Prevalence and mortality of COVID-19 are associated with the L55M functional polymorphism of Paraoxonase 1

Introduction: Accumulating evidence recommends that infectious diseases including coronavirus disease 2019 (COVID-19) are often associated with oxidative stress and inflammation. Paraoxonase 1 (PON1, OMIM: 168,820), a member of the paraoxonase gene family, has antioxidant properties. Enzyme activity of paraoxonase depends on a variety of influencing factors such as polymorphisms of PON1, ethnicity, gender, age, and a number of environmental variables. The PON1 has two common functional polymorphisms, namely, Q192R (rs662) and L55M (rs854560). The R192 and M55 alleles are associated with increase and decrease in enzyme activity, respectively. Objective(s): The present study was conducted to investigate the possible association of rs662 and rs854560 polymorphisms with morbidity and mortality of COVID-19. Method(s): Data for the prevalence, mortality, and amount of accomplished diagnostic test (per 106 people) on 25 November 2020 from 48 countries were included in the present study. The Human Development Index (HDI) was used as a potential confounding variable. Result(s): The frequency of M55 was positively correlated with the prevalence (partial r = 0.487, df = 36, p = 0.002) and mortality of COVID-19 (partial r = 0.551, df = 36, p < 0.001), after adjustments for HDI and amount of the accomplished diagnostic test as possible confounders. Conclusion(s): This means that countries with higher M55 frequency have higher prevalence and mortality of COVID-19.Copyright © The Author(s) 2022.

Correlation Between Diabetes and COVID-19 Indices: A Global Level Ecological Study.

Introduction: Coronavirus is threatening the global public health as a new and widespread crisis. The researchers must keep in mind that one of the most vulnerable groups to COVID-19 are the people with underlying diseases, especially diabetes. Objective: This ecological study aimed to investigate the correlation between diabetes and the epidemiological indices of COVID-19. Methods: This ecological study included 144 countries. Their available data consists of the cumulative incidence rate of cases, cumulative incidence rate of death, recovery rate, case fatality rate, and performed tests of COVID-19, and diabetes. To collect the variables, a data set was provided which included the information of 144 countries based on diabetes and COVID-19 indices. Spearman coefficients were used for assess correlation between diabetes and COVID-19 indices. Also, Scatter plots of diabetes for the studied countries were drawn based on cumulative incidence rate of cases, cumulative incidence rate of death, tests, recovery rate, and case fatality rate of COVID-19. Results: The results of this ecological study showed in total countries, there was a weak positive correlation between diabetes and cumulative incidence rate of cases and also cumulative incidence rate of death. Correlation between diabetes with test of COVID-19 was very weak. Scatter plots showed a weak liner correlation between diabetes and cumulative incidence rate of cases, cumulative incidence rate of death and test of COVID-19. Conclusions: In this study, there was a weak positive correlation between diabetes and cumulative incidence rate of cases, cumulative incidence rate of death, and performed test of COVID-19. This disease is an enormous challenge for health policymakers; therefore, it is necessary to develop strategies and practical guidelines specific to each region to take the necessary care, especially for diabetic patients.

The correlation between attack rates and urban health indicators during the third wave of the COVID-19 outbreak in Turkey.

This study aims to analyze the inter-provincial variation in the increase of attack rates in the third wave of the COVID-19 outbreak in Turkey and to determine their relationship with potential urban health indicators. In this ecological study, dependent variables were selected as the COVID-19 attack rates of provinces before the third wave and during the third peak and the attack rate increase ratio. Urban health indicators that can function as determinants of health were calculated for each province under five headings: demographic, health capacity, economic, environmental, and socio-cultural. The epidemiologic maps were produced to show the spatial distribution of COVID-19 attack rates pre- and during the third wave. The associations with urban indicators were conducted using bivariate analysis, including Pearson or Spearman correlation analysis. A multiple linear regression model was run with variables significantly associated with increased attack rates. The results of our study show significant regional variations in COVID-19 attack rates both at the beginning and during the third wave of the COVID-19 pandemic in Turkey. Among the provinces, the attack rate increase ratio has only shown significant correlations to education level and some economic indicators, such as income, employment, industrial activity measured by electric consumption, and economic activity in the manufacturing industry. The multivariate analysis determined that the indicator of economic activity in the manufacturing industry is related to the increase of the attack rate in the third wave. Our results show that the COVID-19 cases are higher in more developed cities with more manufacturing sector activity. It makes us think that it is mainly related to inequalities arising from access to health institutions and testing. It can be determined that the partly lockdown strategy, which excluded the industrial activity in the country, concluded the higher increase in the attack rates in highly industrialized provinces.

Correlation between human development index and its components with COVID-19 indices: a global level ecologic study.

