Infection Fatality Rate Research Articles

COVID-19 increased existing gender mortality gaps in high-income more than middle-income countries

ObjectiveTo analyze how patterns of excess mortality varied by sex and age groups across countries during the COVID-19 pandemic and their association with country income level. MethodsWe used World Health Organization excess mortality estimates by sex and age groups for 75 countries in 2020 and 62 countries in 2021, restricting the sample to estimates based on recorded all-cause mortality data. We examined patterns across countries using country-specific Poisson regressions with observations consisting of the number of excess deaths by groups defined by sex and age. FindingsMen die at higher rates in nearly all places and at all ages beyond age 45. In 2020, the pandemic amplified this gender mortality gap for the world, but with variation across countries and by country income level. In high-income countries, rates of excess mortality were much higher for men than women. In contrast, in middle-income countries, the sex ratio of excess mortality was similar to the sex ratio of expected all-cause mortality. The exacerbation of the sex ratio of excess mortality observed in 2020 in high-income countries, however, declined in 2021. ConclusionThe COVID-19 pandemic has killed men at much higher rates than women, as has been well documented, but these gender differences have varied by country income. These differences were the result of some combination of variation in gender patterns of infection rates and infection fatality rates across countries. The gender gap in mortality declined in high-income countries in 2021, likely as a result of the faster rollout of vaccination against COVID-19.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Bis-benzylisoquinoline alkaloids inhibit African swine fever virus internalization and replication by impairing late endosomal/lysosomal function.

African swine fever (ASF), caused by the African swine fever virus (ASFV), is a highly infectious disease afflicting domestic pigs and wild boars. It exhibits an alarming acute infection fatality rate of up to 100%. Regrettably, no commercial vaccines or specific drugs for combating this disease are currently available. This study evaluated the anti-ASFV activities in porcine alveolar macrophages, 3D4/21 cells, and PK-15 cells of four bis-benzylisoquinoline alkaloids (BBAs): cepharanthine (CEP), tetrandrine, fangchinoline, and iso-tetrandrine. Furthermore, we demonstrated that CEP, which exhibited the highest selectivity index (SI = 81.31), alkalized late endosomes/lysosomes, hindered ASFV endosomal transport, disrupted virus uncoating signals, and thereby inhibited ASFV internalization. Additionally, CEP disrupted ASFV DNA synthesis, leading to the inhibition of viral replication. Moreover, berbamine was labeled with NBD to synthesize a fluorescent probe to study the cellular location of these BBAs. By co-staining with Lyso-Tracker and lysosome-associated membrane protein 1, we demonstrated that BBAs target the endolysosomal compartments for the first time. Our data together indicated that BBAs are a class of natural products with significant inhibitory effects against ASFV infection. These findings suggest their potential efficacy as agents for the prevention and control of ASF, offering valuable references for the identification of potential drug targets.IMPORTANCEThe urgency and severity of African swine fever (ASF) underscore the critical need for effective interventions against this highly infectious disease, which poses a grave threat to domestic pigs and wild boars. Our study reveals the potent anti-African swine fever virus (ASFV) efficacy of bis-benzylisoquinoline alkaloids (BBAs), particularly evident in the absence of progeny virus production under a 5 µM concentration treatment. The structural similarity among cepharanthine, tetrandrine, fangchinoline, and iso-tetrandrine, coupled with their analogous inhibitory stages and comparable selectivity indexes, strongly suggests a shared antiviral mechanism within this drug category. Further investigation revealed that BBAs localize to lysosomes and inhibit the internalization and replication of ASFV by disrupting the endosomal/lysosomal function. These collective results have profound implications for ASF prevention and control, suggesting the potential of the investigated agents as prophylactic and therapeutic measures. Furthermore, our study offers crucial insights into identifying drug targets and laying the groundwork for innovative interventions.

Large and unequal life expectancy declines during the COVID-19 pandemic in India in 2020.

Global population health during the COVID-19 pandemic is poorly understood because of weak mortality monitoring in low- and middle-income countries. High-quality survey data on 765,180 individuals, representative of one-fourth of India's population, uncover patterns missed by incomplete vital statistics and disease surveillance. Compared to 2019, life expectancy at birth was 2.6 years lower and mortality was 17% higher in 2020, implying 1.19 million excess deaths in 2020. Life expectancy declines in India were larger and had a younger age profile than in high-income countries. Increases in mortality were greater than expected based on observed seroprevalence and international infection fatality rates, most prominently among the youngest and older age groups. In contrast to global patterns, females in India experienced a life expectancy decline that was 1 year larger than losses for males. Marginalized social groups experienced greater declines than the most privileged social group. These findings uncover large and unequal mortality impacts during the pandemic in the world's most populous country.

