scholarly journals The Global Case-Fatality Rate of COVID-19 Has Been Declining Since May 2020

2021 ◽  
Author(s):  
Mohammad Nayeem Hasan ◽  
Najmul Haider ◽  
Florian L. Stigler ◽  
Rumi Ahmed Khan ◽  
David McCoy ◽  
...  
Keyword(s):  
Time Series ◽  
Case Fatality Rate ◽  
Moving Average ◽  
Case Fatality ◽  
Mortality Data ◽  
Fatality Rate ◽  
Peak Period ◽  
Prevalence Of Obesity ◽  
Simple Exponential Smoothing ◽  
Rate Ratios

The objective of this study was to evaluate the trend of reported case fatality rate (rCFR) of COVID-19 over time, using globally reported COVID-19 cases and mortality data. We collected daily COVID-19 diagnoses and mortality data from the WHO’s daily situation reports dated January 1 to December 31, 2020. We performed three time-series models [simple exponential smoothing, auto-regressive integrated moving average, and automatic forecasting time-series (Prophet)] to identify the global trend of rCFR for COVID-19. We used beta regression models to investigate the association between the rCFR and potential predictors of each country and reported incidence rate ratios (IRRs) of each variable. The weekly global cumulative COVID-19 rCFR reached a peak at 7.23% during the 17th week (April 22–28, 2020). We found a positive and increasing trend for global daily rCFR values of COVID-19 until the 17th week (pre-peak period) and then a strong declining trend up until the 53rd week (post-peak period) toward 2.2% (December 29–31, 2020). In pre-peak of rCFR, the percentage of people aged 65 and above and the prevalence of obesity were significantly associated with the COVID-19 rCFR. The declining trend of global COVID-19 rCFR was not merely because of increased COVID-19 testing, because COVID-19 tests per 1,000 population had poor predictive value. Decreasing rCFR could be explained by an increased rate of infection in younger people or by the improvement of health care management, shielding from infection, and/or repurposing of several drugs that had shown a beneficial effect on reducing fatality because of COVID-19.

scholarly journals An Evidence Base for School Health Policy during the COVID-19 Pandemic

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Saif Badran ◽  
Omran Musa ◽  
Somaya Al-maadeed ◽  
Egon Toft ◽  
Suhail Doi
Keyword(s):  
Dose Response ◽  
Case Fatality Rate ◽  
School Health ◽  
Meta Analysis ◽  
Case Fatality ◽  
Evidence Base ◽  
Mortality Data ◽  
Response Analysis ◽  
Fatality Rate ◽  
Epidemiological Characteristics

Objective: Children represent a small fraction of confirmed COVID-19 cases, with a low case fatality rate (CFR). In this paper, we lay out an evidence-based policy for reopening schools. Methods: We gathered age-specific COVID-19 case counts and identified mortality data for 14 countries. Dose-response meta-analysis was used to examine the relationship of the incremental case fatality rate (CFR) to age. In addition, an evidence-to-decision framework (EtD) was used to correlate the dose-response data with other epidemiological characteristics of COVID-19 in childhood. Results: In the dose-response analysis, we found that there was an almost negligible fatality below age 18. CFR rose little between ages 5 to 50 years. The confidence intervals were narrow, suggesting relative homogeneity across countries. Further data suggested decreased childhood transmission from respiratory droplets and a low viral load among children. Conclusions: Opening up schools and kindergartens is unlikely to impact COVID-19 case or mortality rates in both the child and adult populations. We outline a robust plan for schools that recommends that general principles not be micromanaged, with authority left to schools and monitored by public health authorities.

Impact of a multifaceted intervention to improve emergency care on newborn and child health outcomes in Rwanda

2021 ◽  
Author(s):  
Celestin Hategeka ◽  
Larry D Lynd ◽  
Cynthia Kenyon ◽  
Lisine Tuyisenge ◽  
Michael R Law
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Hospital Mortality ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Mortality Rates ◽  
Fatality Rate ◽  
Paediatric Hospital ◽  
Child Health Outcomes ◽  
And Child Health