BACKGROUND: Given that COVID-19 continues to spread worldwide, attempts to restrain the virus and to prevent the effects that critically ill patients with COVID-19 have on healthcare systems, has become a public health priority. This ecological study aimed to investigate the correlation between the Human Development Index (HDI) and the epidemiological indicators of COVID-19, including the cumulative incidence rate of cases, the cumulative incidence rate of death, performed COVID-19 tests per million, recovery rate, and case fatality rate. METHODS: In this ecological study, a data set was provided, which included the epidemiologic indices of COVID-19, HDI, and its components for each country. Correlation coefficients were used to determine linear correlation. Also, the scatter plots of the HDI for the studied countries based on the epidemiologic indices of COVID-19 were drawn. RESULTS: This study showed that HDI and its components had positive correlation with a cumulative incidence rate of cases, the cumulative incidence rate of death, and performed COVID-19 tests (p < 0.001). HDI and two of its components, including literacy and Gross National Income (GNI) components had negative correlation with case fatality rate (CFR). Also, HDI and two of its components, including literacy and life expectancy components had negative correlation with recovery rate. CONCLUSION: Our study showed that the HDI and its components can affect the epidemiological status of COVID-19. As HDI increased, the cumulative incidence rate of cases, cumulative incidence rate of death, and COVID-19 tests increased as well. As HDI increased, CFR and recovery rate decreased as well. Although the HDI is higher in high-income countries, these countries may have also better reporting and surveillance systems.

Correlation between environmental factors and COVID-19 indices: a global level ecological study.

This global level ecological study aimed to investigate the correlation between environmental factors and the COVID-19 indices. This survey is an ecological study, so all studied variables are aggregate variables. To collect the variables in the study, a data set was provided, which includes the information of each country based on the cumulative deaths, case fatality rate, recovery rate, and the number of performed COVID-19 tests. Scatter plots of environmental factors for the studied countries were drawn based on cumulative incidence rate of cases, cumulative incidence rate of death, tests, recovery rate, and case fatality rate of COVID-19. Furthermore, Spearman correlation coefficient was also used to verify the correlation between environmental factors and indicators related to COVID-19. The results of this ecological study showed that among all countries surveyed, Montenegro (60,310.56 per million) and Luxembourg (54,807.89 per million) had the highest cumulative incidence rates of COVID-19 cases, when Tanzania (8.42 per million) and Vietnam (13.78 per million) had the lowest cumulative incidence rates of COVID-19. In addition, in this study, it was shown that the cumulative incidence rate of cases, the cumulative incidence rate of deaths, and performed COVID-19 tests had significant direct correlations with the access to drinking water and the access to sanitation services (p < 0.001). The findings of the present study showed an inverse correlation between the mortality rate due to unhealthy water consumption, poor health status, and a positive correlation between access to drinking water and health services with the cumulative incidence and mortality rates of COVID-19. The differences between our findings and many other studies could be due to the ecological nature of the study. Nevertheless, our findings will help health policymakers to develop timely strategies to reduce the mortality and incidence rate of COVID-19.

Why Are COVID-19 Mortality Rates by Country or Region So Different?: An Ecologic Study of Factors Associated with Mortality from Novel Coronavirus Infections by Country.

BACKGROUND: In order to find out the factors associated with the large disparities in COVID-19 mortality rates by country, we conducted an ecological study by linking existing statistics. In Japan, a large variation was observed in between geographical areas when assessing mortality. We performed a regional correlation analysis to find factors related to regional mortality. METHODS: This study design was an ecologic study. A multiple regression analysis was performed with COVID-19 mortality rates of different countries as the dependent variable together with various health care and economic factors. We calculated the cumulative mortality rate as of June 30, 2020. For the regional correlation analysis of Japan, 47 prefectures were divided into nine regions. The factors examined were health care and tourism. Data for 33 Organization for Economic Co-operation and Development (OECD) countries were analyzed. In Japan's regional analysis, the whole country was classified into nine regions. RESULTS: Factors related to mortality were the incidence of Kawasaki disease (KD), number of computed tomographies (CTs), and alcohol consumption. Mortality was low in countries with high incidence of KD and high number of CTs, as well as in countries with high alcohol consumption. In European countries, high smoking prevalence and a high Gini coefficient were positively related to high mortality. According to a regional analysis in Japan, mortality was related to proportion of population in the densely inhabited districts, the number of foreign visitors per capita, and the number of Chinese visitors per capita. CONCLUSION: Low mortality in East Asia was associated with specific disease morbidity (KD), alcohol consumption, and CT numbers. It was suggested that the mortality gap in Japan was related to the number of foreign tourists and the proportion of population in the densely inhabited districts.

Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil.

Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox regression), incidence and mortality rates (zero-inflated binomial negative regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk.