisis in the US on the health of the population, and the urgent need to increase access to affordable housing to reduce morbidity and mortality from COVID-19.\u003c/p\u003e \u003cp\u003eHousehold overcrowding may increase the risk of COVID-19 mortality. Public health agencies should recognize the importance of effectively allocating resources to areas with overcrowded housing during the COVID-19 pandemic and future disease outbreaks. Our findings emphasize an imperative for further studies exploring the association between overcrowded housing and COVID-19 mortality, as well as mortality attributed to other communicable pathogens.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eLimitations of our work include that our ecological analysis can only provide partial insights regarding the additional barriers experienced by populations in overcrowded housing, such as discrimination or social exclusion. Furthermore, we were unable to account for undocumented or homeless individuals, which are equally important populations to consider when addressing infection and mortality rates. Finally, while the cities in LA County encompass a large portion of the County\u0026rsquo;s population, they do not account for unincorporated areas (regions not governed by municipal corporations), which comprise a sizeable proportion of the County. Regardless of these limitations, our study emphasizes the imperative for further research and data acquisition on the association between household overcrowding and mortality due to COVID-19 infection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLos Angeles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariance inflation factors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis project utilized publicly available data, and ethics approval was hence not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAs ethics approval was not required, participant consent for publication was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: The authors have no conflicts of interest associated with the material presented in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: No funding was provided to any of the authors for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors have no acknowledgements to be made as there were not any other contributors to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization: KV. Data curation: KV, TG. Formal analysis: KV, TG, JA. Funding acquisition: N/A. Methodology: KV, TG, JA. Project administration: N/A. Visualization: KV, JA. Writing \u0026ndash; original draft: KV, JA. Writing \u0026ndash; reviewing \u0026amp; editing: KV, TG, JA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCenters for Disease Control and Prevention [CDC]. COVID Data Tracker.\u003cem\u003e\u0026nbsp;U.S. Department of Health and Human Services\u003c/em\u003e. 2021. Retrieved from https://covid.cdc.gov/covid-data-tracker/#datatracker-home.\u003c/li\u003e\n \u003cli\u003eZonderman AB, Mode NA, Ejiogu N, Evans MK. Race and Poverty Status as a Risk for Overall Mortality in Community-Dwelling Middle-Aged Adults. JAMA Intern Med. 2016;176(9):1394-1395. doi:10.1001/jamainternmed.2016.3649\u003c/li\u003e\n \u003cli\u003eKarmakar M, Lantz PM, Tipirneni R. Association of Social and Demographic Factors With COVID-19 Incidence and Death Rates in the US. JAMA Netw Open. 2021;4(1):e2036462. Published 2021 Jan 4. doi:10.1001/jamanetworkopen.2020.36462\u003c/li\u003e\n \u003cli\u003eNeiderud CJ. How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol. 2015;5:27060. Published 2015 Jun 24. doi:10.3402/iee.v5.27060\u003c/li\u003e\n \u003cli\u003eBlake, K.S, Kellerson, R L., Simic A. Measuring overcrowding in housing. \u003cem\u003eEconometrica, Inc. Bethesda, MD.\u003c/em\u003e 2007. Retrieved from https://www.census.gov/content/dam/Census/programs-surveys/ahs/publications/Measuring_Overcrowding_in_Hsg.pdf\u003c/li\u003e\n \u003cli\u003eMendez AD, Escobar M, Romero M, Wojcicki JM. Overcrowding and exposure to secondhand smoke increase risk for COVID-19 infection among Latinx families in the greater San Francisco Bay Area. Tob Induc Dis. 2021;19:79. Published 2021 Oct 13. doi:10.18332/tid/140827\u003c/li\u003e\n \u003cli\u003eRaisi-Estabragh Z, McCracken C, Bethell MS, et al. Greater risk of severe COVID-19 in Black, Asian and Minority Ethnic populations is not explained by cardiometabolic, socioeconomic or behavioural factors, or by 25(OH)-vitamin D status: study of 1326 cases from the UK Biobank. J Public Health (Oxf). 2020;42(3):451-460.