A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes

Abstract

Background Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Methods and findings We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. Conclusions The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).

COVID-19 Infections and Mortality in Florida Counties: Roles of Race, Ethnicity, Segregation, and 2020 Election Results

“…The political link introduces the possibility that pandemic-associated behaviors in Republican-leaning areas is a new expression of their deaths of despair crises [ 23 , 42 ]. In linking community political preferences with infections, this study bridges research linking such partisan leanings to higher infection rates [ 7 ] and other research linking voter choices to infections [ 21 , 22 ].…”

“…Findings from this study reinforce lessons learned in other studies of COVID-19 outcomes set in the USA. Numerous county and metropolitan level studies have identified a link between population proportions Black or Hispanic with higher COVID-19 infection and mortality rates [ 2 , 3 , 5 – 7 ]; several are even set in Florida [ 8 , 16 ]. Similarly, many studies [ 6 , 8 , 16 , 44 ] find higher poverty rates are associated with higher infection and mortality rates.…”

“…In addition to these factors, other COVID-19 studies have found higher infection and mortality rates associated with community-level poverty, overcrowding, segregation, and average age [ 5 , 6 ]. Counties with higher proportions of females and lower high school graduation rates also experienced higher transmission rates [ 7 ]. A neighborhood-level study of Miami finds economic disadvantage has a stronger influence on infection rates than either race or ethnicity [ 8 ].…”

“…Such uniformity means all counties simultaneously re-opened schools, liberalized bar and restaurant safety precautions [ 10 ], and eliminated mask restrictions. At short time scales, studies find within-county autocorrelated infection rates from one week to the next [ 7 ]. Over the longer time scale, a similar pattern is revealed in a lack of movement among the counties.…”

“…Online spending increased more in Democratic-leaning counties when stay-at-home orders were instituted, consistent with calls for reducing non-essential travel and greater distancing [ 15 ]. Other studies link such mobility decisions to increased infection rates [ 7 ]. Taken together, these studies suggest a causation chain that starts with community political preference as a primary determinant of area mobility behaviors, which are, in turn, strongly related to infections.…”