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

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.

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Case Fatality Rate of COVID-19 and Association With Sociodemographic Characteristics of Populations in Ecuador, 2020

10.21203/rs.3.rs-444898/v1 ◽

2021 ◽

Author(s):

Karla Flores Sacoto ◽

Galo Sánchez Del Hierro ◽

Xavier Jarrín Estupiñan ◽

Felipe Moreno-Piedrahita Hernandez

Keyword(s):

Death Rate ◽

Case Fatality Rate ◽

Case Fatality ◽

Sociodemographic Factors ◽

Cross Sectional Study ◽

Secondary Outcome ◽

Morbidity Rate ◽

Fatality Rate ◽

Cross Sectional ◽

The Impact

Abstract Background COVID-19 has caused deaths worldwide affecting the most vulnerable population with different case fatality rates. Socioeconomic conditions have demonstrated a role regarding the spread of infections and mortality. Socioeconomic characteristics of Ecuador related to poverty, ethnicity and demographic characteristics increase the impact of COVID-19 in certain populations. Methods Objective To analyze the influence of demographic factors on the COVID-19 case fatality rate (CFR) in Ecuador. Design: cross sectional study. Setting 24 provinces in Ecuador-221 cantons. Population: data including 233.277 confirmed COVID-19 cases of Ecuador. Primary and secondary outcome measures COVID-19 CFR and crude cause-specific death rate weight calculated using province-country level data from health ministry of Ecuador in data website. Results Ecuadors CFR is 4,03%, analyzed by cantons the CFR increases to a median of 5,75%, with cantons like Playas with a CFR of 32,39%. The morbidity rate has a median of 795,31 per 100 000 hab. with the highest rate in Isabela-Galápagos (10185,49), Aguarico-Orellana (9506,75) and Baños-Tungurahua (4156,85). And the crude COVID-19 death rate has a median of 39,73 per 100 000 hab. with the highest rate in Penipe-Chimborazo (201,29), 24 de Mayo-Manabí (143,79) and San Pedro de Huaca-Carchi (134,36). The correlations show relations with sociodemographic factors like poverty, ethnicity and scholarity. Conclusion The CFR is the proxy indicator of COVID-19 impact in Ecuador and the analysis made by location give us new information about the specific impact of this disease.

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

10.1101/2020.07.31.20165811 ◽

2020 ◽

Author(s):

Yang Cao ◽

Ayako Hiyoshi ◽

Scott Montgomery

Keyword(s):

Population Size ◽

Spatial Regression ◽

Case Fatality ◽

Country Level ◽

Level Data ◽

Lower Treatment ◽

Active Testing ◽

Large Populations ◽

Spatial Regression Analysis ◽

Testing Policies

We used the COVID-19 dataset obtained from the Our World in Data website and investigated the associations between COVID-19 CFR and nine country-level indices of 209 countries/territories using the Matern correlation regression model. Spatial dependence among the data was controlled using the latitude and longitude of the centroid of the countries/territories. Stratified analyses were conducted by economic level and COVID-19 testing policy. The average of country/territory-specific COVID-19 CFR is about 2-3% worldwide, which is higher than previously reported at 0.7-1.3%. Statistically significant associations were observed between COVID-19 CFR and population size and proportion of female smokers. The open testing policies are associated with decreased CFR. 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. The statistically significant association between population size and COVID-19 CRF suggests the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in females and COVID-19 CFR might be due to that the proportion of female smokers reflected broadly 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.

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Temporal trends and predictors of inhospital death in patients hospitalised for heart failure in Germany

European Journal of Preventive Cardiology ◽

10.1177/2047487320936020 ◽

2020 ◽

pp. 204748732093602 ◽

Cited By ~ 1

Author(s):

Karsten Keller ◽

Lukas Hobohm ◽

Mir A Ostad ◽

Sebastian Göbel ◽

Mareike Lankeit ◽

...