An SIR‐based Bayesian framework for COVID‐19 infection estimation

AbstractEstimating the COVID‐19 infection fatality rate, inferring the latent incidence and predicting the future epidemic evolution are critical to public health surveillance, but often challenging due to limited data availability or quality. Recently, a Bayesian framework combining time series deconvolution of deaths with a parametric Susceptible–Infectious–Recovered (SIR) model was proposed by Irons and Raftery, 2021. We assess the parameter identifiability of the model using the profile likelihood approach and simulations, when only the time series of deaths and seroprevalence survey data are available. The robustness of the model to the more complex but also more realistic Susceptible–Exposed–Infectious–Recovered (SEIR)‐based epidemics is evaluated through simulations; the influence of potential biases in the serosurveys on the inference is also investigated. We use a stationary first‐order autoregressive prior to account for the variability of transmission rate over time. The results suggest that the model is relatively robust to SEIR‐based epidemics, especially when the reproductive number is low, given sufficient information from serosurveys or priors. However, the lack of parameter identifiability under limited data availability cannot be neglected. We apply the model to infer the COVID‐19 infections in Ontario and Quebec, Canada during the Omicron era.

COVID-19 Case Fatality Rates in the Texas Prison System.

The case fatality rate (CFR) is an important metric in the correctional setting because it permits assessment of the lethality of an infectious agent independent of its underlying variations in transmissibility and incidence. Several studies have reported that incarceration is associated with both increased COVID-19 incidence and mortality. CFR, sometimes referred to as infection fatality rate for COVID-19, was used to compare mortality in a population at two points in time. A retrospective cohort study design was used to assess age-adjusted mortality among people diagnosed with COVID-19 in the Texas prison system and the Texas nonincarcerated population from January 1, 2020, through December 31, 2021. For each 6-month period under study, the Texas prison population had a substantially lower age-adjusted CFR compared with the Texas nonincarcerated population. However, in the absence of information on underlying COVID-19 severity, comorbidities, and other potential confounding factors in these two populations, it is difficult to make strong inferences based on a comparison of their CFRs. Future research, with careful attention to bias and confounding, should examine the specific health system factors that may be used to reduce morbidity and mortality associated with infectious disease outbreaks in prisons.

Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods.

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.

Regional European genetic ancestry predicts type I interferon level and risk of severe viral infection.

Viral infection outcomes vary widely between individuals, ranging from mild symptoms to severe organ failure and death, and it is clear that host genetic factors play a role in this variability. Type I interferon (IFN) is a critical anti-viral cytokine, and we have previously noted differences in type I IFN levels between world populations. In this study, we investigate the interrelationship between regional European genetic ancestry, type I IFN levels and severe viral infection outcomes. In cohorts of European ancestry lupus patients living in Europe, we noted higher IFN in the Northwestern populations as compared to Southeastern populations. In an independent cohort of European ancestry lupus patients from the USA with varying proportional regional European genetic admixture, we observed the same Northwest vs. Southeast European ancestry IFN gradient. We developed a model to predict type I IFN level based on regional European ancestry (Area under the curve (AUC) = 0.73, P = 6.1e-6). Examining large databases containing serious viral outcomes data, we found that lower predicted IFN in the corresponding European country was significantly correlated with increased viral infection fatality rate, including Coronavirus Disease 2019 (COVID-19), viral hepatitis and HIV [correlation coefficients: -0.79 (P = 4e-2), -0.94 (P = 6e-3) and -0.96 (P = 8e-2), respectively]. This association between predicted type I IFN level and viral outcome severity suggests a potential causal relationship, as greater intrinsic type I IFN is beneficial in host defense against viruses. Genetic testing could provide insight into individual and population level risk of fatality due to viruses prior to infection, across a wide range of viral pathogens.

