Introduction
First reports of a pneumonia of unknown etiology emerged in Wuhan,
China, on December 31, 2019. The extremely contagious virus was
identified as severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), and spread quickly beyond Wuhan. In the U.S., the first
case of COVID-19, the disease caused by SARS-CoV-2, was reported on
January 22, 2020. Despite unprecedented government action, the number of
cases in the U.S. grew to over 593000 as of April 14, and crossed one
million on April 28.1
The local press and epidemiological research alike have reported local
differences in the outbreak.2 A community’s
susceptibility to any virus is determined by a variety of factors, inter
alia, biological determinants, demographic profiles, and socioeconomic
characteristics.3 These factors vary significantly
across the U.S.; for instance, COVID-19 fatalities in New York, an
epicenter of the outbreak in the U.S., disproportionally affect males
and people belonging to older age groups, from Black/African and
Hispanic ethnicities, and with certain comorbidities.4
As of May 09, 2020, more than half of COVID-19 data reported by the
Centers for Disease Control and Prevention (CDC) are missing race and
ethnicity disaggregation; other individual variables are lacking as
well. To understand local differences in the outbreak and risk of
contracting COVID-19, we therefore deploy an ecological analysis using
contextual factors. A two-level hierarchical linear model with full
maximum likelihood allows us to simultaneously test and disentangle
county- and state-level effects.
Our study contributes to various strands of current COVID-19 research.
First, we note that contextual factors influence the COVID-19 outbreak.
Because significant variations in the outbreak exist between states and
counties within a state (Figures 1 and 2),2 we
recommend policy makers to look at pandemics from the smallest
subdivision possible for effective implementation of countermeasures and
provision of critical resources. Second, we develop an understanding of
how regional cultural differences relate to outbreak variations, driven
by specific psychological functioning of individuals and the enduring
effects of such differences on political processes, governmental
institutions, and public policies.5,6 Third, we debunk
rumors that a state’s leadership, as expressed by the political party in
control or the gender of its governor, has a statistically significant
influence on the outbreak.7 Fourth, we identify how
the virus affects counties differently, depending on their demographic
profile. Fifth, while good personal health is generally associated with
a lower risk, we identify the prevalence of obesity and smoking in
counties to be negatively related with the outbreak. Sixth, while
previous studies link air pollution to the death rate, we show that it
also contributes to the number of cases.