key: cord-261687-632r6uqe authors: Xie, Z.; Li, D. title: Health and Demographic Impact on COVID-19 Infection and Mortality in US Counties date: 2020-05-11 journal: nan DOI: 10.1101/2020.05.06.20093195 sha: doc_id: 261687 cord_uid: 632r6uqe Introduction With the pandemic of COVID-19, the number of confirmed cases and related deaths are increasing in the US. We aimed to understand the potential impact of health and demographic factors on the infection and mortality rates of COVID-19 at the population level. Methods We collected total number of confirmed cases and deaths related to COVID-19 at the county level in the US from January 21, 2020 to April 23, 2020. We extracted health and demographic measures for each US county. Multivariable linear mixed effects models were used to investigate potential correlations of health and demographic characteristics with the infection and mortality rates of COVID-19 in US counties. Results Our models showed that several health and demographic factors were positively correlated with the infection rate of COVID-19, such as low education level and percentage of Black. In contrast, several factors, including percentage of smokers and percentage of food insecure, were negatively correlated with the infection rate of COVID-19. While the number of days since first confirmed case and the infection rate of COVID-19 were negatively correlated with the mortality rate of COVID-19, percentage of elders (65 and above) and percentage of rural were positively correlated with the mortality rate of COVID-19. Conclusions At the population level, health and demographic factors could impact the infection and mortality rates of COVID-19 in US counties. Since the first reported coronavirus disease 2019 (COVID- 19) case was identified in Wuhan, China 1 , it quickly spreads out and was declared as a public health emergency of international concern on January 30, 2020 and a pandemic on March 11, 2020 by the World Health Organization (WHO). By April 28, 2020, there are nearly three millions confirmed COVID-19 cases and over 200,000 confirmed deaths globally 2 . Much effort has been put in understanding the COVID-19 virus infection mechanism and further developing effective vaccines for COVID-19 [3] [4] [5] While COVID-19 becomes an epidemic in the US, there are geographic variations in the number of COVID-19 confirmed cases and related deaths, which might due to the differences in epidemiologic and population-level factors 8 . In this study, at the population level, we . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint investigated potential correlations of health and demographic factors with the infection and mortality rates of COVID-19 in US counties. The number of confirmed COVID-19 cases and related deaths in each US counties from January 21, 2020 to April 23, 2020 were obtained from the website of "1Point3Acres.com" The outcome variables include the infection rate of COVID-19 (the number of confirmed cases divided by the population), and the mortality rate of COVID-19 (the number of deaths divided by the number of confirmed cases) in each US county. We did a log-transformation on the infection and mortality rates of COVID-19. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 11, 2020 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint Multivariable linear mixed effects models were used to investigate the correlations between the outcome variables and prediction variables. The correlations of counties within the same state were considered through the compound symmetry variance-covariance structure, which assumes the correlations of counties are the same within the same state. Covariates controlled for in our final statistical models were selected through purposeful selection of covariates method 9 . The coefficient estimates and their 95% confidence intervals (CIs) were used to quantify the correlation. Positive estimate indicates positive correlation while negative estimate indicates negative correlation between covariates and outcome variables. P-value below 0.05 indicates a significance contribution of a variable to the statistical models. All analyses were conducted using proc mixed procedure in SAS V.9.4 (SAS Institute Inc., Cary, NC). Among 3,143 counties and county equivalents of the United States, by April 23, 2020, based on the website of 1Point3Acres.com, there were 2,743 US counties that had reported COVID-19 cases, ranging from 1 to 147,297 cases with total confirmed cases of 855,979. Considering the difference in total population among different counties, we calculated the COVID-19 infection rate as the number of COVID-19 cases divided by the total population in each county. To investigate the potential correlation of health and demographic factors with the COVID-19 infection rate at the county level, we performed a multivariable linear mixed effects model. Table 1 CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020. While the COVID-19 infection rates were different among US counties, the COVID-19 mortality rate (the number of COVID-19 related deaths divided by the number of confirmed cases) also varied among US counties, ranging from 0 to 1 with a mean of 0.036 (standard deviation = . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint 0.079). To examine the impact of health and demographic factors on the mortality rate of COVID-19 in US counties, a multivariable linear mixed effects model was performed. As shown in Table 2 Our results showed that while the number of days since the first reported case in each county was positively correlated with the infection rate of COVID-19, it was negatively correlated with the mortality rate of COVID-19. In addition, the infection rate was significantly negatively correlated with the mortality rate of COVID-19. One possible explanation could be that health professionals or medical system developed more efficient medical practices (such as increase ICU beds and enhance social distancing) to treat COVID-19 patients, so that the mortality rate was decreasing even though the infection rate was increasing with the spreading of COVID-19 over time. In this study, we showed that the population density was positively correlated with both the infection and mortality rates of COVID-19 even though the estimated coefficients were small. With the higher population density, social distancing might be more challenging, which could contribute to higher infection and mortality rates of COVID-19. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020 13 . Here, we showed, at the population level, percentage of smokers in US counties was negative correlated with the infection rate of COVID-19. However, several studies showed that smoking could worsen the health outcomes of COVID-19 patients 14, 15 . While it requires further investigation on how smoking affects the infection and mortality of COVID-19, careful caution should be taken to interpret these results and smoking should be discouraged. In this study, we showed that percentage of population with 65 and over was positively correlated with the mortality rate of COVID-19 at the population level. One study on COVID-19 patients from China and other countries showed that the older age group (above 60 years) had significantly higher death rate than the younger age group (below 60 years) 16 . The Centers for Disease Control and Prevention reported that 8 out of 10 deaths in the US related to have been in adults with age 65 and over. One possible reason for high death rate in elder patients is that the majority of elder patients with COVID-19 have comorbidities, such as respiratory diseases, kidney failure and heart conditions, which might contribute to the fatal . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint outcome or even more than the contribution of SARS-CoV-2. Therefore, the seniors need to be more careful to avoid COVID-19 infection due to their high fatality rate. It has been shown that there was no association between influenza vaccination and coronavirus 17 . However, one study showed that influenza vaccine derived virus interference was significantly associated with coronavirus 18 . Here, we showed that percentage of people with vaccination was negatively correlated with the mortality rate of COVID-19. Considering the difference between influenza viruses and coronaviruses, the influenza vaccines are less likely to prevent infections with coronavirus. In this study, we did not observe the significant correlation between influenza vaccination and the infection rate of COVID-19. However, without influenza vaccination, the COVID-19 patients might have more severe symptoms with the influenza infection, which might lead to higher fatality rate. Whether influenza vaccines could lead to low mortality rate of COVID-19 requires further investigation. Our statistical model showed that percentage of rural was positively correlated with the mortality rate of COVID-19. Moreover, the counties that are farm-dependent or mining-dependent have high mortality rate of COVID-19. One possible explanation could be that the medical systems in rural areas are not well equipped and prepared for the COVID-19 infection. This finding should be alarming since rural areas have limited medical resources to respond to this pandemic and treat COVID-19 patients 19 . Additional measures (such as professional health experts and wellequipped hospitals) should be taken before the COVID-19 becomes epidemic in rural areas. In our study, the number of confirmed COVID-19 cases and deaths were from 1Point3Acres.com, which is subject to some errors (such as unreported cases). In addition, we only examined the correlation of health and demographic factors with the infection and mortality rates of COVID-19 at the population level. Therefore, the causal relationship could not be . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 11, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 11, 2020. . https://doi.org/10.1101/2020.05.06.20093195 doi: medRxiv preprint Clinical features of patients infected with 2019 novel coronavirus in Wuhan World Health Organization. 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