scholarly journals Over- and under-estimation of COVID-19 deaths

2021 ◽  
Author(s):  
John P. A. Ioannidis
Keyword(s):  
Indirect Effects ◽  
Time Window ◽  
Infection Prevalence ◽  
Fatality Rate ◽  
Excess Death ◽  
Great Caution ◽  
Diagnostic Time ◽  
Testing Practices ◽  
Population Mortality ◽  
Epidemic Wave

AbstractThe ratio of COVID-19-attributable deaths versus “true” COVID-19 deaths depends on the synchronicity of the epidemic wave with population mortality; duration of test positivity, diagnostic time window, and testing practices close to and at death; infection prevalence; the extent of diagnosing without testing documentation; and the ratio of overall (all-cause) population mortality rate and infection fatality rate. A nomogram is offered to assess the potential extent of over- and under-counting in different situations. COVID-19 deaths were apparently under-counted early in the pandemic and continue to be under-counted in several countries, especially in Africa, while over-counting probably currently exists for several other countries, especially those with intensive testing and high sensitization and/or incentives for COVID-19 diagnoses. Death attribution in a syndemic like COVID-19 needs great caution. Finally, excess death estimates are subject to substantial annual variability and include also indirect effects of the pandemic and the effects of measures taken.

scholarly journals Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries and Taiwan

2020 ◽  
Author(s):  
Chen Wei ◽  
Chien-Chang Lee ◽  
Tzu-Chun Hsu ◽  
Wan-Ting Hsu ◽  
Chang-Chuan Chan ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Disease Burden ◽  
Case Fatality ◽  
Correlation Coefficients ◽  
Oecd Countries ◽  
Population Based ◽  
Fatality Rate ◽  
Successful Infection ◽  
Population Mortality ◽  
Testing Coverage

ABSTRACTAlthough testing is widely regarded as critical to fighting the Covid-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics-population testing number and testing coverage-to population mortality outcomes and identify a benchmark for testing adequacy with respect to population mortality and capture of potential disease burden. This ecological study aggregated publicly available data through April 12 on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. All OECD countries and Taiwan were included in this population-based study as a proxy for countries with highly developed economic and healthcare infrastructure. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate, and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. Testing coverage, but not population testing number, was highly correlated with population mortality (rs= −0.79, P=5.975e-09 vs rs = − 0.3, P=0.05) and case fatality rate (rs= −0.67, P=9.067e-06 vs rs= −0.21, P=0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality, whereas above 45, increased testing did not yield significant incremental mortality benefit. Testing coverage was better than population testing number in explaining country performance and can be used as an early and sensitive indicator of testing adequacy and disease burden. This may be particularly useful as countries consider re-opening their economies.

scholarly journals SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation

2021 ◽  
pp. 232102222110543
Author(s):  
Lauren Zimmermann ◽  
Subarna Bhattacharya ◽  
Soumik Purkayastha ◽  
Ritoban Kundu ◽  
Ritwik Bhaduri ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Case Fatality ◽  
Fatality Rate ◽  
Excess Death ◽  
Seir Model ◽  
Model Based ◽  
Quantitative Synthesis ◽  
Primary Focus ◽  
Using Data ◽  
Excess Deaths

Introduction: Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021. Methods: Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021. Results: For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion: When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18

Study on the length of diagnostic time window of CUP-VISAR

2021 ◽  
Author(s):  
Zanyang Guan ◽  
Yu Long Li ◽  
Feng Wang ◽  
Xiangming Liu ◽  
Xiaoshi Peng ◽  
...  
Keyword(s):  
Time Window ◽  
Diagnostic Time

scholarly journals Joint effects of habitat, zooplankton, host stage structure and diversity on amphibian chytrid

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160832 ◽  
Author(s):  
Jessica L. Hite ◽  
Jaime Bosch ◽  
Saioa Fernández-Beaskoetxea ◽  
Daniel Medina ◽  
Spencer R. Hall
Keyword(s):  
Indirect Effects ◽  
Batrachochytrium Dendrobatidis ◽  
Stage Structure ◽  
Infection Prevalence ◽  
Web Based ◽  
Host Diversity ◽  
Host Stage ◽  
Production Host ◽  
Infectious Stage

