Prevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study.

Snoeck, Chantal; Vaillant, Michel; Abdelrahman, Tamir; Satagopam, Venkata; Turner, Jonathan D.; Beaumont, Katy; Gomes, Clarissa P.C.; Fritz, Joëlle V.; Schröder, