Evolution of case fatality rates in the second wave of coronavirus in England: effects of false positives, a Variant of Concern and vaccination.

Objective: To track the statistical case fatality rate (CFR) in the second wave of the UK coronavirus outbreak, and to understand its variations over time. Design: Publicly available UK government data and clinical evidence on the time between first positive PCR test and death are used to determine the relationships between reported cases and deaths, according to age groups and across regions in England. Main Outcome Measures: Estimates of case fatality rates and their variations over time. Results: Throughout October and November 2020, deaths in England can be broadly understood in terms of CFRs which are approximately constant over time. The same CFRs prove a poor predictor of deaths when applied back to September, when prevalence of the virus was comparatively low, suggesting that the potential effect of false positive tests needs to be taken into account. Similarly, increasing CFRs are needed to match cases to deaths when projecting the model forwards into December. The growth of the S gene dropout VOC in December occurs too late to explain this increase in CFR alone, but at 33% increased mortality, it can explain the peak in deaths in January. On our analysis, if there were other factors responsible for the higher CFRs in December and January, 33% would be an upper bound for the higher mortality of the VOC. From the second half of January, the CFRs for older age groups show a marked decline. Since the fraction of the VOC has not decreased, this decline is likely to be the result of the rollout of vaccination. However, due to the rapidly decreasing nature of the raw cases data (likely due to a combination of vaccination and lockdown), any imprecisions in the time-to-death distribution are greatly exacerbated in this time period, rendering estimates of vaccination effect imprecise. Conclusions: The relationship between cases and deaths, even when controlling for age, is not static through the second wave of coronavirus in England. An apparently anomalous low case-fatality ratio in September can be accounted for by a modest 0.4% false-positive fraction. The large jump in CFR in December can be understood in terms of a more deadly new variant B1.1.7, while a decline in January correlates with vaccine roll-out, suggesting that vaccine reduce the severity of infection, as well as the risk.

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Abrupt increase in the UK coronavirus death-case ratio in December 2020

10.1101/2021.01.21.21250264 ◽

2021 ◽

Author(s):

David J Wallace ◽

Graeme J Ackland

Keyword(s):

Case Fatality ◽

Statistical Relationship ◽

Death Case ◽

High Death ◽

Mortality Ratio ◽

Coronavirus Infection ◽

Government Data ◽

The Uk ◽

The Relationship ◽

Second Wave

1AbstractObjectiveto determine the statistical relationship between reported deaths and infections in the UK coronavirus outbreakDesignPublicly available UK government data is used to determine a relationship between reported cases and deaths, taking into account various UK regions, age profiles and prevalence of the variant of concern (VOC) B.1.1.7.Main Outcome MeasuresEstablishing a simple statistical relationship between detected cases and subsequent mortality.ResultsThroughout October and November 2020, deaths in England are well described as 1/55th of detected cases from 12 days previously. After that, the relationship no longer holds and deaths are significantly higher. This is especially true in regions affected by the VOC B.1.1.7ConclusionsIn early December, some new factor emerged to increase the case-fatality rate in the UK.Summary BoxWhat is already known on this topicThe infection-mortality ratio enables one to predict future deaths based on current infections. Incomplete monitoring of infection may be sufficient to predict future trends.What the study addsFor the specific case of the second wave of coronavirus infection in the UK, we show a clear mathematical relationship between detected infections (positive tests) and subsequent deaths. This relationship begins to fail in December, with unexpectedly high death rates. This may be correlated in time and region with the emergence of the Variant of Concern B 1.1.7.

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The increase in the risk of severity and fatality rate of covid-19 in southern Brazil after the emergence of the Variant of Concern (VOC) SARS-CoV-2 P.1 was greater among young adults without pre-existing risk conditions

10.1101/2021.04.13.21255281 ◽

2021 ◽

Author(s):

André Ricardo Ribas Freitas ◽

Daniele Rocha Queiróz Lemos ◽

Otto Albuquerque Beckedorff ◽

Luciano Pamplona de Góes Cavalcanti ◽

Andre M Siqueira ◽

...