Why does the severity of parasite infection differ dramatically across habitats? This question remains challenging to answer because multiple correlated pathways drive disease. Here, we examined habitat–disease links through direct effects on parasites and indirect effects on parasite predators (zooplankton), host diversity and key life stages of hosts. We used a case study of amphibian hosts and the chytrid fungus, Batrachochytrium dendrobatidis , in a set of permanent and ephemeral alpine ponds. A field experiment showed that ultraviolet radiation (UVR) killed the free-living infectious stage of the parasite. Yet, permanent ponds with more UVR exposure had higher infection prevalence. Two habitat-related indirect effects worked together to counteract parasite losses from UVR: (i) UVR reduced the density of parasite predators and (ii) permanent sites fostered multi-season host larvae that fuelled parasite production. Host diversity was unlinked to hydroperiod or UVR but counteracted parasite gains; sites with higher diversity of host species had lower prevalence of infection. Thus, while habitat structure explained considerable variation in infection prevalence through two indirect pathways, it could not account for everything. This study demonstrates the importance of creating mechanistic, food web-based links between multiple habitat dimensions and disease.

scholarly journals COVID-19 in children: analysis of the first pandemic peak in England

2020 ◽  
Vol 105 (12) ◽  
pp. 1180-1185 ◽  
Author(s):  
Shamez N Ladhani ◽  
Zahin Amin-Chowdhury ◽  
Hannah G Davies ◽  
Felicity Aiano ◽  
Iain Hayden ◽  
...  
Keyword(s):  
Primary Care ◽  
Acute Respiratory Infection ◽  
Case Fatality Rate ◽  
Excess Mortality ◽  
Case Fatality ◽  
Fatality Rate ◽  
Large Numbers ◽  
Testing Practices ◽  
Excess Deaths

ObjectivesTo assess disease trends, testing practices, community surveillance, case-fatality and excess deaths in children as compared with adults during the first pandemic peak in England.SettingEngland.ParticipantsChildren with COVID-19 between January and May 2020.Main outcome measuresTrends in confirmed COVID-19 cases, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity rates in children compared with adults; community prevalence of SARS-CoV-2 in children with acute respiratory infection (ARI) compared with adults, case-fatality rate in children with confirmed COVID-19 and excess childhood deaths compared with the previous 5 years.ResultsChildren represented 1.1% (1,408/129,704) of SARS-CoV-2 positive cases between 16 January 2020 and 3 May 2020. In total, 540 305 people were tested for SARS-COV-2 and 129,704 (24.0%) were positive. In children aged <16 years, 35,200 tests were performed and 1408 (4.0%) were positive for SARS-CoV-2, compared to 19.1%–34.9% adults. Childhood cases increased from mid-March and peaked on 11 April before declining. Among 2,961 individuals presenting with ARI in primary care, 351 were children and 10 (2.8%) were positive compared with 9.3%–45.5% in adults. Eight children died and four (case-fatality rate, 0.3%; 95% CI 0.07% to 0.7%) were due to COVID-19. We found no evidence of excess mortality in children.ConclusionsChildren accounted for a very small proportion of confirmed cases despite the large numbers of children tested. SARS-CoV-2 positivity was low even in children with ARI. Our findings provide further evidence against the role of children in infection and transmission of SARS-CoV-2.

scholarly journals Comparative effectiveness of pharmacological interventions to prevent postoperative delirium: a network meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sun-Kyung Park ◽  
Taeyoon Lim ◽  
Hyeyeon Cho ◽  
Hyun-Kyu Yoon ◽  
Ho-Jin Lee ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Atypical Antipsychotics ◽  
Postoperative Delirium ◽  
Randomized Trials ◽  
Time Window ◽  
Meta Analysis ◽  
Significant Heterogeneity ◽  
Cochrane Central Register ◽  
Pharmacological Interventions ◽  
Diagnostic Time

AbstractMany pharmacologic agents were investigated for the effect to prevent delirium. We aimed to comprehensively compare the effect of the pharmacological interventions to prevent postoperative delirium. A Bayesian network meta-analysis of randomized trials was performed using random effects model. PubMed, the Cochrane Central Register of Controlled Trials, and Embase were searched on 20 January 2021. Randomized trials comparing the effect of a drug to prevent postoperative delirium with another drug or placebo in adult patients undergoing any kind of surgery were included. Primary outcome was the postoperative incidence of delirium. Eighty-six trials with 26,992 participants were included. Dexmedetomidine, haloperidol, and atypical antipsychotics significantly decreased the incidence of delirium than placebo [dexmedetomidine: odds ratio 0.51, 95% credible interval (CrI) 0.40–0.66, moderate quality of evidence (QOE); haloperidol: odds ratio 0.59, 95% CrI 0.37–0.95, moderate QOE; atypical antipsychotics: odds ratio 0.27, 95% CrI 0.14–0.51, moderate QOE]. Dexmedetomidine and atypical antipsychotics had the highest-ranking probabilities to be the best. However, significant heterogeneity regarding diagnostic time window as well as small study effects precludes firm conclusion.

