scholarly journals A Comparison of Case Fatality Risk of COVID-19 between Singapore and Japan

2020 ◽  
Vol 9 (10) ◽  
pp. 3326
Author(s):  
Taishi Kayano ◽  
Hiroshi Nishiura
Keyword(s):  
Age Distribution ◽  
Case Fatality ◽  
Standardized Mortality Ratio ◽  
Fatality Risk ◽  
Illness Onset ◽  
High Virulence ◽  
Mortality Ratio ◽  
The Many ◽  
Case Fatality Risk ◽  
Protection Against Infection

The crude case fatality risk (CFR) for coronavirus disease (COVID-19) in Singapore is remarkably small. We aimed to estimate the unbiased CFR by age for Singapore and Japan and compare these estimates by calculating the standardized mortality ratio (SMR). Age-specific CFRs for COVID-19 were estimated in real time, adjusting for the delay from illness onset to death. The SMR in Japan was estimated by using the age distribution of the Singapore population. Among cases aged 60–69 years and 70–79 years, the age-specific CFRs in Singapore were estimated as 1.84% (95% confidence interval: 0.46–4.72%) and 5.57% (1.41–13.97%), respectively, and those in Japan as 5.52% (4.55–6.62%) and 15.49% (13.81–17.27%), respectively. The SMR of COVID-19 in Japan, when compared with Singapore as the baseline, was estimated to be 1.46 (1.09–2.96). The overall CFR for Singapore is lower than that for Japan. It is possible that the circulating variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Singapore causes a milder clinical course of COVID-19 infection compared with other strains. If infection with a low-virulence SARS-CoV-2 variant provides protection against infection by high-virulence strains, the existence of such a strain is encouraging news for the many countries struggling to suppress this virus.

scholarly journals Mortality in Escherichia coli bloodstream infections: a multinational population-based cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Melissa C. MacKinnon ◽  
Scott A. McEwen ◽  
David L. Pearl ◽  
Outi Lyytikäinen ◽  
Gunnar Jacobsson ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Mortality Rate ◽  
Case Fatality ◽  
Population Based ◽  
Day Case ◽  
Fatality Risk ◽  
E Coli ◽  
Factors Associated ◽  
Case Fatality Risk ◽  
Age And Sex

Abstract Background Escherichia coli is the most common cause of bloodstream infections (BSIs) and mortality is an important aspect of burden of disease. Using a multinational population-based cohort of E. coli BSIs, our objectives were to evaluate 30-day case fatality risk and mortality rate, and determine factors associated with each. Methods During 2014–2018, we identified 30-day deaths from all incident E. coli BSIs from surveillance nationally in Finland, and regionally in Sweden (Skaraborg) and Canada (Calgary, Sherbrooke, western interior). We used a multivariable logistic regression model to estimate factors associated with 30-day case fatality risk. The explanatory variables considered for inclusion were year (2014–2018), region (five areas), age (< 70-years-old, ≥70-years-old), sex (female, male), third-generation cephalosporin (3GC) resistance (susceptible, resistant), and location of onset (community-onset, hospital-onset). The European Union 28-country 2018 population was used to directly age and sex standardize mortality rates. We used a multivariable Poisson model to estimate factors associated with mortality rate, and year, region, age and sex were considered for inclusion. Results From 38.7 million person-years of surveillance, we identified 2961 30-day deaths in 30,923 incident E. coli BSIs. The overall 30-day case fatality risk was 9.6% (2961/30923). Calgary, Skaraborg, and western interior had significantly increased odds of 30-day mortality compared to Finland. Hospital-onset and 3GC-resistant E. coli BSIs had significantly increased odds of mortality compared to community-onset and 3GC-susceptible. The significant association between age and odds of mortality varied with sex, and contrasts were used to interpret this interaction relationship. The overall standardized 30-day mortality rate was 8.5 deaths/100,000 person-years. Sherbrooke had a significantly lower 30-day mortality rate compared to Finland. Patients that were either ≥70-years-old or male both experienced significantly higher mortality rates than those < 70-years-old or female. Conclusions In our study populations, region, age, and sex were significantly associated with both 30-day case fatality risk and mortality rate. Additionally, 3GC resistance and location of onset were significantly associated with 30-day case fatality risk. Escherichia coli BSIs caused a considerable burden of disease from 30-day mortality. When analyzing population-based mortality data, it is important to explore mortality through two lenses, mortality rate and case fatality risk.

scholarly journals Estimating the Case Fatality Risk of COVID-19 using Cases from Outside China

