Responding to the Pandemic: A Multicountry Study on Social-Political Factors and Health Outcomes of COVID-19
Authors:   ,   University of Tennessee Health Science Center, Yao Lan 
Published: November 2021

Introduction. Coronavirus Disease 2019 (COVID-19) poses a major global threat to human beings, which has caused devastating consequences of population health, political, and economic crises in many countries. This dissertation was composed of three research activities to study the following aims: (1) review the existing literature focusing on political factors and health outcomes of COVID-19; (2) assess the relationship between democracy and case fatality rate of COVID-19 by controlling for the effect of age, comorbidity, health expenditure, healthcare workforce, and population density; and (3) identify the trajectory pattern cases peak days, deaths peak days, and peak periods. Methods. We accessed data from the World Health Organization (WHO), World Bank, Johns Hopkins University, and the Democracy Index 2019 database. First, we conducted a systematic review that searched three databases and synthesized the articles about democracy and health outcomes of COVID-19. Second, we analyzed data from 148 countries with at least 2,000 confirmed cases of COVID-19 by October 25, 2020. Multiple linear regression was used to examine the association between the Democracy Index and case fatality rate of COVID-19 while controlling for other variables, most notably the age distribution of the population. Lastly, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. Results. Our first research found that of 170 publications in the databases search, 12 publications were screened for systematic review. Among them, one study reported no association between democracy and health outcomes of COVID-19. Eleven articles claimed there was a relationship between democracy level and outcomes of COVID-19. Two papers reported negative associations between democracy and adverse outcomes of the population, while the other nine articles claimed there were positive associations between democracy and the poor health status of populations. When examining the relationship between democracy and health outcomes of COVID-19, the second research demonstrated that the number of hospital beds, the proportion of population above age 65, and current health expenditure as a percentage of the gross domestic product (GDP) are significantly related to the case fatality rates of COVID-19 across 148 countries (p < 0.05). The Democracy Index was not statistically related to the case fatality rates of COVID-19 when considering all 148 countries analyzed but was negatively associated with case fatality rates among 47 high-income countries. In addition, the healthcare workforce, population density, and comorbidity were not statistically significant among the 148 countries. Finally, the findings in the last research suggested that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. Conclusions. This research is the first to our knowledge to study the relationship between democracy and health outcomes of COVID-19 across countries with large sample sizes. According to the multicountry data, the cross-sectional study suggests that enhancing healthcare system facilities is vital to improving clinical outcomes. Protecting the population older than 65 and adjusting the health expenditure budget may need to be considered. The findings suggest that in high-income countries the higher democracy index is associated with more deaths from COVID-19, perhaps due to the decreased ability of the government to control the movement and behavior of its citizens. Besides, the simulated graphical trajectory method identifies statistical biases in surveillance data. This approach incorporates all sources of available data and provides a robust method to characterize the time course of an infectious disease. Regions and countries beginning with high mortality rates from the COVID-19 epidemic will suffer a long, painful period of the disease epidemic. Where the mortality rate is relatively high, healthcare professionals should prepare for a longer period of fighting this pandemic. Data quality is key to case fatality rate estimation which is needed by policymakers to make correct and timely critical decisions.