Prevalence and Levels of Anti-SARS-CoV-2 Antibodies in the Eswatini Population and Subsequent Severity of the Fourth COVID-19 Epidemic Wave

Background: Seroepidemiology studies are useful for quantifying the magnitude of past infections and estimating the extent of population-based immunity to inform risk mitigation strategies for the future. We report on the only national population-based survey of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) immunoglobulin G (IgG) seroprevalence in Eswatini. Methods: The survey was undertaken from 31 August to 30 September 2021, following three earlier waves of coronavirus disease (COVID-19), and preceded the onset of the fourth wave, which was dominated by the Omicron variant of concern. We also report on epidemiological trends of recorded COVID-19 cases and hospitalizations before and after the fourth COVID-19 wave through to March 2022. We evaluated the immunoglobulin G (IgG) seropositivity based on either anti-nucleocapsid (N) or anti-spike (S) antigens. Results: Of 4564 individuals, 58.5% were female, 36.0% were aged 18–50 years, and 863 (18.9%) of adults who were older than 18 years had received at least a single dose of COVID-19 vaccine. Overall, 2769 (60.7%) were seropositive with heterogeneity across sub-regions (53.7%; 95% CI:49.2–58.1 to 68.6%; 95% CI:64.5–72.4), with the highest rates occurring in sub-regions of the Manzini region. Seropositivity was higher in vaccinated individuals (84.5%; 95% CI: 81.9–86.7) compared to unvaccinated individuals (55.1%; 95% CI:53.5–56.7). Amongst unvaccinated individuals, seropositivity was highest in 18–50-year-olds (59.5%;95% CI: 56.9–62.1). Seropositivity was associated with female gender, previous positive SARS-CoV-2 NAAT status and being vaccinated, non-smoking, and being formally employed. We estimated as of 15 September 2021 that there had been 639,475 SARS-CoV-2 infections (95% CI; 620,824–658,003) in Eswatini, which was 25.5-fold greater than the 25,048 COVID-19 cases that had been recorded by then. The national case fatality rate (CFR) based on recorded cases was 4.8%, being 25-fold greater than the infection fatality rate (0.19; 95% CI: 0.18–0.19) based on recorded deaths and extrapolating the force of infection from seroprevalence. Nationally and across all four regions, we report the decoupling of COVID-19 cases from hospitalisations and deaths, observed as early as during the third wave, which was dominated by the Delta variant compared with earlier waves. Conclusions: We identified that 60.7% of people in Eswatini had been infected by SARS-CoV-2 at least once and before the onset of the Omicron wave in mid-November 2021. Despite a modest uptake of COVID-19 vaccines, the evolution of population immunity from infection has likely contributed to the decoupling of infection and severe COVID-19 in Eswatini.

The trade‐off between deaths by infection and socio‐economic costs in the emerging infectious disease

AbstractCOVID‐19, caused by the novel coronavirus (SARS‐CoV‐2), is an emerging infectious disease (EID) with a relatively high infectivity and mortality rate. During the state of emergency announced by the Japanese government in the spring of 2020, citizens were requested to stay home, and the number of infected people was drastically reduced without a legally‐binding lockdown. It is well‐acknowledged that there is a trade‐off between maintaining economic activity and preventing the spread of infectious diseases. We aimed to reduce the total loss caused by the epidemic of an EID like COVID‐19 in the present study. We focused on early and late stages of the epidemic and proposed a framework to reduce the total loss resulted from the damage by infection and the cost for the countermeasure. Mathematical epidemic models were used to estimate the effect of interventions on the number of deaths by infection. The total loss was converted into the monetary base and different policies were compared. In the early stage, we calculated the damage by infection when behavioral restrictions were implemented. The favorable intensity of the intervention depended on the basic reproduction number, infection fatality rate, and the economic impact. In the late stage, we calculated indicators and showed it depended on the ratio of the cost to maintain the hospitalization system to the monetary loss per deaths caused by infection to determine which strategy should be adopted.

Global trends in COVID-19 incidence and case fatality rates (2019-2023): a retrospective analysis.

Analyzing and comparing COVID-19 infection and case-fatality rates across different regions can help improve our response to future pandemics. We used public data from the WHO to calculate and compare the COVID-19 infection and case-fatality rates in different continents and income levels from 2019 to 2023. The Global prevalence of COVID-19 increased from 0.011 to 0.098, while case fatality rates declined from 0.024 to 0.009. Europe reported the highest cumulative infection rate (0.326), with Africa showing the lowest (0.011). Conversely, Africa experienced the highest cumulative case fatality rates (0.020), with Oceania the lowest (0.002). Infection rates in Asia showed a steady increase in contrast to other continents which observed initial rises followed by decreases. A correlation between economic status and infection rates was identified; high-income countries had the highest cumulative infection rate (0.353) and lowest case fatality rate (0.006). Low-income countries showed low cumulative infection rates (0.006) but the highest case fatality rate (0.016). Initially, high and upper-middle-income countries experienced elevated initial infection and case fatality rates, which subsequently underwent significant reductions. COVID-19 rates varied significantly by continent and income level. Europe and the Americas faced surges in infections and low case fatality rates. In contrast, Africa experienced low infection rates and higher case fatality rates, with lower- and middle-income nations exceeding case fatality rates in high-income countries over time.