Keyword(s):

Age Groups ◽

Case Fatality ◽

The State ◽

Epidemiological Surveillance ◽

Fatality Rate ◽

Risk Of Death ◽

New Variant ◽

Before And After ◽

Almost All ◽

Second Wave

ABSTRACTBackgroundThe SARS-CoV-2 P.1 variant has been considered as “variant of concern (VOC)” since the end of 2020 when it was firstly identified in the Brazilian state of Amazonas and from there spread to other regions of Brazil. This variant was associated with an increase in transmissibility and worsening of the epidemiological situation in the places where it was detected. The aim of this study was to analyze the severity profile of covid-19 cases in the Rio Grande do Sul state, southern region of Brazil, before and after the emergence of the P.1 variant, considering also the context of the hospitals overload and the collapse of health services.MethodsWe analyzed data from the Influenza Epidemiological Surveillance Information System, SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) and compare two epidemiological periods: the “first wave” comprised by cases occurred during November and December 2020 (EW 45 to 53) and the “second wave” with cases occurred in February 2021 (EW 5 to 8), considering that in this month there was a predominance of the new variant P.1. We calculated the proportion of severe forms among the total cases of covid-19, the case fatality rates (CFR) and hospital case fatality rate (hCFR) over both waves time set using the date of onset of symptoms as a reference. We analyzed separately the patients without pre-existing conditions of risk, by age and sex. For comparison between periods, we calculated the Risk Ratio (RR) with their respective 95% confidence intervals and the p-values.FindingsWe observed that in the second wave there were an increase in the proportion of severe cases and covid-19 deaths among younger age groups and patients without pre-existing conditions of risk. The proportion of people under the age of 60 among the cases that evolved to death raised from 18% (670 deaths) in November and December (1st wave) to 28% (1370 deaths) in February (2nd wave). A higher proportion of patients without pre-existing risk conditions was also observed among those who evolved to death due to covid-19 in the second wave (22%, 1,077 deaths) than in the first one (13%, 489 deaths). The CFR for covid-19 increased overall and in different age groups, in both sexes. The increase occurred in a greatest intensity in the population between 20 and 59 years old and among patients without pre-existing risk conditions. Female 20 to 39 years old, with no pre-existing risk conditions, were at risk of death 5.65 times higher in February (95%CI = 2.9 - 11.03; p <0.0001) and in the age group of 40 and 59 years old, this risk was 7.7 times higher (95%CI = 5.01-11.83; p <0.0001) comparing with November-December.InterpretationOur findings showed an increase in the proportion of young people and people without previous illnesses among severe cases and deaths in the state of RS after the identification of the local transmission of variant P.1 in the state. There was also an increase in the proportion of severe cases and in the CFR, in almost all subgroups analyzed, this increase was heterogeneous in different age groups and sex. As far as we know, these are the first evidences that the P.1 variant can disproportionately increase the risk of severity and deaths among population without pre-existing diseases, suggesting related changes in pathogenicity and virulence profiles. New studies still need to be done to confirm and deepen these findings.

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Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany

BMC Public Health ◽

10.1186/s12889-021-11127-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Christian Staerk ◽

Tobias Wistuba ◽

Andreas Mayr

Keyword(s):

Age Distribution ◽

Age Groups ◽

Case Fatality ◽

Time Dependent ◽

Temporary Increase ◽

International Studies ◽

Age Group ◽

Fatality Rate ◽

Substantial Fraction ◽

Second Wave

Abstract Background The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals – not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. Methods Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. Conclusions The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

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Abstract 88: Increasing False Positive Diagnoses May Lead to Overestimation of Stroke Incidence, Particularly in the Young

Stroke ◽

10.1161/str.51.suppl_1.88 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Abhinav J Appukutty ◽

