COVID-19 Case Fatality Rates Research Articles

Sex differences in COVID-19 case fatality: do we know enough?

Sex differences in COVID-19 case fatality: do we know enough?

Lifestyle Acquired Immunity, Decentralized Intelligent Infrastructures, and Revised Healthcare Expenditures May Limit Pandemic Catastrophe: A Lesson From COVID-19.

Throughout history, the human race has often faced pandemics with substantial numbers of fatalities. As the COVID-19 pandemic has now affected the whole planet, even countries with moderate to strong healthcare support and expenditure have struggled to contain disease transmission and casualties. Countries affected by COVID-19 have different demographics, socioeconomic, and lifestyle health indicators. In this context, it is important to find out to what extent these parametric variations are modulating disease outcomes. To answer this, this study selected demographic, socioeconomic, and health indicators e.g., population density, percentage of the urban population, median age, health expenditure per capita, obesity, diabetes prevalence, alcohol intake, tobacco use, case fatality of non-communicable diseases (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g., COVID-19 positive tests, case fatality, and case recovery rates were statistically analyzed. The results suggested that countries with variable median age had a significantly different outcome on positive test rate (P < 0.01). Both the median age (P = 0.0397) and health expenditure per capita (P = 0.0041) showed a positive relation with case recovery. An increasing number of tests per 100 K of the population showed a positive and negative relationship with the number of positives per 100 K population (P = 0.0001) and the percentage of positive tests (P < 0.0001), respectively. Alcohol intake per capita in liter (P = 0.0046), diabetes prevalence (P = 0.0389), and NCDs mortalities (P = 0.0477) also showed a statistical relation to the case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age and increased urban population showed more case fatality but also had a better recovery rate. Investment in the health sector alone is insufficient in controlling the severity of the pandemic. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease transmission and comorbidity induced fatalities, respectively.

Explaining among-country variation in COVID-19 case fatality rate

While the epidemic of SARS-CoV-2 has spread worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate had varied temporally (as the epidemic has progressed) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries. We used data on the temporal trajectory of case fatality rate provided by the European Center for Disease Prevention and Control, and country-specific data on different metrics describing the incidence of known comorbidity factors associated with an increased risk of COVID-19 mortality at the individual level. We also compiled data on demography, economy and political regimes for each country. We found that temporal trajectories of case fatality rate greatly vary among countries. We found several factors associated with temporal changes in case fatality rate both among variables describing comorbidity risk and demographic, economic and political variables. In particular, countries with the highest values of DALYs lost to cardiovascular, cancer and chronic respiratory diseases had the highest values of COVID-19 CFR. CFR was also positively associated with the death rate due to smoking in people over 70 years. Interestingly, CFR was negatively associated with share of death due to lower respiratory infections. Among the demographic, economic and political variables, CFR was positively associated with share of the population over 70, GDP per capita, and level of democracy, while it was negatively associated with number of hospital beds ×1000. Overall, these results emphasize the role of comorbidity and socio-economic factors as possible drivers of COVID-19 case fatality rate at the population level.

Joint estimation of case fatality rate of COVID-19 and power of quarantine strategy performed in Wuhan, China.

From the first case of COVID-19 confirmed in Wuhan, the capital of Hubei Province, China, in early December 2019, it has been found in more than 160 countries and caused over 11,000 deaths as of March 20, 2020. Wuhan, as the city where the epidemic first broke out, has made great sacrifices to block the possible transmission. In this research, we estimate the case fatality rate (CFR) of COVID-19 and quantify the effect of quarantine strategy utilized in Wuhan by developing an extended Susceptible-Infected-Recovered (SIR) model. The outcomes suggest that the CFR is 4.4% (95% CI [3.6%, 5.2%]) and the effect of the quarantine strategy is 99.3% (95% CI [99.2%, 99.5%]), which implies that such a method can significantly reduce the number of infections.

COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data

ObjectiveTo investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally.DesignPublicly available register-based ecological study.SettingTwo hundred and nine countries/territories in the world.ParticipantsAggregated data including 10...

Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country.

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 population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR.

Demystifying the varying case fatality rates (CFR) of COVID-19 in India: Lessons learned and future directions.

At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.