2020 ◽  
Author(s):  
Nick Wilson ◽  
Amanda Kvalsvig ◽  
Lucy Telfar Barnard ◽  
Michael G Baker
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Meta Analysis ◽  
Case Fatality ◽  
Large Uncertainty ◽  
Icu Patients ◽  
Fatality Risk ◽  
Case Fatality Risk ◽  
Respiratory Conditions

AbstractThere is large uncertainty around the case fatality risk (CFR) for COVID-19 in China. Therefore, we considered symptomatic cases outside of China (countries/settings with 20+ cases) and the proportion who are in intensive care units (4.0%, 14/349 on 13 February 2020). Given what is known about CFRs for ICU patients with severe respiratory conditions from a meta-analysis, we estimated a CFR of 1.37% (95%CI: 0.57% to 3.22%) for COVID- 19 cases outside of China.

scholarly journals Case Fatality Risk of the First Pandemic Wave of Coronavirus Disease 2019 (COVID-19) in China

2020 ◽  
Author(s):  
Xiaowei Deng ◽  
Juan Yang ◽  
Wei Wang ◽  
Xiling Wang ◽  
Jiaxin Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Influenza Pandemic ◽  
Mainland China ◽  
Case Fatality ◽  
Likelihood Estimation ◽  
H1n1 Influenza ◽  
Right Censoring ◽  
Time To Event ◽  
Fatality Risk ◽  
Case Fatality Risk

Abstract Background To assess the case fatality risk (CFR) of COVID-19 in mainland China, stratified by region and clinical category, and estimate key time-to-event intervals. Methods We collected individual information and aggregated data on COVID-19 cases from publicly available official sources from 29 December 2019 to 17 April 2020. We accounted for right-censoring to estimate the CFR and explored the risk factors for mortality. We fitted Weibull, gamma, and log-normal distributions to time-to-event data using maximum-likelihood estimation. Results We analyzed 82 719 laboratory-confirmed cases reported in mainland China, including 4632 deaths and 77 029 discharges. The estimated CFR was 5.65% (95% confidence interval [CI], 5.50–5.81%) nationally, with the highest estimate in Wuhan (7.71%) and lowest in provinces outside Hubei (0.86%). The fatality risk among critical patients was 3.6 times that of all patients and 0.8–10.3-fold higher than that of mild-to-severe patients. Older age (odds ratio [OR], 1.14 per year; 95% CI, 1.11–1.16) and being male (OR, 1.83; 95% CI, 1.10–3.04) were risk factors for mortality. The times from symptom onset to first healthcare consultation, to laboratory confirmation, and to hospitalization were consistently longer for deceased patients than for those who recovered. Conclusions Our CFR estimates based on laboratory-confirmed cases ascertained in mainland China suggest that COVID-19 is more severe than the 2009 H1N1 influenza pandemic in hospitalized patients, particularly in Wuhan. Our study provides a comprehensive picture of the severity of the first wave of the pandemic in China. Our estimates can help inform models and the global response to COVID-19.

scholarly journals Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

2020 ◽  
Vol 9 (2) ◽  
pp. 538 ◽  
Author(s):  
Natalie Linton ◽  
Tetsuro Kobayashi ◽  
Yichi Yang ◽  
Katsuma Hayashi ◽  
Andrei Akhmetzhanov ◽  
...  
Keyword(s):  
Incubation Period ◽  
Case Fatality ◽  
Illness Onset ◽  
Event Date ◽  
History Of ◽  
Novel Coronavirus ◽  
Case Fatality Risk ◽  
Right Truncation ◽  
Epidemiological Characteristics ◽  
Mean Time

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

scholarly journals The role of age distribution, time lag between reporting and death and healthcare system capacity on case fatality estimates of COVID-19

2020 ◽  
Author(s):  
Patrizio Vanella ◽  
Christian Wiessner ◽  
Anja Holz ◽  
Gerard Krause ◽  
Annika Moehl ◽  
...  
Keyword(s):  
Time Lag ◽  
Age Distribution ◽  
Case Fatality ◽  
System Capacity ◽  
Time Lags ◽  
Cross Country ◽  
The Difference ◽  
Case Fatality Risk ◽  
The Impact

European countries report large differences in coronavirus disease (COVID-19) case fatality risk (CFR). CFR estimates depend on demographic characteristics of the cases, time lags between reporting of infections and deaths and infrastructural characteristics, such as healthcare and surveillance capacities. We discuss the impact of these factors on the CFR estimates for Germany, Italy, France, and Spain for the COVID-19 pandemic from early March to mid-April, 2020. We found that, first, a large proportion of the difference in CFRs can be attributed to different age structures of the cases. Second, lags of 5-10 days between day of case report and death should be used, since these provide the most constant estimates. Third, for France, Italy, and Spain, intensive care beds occupied by COVID-19 patients were positively associated with fatality risks of hospitalized cases. Our results highlight that cross-country comparisons of crude CFR estimates can be misleading and should be avoided.

