Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries (original) (raw)
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International Journal of Environmental Research and Public Health
Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent popula...
2021
COVID-19 pandemic raises an extraordinary challenge to the healthcare systems globally. The governments are taking key measures to constrain the corresponding health, social, and economic impacts, however, these measures vary depending on the nature of the crisis and country-specific circumstances. Objectives: Considering different incidence and mortality rates across different countries, we aimed at explaining variance of these variables by performing accurate and precise multivariate analysis with aid of suitable predictors, accordingly, the model would proactively guide the governmental responses to the crisis. Methods: Using linear and exponential time series analysis, this research aimed at studying the incidence and mortality rates of COVID-19 in 18 countries during the first six months of the pandemic, and further utilize multivariate techniques to explain the variance in monthly exponential growth rates of cases and deaths with aid of a set of different predictors: the recor...
Estimating Excess Mortality Associated with COVID-19 Pandemic: A 151 cross-countries study
2021
In this paper, we aim to estimate the excess of mortality associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2/COVID-19) pandemic. For this analysis, we merged population data and deaths number provided by the World Bank database and the projections of COVID-19 deaths of 151 countries published by the Institute for Health Metrics and Evaluation (IHME). These projections are computed using a deterministic SEIR model according to the effects of non-pharmaceutical interventions. We had predicted on 12/31/2020 the new death crude rate (DCR) associated with COVID-19, its growth rate, the share of this cause of deaths in the yearly total number of deaths, and in the daily number of deaths on 12/31/2020. Excess of mortality (vs baseline growth of DCR) is varying markedly across countries. Regardless of the Scenario, Peru would be the country with the highest increase in its DCR. Many European countries, like Belgium, Italy, United Kingdom would also know a significant increase in their DCR. Tunisia would have the highest in Africa regardless of the scenario. Since the data used for this paper is dynamic and regularly updated, we had built a Shiny web application to keep the results of this paper up to date.
A Statistical Analysis of Positive Excess Mortality at Covid 19 in 2020 2021
When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the measurement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthinkable in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic's effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under 'normal' circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 "Spanish Flu"6. Positive excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affects the mortality rate. The estimates of positive excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the Methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lacking complete reported data using the Poisson, Negative Binomial count framework. By overdispersion test, we checked the assumption of the Poisson model, and then we chose the negative binomial as a good fitting model for this analysis through Akaike Information Criteria (AIC) and Standardized residual plots, after that checking the P-value<0.05; we found some significant predictors from our choosing model Negative binomial model, and the coefficient of all predictors gave the information that some factors have a positive effect, and some has a negative effect at positive excess mortality at COVID-19 (2020-2021).
Population Risk Factors for COVID-19 Mortality in 93 Countries
Journal of Epidemiology and Global Health
Death rates due to COVID-19 pandemic vary considerably across regions and countries. Case Mortality Rates (CMR) per 100,000 population are more reliable than case-fatality rates per 100 test-positive cases, which are heavily dependent on the extent of viral case testing carried out in a country. We aimed to study the variations in CMR against population risk factors such as aging, underlying chronic diseases and social determinants such as poverty and overcrowding. Data on COVID-19 CMR in 93 countries was analyzed for associations with preexisting prevalence rates of eight diseases [asthma, lung cancer, Chronic Obstructive Pulmonary Disease (COPD), Alzheimer's Disease (AD), hypertension, ischemic heart disease, depression and diabetes], and six socio-demographic factors [Gross Domestic Product (GDP) per capita, unemployment, age over 65 years, urbanization, population density, and socio-demographic index]. These data were analyzed in three steps: correlation analysis, bivariate comparison of countries, and multivariate modelling. Bivariate analysis revealed that COVID-19 CMR were higher in countries that had high prevalence of population risk factors such as AD, lung cancer, asthma and COPD. On multivariate modeling however, AD, COPD, depression and higher GDP predicted increased death rates. Comorbid illnesses such as AD and lung diseases may be more influential than aging alone.
Factors affecting COVID-19 mortality: an exploratory study
Journal of Health Research
PurposeThe purpose of this paper is to study the factors affecting COVID-19 mortality.Design/methodology/approachAn empirical model is developed in which the mortality rate per million is the dependent variable, and life expectancy at birth, physician density, education, obesity, proportion of population over the age of 65, urbanization (population density) and per capita income are explanatory variables. Crosscountry data from 184 countries are used to estimate the quantile regression that is employed.FindingsThe estimated results suggest that obesity, the proportion of the population over the age of 65 and urbanization have a positive and statistically significant effect on COVID-19 mortality. Not surprisingly, per capita income has a negative and statistically significant effect on COVID-19 death rate.Research limitations/implicationsThe study is based on the COVID-19 mortality data from June 2020, which have constantly being changed. What data reveal today may be different after...