Lesli E Skolarus ◽

Mellanie V Springer ◽

William J Meurer ◽

James F Burke

Keyword(s):

Older Adults ◽

False Positive ◽

Age Groups ◽

Vascular Risk Factor ◽

The United States ◽

Cross Sectional ◽

Stroke Incidence ◽

Ed Visits ◽

Over Time

Introduction: Stroke incidence is reportedly increasing in younger adults. While increasing vascular risk factor prevalence has been suggested as a cause, the reasons for rising stroke incidence in the young are not clear. We explored several alternate explanations: trends in neurologically-focused emergency department (ED) visits, differential diagnostic classification of stroke and TIA over time, and changes in the use of advanced imaging in young and older adults. Methods: We performed a retrospective, serial, cross-sectional study on a nationally representative sample of all ED visits in the United States to quantify changes in patterns of neurologically-focused ED visits, stroke and TIA diagnoses, and rates of MRI utilization for young (18 – 44 years) and older (65+ years) adults over a 17-year period (1995 – 2000; 2005 – 2015) using National Hospital Ambulatory Medical Care Survey (NHAMCS) data. Results: In young adults, 0.4% (95% CI 0.3% – 0.5%) of neurologically-focused ED visits resulted in a primary diagnosis of stroke vs. 6.8% (95% CI 6.2% – 7.5%) for older adults. In both populations, the incidence of neurologically-focused ED visits has increased over time (+111/100,000 population/year, 95% CI +94 – +130 in the young vs. +70/100,000 population/year, 95% CI +34 – +108 in older adults). There was no evidence of differential classification of TIA to stroke over time (OR 1.001 per year, 95% CI 0.926 – 1.083 in the young; OR 1.003 per year, 95% CI 0.982 – 1.026 in older adults) and no evidence of disproportionate rise in MRI utilization for neurologically-focused ED visits in the young (OR 1.057 per year, 95% CI 1.028 – 1.086 in the young; OR 1.095 per year, 95% CI 1.066 – 1.125 in older adults). Conclusions: If the specificity of stroke diagnosis amongst ED visits is similar amongst young and older populations, then the combination of data observed here, including (1) a lower prior probability of stroke diagnoses in the young and (2) an increasing trend in neurologically-focused ED visits in both age groups, suggests that false positive diagnoses will increase over time, with a faster rise in the young compared to older adults. These data suggest a potential explanation that may contribute to higher stroke incidence in the young and merits further scrutiny.

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Modelling optimal vaccination strategy for SARS-CoV-2 in the UK.

10.1101/2020.09.22.20194183 ◽

2020 ◽

Cited By ~ 4

Author(s):

Sam Moore ◽

Edward M Hill ◽

Louise Dyson ◽

Michael Tildesley ◽

Matt J Keeling

Keyword(s):

Effective Treatment ◽

Age Groups ◽

Vaccination Strategy ◽

Quality Adjusted Life Year ◽

Reproductive Number ◽

Large Pool ◽

Complete Relaxation ◽

The Uk ◽

Second Wave ◽

Quality Adjusted Life

The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission- successfully reducing the reproductive number, R, below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial second wave. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and can avoid a second wave if the vaccine prevents transmission as well as disease.

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Spotlight on the dark figure: Exhibiting dynamics in the case detection ratio of COVID-19 infections in Germany

10.1101/2020.12.23.20248763 ◽

2020 ◽

Author(s):

Marc Schneble ◽

Giacomo De Nicola ◽

Göran Kauermann ◽

Ursula Berger

Keyword(s):

