Jobs, Housing, and Mask Wearing: Cross-Sectional Study of Risk Factors for COVID-19

Abstract

Background Many studies have focused on the characteristics of symptomatic patients with COVID-19 and clinical risk factors. This study reports the prevalence of COVID-19 in an asymptomatic population of a hospital service area (HSA) and identifies factors that affect exposure to the virus. Objective The aim of this study is to measure the prevalence of COVID-19 in an HSA, identify factors that may increase or decrease the risk of infection, and analyze factors that increase the number of daily contacts. Methods This study surveyed 1694 patients between April 30 and May 13, 2020, about their work and living situations, income, behavior, sociodemographic characteristics, and prepandemic health characteristics. This data was linked to testing data for 454 of these patients, including polymerase chain reaction test results and two different serologic assays. Positivity rate was used to calculate approximate prevalence, hospitalization rate, and infection fatality rate (IFR). Survey data was used to analyze risk factors, including the number of contacts reported by study participants. The data was also used to identify factors increasing the number of daily contacts, such as mask wearing and living environment. Results We found a positivity rate of 2.2%, a hospitalization rate of 1.2%, and an adjusted IFR of 0.55%. A higher number of daily contacts with adults and older adults increases the probability of becoming infected. Occupation, living in an apartment versus a house, and wearing a face mask outside work increased the number of daily contacts. Conclusions Studying prevalence in an asymptomatic population revealed estimates of unreported COVID-19 cases. Occupational, living situation, and behavioral data about COVID-19–protective behaviors such as wearing a mask may aid in the identification of nonclinical factors affecting the number of daily contacts, which may increase SARS-CoV-2 exposure.

A Cross-Sectional Study of Exposure Factors Associated with Seropositivity for SARS-CoV-2 Antibodies during the Second Epidemic Wave among a Sample of the University of Corsica (France)

“…Furthermore, we know that SARS-CoV-2 can remain airborne for up to three hours and survive on surfaces [ 17 ]. In agreement with previous studies [ 18 , 19 ], we observed that participants living in houses were at a lower risk of infection than were participants living in apartments. It can be hypothesized that single-family homes allow more frequent access to the outdoors than do apartments.…”

Effects of 105 biological, socioeconomic, behavioural, and environmental factors on the risk of SARS-CoV-2 infection and a severe course of Covid-19: A prospective longitudinal study

“…People who live in larger cities and individuals with higher education, especially women, had a lower risk of infection, which is in agreement with published data 20 . Household size, and in men especially the number of children under 20 years of age, was associated with a higher risk of infection, which again agrees with published data 3,4 . People living on their own had a much lower risk of infection than those who share household with someone else and singles also reported a less severe course of Covid-19.…”

“…Moreover, the range of factors examined by these retrospective or cross-sectional studies was rather limited. Factors significantly or non-significantly associated with Covid-19 were sex 1 , age 1 , ethnicity 2 , urbanisation 3 , residence in a multifamily unit 4 , BMI and obesity 2,5,6 , smoking 2,7 , physical fitness and forced expiratory volume 2 , the number of daily contacts 3 , wearing masks and washing hands 3 , socioeconomic deprivation 2 , particular AB0 blood groups [8-10], Rh factor [10], vitamin D deficiency [11], high-density lipoprotein level 2 , use of immunosuppressants 8 , and a growing set of comorbidities – cardiovascular disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease, dementia, hypertension and functional dependence 6 , and toxoplasmosis 9 . Other factors, such as contact with animals, have been suggested only on a theoretical basis 10 or are merely discussed in non-scientific sources, such as popular literature or the internet.…”

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

“…For social determinants, we considered a limited set of measures previously shown to be associated with test-positivity; [33] and mechanistically drive transmission dynamics by considering frequency of contacts and who contacts whom. [34] These include: socio-demographic variables that are proxies of economic barriers and systemic racism (income;% visible minority;% recent immigration); dwelling-related variables (% suitable housing; [ 35 , 36 ]% multigenerational households); and occupation-related variables (% working in essential services [health; trades, transport and equipment operation; sales and services; manufacturing and utilities; resources, agriculture, and production]) [37] …”