Abstract Implementing context-appropriate neonatal and paediatric advanced life support management interventions has increasingly been recommended as one of the approaches to reduce under-five mortality in resource-constrained settings like Rwanda. One such intervention is ETAT+, which stands for Emergency Triage, Assessment and Treatment plus Admission care for severely ill newborns and children. In 2013, ETAT+ was implemented in Rwandan district hospitals. We evaluated the impact of the ETAT+ intervention on newborn and child health outcomes. We used monthly time-series data from the DHIS2-enabled Rwanda Health Management Information System from 2012 to 2016 to examine neonatal and paediatric hospital mortality rates. Each hospital contributed data for 12 and 36 months before and after ETAT+ implementation, respectively. Using controlled interrupted time-series analysis and segmented regression model, we estimated longitudinal changes in neonatal and paediatric hospital mortality rates in intervention hospitals relative to matched concurrent control hospitals. We also studied changes in case fatality rate specifically for ETAT+-targeted conditions. Our study cohort consisted of 7 intervention hospitals and 14 matched control hospitals contributing 142 424 neonatal and paediatric hospital admissions. After controlling for secular trends and autocorrelations, we found that the ETAT+ implementation had no statistically significant impact on the rate of all-cause neonatal and paediatric hospital mortality in intervention hospitals relative to control hospitals. However, the case fatality rate for ETAT+-targeted neonatal conditions decreased immediately following implementation by 5% (95% confidence interval: −9.25, −0.77) and over time by 0.8% monthly (95% confidence interval: −1.36, −0.25) in intervention hospitals compared with control hospitals. Case fatality rate for ETAT+-targeted paediatric conditions did not decrease following the ETAT+ implementation. While ETAT+ focuses on improving the quality of hospital care for both newborns and children, we only found an impact on neonatal hospital mortality for ETAT+-targeted conditions that should be interpreted with caution given the relatively short pre-intervention period and potential regression to the mean.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

scholarly journals Pre-existing comorbidity, the highest risk factor for poor prognosis of COVID-19 among the Mexican population

Nova Scientia ◽  
2021 ◽  
Vol 13 ◽  
Author(s):  
Basilio Calixto-Calderón ◽  
María F. Vázquez-González ◽  
Rafael Martínez Peláez ◽  
Josué R. Bermeo-Escalona ◽  
Vicente García ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Severe Disease ◽  
Case Fatality ◽  
Mexican Population ◽  
Age Group ◽  
Fatality Rate ◽  
Medical Comorbidity ◽  
Factors Associated ◽  
Risk Of Death ◽  
Prevalence Of Obesity

The coronavirus disease 2019 (COVID-19) pandemic represents a challenge for public health and a high risk for patients with pre-existing comorbidity. As of July 20, 2020, the Case Fatality Rate (CFR) was 11.30% and the Mortality Rate (MR) was 31.28 deaths per 100,000 population. In Mexico, the prevalence of obesity, diabetes mellitus, and hypertension among the adult Mexican population is 30%, 9.2%, and 40%, respectively. The objective of this research was to identify the risk factors associated with eight comorbidities and their dependency on age for death caused by COVID-19. Method: This study used the dataset published on July 20, 2020, by the General Directorate of Epidemiology of the Ministry of Health of Mexico. From this dataset, we analysed 130,896 positive COVID-19 cases, where 35,483 (27.107%) patients had one comorbidity, and 95,413 (72.892%) patients had not medical comorbidity. Statistical analyses include the Case Fatality Rate (CFR), the estimation of the Odds Ratio (OR), and its 95% Confidence Interval (CI). Results: The highest CFR was 14.382% for COPD, 10.266% for CKD, 10.126% for diabetes, and 8.954% for hypertension. The obesity CFR was 3.535%. Moreover, we detected a higher risk for patients with COPD, diabetes, and CKD, resulting in OR of 4.443 (95% CI: 3.404-5.799), 3.283 (95% CI: 3.018-3.570), and 3.016 (95% CI: 2.248-4.047), respectively.   Conclusion: This study corroborates that the highest risk for severe disease and death caused by COVID-19 among the Mexican population are pre-existing comorbidities. Findings show that COPD, CKD, diabetes, hypertension, and cardiovascular disease increase the risk of death for patients older than 54 years. The most vulnerable age group is older than 65 years.

scholarly journals The role of population structure when measuring COVID-19 impact across countries

2020 ◽  
Author(s):  
Octavio Bramajo ◽  
Mauro Infantino ◽  
Rafael Unda ◽  
Walter D Cardona-Maya ◽  
Pablo Richly
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Mortality Data ◽  
Significant Heterogeneity ◽  
Fatality Rate ◽  
Crude Death Rate ◽  
Using Data ◽  
Rate Case