The evolution and interpretation of seroprevalence of SARS-CoV-2 antibodies among South African blood donors from the Beta to Omicron variant-driven waves.

Confirmed COVID-19 diagnoses underestimate the total number of infections. Blood donors can provide representative seroprevalence estimates, which can be leveraged into reasonable estimates of total infection counts and infection fatality rate (IFR). Blood donors who donated after each of three epidemic waves (Beta, Delta and first Omicron waves) were tested for anti-SARS-CoV-2 nucleocapsid antibodies using the Roche Elecsys anti-SARS-CoV-2 total immunoglobulin assay. Roche Elecsys anti-spike antibody testing was done for the post-Omicron sampling. Prevalence of antibodies was estimated by age, sex, race and province and compared to official case reporting. Province and age group-specific IFRs were estimated using external excess mortality estimates. The nationally weighted anti-nucleocapsid seroprevalence estimates after the Beta, Delta and Omicron waves were 47% (46.2%-48.6%), 71% (68.8%-73.5%) and 87% (85.5%-88.4%), respectively. There was no variation by age and sex, but there were statistically and epidemiologically significant differences by province (except at the latest time point) and race. There was a 13-fold higher seroprevalence than confirmed case counts at the first time point. Age-dependent IFR roughly doubled for every 10 years of age increase over 6 decades from 0.014% in children to 6.793% in octogenarians. Discrepancies were found between seroprevalence and confirmed case counts. High seroprevalence rates found among Black African donors can be ascribed to historical inequities. Our IFR estimates were useful in refining previous large disagreements about the severity of the epidemic in South Africa. Blood donor-based serosurveys provided a valuable and efficient way to provide near real-time monitoring of the ongoing SARS-CoV-2 outbreak.

Assessing the Epidemiological Effects of COVID-19 Vaccination in Countries around the World: A Cross-Sectional Study

Relevance. To maximize the success of COVID-19 vaccination, it is necessary to consider the sensitivity of population health not only to these specific exposures, but also to the inherent socioeconomic factors that can modulate the effectiveness of health interventions. Aims. To assess the relationships between infection, mortality and the fatality ratio from COVID-19 in the countries of the world with the proportion of vaccinated in the population, as well as the relationship of the epidemic situation on COVID-19 with the parameters of the well-being of the population based on a cross-sectional study. Materials and methods. Statistical analysis used the data of the portal Our World in Data as of 15/12/2021, as well as UNO data on human development. Results. The frequencies of infection and death were higher the higher the human development index of countries. The inverse correlation of vaccination rates with infection and mortality rates was typical only for countries with very high human development. In the same group of countries, the infection fatality rate was the lowest. Conclusion. The nature of the correlations between the level of vaccination and the parameters of the epidemic situation is not the same for countries with different levels of human development. Apparently, a number of socio-economic factors, including the integral level development of the country and the level of urbanization, can influence the effectiveness of vaccination.

An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI)

BackgroundMost COVID-19 vulnerability indices rely on measures that are biased by rates of exposure or are retrospective like mortality rates that offer little opportunity for intervention. The Moore-Hill Vulnerability Index (MHVI) is a precision public health early warning alternative to traditional infection fatality rates that presents avenues for mortality prevention.MethodsWe produced an infection-severity vulnerability index by calculating the proportion of all recorded positive cases that were severe and attended by ambulances at small area scale for the East Midlands of the UK between May 2020 and April 2022. We produced maps identifying regions with high and low vulnerability, investigated the accuracy of the index over shorter and longer time periods, and explored the utility of the MHVI compared to other common proxy measures and indices. Analysis included exploring the correlation between our novel index and the Index of Multiple Deprivation (IMD).ResultsThe MHVI captures geospatial dynamics that single metrics alone often overlook, including the compound health challenges associated with disadvantaged and declining coastal towns inhabited by communities with post-industrial health legacies. A moderate negative correlation between MHVI and IMD reflects spatial analysis which suggests that high vulnerability occurs in affluent rural as well as deprived coastal and urban communities. Further, the MHVI estimates of severity rates are comparable to infection fatality rates for COVID-19.ConclusionsThe MHVI identifies regions with known high rates of poor health outcomes prior to the pandemic that case rates or mortality rates alone fail to identify. Pre-hospital early warning measures could be utilised to prevent mortality during a novel pandemic.

