scholarly journals Ecologic correlation between underlying population level morbidities and COVID-19 case fatality rate among countries infected with SARS-CoV-2

2020 ◽  
Author(s):  
Evaezi Okpokoro ◽  
Victoria Igbinomwanhia ◽  
Elima Jedy-Agba ◽  
Gbenga Kayode ◽  
Ezenwa James Onyemata ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Tobacco Use ◽  
Negative Correlation ◽  
Case Fatality Rate ◽  
Ecological Study ◽  
Case Fatality ◽  
Population Level ◽  
Hiv Prevalence ◽  
Fatality Rate

AbstractBackgroundThe ongoing Coronavirus disease 2019 (COVID-19) pandemic is unprecedented in scope. High income countries (HIC) seemingly account for the majority of the mortalities considering that these countries have screened more persons. Low middle income countries (LMIC) countries may experience far worse mortalities considering the existence of a weaker health care system and the several underlying population level morbidities. As a result, it becomes imperative to understand the ecological correlation between critical underlying population level morbidities and COVID-19 case fatality rates (CFR).MethodThis is an ecological study using data on COVID-19 cases, prevalence of COPD, prevalence of tobacco use, adult HIV prevalence, quality of air and life expectancy. We plotted a histogram, performed the Shapiro-Wilk normality test and used spearman correlation to assess the degree of correlation between COVID-19 case fatality rate (CFR) and other covariates mentioned above.ResultAs at the 31st of March 2020, there were a total of 846,281 cases of COVID-19 from 204 countries and a global case fatality rate of 5% (range 0% to 29%). Angola and Sudan both had the highest CFR of 29%, while Italy had the highest number of deaths (i.e. 12,428) as at 31st of March 2020. Adult HIV prevalence has a significant but weak negative correlation with CFR (correlation coefficient = - 0.24, p value =0.01) while all the other variables have positive correlation with CFR due to COVID-19 though not statistically significant. Of the 204 countries analyzed, only 11 countries (i.e. 5%) had complete datasets across all 5 population level morbidities (i.e. prevalence of COPD, prevalence of tobacco use, life expectancy, quality of air, and adult HIV prevalence variables). Correlations of CFR from these 11 countries were similar to that from the 204 countries except for the correlation with quality of air and prevalence of tobacco use. Conclusion: While we interpret our data with caution given the fact that this is an ecological study, our findings suggest that population level factors such as prevalence of COPD, prevalence of tobacco use, life expectancy and quality of air are positively correlated with CFR from COVID-19 but, adult HIV prevalence has a weak and negative correlation with COVID-19 CFR and would require extensive research.

scholarly journals Factors influencing COVID-19 case burden and fatality rates findings from secondary data analysis of major urban agglomerations in India

2020 ◽  
Vol 7 (8) ◽  
pp. 3284
Author(s):  
Deodatt M. Suryawanshi ◽  
Raghuram Venugopal ◽  
Ramchandra Goyal
Keyword(s):  
Data Analysis ◽  
Negative Correlation ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Secondary Data ◽  
Health Financing ◽  
Secondary Data Analysis ◽  
Fatality Rate ◽  
Health Index ◽  
Significant Difference

In December 2019, SARS COV-2 which originated in the Chinese city of Wuhan achieved pandemic proportions and spread rapidly to countries through International air traffic causing acute respiratory infection and deaths. Presence of International airports, demography, health financing and human developments factors were assumed to influence COVID-19 cases burden and case fatality rate (CFR). So, this study was undertaken to find a association between these factors and COVID-19 cases and deaths. The study used 48 districts using purposive sampling as proxy for cities and used secondary data analysis. Data was obtained for various variables like demographic, Health Financing, Indices and Testing infrastructure, COVID cases burden and case fatality from trusted sources. Descriptive statistics correlational statistics using Pearsons coefficient students T was used to describe, correlate and find significant difference in the data. The analysis found a significant difference between COVID cases burden in districts with International Airports (p<0.039) and those without it. Positive correlation of population density (r=0.65) with COVID-19 case burden and negative correlation of case fatality rate with NITI Aayogs health index (r=-0.12), human development index (HDI) (r=-0.18), per-capita expenditure on health (r=-0.072) and a correlation of r=0.16 was observed for gross state domestic product. Decongestion of cities through perspective urban planning is the need of the hour. Stricter quarantine measures in those districts with international airports can help reduce the transmission. Negative correlation of HDI and NITI Aayogs health index with CFR emphasizes the importance of improvements in social determinants of health.

scholarly journals Global Epidemiology of First 90 Days into COVID-19 Pandemic: Disease Incidence, Prevalence, Case Fatality Rate and Their Association with Population Density, Urbanisation and Elderly Population

2020 ◽  
Vol 22 (2) ◽  
pp. 117-128 ◽  
Author(s):  
Shivam Gupta ◽  
Kamalesh Kumar Patel ◽  
Shobana Sivaraman ◽  
Abha Mangal
Keyword(s):  
Population Density ◽  
Case Fatality Rate ◽  
Elderly Population ◽  
Disease Incidence ◽  
Case Fatality ◽  
Population Level ◽  
Epidemiological Data ◽  
Fatality Rate ◽  
Global Epidemiology ◽  
First 90 Days