Age Groups ◽

Case Detection ◽

Testing Strategies ◽

True Number ◽

Detection Ratio ◽

True Infection ◽

Changes Over Time ◽

Dark Figure ◽

Second Wave ◽

Over Time

AbstractThe case detection ratio of COVID-19 infections varies over time due to changing testing capacities, modified testing strategies and also, apparently, due to the dynamics in the number of infected itself. In this paper we investigate these dynamics by jointly looking at the reported number of detected COVID-19 infections with non-fatal and fatal outcomes in different age groups in Germany. We propose a statistical approach that allows us to spotlight the case detection ratio and quantify its changes over time. With this we can adjust the case counts reported at different time points so that they become comparable. Moreover we can explore the temporal development of the real number of infections, shedding light on the dark number. The results show that the case detection ratio has increased and, depending on the age group, is four to six times higher at the beginning of the second wave compared to what it was at the peak of the first wave. The true number of infection in Germany in October was considerably lower as during the peak of the first wave, where only a small fraction of COVID-19 infections were detected. Our modelling approach also allows quantifying the effects of different testing strategies on the case detection ratio. The analysis of the dynamics in the case detection rate and in the true infection figures enables a clearer picture of the course of the COVID-19 pandemic.

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Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data

BMC Medical Research Methodology ◽

10.1186/s12874-021-01314-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ian C. Marschner

Keyword(s):

Age Groups ◽

Case Fatality ◽

The Elderly ◽

Specific Model ◽

Fatality Risk ◽

Individual Level ◽

Time To Death ◽

History Of ◽

Case Fatality Risk ◽

Dynamics Models

Abstract Background Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution. Methods Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions. Results It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns. Conclusions Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

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140 Killed in Action (KIA): an analysis of military personnel who died of their injuries before reaching a definitive medical treatment facility in Afghanistan (2004–2014)

Emergency Medicine Journal ◽

10.1136/emj-2020-rcemabstracts.25 ◽

2020 ◽

Vol 37 (12) ◽

pp. 838.2-838

Author(s):

Stacey Webster ◽

Ed Barnard ◽

Jason Smith ◽

Max Marsden ◽

Chris Wright

Keyword(s):

Military Personnel ◽

Injury Severity ◽

Case Fatality ◽

Primary Objective ◽

Wilcoxon Test ◽

Time Interval ◽

Exact Test ◽

Time To Death ◽

Medical Treatment Facility ◽

The Uk

Aims/Objectives/BackgroundMost fatalities from trauma, in civilian and military settings, die before reaching a hospital. However, no previous studies have comprehensively examined this phase of care. The aim of this study was to define the time interval between injury and death in UK military personnel who died pre-hospital from enemy action (Killed in Action, KIA).Methods/DesignThe UK Joint Trauma Theatre Registry (JTTR) was used to identify all UK military personnel who died in Afghanistan (2004–2014). Through novel linkage of medical and tactical databases, an accurate timeline of events was obtained. Cause of death was determined from post mortem review. The primary objective was to report time between injury and death. Secondary objectives: mortality at key timepoints, the temporal lethality of different anatomical injuries, and trends in the case fatality rate (CFR, defined as deaths/injuries x100). Data are reported as n(%), and median [inter-quartile range]. Proportions compared with a Fisher’s exact test, and survival was with a Gehan-Breslow-Wilcoxon test; level of significance was corrected by Bonferroni.Results/Conclusions2413 UK personnel were injured in Afghanistan from 2004–2014; 448 died, a CFR of 18.6%. 390 (87.1%) of total deaths (KIA + Killed Non-Enemy Action) were prehospital. Complete timeline data were available for n=303 (87.1%) KIA – this cohort had a median injury severity score of 75.0 [55.5–75.0]. The median time between injury and death in KIA was 0.0 [0.0–21.8] minutes; 173 (57.1%) died immediately, and by 10 min more than two-thirds had died. Primary injury to the head had a significantly shorter time to death compared to the abdomen and lower extremity (both p<0.01). Significant improvement in survival over the decade was due to a reduction in pre-hospital CFR without an increase in in-hospital CFR.Over two-thirds of KIA deaths occurred within 10 min of injury. Improvement in the CFR in Afghanistan was predominantly in the prehospital phase.

