scholarly journals Spatial variability in the risk of death from COVID-19 in Italy, 2020

2020 ◽  
Author(s):  
Kenji Mizumoto ◽  
Sushma Dahal ◽  
Gerardo Chowell
Keyword(s):  
Time Delay ◽  
Care Delivery ◽  
Case Fatality ◽  
Medical Support ◽  
Island Region ◽  
Case Fatality Ratio ◽  
Risk Of Death ◽  
Geographic Regions ◽  
Northwest Region ◽  
The City

AbstractObjectivesItaly is bearing the brunt of the COVID-19 pandemic, as the death toll there has already surpassed that in Wuhan, the city in China where the coronavirus emerged in December 2019. Here we employ statistical methods to assess the severity of COVID-19 pandemic across different regions of Italy.MethodWe manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 stratified by region in Italy. We estimated both the crude and time-delay adjusted case fatality ratio across five geographic regions of Italy.ResultsThe Northwest that includes Lombardy exhibited the highest time-delay-adjusted CFR at 23.0% followed by the Northeast (14.2%), the Center region (11.6%) and the South region (12.1%). The Island region has exhibited the lowest CFR (9.6%).ConclusionOur estimates in the Northwest and the Northeast are higher than those reported from most affected areas in China. The CFR in Northwest Italy is 1.9-fold higher than that in Wuhan. Our finding reflects the need of urgent medical support in the Northwest region and the appropriate planning and supplies procurement in other regions of Italy focusing on medical care delivery to those who are at the highest risk of poorer outcomes due to COVID-19.

scholarly journals The Characteristics, Dynamics, and the Risk of Death in COVID-19 Positive Dialysis Patients in London, UK

Kidney360 ◽  
2020 ◽  
Vol 1 (11) ◽  
pp. 1226-1243
Author(s):  
Dalvir Kular ◽  
Irina Chis Ster ◽  
Alexander Sarnowski ◽  
Eirini Lioudaki ◽  
Dandisonba C.B. Braide-Azikiwe ◽  
...  
Keyword(s):  
Risk Factors ◽  
Performance Status ◽  
Age Groups ◽  
Case Fatality ◽  
Smoking History ◽  
Case Fatality Ratio ◽  
Risk Of Death ◽  
Dialysis Vintage ◽  
Patients With Diabetes ◽  
Potential Risk Factors

BackgroundPatients on dialysis with frequent comorbidities, advanced age, and frailty, who visit treatment facilities frequently, are perhaps more prone to SARS-CoV-2 infection and related death—the risk factors and dynamics of which are unknown. The aim of this study was to investigate the hospital outcomes in patients on dialysis infected with SARS-CoV-2.MethodsData on 224 patients on hemodialysis between February 29, 2020 and May 15, 2020 with confirmed SARS-CoV-2 were analyzed for outcomes and potential risk factors for death, using a competing risk-regression model assessed by subdistribution hazards ratio (SHR).ResultsCrude data analyses suggest an overall case-fatality ratio of 23% (95% CI, 17% to 28%) overall, but that varies across age groups from 11% (95% CI, 0.9% to 9.2%) in patients ≤50 years old and 32% (95% CI, 17% to 48%) in patients >80 years; with 60% of deaths occurring in the first 15 days and 80% within 21 days, indicating a rapid deterioration toward death after admission. Almost 90% of surviving patients were discharged within 28 days. Death was more likely than hospital discharge in patients who were more frail (WHO performance status, 3–4; SHR, 2.16 [95% CI, 1.25 to 3.74]; P=0.006), had ischemic heart disease (SHR, 2.28 [95% CI, 1.32 to 3.94]; P=0.003), cerebrovascular disease (SHR, 2.11 [95% CI, 1.20 to 3.72]; P=0.01), smoking history (SHR, 2.69 [95% CI, 1.33 to 5.45]; P=0.006), patients who were hospitalized (SHR, 10.26 [95% CI, 3.10 to 33.94]; P<0.001), and patients with high CRP (SHR, 1.35 [95% CI, 1.10 to 1.67]) and a high neutrophil:lymphocyte ratio (SHR, 1.03 [95% CI, 1.01 to 1.04], P<0.001). Our data did not support differences in the risk of death associated with sex, ethnicity, dialysis vintage, or other comorbidities. However, comparison with the entire dialysis population attending these hospitals, in which 13% were affected, revealed that patients who were non-White (62% versus 52% in all patients, P=0.001) and those with diabetes (54% versus 22%, P<0.001) were disproportionately affected.ConclusionsThis report discusses the outcomes of a large cohort of patients on dialysis. We found SARS-CoV-2 infection affected more patients with diabetes and those who were non-White, with a high case-fatality ratio, which increased significantly with age, frailty, smoking, increasing CRP, and neutrophil:lymphocyte ratio at presentation.