Targeting Crucial Host Factors of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide since its first incidence in Wuhan, China, in December 2019. Although the case fatality rate of COVID-19 appears to be lower than that of SARS and Middle East respiratory syndrome (MERS), the higher transmissibility of SARS-CoV-2 has caused the total fatality to surpass other viral diseases, reaching more than 1 million globally as of October 6, 2020. The rate at which the disease is spreading calls for a therapy that is useful for treating a large population. Multiple intersecting viral and host factor targets involved in the life cycle of the virus are being explored. Because of the frequent mutations, many coronaviruses gain zoonotic potential, which is dependent on the presence of cell receptors and proteases, and therefore the targeting of the viral proteins has some drawbacks, as strain-specific drug resistance can occur. Moreover, the limited number of proteins in a virus makes the number of available targets small. Although SARS-CoV and SARS-CoV-2 share common mechanisms of entry and replication, there are substantial differences in viral proteins such as the spike (S) protein. In contrast, targeting cellular factors may result in a broader range of therapies, reducing the chances of developing drug resistance. In this Review, we discuss the role of primary host factors such as the cell receptor angiotensin-converting enzyme 2 (ACE2), cellular proteases of S protein priming, post-translational modifiers, kinases, inflammatory cells, and their pharmacological intervention in the infection of SARS-CoV-2 and related viruses.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

Background: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. Methods: We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5 and O3 and county-level COVID-19 case-fatality and mortality rates in the US. We used both single and multipollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level healthcare capacity, phase-of-epidemic, population mobility, sociodemographics, socioeconomic status, behavioral risk factors, and meteorology. Findings: 1,027,799 COVID-19 cases and 58,489 deaths were reported in 3,122 US counties from January 22 to April 29, 2020, with an overall observed case-fatality rate of 5·8%. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality and mortality rates in single-, bi-, and tri-pollutant models (p-values&lt;0·05). Per inter-quartile range (IQR) increase in NO2 (4·6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 7·1% (95% CI 1·2% to 13·4%) and 11·2% (95% CI 3·4% to 19·5%), respectively. We did not observe significant associations with long-term exposure to PM2.5 or O3. Interpretation: Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case-fatality and mortality.

Negative Correlation between Altitude and COVID-19 Pandemic in Colombia: A Preliminary Report.

.It has been suggested that high altitude can reduce the infectivity and case fatality rate of COVID-19. We investigated the relationship between altitude and the COVID-19 pandemic in Colombia. Epidemiological data included the number of positive cases, deaths, and the case fatality rate of COVID-19. In particular, we analyzed data from 70 cities with altitudes between 1 and 3,180 m. Correlations and linear regression models adjusted to population density were performed to examine the relationship and contribution of altitude to epidemiological variables. The case fatality rate was negatively correlated with the altitude of the cities. The incidence of cases and deaths from COVID-19 had an apparent correlation with altitude; however, these variables were better explained by population density. In general, these findings suggest that living at high altitude can reduce the impact of COVID-19, especially the case fatality rate.

Older age groups and country-specific case fatality rates of COVID-19 in Europe, USA and Canada

PurposeTo evaluate the association between the percentages of older age groups among confirmed SARS-CoV-2 infections and the country-specific case fatality rate (CFR).MethodsThis ecological study analyzed data from the 20 most severely affected European countries, USA and Canada, in which national health authorities provided data on age distribution and gender among confirmed SARS-CoV-2 cases and deaths.ResultsThe proportion of individuals older than 70 years among confirmed SARS-CoV-2 cases differed markedly between the countries, ranging from 4.9 to 40.4%. There was a strong linear association between the proportion of individuals older than 75 years and the country-specific CFRs (R2 = 0.803 for all countries, R2 = 0.961 after exclusion of three countries with incongruent data). Each 5% point increase of this older age group among confirmed SARS-CoV-2 cases was associated with an increase in CFR of 2.5% points (95% CI 1.9–3.1).ConclusionData from 20 European countries and the USA and Canada showed that the variance of crude CFR of COVID-19 is predominantly (80–96%) determined by the proportion of older individuals who are diagnosed with SARS-CoV-2. The age distribution of SARS-CoV-2 infections is still far from being homogeneous. Detailed demographic data have to be taken into account in all the analyses on COVID-19-associated mortality. We urgently call for standardized data collection by national health authorities.

Evidence of protective role of Ultraviolet-B (UVB) radiation in reducing COVID-19 deaths

Prior studies indicate the protective role of Ultraviolet-B (UVB) radiation in human health, mediated by vitamin D synthesis. In this observational study, we empirically outline a negative association of UVB radiation as measured by ultraviolet index (UVI) with the number of COVID-19 deaths. We apply a fixed-effect log-linear regression model to a panel dataset of 152 countries over 108 days (n = 6524). We use the cumulative number of COVID-19 deaths and case-fatality rate (CFR) as the main dependent variables and isolate the UVI effect from potential confounding factors. After controlling for time-constant and time-varying factors, we find that a permanent unit increase in UVI is associated with a 1.2 percentage points decline in daily growth rates of cumulative COVID-19 deaths [p < 0.01] and a 1.0 percentage points decline in the CFR daily growth rate [p < 0.05]. These results represent a significant percentage reduction in terms of daily growth rates of cumulative COVID-19 deaths (− 12%) and CFR (− 38%). We find a significant negative association between UVI and COVID-19 deaths, indicating evidence of the protective role of UVB in mitigating COVID-19 deaths. If confirmed via clinical studies, then the possibility of mitigating COVID-19 deaths via sensible sunlight exposure or vitamin D intervention would be very attractive.