Keyword(s):

Heart Failure ◽

Case Fatality Rate ◽

Nationwide Inpatient Sample ◽

Event Rate ◽

Temporal Trends ◽

Case Fatality ◽

Patient Characteristics ◽

Cerebrovascular Event ◽

Fatality Rate ◽

Cerebrovascular Events

Aims We investigated trends in incidence, case fatality rate, patient characteristics and adverse inhospital events of patients hospitalised for heart failure in Germany. Methods and results The German nationwide inpatient sample (2005–2016) was used for this analysis. Patients hospitalised due to heart failure were selected for analysis. Temporal trends in the incidence of hospitalisations, case fatality rate and treatments were analysed and predictors of inhospital death were identified. The analysis comprised a total number of 4,539,140 hospitalisations (52.0% women, 81.0% aged ≥70 years) due to heart failure. Although hospitalisations increased from 381 (2005) to 539 per 100,000 population (2016) (β estimate 0.06, 95% confidence interval (CI) 0.06 to 0.07, P < 0.001) in parallel with median age and prevalence of comorbidities, the inhospital case fatality rate decreased from 11.1% to 8.1% (β estimate –0.36, 95% CI –0.37 to –0.35, P < 0.001) and the rate of major adverse cardiovascular and cerebrovascular events (β estimate –0.24, 95% CI –0.25 to –0.23, P < 0.001) decreased from 12.7% to 10.3%. Age 70 years and older (odds ratio (OR) 2.60, 95% CI 2.57 to 2.63, P < 0.001) and cancer (OR 1.93, 95% CI 1.91 to 1.96, P < 0.001) were independent predictors of inhospital death. Conclusion Hospitalisations for heart failure increased in Germany from 2005 to 2016, whereas the major adverse cardiovascular and cerebrovascular event rate and inhospital case fatality rate decreased during this period despite higher patient age and increasing prevalence of comorbidities.

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Temporal trends in stroke incidence and case-fatality rates in Arcadia, Greece: A sequential, prospective, population-based study

International Journal of Stroke ◽

10.1177/1747493021995594 ◽

2021 ◽

pp. 174749302199559

Author(s):

Eleni Karantali ◽

Konstantinos Vemmos ◽

Evangelos Tsampalas ◽

Konstantinos Xynos ◽

Persefoni Karachalia ◽

...

Keyword(s):

Case Fatality Rate ◽

Temporal Trends ◽

Case Fatality ◽

Population Based ◽

Incidence Rates ◽

European Population ◽

Fatality Rate ◽

Population Based Study ◽

Stroke Incidence ◽

Southern Greece

Background Stroke incidence and case-fatality are reported to decline in high-income countries during the last decades. Epidemiological studies are important for health services to organize prevention and treatment strategies. Aims The aim of this population-based study was to determine temporal trends of stroke incidence and case-fatality rates of first-ever stroke in Arcadia, a prefecture in southern Greece. Methods All first-ever stroke cases in the Arcadia prefecture were ascertained using the same standard criteria and multiple overlapping sources in three study periods: from November 1993 to October 1995; 2004; and 2015–2016. Crude and age-adjusted to European population incidence rates were compared using Poisson regression. Twenty-eight days case fatality rates were estimated and compared using the same method. Results In total, 1315 patients with first-ever stroke were identified. The age-standardized incidence to the European population was 252 per 100,000 person-years (95% CI 231–239) in 1993/1995, 252 (95% CI 223–286) in 2004, and 211 (192–232) in 2015/2016. The overall age- and sex-adjusted incidence rates fell by 16% (incidence rates ratio 0.84, 95% CI: 0.72–0.97). Similarly, 28-day case-fatality rate decreased by 28% (case fatality rate ratio = 0.72, 95% CI: 0.58–0.90). Conclusions This population-based study reports a significant decline in stroke incidence and mortality rates in southern Greece between 1993 and 2016.