National case fatality rates of the COVID-19 pandemic
Clinical Microbiology and Infection, 2021
Objectives: The case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) varies significantly between countries. We aimed to describe the associations between health indicators and the national CFRs of COVID-19. Methods: We identified for each country health indicators potentially associated with the national CFRs of COVID-19. We extracted data for 18 variables from international administrative data sources for 34 member countries of the Organization for Economic Cooperation and Development (OECD). We excluded the collinear variables and examined the 16 variables in multivariable analysis. A dynamic web-based model was developed to analyse and display the associations for the CFRs of COVID-19. We followed the Guideline for Accurate and Transparent Health Estimates Reporting (GATHER). Results: In multivariable analysis, the variables significantly associated with the increased CFRs were percentage of obesity in ages >18 years (b ¼ 3.26; 95%CI ¼ 1.20, 5.33; p 0.003), tuberculosis incidence (b ¼ 3.15; 95%CI ¼ 1.09, 5.22; p 0.004), duration (days) since first death due to COVID-19 (b ¼ 2.89; 95% CI ¼ 0.83, 4.96; p 0.008), and median age (b ¼ 2.83; 95%CI ¼ 0.76, 4.89; p 0.009). The COVID-19 test rate (b ¼ e3.54; 95%CI ¼ e5.60, e1.47; p 0.002), hospital bed density (b ¼ e2.47; 95%CI ¼ e4.54, e0.41; p 0.021), and rural population ratio (b ¼ e2.19; 95%CI ¼ e4.25, e0.13; p 0.039) decreased the CFR. Conclusions: The pandemic hits population-dense cities. Available hospital beds should be increased. Test capacity should be increased to enable more effective diagnostic tests. Older patients and patients with obesity and their caregivers should be warned about a potentially increased risk. €
Determinants of COVID-19 cases and deaths in OECD countries
Journal of Public Health
Aim This research aims to examine the effects of variables that can affect COVID-19 deaths and cases in Organisation for Economic Cooperation and Development (OECD) countries during the years 2020 (first wave), 2021 (vaccine available), and 2022 (vaccine available and Omicron variant appeared). Material and method The factors that are thought to affect the case and death rates in 37 OECD countries were examined by multiple linear regression analysis using SPSS 22. The dependent variables were the COVID-19 deaths and cases per 10,000 (in 2020, 2021, and 2022); the independent variables were universal health coverage, physicians, nurses, intensive care beds, hospital beds, non-communicable diseases mortality per 100,000 people, population over 65 years of age, out-ofpocket expenditure, private expenditure, and health expenditure per capita and percent of % GDP. Results It was determined that the non-communicable diseases mortality is the relatively important variable COVID-19 cases and deaths in 2020 and 2021. After controlling for the scores of other variables, according to the ß coefficients, a oneunit increase in the number of physicians variable increases COVID-19 cases by 1.14 units in 2022; a one-unit increase in the universal coverage variable decreases COVID-19 deaths by 0.33 units in 2022. Conclusion The results of this research provide evidence that the effects of the COVID-19 outbreak have changed between 2020, the first wave of the epidemic, 2021, when the vaccine is available, and 2022, when both the vaccine is available and the Omicron variant is seen. With the increase in vaccination in 2022, the impact of non-communicable diseases mortality on the number of COVID-19 cases has decreased.
Estimation of risk factors for COVID-19 mortality - preliminary results
Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China CDC has reported results of a descriptive exploratory analysis of all cases diagnosed until the 11th February 2020, presenting the epidemiologic curves and geo-temporal spread of COVID-19 along with case fatality rate according to some baseline characteristics, such as age, gender and several well-established high prevalence comorbidities. Despite this, we intend to increase even further the predictive value of that manuscript by presenting the odds ratio for mortality due to COVID-19 adjusted for the presence of those comorbidities and baseline characteristics such as age and gender. Besides, we present a way to determine the risk of each particular patient, given his characteristics. We found that age is the variable that presents higher risk of COVID-19 mortality, where 60 or older patients have an OR = 18.8161 (CI95%[7.1997; 41.5517]). Regardin...
Journal of Clinical Medical Research, 2023
Background: Coronavirus disease 2019 (COVID-19) has costed the lives of more than 2 million people during the first wave of the pandemic with the number of fatalities varying among different countries. Thus, we aimed to assess the associations of specific demographic, clinical and political variables with COVID-19 related fatalities in various countries, during the first three months of the pandemic. Methods: We analyzed publicly available data from 192 regions (114 countries) and built a loglinear regression model with random intercepts in order to identify predictors of COVID-19 fatalities during the first three months of the pandemic. We used country-level data including total population, the percentage of people aged 65 and above, number of ICU beds per 100,000 people, geographical latitude and number of days from the first confirmed COVD-19 case to establishment of specific preventive control measures as explanatory variables of our statistical model. Results: In our multivariate statistical model, one unit increases in the total population (in 10,000,000 units), percentage of population aged 65 and above and the number of days from the first confirmed COVID-19 case to the imposition of preventive measures, were related to 3.8% (95% CI: 0.8% to 6.9%), 7.1% (95% CI: 0.9% to 13.6%) and 1.8% (95% CI: 0.3% to 3.6%), respectively, higher number of COVID-19 related deaths. Conclusion: Our findings imply positives associations of total population, percentage of population aged 65 and above and number of days from the first confirmed COVID-19 case to the imposition of preventive measures with COVID-19 fatal cases, during the first three months of the pandemic. Future non-ecological studies are warranted to confirm our results.