scholarly journals Estimating the Risk of COVID-19 Death during the Course of the Outbreak in Korea, February–May 2020

2020 ◽  
Vol 9 (6) ◽  
pp. 1641 ◽  
Author(s):  
Eunha Shim ◽  
Kenji Mizumoto ◽  
Wongyeong Choi ◽  
Gerardo Chowell
Keyword(s):  
Time Delay ◽  
Early Stage ◽  
Case Fatality ◽  
Credible Interval ◽  
Adjusted Risk ◽  
Risk Of Death ◽  
Early Implementation ◽  
Daily Series ◽  
Case Fatality Risk ◽  
Reporting Delay

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country. Results: Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%–33.6%), 20.8% (95% CrI: 18.1%–24.0%) in Daegu, and 1.7% (95% CrI: 1.1%–2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%–11.5%). Conclusions: The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.

scholarly journals Estimating the risk of COVID-19 death during the course of the outbreak in Korea, February-March, 2020

2020 ◽  
Author(s):  
Eunha Shim ◽  
Kenji Mizumoto ◽  
Wongyeong Choi ◽  
Gerardo Chowell
Keyword(s):  
Time Delay ◽  
Early Stage ◽  
Case Fatality ◽  
Adjusted Risk ◽  
Early Implementation ◽  
Daily Series ◽  
Geographic Differences ◽  
Case Fatality Risk ◽  
The City ◽  
Reporting Delay

AbstractBackgroundIn Korea, a total of 8,799 confirmed cases of COVID-19 including 102 deaths have been recorded as of Mar 21, 2020. The time-delay adjusted case fatality risk of COVID-19 in Korea is yet to be estimated.MethodsWe obtained the daily series of confirmed cases and deaths in Korea reported prior to March 21, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in the city of Daegu, Gyeongsangbuk-do, other regions in Korea, as well as for the entire country.ResultsOur model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do, partially due to the reporting delay. Our estimates of the risk for death in Gyeongsangbuk-do reached 2.4% (95% CrI: 1.6-3.4%), 1.3% (95% CrI: 1.0-1.6%) in Daegu and 0.7% (95% CrI: 0.3-1.4%) in other regions, whereas the national estimate of the risk for death was estimated at 1.4% (95% CrI: 1.2-1.7%) in Korea.ConclusionsThe relatively low CFRs are associated with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings in Korea. Geographic differences in CFR are likely influenced by clusters of nosocomial transmission.

scholarly journals The age structure of cases as the key of COVID-19 severity: Longitudinal population-based analysis of European countries during 150 days

2021 ◽  
pp. 140349482110424
Author(s):  
Klára Hulíková Tesárková ◽  
Dagmar Dzúrová
Keyword(s):  
Time Delay ◽  
Age Structure ◽  
Case Fatality ◽  
The Elderly ◽  
Population Based ◽  
European Countries ◽  
Fatality Rate ◽  
Risk Of Death ◽  
Cross Country ◽  
The Impact

Aims: Over a million confirmed cases of the coronavirus disease (COVID-19) across 16 European countries were observed during the first wave of the pandemic. Epidemiological measures like the case fatality rate (CFR) are generally used to determine the severity of the illness. The aim is to investigate the impact of the age structure of reported cases on the reported CFR and possibilities of its demographic adjustment for a better cross-country comparison (age-standardized CFRs, time delay between cases detection and death). Methods: This longitudinal study uses prospective, population-based data covering 150 days, starting on the day of confirmation of the 100th case in each country. COVerAGE-DB and the Human Mortality Database were used in this regard. The age-standardized CFRs were calculated with and without the time delay of the number of deaths after the confirmation of the cases. Results: The observed decline in the CFRs at the end of the first wave is partly given by the changes in the age structure of confirmed cases. Using the adjusted (age-standardized) CFRs with time delay, the risk of death among confirmed cases is much more stable in comparison to crude (observed) CFRs. Conclusions: Preventing the spread of COVID-19 among the elderly is an important way to positively influence the overall fatality rate, decrease the number of deaths, and not overload the health systems. The crude CFRs (still often presented) are not sufficient for a proper evaluation of the development across populations nor as a means of identifying the influencing factors.

scholarly journals Spatial variability in the risk of death from COVID-19 in Italy

2020 ◽  
Vol 24 (8) ◽  
pp. 829-837
Author(s):  
K. Mizumoto ◽  
S. Dahal ◽  
G. Chowell
Keyword(s):  
Health Care ◽  
Multivariate Analysis ◽  
Multivariate Regression ◽  
Case Fatality ◽  
Multivariate Regression Analysis ◽  
Morbidity Rate ◽  
Case Fatality Ratio ◽  
Risk Of Death ◽  
Italian Regions ◽  
Transmission Factors