scholarly journals Sex Differences In COVID-19 Mortality In The Netherlands

2021 ◽  
Author(s):  
Annabel Niessen ◽  
Anne Teirlinck ◽  
Scott McDonald ◽  
Wim van der Hoek ◽  
Rianne van Gageldonk-Lafeber ◽  
...  
Keyword(s):  
Sex Differences ◽  
Case Fatality ◽  
Standardized Mortality Ratio ◽  
Predisposing Factor ◽  
Female Ratio ◽  
Specific Mortality ◽  
Male Female Ratio ◽  
Mortality Ratio ◽  
Underlying Mechanisms ◽  
Population Mortality

Abstract Introduction: Since the first reports of COVID-19 cases, sex-discrepancies have been reported in COVID-19 mortality. We provide a detailed description of these sex differences in relation to age and comorbidities among notified cases as well as in relation to age and sex specific mortality in the general Dutch population. Methods: Data on COVID-19 cases and mortality until May 31st was extracted from the national surveillance database with exclusion of healthcare workers. Association between sex and case fatality was analyzed with multivariable logistic regression. Subsequently, male-female ratio in standardized mortality ratios and population mortality rates relative to all-cause and infectious diseases-specific mortality were computed stratified by age.Results: Male-female odds ratio for case fatality was 1.33 [95% CI 1.26-1.41] and among hospitalized cases 1.27 [95% CI 1.16-1.40]. This remained significant after adjustment for age and comorbidities. The male-female ratio of the standardized mortality ratio was 1.70 [95%CI 1.62-1.78]. The population mortality rate was 35.1 per 100.000, with a male-female rate ratio of 1.25 (95% CI 1.18-1.31) which was higher than in all-cause and infectious disease mortality.Conclusion: Our study confirms male sex is a predisposing factor for severe outcomes of COVID-19, independent of age and comorbidities. The underlying mechanisms are likely to be COVID-19 specific.

scholarly journals Clinical Determinants of the Severity of Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis

2020 ◽  
Author(s):  
Yanling Wu ◽  
Hu Li ◽  
Shengjin Li
Keyword(s):  
Risk Factors ◽  
Cerebrovascular Disease ◽  
Respiratory Disease ◽  
Meta Analysis ◽  
Case Fatality ◽  
Older Age ◽  
Fatality Risk ◽  
Male Sex ◽  
Case Fatality Risk ◽  
D Dimer

Abstract Background: SARS-CoV-2 is an emerging pathogen, and coronavirus disease 2019 (COVID-19) has been declared a global pandemic. We aim to summarize current evidence regarding the risk of death and the severity of COVID-19 as well as risk factors for severe COVID-19.Methods: The PubMed, Embase, and Web of Science databases as well as some Chinese databases were searched for clinical and epidemiological studies on COVID-19. We conducted a meta-analysis to examine COVID-19-related death and risk factors for the severity of COVID-19.Results: A total of 55 studies fulfilled the criteria for this review. The case fatality risk ranged from 0 to 61.5%, with a pooled estimate of 3.3%. The risks of ICU admission, acute respiratory distress syndrome (ARDS)and severe COVID-19 were 24.9%, 20.9% and 26.6%, respectively. Factors related to the risk of severe COVID-19 were older age (MD=10.09, 95% CI:7.03, 13.16), male sex (OR=1.62, 95% CI:1.32, 1.99), hypertension (OR=2.34, 95% CI:1.47, 3.73), diabetes (OR=2.25, 95% CI:1.68, 3.03), chronic renal disease (OR=3.60, 95% CI:1.53, 8.46), heart disease (OR=2.76, 95% CI:1.78, 4.30), respiratory disease (OR=3.74, 95% CI:2.15, 6.49), cerebrovascular disease (OR=2.21, 95% CI:1.23, 3.98), higher D-dimer levels (SMD=0.62, 95% CI:0.28, 0.96), and higher IL-6 levels (SMD=2.21, 95% CI:0.11, 4.31). However, liver disease (OR=0.63, 95% CI: 0.36, 1.10) was found to be a nonsignificant predictor of the severity of COVID-19.Conclusions: The case fatality risk of COVID-19 and the risk of severe manifestations were not very high, and variances in the study designs and regions led to high heterogeneity among the studies. Male sex, older age, comorbidities such as hypertension, diabetes, cardiovascular disease, respiratory disease and cerebrovascular disease could increase the risk of developing a severe case of COVID-19. Laboratory parameters, such as D-dimer and IL-6 levels, could affect the prognosis of COVID-19.

Sign in / Sign up

Export Citation Format

Share Document