Discussion of results
In the absence of national-level data controlled for location and
disaggregated by race and ethnicity, demographics, information about
comorbidity and other personal health variables, an ecological analysis
provides an alternative way of measuring the disproportionate impact of
COVID-19 across the U.S. and among segments of Americans. It may be
contrary to expectations that the outbreak rate of a new pathogen, which
is able to infect virtually anyone, manifests contextual disparities.
But for other conditions, such as HIV and cancer, regional health
disparities have been reported before;33,34 and with
the current study we show that contextual factors in the U.S. also
create a variation in COVID-19 cases.
Our analysis indicates that higher
outbreak rates can be found in U.S. states characterized by a higher
cultural value of collectivism (coefficient 0.998, confidence interval
[0.351; 1.645], p = 0.004). As Table 2 shows, collectivistic
values are more prevalent in counties that are warmer (correlation with
temperature 0.715, p < 0.001) and have a higher
percentage of people with a Black/African background (with Black/African
American 0.539, p < 0.001). This mirrors findings from
international cultural research.12 Conversely, we
cannot find any statistical evidence that the government spending, the
gender of the governor, or the party in control would be in any way
linked to the outbreak. This certainly debunks myths spread by the
popular media.
A disproportionately stronger outbreak of COVID-19 cases can be found in
counties with a higher percentage of Black/African (1.158, [0.725;
1.591], p < 0.001) and Asian Americans (1.305,
[0.166; 2.444], p = 0.025), which supports prior infection
and mortality studies in the U.S. and U.K.18,35 The
former counties are also characterized by a higher rate of sleep
deprivation (0.568, p < 0.001) and warmer temperatures
(0.533, p < 0.001). The latter have a higher population
density (0.553, p < 0.001). While we found sleep
deprivation to be associated with a higher outbreak rate (1.557;
[0.412; 2.702], p = 0.008), a positive influence of
population density (0.050, [-0.009; 0.109], p = 0.095) and
temperature (0.301, [-0.518; 1.120], p = 0.472) are only
directionally informative, but not statistically significant. In the
first robustness test, higher average temperatures are positively and
significantly related to the outbreak (1.027, [0.235, 1.861],p = 0.011), potentially related to more time spent indoor with
air conditioning.
Conversely, counties with more Hispanic Americans are less affected by
the pandemic, with borderline statistical significance (-0.447,
[-0.915; 0.021], p = 0.061). We could not find a significant
effect for counties with a higher Native American (0.763, [-0.209;
1.735], p = 0.124) or Hawaiian population (1.478, [0.506;
2.450], p = 0.538) though.
We see that higher income and education levels are associated with a
less aggressive outbreak (household income: -3.854, [-7.437;
-0.271]; p = 0.035; nonproficiency in English: 2.090; [0.547;
3.633]; p = 0.008; math grade: -0.002, [-0.004; 000];p < 0.001). In counties with a higher household income,
the obesity rate and the percentage of smokers tends to be lower
(-0.518, p < 0.001 and -0.666,p < 0.001 respectively). Both are negatively associated
with the outbreak rate. The effect of the obesity rate is highly
significant (-1.093, [-1.828; -0.358], p = 0.004), but the
effect of the percentage of smokers is only directionally informative
(-0.784, [-3.150; 1.582], p = 0.516). Studies report that
people with obesity are at increased risk of developing severe COVID-19
symptoms,36 but, to the best of our knowledge, a link
to the infection rate has not yet been established. A potential
explanation of this is that people with obesity heed the warnings issued
by the CDC, and are extra careful in avoiding social contact, in line
with the value expectancy concepts of the health belief
model.22 Other studies report that smoking or
medicinal nicotine might be a protective factor against infection by
SARS-CoV-2;23 our ecological data does not contradict
this finding. Many other variables related to good personal health are
associated with a slower outbreak (social associations: -2.027,
[-2.911; -1.143], p < 0.001; sleep deprivation:
1.557, [0.412; 2.702], p = 0.008; preventable
hospitalization: 0.001, [-0.001; 0.003], p = 0.207).
Regarding age-related demographics, we confirm early observations that
counties with an older population are more affected by the outbreak,
with borderline significance (median age: 0.657,
[-0.033; 1.347], p = 0.062). Notably, the percentage of
persons under 18 years is positively associated with the outbreak rate,
again with borderline significance (1.066, [-0.014; 2.146],p = 0.053). A possible reason is that younger people physically
interact more frequently, closer, and longer with their friends, thus
contributing to the spread of the virus. Conversely, we find no effect
of differences in gender (0.167, [-0.880; 1.214], p = 0.755).
None of these demographic variables are strongly correlated with any
other variable.
Air pollution is a significant contributor to the outbreak (3.329,
[1.465; 5.193], p < 0.001), and, concurrently,
counties with a rural environment experience a slower outbreak (-0.443,
[-0.574;
-0.312], p < 0.001). This calls for studies linking
air pollution to the lethality of COVID-1924,25 to
include the outbreak rate as a potential confounding variable.
Contrariwise, a better food environment is associated with a higher
outbreak rate (5.996, [1.286; 10.706], p = 0.016). While the
food environment index is usually associated with a healthier lifestyle,
better access to grocery stores and supermarkets in the vicinity also
means more interaction with other people, and thus an increased
likelihood of transmission.
As a final point, we want to note that we have presented associations
between contextual factors and the COVID-19 outbreak which are
consistent with the deliberations leading to our research model.
However, these associations, even when statistically significant, are
not an inference of causality. Establishing causal inference is, of
course, critical for our understanding of and fight against COVID-19,
but this represents a direction for further research using more detailed
data at the level of individual patients.