Community and Socioeconomic Factors Associated with COVID-19 in the United States: Zip code level cross sectional analysis

Abstract

Background: Multiple reports have pointed towards involvement of community and socioeconomic characteristics of people in the United States may be associated with COVID-19 cases and deaths. Methods:In this study, zip-code level data from 5 major metropolitan areas, was utilized to study the effect of multiple demographic & socioeconomic factors -including race, age, income, chronic disease comorbidity, population density, number of people per household on number of positive cases and ensuing death. Adjusted linear regression analysis using 13 to 16 such variables was performed. Results:Overall, 442 zip codes reporting 93,170 positive COVID-19 cases and 138 zip codes reporting mortality ranging from 0 to 25 were included in this study. A multivariable linear regression model noted that 1% increase in the proportion of residents above the age of 65 years, proportion of African American residents, proportion of females, persons per household and population density of the zip code increased the proportion of positive cases by 0.77%, 0.23%, 1.64%, 1.83% and 0.46% respectively (P<0.01) with only population density remaining significant in zip codes with greater than median number of cases. In zips with greater than median number of deaths, no community/socio-economic factor contributed significantly to death. Conclusion:This study gives early signals of gender, and racial inequalities while providing overwhelming evidence of how population density may contribute to an increase in the number of positive cases of COVID-19.

Excess Out-Of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of EMS Data in the COVID-19 Crisis in Tijuana, Mexico

“…There is a notable difference between respiratory cases and deaths, which may suggest that the profile of individuals who have the economic or social capital to seek care early for respiratory symptoms in Tijuana differs from those who do not interact with the medical system until after their death. This finding adds to a growing body of literature and social commentary suggesting that social inequalities may be translating into inequalities in the risk of infection or death from COVID-19 in numerous contexts [37][38][39][40][41][42][43] Conclusions EMS data provide a valuable tool to rapidly track the health of populations at risk of COVID-19 in LMICs, where other forms of real-time data may not be available. EMS information can be used to track excess out-of-hospital mortality and respiratory burden, as well as changing clinical or demographic features.…”

The COVID-19 Pandemic: Does Our Early Life Environment, Life Trajectory and Socioeconomic Status Determine Disease Susceptibility and Severity?

“…Detailed data from New York showed that the number of COVID-19 cases associated with the percentage of dependents in the local population, the male:female ratio, and low-income neighborhoods [21]. United States-wide data gave a similar result, with proportion of residents >65 years old, ethnic minorities, male:females ratio, and the overall population density associating with increased frequency of COVID-19 [22]. The United Kingdom followed a similar profile.…”

A delayed modulation of solar ultraviolet radiation on the COVID-19 transmission reflects an incubation period

“…The UV correlation exhibits a delay of about seven days, at the temporal scale of the incubation period. While the spatial clusters and the correlation might be attributed to the spatial difference of socioeconomic factors 20,21 , the delay cannot. The variations of socioeconomic factors are overall at temporal scales much longer than that of the delay, which can neither modulate the COVID-19 nor respond to the UV flux at the time scale of the incubation period.…”