In this study, we demonstrated that the timing of state mandated stay-at-home orders had the most significant effect on all three outcomes: the total cases/100,000, daily case rate, and total deaths/100,000 after adjusting for multiple socio-demographic, geographic and health related factors. A proclamation of this order in the counties that implemented it, even a day earlier, would have reduced the DCR by 0.04, confirmed cases by 12/100,000, and deaths by 0.87/100,000.
Health disparities between the counties in the United States have long existed and been previously well studied 15 16 17. We sought to determine the effects of these disparities on the incidence and spread of COVID-19 at the county level. Socio-economic factors including gender, race, age, income and insurance have been shown to be associated with the incidence and spread of COVID-19 18. Counties with low population in the USA may not have the adequate health infrastructure to manage complicated COVID-19 patients, leading to movement of these patients to adjoining counties with more advanced facilities. Patients from smaller counties may be referred to larger health centers in adjacent counties with larger population, leading to sampling bias in the analysis. Furthermore, individuals with appropriate health insurance coverage may seek out referral centers in adjacent counties with better health facilities. We included counties with a population of > 100,000 population and a minimum case rate of 50/100,000 in an attempt to reduce these biases.
DCR:
We considered a variety of factors which may be hypothesized to have an association with COVID-19 incidence and spread, but the factor that emerged with the most significant association was the days from stay-at-home order (coefficient of correlation -0.73 and a standardized beta of -0.69). The differential effects of the timing of intervention in terms of social distancing, to prevent the spread of the virus, has already been established very recently by Pei (2020) 19. In this study, we were able to show that after adjusting for all possible independent factors, enforced social distancing measures (days from the order of stay-at-home) had the largest effect size in preventing the spread of virus. Uninsured patients had a significant negative correlation with daily case rate, implying that higher uninsured population in a county would be associated with lower COVID-19 cases. The differential accessibility of uninsured population to testing is the likely explanation for this finding. Uninsured people are more likely to experience poverty and have transportation difficulties, therefore they may not be able to reach the few and distant COVID-19 testing sites 20.
Confirmed cases per 100,000:
Significant positive correlation in the best fit multiple regression model was observed between housing problem, population density, lower education, African American race and number of people that received influenza vaccine. Previous research has shown that housing problems may lead to overcrowding, an important determinant for the spread of virus 21. High population density may be associated with increased spread of the virus (Rocklöv and Sjödin, 2020). In our study, higher population density had a large standardized beta coefficient of 0.17 and 0.37 with confirmed cases/100,000 and deaths/100,000 respectively, making this the second most important factor after days from stay-at-home order to affect the outcomes. It is well known that racial and ethnic minority groups are often affected disproportionately more by pandemics. Non-Hispanic African Americans not only have a higher case burden, but also are hospitalized more often and have a higher death rate (CDC, 2020) 24 25. Our study showed a significant association between the number of cases and African American race. Recently, questions were raised about the adverse effects of influenza vaccine on the susceptibility of individuals to coronavirus but other studies have refuted the possibility 26 27. The association of higher rate of influenza vaccination with confirmed cases may be attributed to the fact that in Medicare population, those who have received the influenza vaccination are more likely to get tested and treated for COVID-19. We were unable to determine an alternative explanation for this finding. Significant negative association was observed for days from the stay-at-home orders and uninsured population among the counties.
Confirmed deaths per 100,000:
Several factors that were associated with confirmed cases were also associated with deaths, such as days from stay-at-home order, housing problem, population density and the uninsured. The percentage of unemployed and age > 65 years, both showing positive correlation, were the new factors associated with COVID-19 related deaths. Unemployment is an important socioeconomic factor indirectly related to the higher death rate due to the virus. Lack of timely access to testing, housing problems, chronic illnesses and delayed access to medical treatment can be associated with unemployment, as well as increased number of deaths from the virus. Age > 65 years have been shown to be strongly associated with deaths 28.
Limitations:
The major limitation of the study is that cases of COVID-19 in the counties cannot be directly linked to the subjects with the demographic, socioeconomic or geographic factor. This study merely compares the population characteristics of the counties with the outcomes within the same and other counties. In addition, we considered about 20% of the most populated counties of the USA, which may lead to a bias resulting from the factors associated with less populated counties. Population movement due to referrals from satellite hospitals to large health centers may also lead to statistical bias.