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.

scholarly journals Strong Correlation between the Case Fatality Rate of COVID-19 and the rs6598045 Single Nucleotide Polymorphism (SNP) of the Interferon-Induced Transmembrane Protein 3 (IFITM3) Gene at the Population-Level

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Yong-Chan Kim ◽  
Byung-Hoon Jeong
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Ethnic Group ◽  
Strong Correlation ◽  
Case Fatality Rate ◽  
Transmembrane Protein ◽  
Case Fatality ◽  
Population Level ◽  
Fatality Rate ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide

Coronavirus disease 2019 (COVID-19) is a fatal pandemic disease that is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 13 December, 2020, over 70,000,000 cases and 1,500,000 deaths have been reported over a period of several months; however, the mechanism underlying the pathogenesis of COVID-19 has not been elucidated. To identify the novel risk genetic biomarker for COVID-19, we evaluated the correlation between the case fatality rate of COVID-19 and the genetic polymorphisms of several potential COVID-19-related genes, including interferon-induced transmembrane protein 3 (IFITM3), the angiotensin I converting enzyme 2 (ACE2) gene, transmembrane protease, serine 2 (TMPRSS2), interleukin 6 (IL6), leucine zipper transcription factor-like protein 1 (LZTFL1), and the ABO genes, in various ethnic groups. We obtained the number of COVID-19 cases and deaths from the World Health Organization (WHO) COVID-19 dashboard and calculated the case fatality rate of each ethnic group. In addition, we obtained the allele distribution of the polymorphisms of the IFITM3, ACE2, TMPRSS2, IL6, LZTFL1, and ABO genes from the 1000 Genomes Project and performed Log-linear regression analysis using SAS version 9.4. We found different COVID-19 case fatality rates in each ethnic group. Notably, we identified a strong correlation between the case fatality rate of COVID-19 and the allele frequency of the rs6598045 single nucleotide polymorphism (SNP) of the IFITM3 gene. To the best of our knowledge, this report is the first to describe a strong correlation between the COVID-19 case fatality rate and the rs6598045 SNP of the IFITM3 gene at the population-level.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gabriele Sorci ◽  
Bruno Faivre ◽  
Serge Morand
Keyword(s):  
Case Fatality Rate ◽  
Respiratory Infections ◽  
Case Fatality ◽  
Population Level ◽  
Fatality Rate ◽  
Negatively Associated ◽  
Chronic Respiratory Diseases ◽  
Factors Associated ◽  
Increased Risk ◽  
Political Variables

Abstract 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.

scholarly journals Averting Maternal Death and Disability : Role of Eoc in Chittagong District

2012 ◽  
Vol 23 (1) ◽  
pp. 7-10
Author(s):  
Rowshan Akhtar ◽  
Afroza Ferdous ◽  
Umme Kulsum ◽  
Syeda Nurjahan Bhuiyan
Keyword(s):  
Rural Areas ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
Cesarean Deliveries ◽  
The People ◽  
Grass Root ◽  
Grass Root Level

Objectives of this study are: 1. To find out the number of facitilities providing EmOC services in rural areas of Chittagong district. 2. To assess the proportion of women who deliver at Emoc facilities. 3. To find out the “METNEED” at EmOC facilities. 4. To find out the caesarean deliveries as a proportion of all births at EmOC. 5. To see the “Case fatality rate” which reflects the quality of care & facility performance. This is a retrospective study between January 2009 to December 2009 done in thirteen upazilla health complexes in Chittagong district of population size-52,39,000. Outcome measures are availability of EmOC, Proportion of births in EmOC facilities, Met need, Cesarean deliveries &case fatality rate. About 6.7 & of births take place in Comprensive EmOC facilities and 2.4% in Basic EmOC (i.e. About 9.1% births are institutional). Study shows that “Met Need” is about 18%. Only <0.8% of all births in the population is delivered by casesarean section. In this study case fatality rate is only .067%. This study describes the baseline indicates calculated in different upazillas. In Chittagong only 5 Comprehensive EmOC services are not sufficient to cover the largely populated area. If we expand the Basic EmOC and Comprehensive EmOC we can help the people even in grass root level. JCMCTA 2012; 23(1): 7-10

scholarly journals Early trends of socio-economic and health indicators influencing case fatality rate of COVID-19 pandemic

2020 ◽  
Vol 90 (3) ◽  
Author(s):  
Shahir Asfahan ◽  
Aneesa Shahul ◽  
Gopal Chawla ◽  
Naveen Dutt ◽  
Ram Niwas ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Health Indicators ◽  
World Health ◽  
Fatality Rate ◽  
Medical Doctors ◽  
Selection Of Variables ◽  
The World ◽  
Per Capita

