scholarly journals Change in country-level COVID-19 case fatality rate is associated with improved testing; no apparent role of medical care or disease-specific knowledge

2021 ◽  
Author(s):  
Maia P. Smith
Keyword(s):  
Medical Care ◽  
Case Fatality Rate ◽  
Clinical Care ◽  
Case Fatality ◽  
Fatality Rate ◽  
Minimal Effect ◽  
Medical Interventions ◽  
Medical Spending ◽  
Country Level ◽  
Total Death

Abstract Observed case fatality rate (CFR) of COVID-19 has decreased since the beginning of the pandemic. Reasons for this decline include improved knowledge of COVID-19 pathogenesis, leading to improved medical care of confirmed cases. However, ascertainment also plays a role: as more low-risk individuals are tested and more mild cases identified, observed CFR will decline. Previously I showed that geography-level CFR was cross-sectionally negatively associated with test density; here I test for similar trends within geography over six months, and check plausibility of various posited causes. Although CFR varied between geographies, its association with testing did not: in 141 geographies, CFR dropped by an average of 18% for each doubling of test density. Change in CFR within a given geography was not associated either with that geography’s medical spending or with whether the bulk of cases occurred early or late in the pandemic. This shows that medical interventions, including those specific to COVID-19, have only a minimal effect on total CFR. Two major conclusions follow. First, interventions to reduce CFR should be evaluated by comparing groups that received the intervention to those who did not: decline in CFR after an intervention is not evidence of effectiveness. Second, improving clinical care of confirmed COVID-19 cases has only a minimal effect on death rates. To minimize the total death toll of COVID-19, policymakers should prioritize reducing infections.

scholarly journals Change in country-level COVID-19 case fatality rate is explicable by improved testing; no apparent role of medical care or disease-specific knowledge

2021 ◽  
Author(s):  
Maia P. Smith
Keyword(s):  
Medical Care ◽  
Case Fatality Rate ◽  
Clinical Care ◽  
Case Fatality ◽  
Fatality Rate ◽  
Minimal Effect ◽  
Medical Interventions ◽  
Medical Spending ◽  
Country Level ◽  
Total Death

Abstract Observed case fatality rate (CFR) of COVID-19 has decreased since the beginning of the pandemic. Reasons for this decline include improved knowledge of COVID19 pathogenesis, leading to improved medical care of confirmed cases. However, ascertainment also plays a role: as more low-risk individuals are tested and more mild cases identified, observed CFR will decline. Previously I showed that geography-level CFR was cross-sectionally negatively associated with test density; here I test for similar trends within geography over six months, and check plausibility of various posited causes. Although CFR varied between geographies, its association with testing did not: in 162 geographies, CFR dropped by an average of 18% (median 21; IQR 5–30) for each doubling of test density. Change in CFR within a given geography was not associated either with that geography’s medical spending or with whether the bulk of cases occurred early or late in the pandemic. This shows that medical interventions, including those specific to COVID-19, have only a minimal effect on total CFR. Two major conclusions follow. First, interventions to reduce CFR should be evaluated by comparing groups that received the intervention to those who did not: decline in CFR after an intervention is not evidence of effectiveness. Second, improving clinical care of confirmed COVID-19 cases has only a minimal effect on death rates. To minimize the total death toll of COVID-19, policymakers should prioritize reducing infections.

scholarly journals Differences by country-level income in COVID-19 cases, deaths, case-fatality rates, and rates per million population in the first five months of the pandemic

2020 ◽  
Author(s):  
Wrishmeen Sabawoon
Keyword(s):  
Low Income ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Incidence Rates ◽  
High Income ◽  
Fatality Rate ◽  
Middle Income ◽  
Death Rates ◽  
Middle Income Countries ◽  
Country Level