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKamis C, Stolte A, West JS, Fishman SH, Brown T, Brown T, Farmer HR. Overcrowding and COVID-19 mortality across US counties: Are disparities growing over time?. SSM-Population Health. 2021 Jun 12:100845.\u003c/li\u003e\n \u003cli\u003eU.S. Census Bureau. Datasets. \u003cem\u003eUnited States Census Bureau.\u0026nbsp;\u003c/em\u003e2021. Retrieved from: https://www.census.gov/data/datasets.html\u003c/li\u003e\n \u003cli\u003eCoronavirus Resource Center. COVID-19 United States Cases by County. \u003cem\u003eJohns Hopkins University.\u0026nbsp;\u003c/em\u003e2021.\u003cem\u003e\u0026nbsp;\u003c/em\u003eRetrieved from https://coronavirus.jhu.edu/us-map.\u003c/li\u003e\n \u003cli\u003eCounty of Los Angeles Public Health [County of LA Public Health]. \u0026nbsp;\u003cem\u003eLA County Daily COVID-19 Data.\u0026nbsp;\u003c/em\u003e2021.\u003cem\u003e\u0026nbsp;\u003c/em\u003eRetrieved from:\u0026nbsp;http://publichealth.lacounty.gov/media/coronavirus/data/index.htm\u003c/li\u003e\n \u003cli\u003eRozenfeld Y, Beam J, Maier H, et al. A model of disparities: risk factors associated with COVID-19 infection. \u003cem\u003eInt J Equity Health\u003c/em\u003e. 2020;19(1):126. Published 2020 Jul 29. doi:10.1186/s12939-020-01242-z\u003c/li\u003e\n \u003cli\u003eGe Y, Martinez L, Sun S, et al. COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19: A Population-Based Cohort Study in Zhejiang Province, China. \u003cem\u003eJAMA Intern Med.\u003c/em\u003e 2021;181(10):1343\u0026ndash;1350. doi:10.1001/jamainternmed.2021.4686\u003c/li\u003e\n \u003cli\u003eCenters for Disease Control and Prevention. (n.d.). \u003cem\u003eHow to protect yourself \u0026amp; others\u003c/em\u003e. Centers for Disease Control and Prevention. Retrieved January 14, 2022, from https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, mortality, housing, inequities, prevention","lastPublishedDoi":"10.21203/rs.3.rs-1323646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1323646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOvercrowded housing is a sociodemographic variable associated with increased infection and mortality rates from communicable diseases. It is not well understood if this association exists for COVID-19. Our objective was to hence determine the association between household overcrowding and risk of mortality from COVID-19, and this was done by performing bivariable and multivariable analyses using COVID-19 data from cities in Los Angeles County.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBivariate regression revealed that overcrowded households were positively associated with COVID-19 deaths (standardized β=0.863, p\u0026lt;0.001). COVID-19 case totals, people aged 60+, and the number of overcrowded households met conditions for inclusion in the backwards stepwise linear regression model. Analysis revealed all independent variables were positively associated with mortality rates, primarily for individuals 60+ (standardized β\u003csub\u003e­1­­\u003c/sub\u003e=0.375, p=0.001), followed by overcrowded households (standardized β\u003csub\u003e­2­­\u003c/sub\u003e=0.346, p=0.014), and total COVID-19 cases (standardized β\u003csub\u003e­3\u003c/sub\u003e=0.311, p\u0026lt;0.001).\u003csub\u003e \u003c/sub\u003eOur findings highlight that residing in overcrowded households may be an important risk factor for COVID-19 mortality. Public health entities should consider this population when allocating resources for prevention and control of COVID-19 mortality and future disease outbreaks.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Overcrowded Housing Increases Risk for COVID-19 Mortality: An Ecological Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-28 21:42:01","doi":"10.21203/rs.3.rs-1323646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-02-26T06:08:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-02-25T22:01:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2022-02-05T04:31:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3155eff9-9f62-4fd9-95a2-e2da22043ddd","owner":[],"postedDate":"February 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2022-04-05T11:52:26+00:00","versionOfRecord":{"articleIdentity":"rs-1323646","link":"https://doi.org/10.1186/s13104-022-06015-1","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2022-04-05 11:52:26","publishedOnDateReadable":"April 5th, 2022"},"versionCreatedAt":"2022-02-28 21:42:01","video":"","vorDoi":"10.1186/s13104-022-06015-1","vorDoiUrl":"https://doi.org/10.1186/s13104-022-06015-1","workflowStages":[]},"version":"v1","identity":"rs-1323646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1323646","identity":"rs-1323646","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}