Coronavirus disease 2019, i.e. COVID-19, started as an outbreak in a district of China and has engulfed the world in a matter of 3 months. It is posing a serious health and economic challenge worldwide. However, case fatality rates (CFRs) have varied amongst various countries ranging from 0 to 8.91%. We have evaluated the effect of selected socio-economic and health indicators to explain this variation in CFR. Countries reporting a minimum of 50 cases as on 14th March 2020, were selected for this analysis. Data about the socio-economic indicators of each country was accessed from the World bank database and data about the health indicators were accessed from the World Health Organisation (WHO) database. Various socioeconomic indicators and health indicators were selected for this analysis. After selecting from univariate analysis, the indicators with the maximum correlation were used to build a model using multiple variable linear regression with a forward selection of variables and using adjusted R-squared score as the metric. We found univariate regression results were significant for GDP (Gross Domestic Product) per capita, POD 30/70 (Probability Of Dying Between Age 30 And Exact Age 70 From Any of Cardiovascular Disease, Cancer, Diabetes or Chronic Respiratory Disease), HCI (Human Capital Index), GNI(Gross National Income) per capita, life expectancy, medical doctors per 10000 population, as these parameters negatively corelated with CFR (rho = -0.48 to -0.38 , p<0.05). Case fatality rate was regressed using ordinary least squares (OLS) against the socio-economic and health indicators. The indicators in the final model were GDP per capita, POD 30/70, HCI, life expectancy, medical doctors per 10,000, median age, current health expenditure per capita, number of confirmed cases and population in millions. The adjusted R-squared score was 0.306. Developing countries with a poor economy are especially vulnerable in terms of COVID-19 mortality and underscore the need to have a global policy to deal with this on-going pandemic. These trends largely confirm that the toll from COVID-19 will be worse in countries ill-equipped to deal with it. These analyses of epidemiological data are need of time as apart from increasing situational awareness, it guides us in taking informed interventions and helps policy-making to tackle this pandemic.

scholarly journals COVID-19: urgent questions for estimating morbidity, prevalence, case fatality rate and mortality rate

2020 ◽  
Vol 19 (3) ◽  
pp. 2585 ◽  
Author(s):  
O. M. Drapkina ◽  
I. V. Samorodskaya ◽  
M. G. Sivtseva ◽  
E. P. Kakorina ◽  
N. I. Briko ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Case Fatality Rate ◽  
Statistical Data ◽  
Public Health System ◽  
Case Fatality ◽  
Prognostic Models ◽  
Fatality Rate ◽  
The Public ◽  
Managerial Decisions

During epidemics, the usual statistical approaches will not allow determining the readiness of the public health system to take urgent measures to counteract the increase in morbidity, spread and mortality of the population. The quality of the medical, socio-economic and managerial decisions at all levels will depend on the accuracy of statistical data and the possibility of creating adequate prognostic models. However, there are still problems with the identification of COVID-19 cases and the diagnostic accuracy of the methods used. Complex analytical efforts require in order to determine the COVID-19 impact on the health status and case fatality rate/mortality rate.

scholarly journals Clinical and epidemiological variability in severe sepsis: an ecological study

2018 ◽  
Vol 72 (8) ◽  
pp. 741-745 ◽  
Author(s):  
J Priyanka Vakkalanka ◽  
Karisa K Harland ◽  
Morgan B Swanson ◽  
Nicholas M Mohr
Keyword(s):  
Severe Sepsis ◽  
Case Fatality Rate ◽  
Ecological Study ◽  
Case Fatality ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Administrative Database ◽  
Fatality Rate ◽  
Diagnostic Coding ◽  
Hospital Administrative

BackgroundTo assess clinical and epidemiological trends of severe sepsis.MethodsEcological study of patients presenting to the emergency department with severe sepsis or septic shock between 2005 and 2013. Patients were identified using the state-wide hospital administrative database. Key outcomes included incidence rates (IRs) and mortality rates (per 1000 population) by age and medically underserved areas (MUAs), sepsis case fatality rate (deaths per 100 sepsis cases), and proportions of transfer and comorbidities.ResultsThere were 154 019 sepsis cases identified. In 2005, 85+ yo in non-MUAs had a 44% increase in IR compared with those in MUAs, and this difference rose to 74% by 2013. Mortality rates were 1.6 (95% CI 1.3 to 1.8) times greater among 85+ yo in non-MUAs. Mortality rates increased by 1.8% annually, while the sepsis case fatality rate decreased by 7.7%. The proportion of transfer among sepsis cases decreased by 2.1% per year (3.8% in non-MUAs, 0.7% in MUAs).ConclusionsSepsis incidence varies geographically, and access to healthcare is one proposed mechanism that may explain heterogeneity. Over time, we may be capturing higher acuity sepsis cases with better recognition and management, as well as observing differential diagnostic coding documentation by location.

scholarly journals COVID-19 Infection Fatality Rate Associated with Incidence—A Population-Level Analysis of 19 Spanish Autonomous Communities

Biology ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 128 ◽  
Author(s):  
Chris Kenyon
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Population Level ◽  
Fatality Rate ◽  
Autonomous Communities ◽  
Spanish Regions ◽  
Level Analysis

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69–1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

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