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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

10.1101/2020.07.13.20153064 ◽

2020 ◽

Cited By ~ 1

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

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Estimation of undetected COVID-19 infections in India

10.1101/2020.04.20.20072892 ◽

2020 ◽

Cited By ~ 4

Author(s):

Siuli Mukhopadhyay ◽

Debraj Chakraborty

Keyword(s):

Death Rate ◽

Case Fatality Rate ◽

Indian Population ◽

Case Fatality ◽

Infection Model ◽

Fatality Rate ◽

Single Digit ◽

High Death Rate ◽

True Number ◽

High Death

Background and ObjectivesWhile the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected COVID-19 cases is urgently needed for an effective tackling of the pandemic and as a guide to lifting the lockdown. The aim of this work is to estimate and predict the true number of COVID-19 (detected and undetected) infections in India for short to medium forecast horizons. In particular, using publicly available COVID-19 infection data up to 28th April 2020, we forecast the true number of infections in India till the end of lockdown (3rd May) and five days beyond (8th May).MethodsThe high death rate observed in most COVID-19 hit countries is suspected to be a function of the undetected infections existing in the population. An estimate of the age weighted infection fatality rate (IFR) of the disease of 0.41%, specifically calculated by taking into account the age structure of Indian population, is already available in the literature. In addition, the recorded case fatality rate (CFR= 1%) of Kerala, the first state in India to successfully flatten the curve by consistently reporting single digit new infections from 12-20 April, is used as a second estimate of the IFR. These estimates are used to formulate a relationship between deaths recorded and the true number of infections and recoveries. The estimated undetected and detected cases time series based on these two IFR estimates are then used to fit a discrete time multivariate infection model to predict the total infections at the end of the formal lockdown period.ResultsOver three consecutive fortnight periods during the lockdown, it was noted that the rise in detected infections has decreased by 8.2 times. For an IFR of 0.41%, the rise in undetected infections decreased 2.5 times, while for the higher IFR value of 1%, undetected cases decreased by 2.4 times. The predicted number of total infections in India on 3rd May for both IFRs varied from 2.8 - 6.8 lakhs.Interpretation and ConclusionsThe behaviour of the undetected cases over time effectively illustrates the effects of lockdown and increased testing. From our estimates, it is found that the lockdown has brought down the undetected to detected cases ratio, and has consequently dampened the increase in the number of total cases. However, even though the rate of rise in total infections has fallen, the lifting of the lockdown should be done keeping in mind that 2.3 to 6.4 lakhs undetected cases will already exist in the population by 3rd May.

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

10.21203/rs.3.rs-471645/v1 ◽

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.

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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15819.2 ◽

2020 ◽

Vol 5 ◽

pp. 56 ◽

Cited By ~ 1

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.

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

10.1101/2020.11.30.20239947 ◽

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.

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COVID-19 Fatality and Comorbidity Risk Factors among Diagnosed Patients in Mexico

10.1101/2020.04.21.20074591 ◽

2020 ◽

Cited By ~ 10

Author(s):

Patricio Solís ◽

Hiram Carreño

Keyword(s):

Case Fatality Rate ◽

Case Fatality ◽

Fatality Rate ◽

Cross Sectional ◽

Fatality Risk ◽

Individual Level ◽

Level Data ◽

Younger Age ◽

High Prevalence ◽

Right Censorship

AbstractAs of April 18, 2020, 2.16 million patients in the world had been tested positive with Coronavirus (COVID-19) and 146,088 had died, which accounts for a case fatality rate of 6.76%. In Mexico, according to official statistics (April 18), 7,497 cases have been confirmed with 650 deaths, for a case fatality rate of 8.67%. These estimates, however, may not reflect the final fatality risk among COVID-19 confirmed patients, because they are based on cross-sectional counts of diagnosed and deceased patients, and therefore are not adjusted by time of exposure and right-censorship. In this paper we estimate fatality risks based on survival analysis methods, calculated from individual-level data on symptomatic patients confirmed with COVID-19 recently released by the Mexican Ministry of Health. The estimated fatality risk after 35 days of onset of symptoms is 12.38% (95% CI: 11.37-13.47). Fatality risks sharply rise with age, and significantly increase for males (59%) and individuals with comorbidities (38%-168%, depending on the disease). Two reasons may explain the high COVID-19 related fatality risk observed in Mexico, despite its younger age structure: the high selectivity and self-selectivity in testing and the high prevalence of chronic-degenerative diseases.

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