scholarly journals Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19

2021 ◽  
Author(s):  
Jörg Stoye
Keyword(s):  
Odds Ratio ◽  
Early Stage ◽  
Partial Identification ◽  
Infection Prevalence ◽  
Test Accuracy ◽  
The Novel ◽  
Fatality Rate ◽  
Infection Status ◽  
The One ◽  
True Infection

Abstract I propose novel partial identification bounds on infection prevalence from information on test rate and test yield. The approach utilizes user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful elsewhere. Evaluated on data from the pandemic’s early stage, even the weakest of the novel bounds are reasonably informative. Notably, and in contrast to speculations that were widely reported at the time, they place the infection fatality rate for Italy well above the one of influenza by mid-April.

scholarly journals Analysis of case fatality rate of SARS-CoV-2 infection in the Spanish Autonomous Communities between March and May 2020

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260769
Author(s):  
Martín-Sánchez V. ◽  
Calderón-Montero A. ◽  
Barquilla-García A. ◽  
Vitelli-Storelli F. ◽  
Segura-Fragoso A. ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Statistical Significance ◽  
Case Fatality ◽  
Infection Prevalence ◽  
Fatality Rate ◽  
Autonomous Communities ◽  
La Mancha ◽  
The Difference ◽  
Regional Health Systems ◽  
Spanish Health

Objective The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA.

scholarly journals One-Year Prospective Study of Plasma Biomarkers From CNS in Patients With Mild Traumatic Brain Injury

2021 ◽  
Vol 12 ◽  
Author(s):  
Gerard Janez Brett Clarke ◽  
Toril Skandsen ◽  
Henrik Zetterberg ◽  
Cathrine Elisabeth Einarsen ◽  
Casper Feyling ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Glial Fibrillary Acidic Protein ◽  
Mild Traumatic Brain Injury ◽  
Time Window ◽  
Brain Mri ◽  
Demographic Variables ◽  
Acidic Protein ◽  
Observation Study ◽  
Diagnostic Time

Objective: To investigate the longitudinal evolution of three blood biomarkers: neurofilament light (NFL), glial fibrillary acidic protein (GFAP) and tau, in out-patients and hospitalized patients with mild traumatic brain injury (mTBI) compared to controls, along with their associations—in patients—with clinical injury characteristics and demographic variables, and ability to discriminate patients with mTBI from controls.Methods: A longitudinal observation study including 207 patients with mTBI, 84 age and sex-matched community controls (CCs) and 52 trauma controls (TCs). Blood samples were collected at 5 timepoints: acute (&lt;24 h), 72 h (24–72 h post-injury), 2 weeks, 3 and 12 months. Injury-related, clinical and demographic variables were obtained at inclusion and brain MRI within 72 h.Results: Plasma GFAP and tau were most elevated acutely and NFL at 2 weeks and 3 months. The group of patients with mTBI and concurrent other somatic injuries (mTBI+) had the highest elevation in all biomarkers across time points, and were more likely to be victims of traffic accidents and violence. All biomarkers were positively associated with traumatic intracranial findings on MRI obtained within 72 h. Glial fibrillary acidic protein and NFL levels were associated with Glasgow Coma Scale (GCS) score and presence of other somatic injuries. Acute GFAP concentrations showed the highest discriminability between patients and controls with an Area Under the Curve (AUC) of 0.92. Acute tau and 2-week NFL concentrations showed moderate discriminability (AUC = 0.70 and AUC = 0.75, respectively). Tau showed high discriminability between mTBI+ and TCs (AUC = 0.80).Conclusions: The association of plasma NFL with traumatic intracranial MRI findings, together with its later peak, could reflect ongoing secondary injury or repair mechanisms, allowing for a protracted diagnostic time window. Patients experiencing both mTBI and other injuries appear to be a subgroup with greater neural injury, differing from both the mTBI without other injuries and from both control groups. Acute GFAP concentrations showed the highest discriminability between patients and controls, were highly associated with intracranial traumatic injury, and showed the largest elevations compared to controls at the acute timepoint, suggesting it to be the most clinically useful plasma biomarker of primary CNS injury in mTBI.

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