OBJECTIVES: Italy has been badly affected by the COVID-19 pandemic and has one of the highest death tolls. We analyzed the severity of COVID-19 across all 20 Italian regions.METHOD: We manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 in each region, and estimated the crude case fatality ratio and time delay-adjusted case fatality ratio (aCFR). We then assessed the association between aCFR and sociodemographic, health care and transmission factors using multivariate regression analysis.RESULTS: The overall aCFR in Italy was estimated at 17.4%. Lombardia exhibited the highest aCFR (24.7%), followed by Marche (19.3%), Emilia Romagna (17.7%) and Liguria (17.6%). Our aCFR estimate was greater than 10% for 12 regions. Our aCFR estimates were statistically associated with population density and cumulative morbidity rate in a multivariate analysis.CONCLUSION: Our aCFR estimates for Italy as a whole and for seven out of the 20 regions exceeded those reported for the most badly affected region in China. These findings highlight the importance of social distancing to suppress transmission to avoid overwhelming the health care system and reduce the risk of death.

A Note on Estimating the Case Fatality Ratio for Novel Corona Virus Disease (Covid-19)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Prafulla Kumar Swain
Keyword(s):  
Confidence Interval ◽  
Graphical Representation ◽  
Virus Disease ◽  
Case Fatality ◽  
Case Fatality Ratio ◽  
Healthcare Planning ◽  
Robust Estimates ◽  
University Of Oxford ◽  
Pros And Cons ◽  
Death Cases

Background: In this paper an attempt has been made to estimate the Case Fatality Ratio (CFR) for coronavirus disease of India and few selected countries. and Also, highlighted the pros and cons of obtaining crude and adjusted CFR of COVID-19 pandemic. Material and Methods: Data extracted from WHO situation report and University of Oxford website have been used for this analysis. The CFR and its 95% confidence interval were computed, trend and bar plot was used for graphical representation. Results: The worldwide crude CFR stands 6.73% (95% CI 6.69 to 6.76) based on 21, 83, 877 confirmed and 1,46,872 death cases(as on 17th April,2020). Belgium was highest CFR 13.95% as compared to others. However, India’s CFR was found to be around 3.26% (as on 17th April, 2020). Conclusion: In conclusion, the estimation and interpretation of CFR is critical in response to ongoing COVID-19. The initial CFR estimates are subject to change, still it is useful for healthcare planning over the coming months. Moreover, the precise and robust estimates of CFR will be available only at the end of the epidemic.

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Correlates of SARS-CoV-2 Variants on Deaths, Case Incidence and Case Fatality Ratio among the Continents for the Period of 1 December 2020 to 15 March 2021

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1061
Author(s):  
Wajdy J. Al-Awaida ◽  
Baker Jawabrah Al Hourani ◽  
Samer Swedan ◽  
Refat Nimer ◽  
Foad Alzoughool ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Significant Positive Correlation ◽  
Vaccine Development ◽  
Case Fatality ◽  
Significant Negative Correlation ◽  
Reference Sequence ◽  
Genome Sequences ◽  
Positive Correlation ◽  
Case Fatality Ratio ◽  
Epidemiological Surveys

The outbreak of coronavirus disease 2019 (COVID-19), by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly developed into a worldwide pandemic. Mutations in the SARS-CoV-2 genome may affect various aspects of the disease including fatality ratio. In this study, 553,518 SARS-CoV-2 genome sequences isolated from patients from continents for the period 1 December 2020 to 15 March 2021 were comprehensively analyzed and a total of 82 mutations were identified concerning the reference sequence. In addition, associations between the mutations and the case fatality ratio (CFR), cases per million and deaths per million, were examined. The mutations having the highest frequencies among different continents were Spike_D614G and NSP12_P323L. Among the identified mutations, NSP2_T153M, NSP14_I42V and Spike_L18F mutations showed a positive correlation to CFR. While the NSP13_Y541C, NSP3_T73I and NSP3_Q180H mutations demonstrated a negative correlation to CFR. The Spike_D614G and NSP12_P323L mutations showed a positive correlation to deaths per million. The NSP3_T1198K, NS8_L84S and NSP12_A97V mutations showed a significant negative correlation to deaths per million. The NSP12_P323L and Spike_D614G mutations showed a positive correlation to the number of cases per million. In contrast, NS8_L84S and NSP12_A97V mutations showed a negative correlation to the number of cases per million. In addition, among the identified clades, none showed a significant correlation to CFR. The G, GR, GV, S clades showed a significant positive correlation to deaths per million. The GR and S clades showed a positive correlation to number of cases per million. The clades having the highest frequencies among continents were G, followed by GH and GR. These findings should be taken into consideration during epidemiological surveys of the virus and vaccine development.

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