Trends in case-fatality rates of COVID-19 in the World, between 2019 - 2020

Introduction: CoV infections can potentially cause from a simple cold to a severe respiratory syndrome, such as the Severe Acute Respiratory Syndrome and the Middle East Respiratory Syndrome (MERS-CoV). The COVID-19 created a new reality for global healthcare models. Objetive: To evaluate trends in case fatality rates of COVID-19 in the World. Methods: We conducted a population based time-series study using public and official data of cases and deaths from COVID-19 in Argentina, Australia, Brazil, Chile, China, Colombia, France, Germany, India, Iran, Italy, Japan, Mexico, Morocco, New Zealand, Nigeria, Peru, Saudi Arabia, South Africa, South Korea, Spain, Switzerland, United Kingdom, United States and Russian, between December, 2019 and August, 2020. Data were based on reports from European Centre for Disease Prevention and Control. COVID-19 was defined by the International Classification of Diseases, 10th revision (U07.1). A Prais-Winsten regression model was performed and the Daily Percentage Change (DPC) calculated determine rates as increasing, decreasing or flat. Results: During the study period, trends in case-fatality rates in the world were flat (DPC = 0.3; CI 95% [-0.2: 0.7]; p = 0.225). In Africa, Morocco had decreasing trends (DPC = -1.1; CI 95% [-1.5: -0.7]; p &lt; 0.001), whereas it were increasing in South Africa (p &lt; 0.05) and flat in Nigeria (p &gt; 0.05). In the Americas, Argentina showed a decreasing trend in case-fatality rates (DPC = -0.6; CI 95% [-1.1: -0.2]; p = 0.005), the U.S. had flat trends (p &gt; 0.05) and all other American countries had increasing trends (p &lt; 0.05). In Asia, Iran had decreasing trends (DPC = -1.5; CI 95% [-2.6 : -0.2]; p = 0.019); China and Saudi Arabia showed increasing trends (p &lt; 0.05), while in India, Japan and South Korea they were flat (p &gt; 0.05). European countries had mostly increasing trends (p &lt; 0.05): Germany, Italy, Spain, the UK and Russia; France and Switzerland had flat trends (p &gt; 0.05). Finally, in Oceania, trends in case-fatality rates were flat in Australia (p &gt; 0.05) and increasing in New Zealand (p &lt; 0.05). Conclusion: Trends in case-fatality rates of COVID-19 in the World were flat between December, 31 and August, 31. Argentina, Iran and Morocco were the only countries with decreasing trends. On the other hand, South Africa, Brazil, Canada, Chile, Colombia, Mexico, Peru, China, Saudi Arabia, Germany, Spain, United Kingdom, Russian and New Zealand had increasing trends in case-fatality rate. All the other countries analyzed had flat trends. Based on case-fatality rate data, our study supports that COVID-19 pandemic is still in progress worldwide.

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach

The COVID-19 virus has infected more than 38 million people and resulted in more than one million deaths worldwide as of October 14, 2020. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, economic inequality enhanced the risk of COVID-19 transmission. The per capita hospital beds correlated negatively with COVID-19 deaths. Blood types B and AB were protective factors for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV and influenza and pneumonia was associated with reduced COVID-19 risk. Increased intake of vegetables, edible oil, protein, vitamin D, and vitamin K was associated with reduced COVID-19 risk, while increased intake of alcohol was associated with increased COVID-19 risk. Other factors included age, sex, temperature, humidity, social distancing, smoking, health investment, urbanization level, and race. High temperature is a more compelling factor mitigating COVID-19 transmission than low temperature. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.

Robust and fragile determinants of the infection and case fatality rates of Covid-19: international cross-sectional evidence

ABSTRACT An attempt is made to identify the factors that can explain inter-country differences in the severity of Covid-19, measured by the infection and case fatality rates. To circumvent the problem of the sensitivity of the results with respect to the selected set of explanatory variables, extreme bounds analysis (EBA) is applied to a cross-sectional sample of 154 countries. The results show that the infection and fatality rates depend on different factors, except for the number of tests, which is a robust determinant of both. An interesting result is that the infection rate depends on urban population rather than the overall population density. Another interesting result is that the fatality rate depends on the age structure of the population and population density but not on the percentage of urban population.

Outbreak of COVID-19 and SARS in mainland China: a comparative study based on national surveillance data

ObjectiveTo compare the epidemiological characteristics and transmission dynamics in relation to interventions against the COVID-19 and severe acute respiratory syndrome (SARS) outbreak in mainland China.DesignComparative study based on a unique...