Forecasting of COVID-19 spread in Poland as part of health needs mapping

Abstract The development of a robust model for COVID-19 forecasting was a crucial task undertaken by the Ministry of Health of the Republic of Poland in response to the pandemic. High-quality forecasting rendered the inclusion of COVID-19 modelling in health needs assessment indispensable at the regional as well as country level. The model served as a basis for data-driven policy-making, such as the introduction or lifting of non-pharmaceutical interventions and the management of hospital resource allocation. The proposed model draws upon the population-adjusted infection fatality rates, vaccine-induced immunity, reported test positivity rates, and COVID-19-related mortality to infer the dark figure of cases. The estimates of the true number of infections lay the foundations for the computation of variant-dependent effective reproduction numbers, which constitute the workhorse of the model. The algorithm utilises the Bayesian prediction of those metrics along with the estimation of hybrid immunity levels to arrive at a short-term forecast of the course of the pandemic. The implemented model yields reliable results throughout the outbreak, allowing for the assessment of the strain of consecutive epidemic waves on the healthcare system. Key messages • Forecasting of COVID-19 spread is vital for health needs assessment and health crisis management policy-making. • Short-term forecasting of COVID-19 spread using effective reproduction numbers yields high-quality results.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure.From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health.Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Magnitude and associated factors of mortality among patients admitted with COVID-19 in Addis Ababa, Ethiopia.

The COVID-19 pandemic continues to grow around the world and has caused enormous mortality and morbidity. The severity and mortality of coronavirus disease are associated with various comorbidities. The infection fatality rate was reported to be inconsistent with different studies. Therefore, the aim of this study was to assess the magnitude and factors associated with mortality among patients admitted to Eka Kotebe General Hospital, Addis Ababa, Ethiopia. An institutional-based cross-sectional study was conducted at Eka Kotebe General Hospital among patients who were admitted for COVID-19 from January 15, 2021, to June 30, 2021. A total of 393 records of patients were selected by simple random sampling. Data was extracted from compiled data forms where available information was already tabulated. Data was entered and analyzed using SPSS version 25. The determinant factors associated with mortality among COVID-19 patients were identified using bivariate and multivariable logistic regression analysis. A statistical association was declared with multivariable logistic regression using a 95% confidence interval and a P-value of less than 0.05. The proportion of COVID-19 mortality among patients admitted to Eka Kotebe General Hospital was 8.1% (95% CI (5.4-10.8%)). Age >50 years [AOR = 7.91; 95% CI (2.34-25.70)], being male [AOR = 2.09; 95% CI (1.20-3.65)], having diabetes mellitus [AOR = 2.64; 95% CI (1.30-5.35)], having hypertension [AOR = 2.67; 95% CI (1.22-5.88)] and having chronic kidney disease [AOR = 12.04; 95% CI (4.03-14.22)] were determinant factors of COVID-19 mortality. The current study findings revealed that COVID-19 mortality was high among hospitalized COVID-19 patients. Furthermore, age, gender, diabetes mellitus, hypertension, and chronic kidney disease were discovered to be independent predictors of COVID-19 mortality. Therefore, older COVID-19 patients and those with established comorbidities such as hypertension, diabetes, and end-stage renal disease should receive comprehensive preventative efforts, including vaccination.

Does tourism affect the long term course of COVID-19 pandemic in a country of destination? Evidence from a popular Greek island in 2020 where control measures were implemented.

Greece opened its points of entry on July 1, 2020, with specific guidelines for travellers arriving by sea, air or land. The aim of this article is to examine the effect of tourism on the long term course of the Coronavirus Disease 2019 (COVID-19) pandemic during the pre-vaccination era (June to December 2020) on the popular Greek island of Crete. To achieve this, a cross-sectional serosurvey, repeated at monthly intervals, was conducted to compare the seroprevalence in Crete with seroprevalence in the mainland of Greece. Crete welcomed nearly 2,000,000 travellers during the 2020 summer season. Left-over serum samples were collected and obtained from public and private laboratories located in Greece, including the island of Crete. These samples were tested for the presence of anti-SARS-CoV-2 IgG antibodies. A total of 55,938 samples were collected, 3,785 of which originated from Crete. In Crete, the seroprevalence ranged between 0% (June 2020) and 2.58% (December 2020), while the corresponding seroprevalence in Greece was 0.19% and 10.75%, respectively. We identified 4.16 times lower seropositivity in Crete (2.58%) in comparison with the mainland of Greece (10.75%) during December 2020. Moreover, the monthly infection fatality rate (IFR) in Crete was calculated at 0.09%, compared with 0.21% in mainland Greece for December 2020. The island of Crete presented more than four times lower seroprevalence than the mainland of Greece, despite being a highly attractive tourist destination. This evidence supports the idea that tourism may not have affected the long term course of the COVID-19 pandemic in Greece. However, due to contradicting results from previous studies, further investigation is needed.