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Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England

10.1101/2021.11.24.21266818 ◽

2021 ◽

Author(s):

Thomas Beaney ◽

Ana Luisa Neves ◽

Ahmed Alboksmaty ◽

Kelsey Flott ◽

Aidan Fowler ◽

...

Keyword(s):

Risk Factors ◽

Mortality Risk ◽

Hospital Admissions ◽

Age Groups ◽

Case Fatality ◽

Individual Risk ◽

Nationally Representative ◽

Individual Risk Factors ◽

Underlying Risk ◽

Over Time

Background The Covid-19 case fatality ratio varies between countries and over time but it is unclear whether variation is explained by the underlying risk in those infected. This study aims to describe the trends and risk factors for admission and mortality rates over time in England. Methods In this retrospective cohort study, we included all adults (≥18 years) in England with a positive Covid-19 test result between 1st October 2020 and 30th April 2021. Data were linked to primary and secondary care electronic health records and death registrations. Our outcomes were i) one or more emergency hospital admissions and ii) death from any cause, within 28 days of a positive test. Multivariable multilevel logistic regression was used to model each outcome with patient risk factors and time. Results 2,311,282 people were included in the study, of whom 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days. There was significant variation in the case hospitalisation and mortality risk over time, peaking in December 2020-February 2021, which remained after adjustment for individual risk factors. Older age groups, males, those resident in more deprived areas, and those with obesity had higher odds of admission and mortality. Of risk factors examined, severe mental illness and learning disability had the highest odds of admission and mortality. Conclusions In one of the largest studies of nationally representative Covid-19 risk factors, case hospitalisation and mortality risk varied significantly over time in England during the second pandemic wave, independent of the underlying risk in those infected.

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On the association between SARS-COV-2 variants and COVID-19 mortality during the second wave of the pandemic in Europe

10.1101/2021.03.25.21254289 ◽

2021 ◽

Author(s):

Katarzyna Jablonska ◽

Samuel Aballea ◽

Pascal Auquier ◽

Mondher Toumi

Keyword(s):

Linear Models ◽

Peak Height ◽

Cumulative Number ◽

Peak Period ◽

New Variant ◽

Average Proportion ◽

The Uk ◽

Selection Algorithms ◽

Second Wave ◽

Pearson Correlations

BACKGROUND: Preliminary clinical evidence suggests an increased COVID-19 mortality associated with the variant of concern 20I/501Y.V1. The evidence outside the UK and a real-world comparison of variants spread and mortality is sparse. This study aims at investigating the association between COVID-19 mortality and SARS-COV-2 variants spread during the second wave of the COVID-19 pandemic in Europe. METHODS: For 38 European countries, publicly available data were collected on numbers of COVID-19 deaths, SARS-COV-2 variants spread through time using Nextstrain classification and countries demographic and health characteristics. The cumulative number of COVID-19 deaths and the height of COVID-19 daily deaths peak during the second wave of the pandemic were considered as outcomes. Pearson correlations and multivariate generalized linear models with selection algorithms were used. FINDINGS: The average proportion of 20I/501Y.V1 variant (B.1.1.7) was found to be a significant predictor of cumulative number of COVID-19 deaths within two months before the deaths peak and between 1 January - 25 February 2021, as well as of the deaths peak height when calculating the proportion during the second wave and the pre-peak period. The average proportion of 20A.EU2 variant (S:477N) was a significant predictor of cumulative COVID-19 deaths in the pre-peak period. INTERPRETATION: Our findings suggest that the spread of a new variant of concern 20I/501Y.V1 had a significant impact on the mortality during the second wave of COVID-19 pandemic in Europe and that proportions of 20A.EU2 and 20I/501Y.V1 variants were associated with increased mortality in the initial phase of that wave. KEYWORDS: COVID-19, mortality, SARS-COV-2 variants, variant of concern.

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