Methods
We now explain the estimation of the outbreak rate, and the reasons for
including certain contextual factors; Table 1 summarizes the data
sources.
Outbreak rate
We obtain COVID-19 outbreak data from USA Facts, as of April 14,
2020.1 Since January 22, this database has aggregated
data from the CDC and other public health agencies. The 21 cases on the
Grand Princess cruise ship are not attributed to any counties in
California. We discard cases only allocated at the state-level due to
lack of information. On average, these are only 308 cases per state, but
a few states have as many as 4866 (New Jersey), 1300 (both Rhode Island
and Georgia), or 1216 (Washington State). Following approaches by the
Institute for Health Metrics and Evaluation at the University of
Washington8 and the COVID-19 Modeling Consortium at
the University of Texas at Austin,9 we model the
outbreak using the exponential growth equation\(\frac{\text{dy}}{\text{dt}}=b\ y\), where b is a positive
constant called the relative growth rate with units of inverse
time. Going forward, we simply refer to b as the
outbreak rate. The shape of the trends in case counts enables us to see
differences between counties.10 Solutions to this
differential equation have the form \(y=a\ e^{\text{bt}}\), wherea is the initial value of cases y . The doubling timeTd can be calculated as\(T_{d}=\frac{ln(2)}{b}\). We estimate the outbreak rate for 1987 out
of 3142 counties in the 50 U.S. states that have a minimum of 10
reported cases. This is a statistical, but not an epidemiological model,
that is, we are neither trying to model infection transmission
nor estimate epidemiological parameters, such as the pathogen’s
reproductive or attack rate. Instead, we are fitting curves to observed
outbreak data at the county level. A change-point analysis using the
Fisher discriminant ratio as a kernel function does not show any
significant change points in the outbreak, and therefore justifies
modeling the COVID-19 outbreak as a phenomenon of unrestricted
population growth.11 We cannot forecast outbreak
dynamics with this statistical approach, though we do not require
extrapolated data in our work.
Cultural values
Culture can be defined as a set of values that are shared in a given
social group. While cultural values are often used to distinguish
countries,12 more than 80% of cultural variation
resides within countries.13 The original North
American colonies were settled by people hailing from various countries,
who have spread their influence across mutually exclusive areas. Their
distinct cultures are still with us today.6 Although
today’s U.S. states are not strictly synonymous with these cultural
areas, there is abundant evidence that political boundaries can serve as
useful proxies for culture.14
One of the most useful constructs to emerge from cultural social
psychology is the individualism-collectivism bipolarity. It has proven
useful in describing cultural variations in behaviors, attitudes, and
values. Briefly, individualism is a preference for a loosely knit social
framework, whereas collectivism represents a preference for a tightly
knit framework, in which its members are interdependent and expected to
look after each other in exchange for unquestioning loyalty. While the
majority of research on collectivism involves comparing countries12, we use an index developed at state-level solely
within the U.S. 5. Previous studies have shown that
the regional prevalence of pathogens and international differences in
the COVID-19 outbreak are positively associated with
collectivism.14,15
Institutional confounders
In addition to culture, we include various institutional confounders at
the state-level, such as the political affiliation of a state’s
governor, the gender of the governor, and government spending per
capita. Government plays a critical role in policy development and
implementation, and so state-level differences could influence the
outbreak rate.16
Racial composition
While first systematic reviews about COVID-19 incidences from China
relied on ethnically homogenous cohorts17,18ethnically diverse populations, such as in the U.K. and U.S. may exhibit
different susceptibility or response to infection because of
socioeconomic, cultural, or lifestyle factors, genetic predisposition
and pathophysiological differences. Certain vitamin or mineral
deficiencies, differences in insulin resistance or vaccination policies
in countries of birth may also be contributing
factors.18 We include variables measuring the
composition of U.S. counties regarding racial and ethnic groups.
Income and education
Poverty is arguably the greatest risk factor for acquiring and
succumbing to disease worldwide, but has historically received less
attention from the medical community than genetic or environmental
factors. The global HIV crisis brought into sharp relief the
vulnerability of financially strapped health systems, and revealed
disparities in health outcomes along economic fault
lines.19 We include the median household income to
quantify potential economic disparities between U.S. counties. In
addition, we measure non-proficiency in English and math performance of
students. Lower educational levels may result in a lower aptitude as it
relates to understanding and effectively responding to the pandemic.
Other demographics
Age and gender also play a potential role in a population’s
susceptibility. During the aging process, immune functions decline,
rendering the host more vulnerable to certain
viruses.20 We use the percentage of population below
18 years of age and their median age to determine potential effect of
differences in mobility, response, and lifestyle factors. We also
control for the percentage of the population that is female, as one
COVID-19
study
in Italy showed that about 82% of critically ill people admitted into
intensive care were men.21
Personal health
Good overall personal health is a general indicator for disease
resistance. Additionally, the health belief model suggests that a
person’s belief in a personal threat of a disease, together with faith
in the effectiveness of behavioral recommendations, predicts the
likelihood of the person adopting the
recommendation.22 We use the percentage of the
population that reports insufficient amount of sleep, is obese (as
defined by a body mass index above 30), and smokes daily. Given the
latter two are publicized risk factors for COVID-19, there is a
potential for greater caution following the value-expectancy concepts of
the health belief model. Yet, medicinal nicotine has been identified as
a potential protective factor against infection by
SARS-CoV-2.23 We also measure the preventable
hospitalization rate (that is, the rate of hospital stays for
ambulatory-care sensitive conditions) as a potential indicator of poor
personal health and the social association rate (that is, the average
number of membership associations), which is generally connected with
positive mental health and happiness.
External health
Previous studies suggest that exposure to pollution can suppress immune
responses and proliferate the transmission of infectious
diseases,24 and that the COVID-19 mortality rate is
associated with air pollution.25 However, the impact
of air pollution on the spread of COVID-19 is not yet
known.24 We use the 2014 average daily density of fine
particulate matter PM2.5 to measure air pollution across
U.S. counties, and the percentage of population living in rural areas to
account for physical distancing being more prevalent in rural areas. In
addition, the food environment index reflects access to grocery stores
and healthy foods.
Other confounders
Population density and overcrowding is significant when considering
public health crises, facilitating the spread of diseases in developing
and developed countries alike.26 As the climate is
another highly publicized confounder potentially influencing the
COVID-19 transmission rate,27 we also include each
county’s average temperature during February and March 2020. To control
for the temporality of the outbreak, we bring in a variable representing
the number of days between January 01 and the 10thconfirmed case reported.