Gender and age group-wise inequality in health burden and value of premature death from COVID-19 in India

BackgroundEarlier studies have focused on the age-group-wise health burden of COVID-19 while few studies have focused on the gender-wise analysis of the burden of COVID-19. The present study estimated the health burden and value of premature mortality from COVID-19 based on gender and age. MethodsThis study was based on secondary data collected from several sources of the government of India. To quantify the health burden, the disability-adjusted life years (DALY) method was used. An abridged life table was used to estimate the fall in life expectancy due to COVID-19. The value of premature mortality was estimated by using the human capital approach. ResultsAmong COVID-19 cases, 65.08% were males and 34.92% were females. The overall health burden caused by COVID-19 was 19,24,107 DALY in 2020, 43,40,526 DALY in 2021, and 8,08,124 DALY in 2022. The health burden per 1000 males was more than double that per 1000 females. This was due to higher rates of infection and case fatality rate among males compared to females. The age group 60–64 years experienced the highest loss of healthy life years per 1000 people, while the age group 55–59 years had the highest overall loss. Due to additional deaths from COVID-19, life expectancy decreased by 0.24 years in 2020, 0.47 years in 2021, and 0.07 years in 2022. The total value of premature death in the first three years of the COVID-19 pandemic amounted to 15,849.99 crores INR. ConclusionIn India, males and the older population were more susceptible to the impact of COVID-19.

A global analysis of COVID-19 infection fatality rate and its associated factors during the Delta and Omicron variant periods: an ecological study.

The Omicron variant of SARS-CoV-2 is more highly infectious and transmissible than prior variants of concern. It was unclear which factors might have contributed to the alteration of COVID-19 cases and deaths during the Delta and Omicron variant periods. This study aimed to compare the COVID-19 average weekly infection fatality rate (AWIFR), investigate factors associated with COVID-19 AWIFR, and explore the factors linked to the increase in COVID-19 AWIFR between two periods of Delta and Omicron variants. An ecological study has been conducted among 110 countries over the first 12 weeks during two periods of Delta and Omicron variant dominance using open publicly available datasets. Our analysis included 102 countries in the Delta period and 107 countries in the Omicron period. Linear mixed-effects models and linear regression models were used to explore factors associated with the variation of AWIFR over Delta and Omicron periods. During the Delta period, the lower AWIFR was witnessed in countries with better government effectiveness index [β = -0.762, 95% CI (-1.238)-(-0.287)] and higher proportion of the people fully vaccinated [β = -0.385, 95% CI (-0.629)-(-0.141)]. In contrast, a higher burden of cardiovascular diseases was positively associated with AWIFR (β = 0.517, 95% CI 0.102-0.932). Over the Omicron period, while years lived with disability (YLD) caused by metabolism disorders (β = 0.843, 95% CI 0.486-1.2), the proportion of the population aged older than 65 years (β = 0.737, 95% CI 0.237-1.238) was positively associated with poorer AWIFR, and the high proportion of the population vaccinated with a booster dose [β = -0.321, 95% CI (-0.624)-(-0.018)] was linked with the better outcome. Over two periods of Delta and Omicron, the increase in government effectiveness index was associated with a decrease in AWIFR [β = -0.438, 95% CI (-0.750)-(-0.126)]; whereas, higher death rates caused by diabetes and kidney (β = 0.472, 95% CI 0.089-0.855) and percentage of population aged older than 65 years (β = 0.407, 95% CI 0.013-0.802) were associated with a significant increase in AWIFR. The COVID-19 infection fatality rates were strongly linked with the coverage of vaccination rate, effectiveness of government, and health burden related to chronic diseases. Therefore, proper policies for the improvement of vaccination coverage and support of vulnerable groups could substantially mitigate the burden of COVID-19.