A Case of Covid-19 Patient Presenting with Gastrointestinal-Specific Symptoms in Indonesia

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease officially declared as a pandemic on 11 March 2020 by the World Health Organization (WHO). Indonesia’s COVID-19 case fatality rate remain consistently high, approximately twice the global case fatality rate available. Patients typically present with fever, dry cough and dyspnea. However, there were reports of atypical COVID-19 symptoms such as myalgia, fatigue, diarrhea, nausea, and vomiting. These atypical presentations were suggested to indicate a more severe COVID-19. Here, we present a case of COVID-19 patient presenting with gastrointestinal-specific symptoms in Indonesia.

The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors

The Italian government has been one of the most responsive to COVID-2019 emergency, through the adoption of quick and increasingly stringent measures to contain the outbreak. Despite this, Italy has suffered a huge human and social cost, especially in Lombardy. The aim of this paper is dual: i) first, to investigate the reasons of the case fatality rate (CFR) differences across Italian 20 regions and 107 provinces, using a multivariate OLS regression approach; and ii) second, to build a “taxonomy” of provinces with similar mortality risk of COVID-19, by using the Ward's hierarchical agglomerative clustering method. I considered health system metrics, environmental pollution, climatic conditions, demographic variables, and three ad hoc indexes that represent the health system saturation. The results showed that overall health care efficiency, physician density, and average temperature helped to reduce the CFR. By the contrary, population aged 70 and above, car and firm density, air pollutants concentrations (NO2, O3, PM10, and PM2.5), relative average humidity, COVID-19 prevalence, and all three indexes of health system saturation were positively associated with the CFR. Population density, social vertical integration, and altitude were not statistically significant. In particular, the risk of dying increases with age, as 90 years old and above had a three-fold greater risk than the 80–to–89 years old and four-fold greater risk than 70–to–79 years old. Moreover, the cluster analysis showed that the highest mortality risk was concentrated in the north of the country, while the lowest risk was associated with southern provinces. Finally, since prevalence and health system saturation indexes played the most important role in explaining the CFR variability, a significant part of the latter may have been caused by the massive stress of the Italian health system.

Case Fatality in COVID-19; a District Perspective

Objectives: To determine the case fatality rate of COVID-19 by evaluating the data of Patients died due to COVID-19.&#x0D; Materials and Methods: A total of 243 patients with PCR done from the government designated Public health research laboratory of Khyber Medical University were included in a cross sectional comparative study. Chi-square test, risk analysis, probability testing and survival analysis using Kaplan Meir test was done on data sheet prepared in SPPS version 25 in accordance with the objectives of the study.&#x0D; Results: Out of total 243 patients, 165 (67.90%) were negative by PCR testing and 78 (32.09%) were COVID-19 positive. The Mean age with SD was 36+17 years. Out of total 178 (73.3%) were males and 65 (26.7%) were females. 34 (13.99%) had age more than 55 years, where 4/5 (80%) of the deaths were recorded. The relationship of an increase in age with rate of mortality was statistically significant (p=0.001). The over all probability of death in our population in age more than 55 years is 24 times higher (OR=24,95CI: 2.6-221.24) with relative risk of 1.1 (rr=1.11, 95CI:1-1.24). Mortality rate was 6.41%. A significant correlation of mortality with case positivity (p=0.003) with relative risk of 1.06 (rr=1.06, 95CI: 1.008-1.13). Three out of 5 of the COVID-19 positive deceased had cardiac diseases and 2/5 had respiratory disease/viral pneumonia. Using Kaplan Meir test, the survival graphs of COVID positive vs COVID negative cases had similar pattern that shows the mortality rate in the positive cases was not solely due to COVID-19, but has aggravated the pre-existing illness to cause death.&#x0D; Conclusion: The mortality rate was 6.41%, more in age&gt;55 years (80%) and almost all the deceased had chronic co-morbid conditions like CAD, CCF and COPD at time of presentation. COVID-19 is not the sole killer, it contributes killing in immune-deficient patients.

National case fatality rates of the COVID-19 pandemic

ObjectivesThe 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. MethodsWe 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). ResultsIn multivariable analysis, the variables significantly associated with the increased CFRs were percentage of obesity in ages >18 years (β = 3.26; 95%CI = 1.20, 5.33; p 0.003), tuberculosis incidence (β = 3.15; 95%CI = 1.09, 5.22; p 0.004), duration (days) since first death due to COVID-19 (β = 2.89; 95%CI = 0.83, 4.96; p 0.008), and median age (β = 2.83; 95%CI = 0.76, 4.89; p 0.009). The COVID-19 test rate (β = –3.54; 95%CI = –5.60, –1.47; p 0.002), hospital bed density (β = –2.47; 95%CI = –4.54, –0.41; p 0.021), and rural population ratio (β = –2.19; 95%CI = –4.25, –0.13; p 0.039) decreased the CFR. ConclusionsThe 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.