Abstract Objective: To describe differences by country-level income in COVID-19 cases, deaths, case-fatality rates, incidence rates, and death rates per million population. Methods: Publicly available data on COVID-19 cases and deaths from December 31, 2019 to June 3, 2020 were analyzed. Kruskal-Wallis tests were used to examine associations of country-level income with COVID-19 cases, deaths, case-fatality rates, incidence rates, and death rates. Results: A total of 380,803 deaths out of 6,348,204 COVID-19 cases were reported from 210 countries and territories globally in the period under study, and the global case-fatality rate was 6.0%. Of the total globally reported cases and deaths, the percentages of cases and deaths were 59.9% and 75.0% for high-income countries, and 30.9% and 20.7% for upper-middle-income countries. Countries in higher-income categories had higher incidence rates and death rates. Between April and May, the incidence rates in higher-income groups of countries decreased, but in other groups, it increased. Conclusions In the first five months of the COVID-19 pandemic, most cases and deaths were reported from high-income and upper-middle-income countries, and those countries had higher incidence rates and death rates per million population than did lower-middle and low-income countries. Keywords: COVID-19, incidence rate, death rate, case fatality rate, income, and country

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.

scholarly journals Case- fatality rate in COVID- 19 patients: A meta- analysis of publicly accessible database

2020 ◽  
Author(s):  
Souvik Maitra ◽  
Mansij Biswas ◽  
Sulagna Bhattacharjee
Keyword(s):  
Case Fatality Rate ◽  
Meta Analysis ◽  
Random Effect ◽  
Case Fatality ◽  
Fatality Rate ◽  
Web Database ◽  
Analysis Model ◽  
Country Level ◽  
Meta Regression Analysis ◽  
Novel Coronavirus

AbstractA novel coronavirus was reported in Wuhan, China in December 2019 to cause severe acute respiratory symptoms (COVID-19). In this meta-analysis, we estimated case fatality rate from COVID-19 infection by random effect meta-analysis model with country level data. Publicly accessible web database WorldOMeter (https://www.worldometers.info/coronavirus/) was accessed on 24th March 2020 GMT and reported total number of cases, total death, active cases and seriously ill/ critically ill patients were retrieved. Primary outcome of this meta-analysis was case fatality rate defined by total number of deaths divided by total number of diagnosed cases. Pooled case fatality rate (95% CI) was 1.78 (1.34-2.22) %. Between country heterogeneity was 0.018 (p<0.0001). Pooled estimate of composite poor outcome (95% CI) was 4.06 (3.24-4.88) % at that point of time after exclusion of countries reported small number of cases. Pooled mortality rate (95% CI) was 33.97 (27.44-40.49) % amongst closed cases (where patients have recovered or died) with. Meta regression analysis identified statistically significant association between health expenditure and case fatality rate (p=0.0017).

scholarly journals Why does COVID-19 case fatality rate vary among countries?

2020 ◽  
Author(s):  
Gabriele Sorci ◽  
Bruno Faivre ◽  
Serge Morand
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Political Regime ◽  
Fatality Rate ◽  
Political Factors ◽  
Individual Level ◽  
Country Level ◽  
Increased Risk ◽  
The Individual ◽  
And Control

SummaryBackgroundWhile the epidemic of SARS-CoV-2 is spreading worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate varies temporally (as the epidemic progresses) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries.MethodsWe 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 (Institute for Health Metrics and Evaluation). We also compiled data on demography, economy and political regimes for each country.FindingsWe first showed that temporal trajectories of case fatality rate greatly vary among countries. We found no evidence for association between comorbidities and case fatality rate at the country level. Case fatality rate was negatively associated with number of hospital beds x1,000 inhabitants. We also report evidence suggesting an association between case fatality rate and the political regime, with democracies suffering from the highest mortality burden, compared to autocratic regimes. However, most of the among-country variance in case fatality rate remained unexplained.InterpretationOverall, these results emphasize the role of socio-economic and political factors as possible drivers of COVID-19 case fatality rate at the country level.FundingNone.

scholarly journals COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043560 ◽  
Author(s):  
Yang Cao ◽  
Ayako Hiyoshi ◽  
Scott Montgomery
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Spatial Regression ◽  
Temporal Trends ◽  
Case Fatality ◽  
Secondary Outcome ◽  
Fatality Rate ◽  
Country Level ◽  
Level Data ◽  
Testing Policies

ObjectiveTo investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally.DesignPublicly available register-based ecological study.SettingTwo hundred and nine countries/territories in the world.ParticipantsAggregated data including 10 445 656 confirmed COVID-19 cases.Primary and secondary outcome measuresCOVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website.ResultsThe average of country/territory-specific COVID-19 CFR is about 2%–3% worldwide and higher than previously reported at 0.7%–1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR.ConclusionThe association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.

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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