AbstractThe search for accurate indicators to compare the pandemic impact between countries is still a challenge. The crude death rate, case fatality rate by country and sex, standardized fatality rate, and standardized death rate were calculated using data from Argentina and Colombia countries. We show that even when frequently used indicator as deaths per million are quite similar, 512 deaths per million in Argentina and 522 deaths per million in Colombia, a significant heterogeneity can be found when the mortality data is decomposed by sex or age.

scholarly journals Temporal Association Between Particulate Matter Pollution and Case Fatality Rate of COVID-19 in Wuhan, China

2020 ◽  
Author(s):  
Ye Yao ◽  
Jinhua Pan ◽  
Zhixi Liu ◽  
Xia Meng ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Time Series Analysis ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Temporal Scale ◽  
Temporal Association ◽  
Fatality Rate ◽  
Day By Day ◽  
Particulate Matter Pollution

AbstractThe Coronavirus (COVID-19) epidemic, which was first reported in December 2019 in Wuhan, China, has caused 3,314 death as of March 31, 2020 in China. This study aimed to investigate the temporal association between case fatality rate (CFR) of COVID-19 and particulate matter (PM) in Wuhan. We conducted a time series analysis to explore the temporal day-by-day associations. We found COVID-19 held higher case fatality rate with increasing concentrations of PM2.5 and PM10 in temporal scale, which may affect the process of patients developed from mild to severe and finally influence the prognosis of COVID-19 patients.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

Impact of hydroxychloroquin/azithromycin protocol on COVID-19 case-fatality rate reduction in Algeria. (Preprint)

2020 ◽  
Author(s):  
Ahmed Youssef Kada
Keyword(s):  
Case Fatality Rate ◽  
Doubling Time ◽  
Case Fatality ◽  
World Health ◽  
Fatality Rate ◽  
Therapeutic Protocol ◽  
First Case ◽  
The World ◽  
The Impact ◽  
At Home

BACKGROUND Covid-19 is an emerging infectious disease like viral zoonosis caused by new coronavirus SARS CoV 2. On December 31, 2019, Wuhan Municipal Health Commission in Hubei province (China) reported cases of pneumonia, the origin of which is a new coronavirus. Rapidly extendable around the world, the World Health Organization (WHO) declares it pandemic on March 11, 2020. This pandemic reaches Algeria on February 25, 2020, date on which the Algerian minister of health, announced the first case of Covid-19, a foreign citizen. From March 1, a cluster is formed in Blida and becomes the epicentre of the coronavirus epidemic in Algeria, its total quarantine is established on March 24, 2020, it will be smoothly alleviated on April 24. A therapeutic protocol based on hydroxychloroquine and azithromycin was put in place on March 23, for complicated cases, it was extended to all the cases confirmed on April 06. OBJECTIVE This study aimed to demonstrate the effectiveness of hydroxychloroquin/azithromycin protocol in Algeria, in particular after its extension to all patients diagnosed COVID-19 positive on RT-PCR test. We were able to illustrate this fact graphically, but not to prove it statistically because the design of our study, indeed in the 7 days which followed generalization of therapeutic protocol, case fatality rate decrease and doubling time increase, thus confirming the impact of wide and early prescription of hydroxychloroquin/azithromycin protocol. METHODS We have analyzed the data collected from press releases and follow-ups published daily by the Ministry of Health, we have studied the possible correlations of these data with certain events or decisions having a possible impact on their development, such as confinement at home and its reduction, the prescription of hydroxychloroquine/azithromycin combination for serious patients and its extension to all positive COVID subjects. Results are presented in graphics, the data collection was closed on 31/05/2020. RESULTS Covid-19 pandemic spreads from February 25, 2020, when a foreign citizen is tested positive, on March 1 a cluster is formed in the city of Blida where sixteen members of the same family are infected during a wedding party. Wilaya of Blida becomes the epicentre of coronavirus epidemic in Algeria and lockdown measures taken, while the number of national cases diagnosed begins to increases In any event, the association of early containment measures combined with a generalized initial treatment for all positive cases, whatever their degree of severity, will have contributed to a reduction in the fatality rate of COVID 19 and a slowing down of its doubling time. CONCLUSIONS In Algeria, the rapid combination of rigorous containment measure at home and early generalized treatment with hydroxychloroquin have demonstrated their effectiveness in terms of morbidity and mortality, the classic measures of social distancing and hygiene will make it possible to perpetuate these results by reducing viral transmission, the only unknown, the reopening procedure which can only be started after being surrounded by precautions aimed at ensuring the understanding of the population. CLINICALTRIAL Algeria, Covid-19, pandemic, hydroxychloroquin, azithromycin, case fatality rate

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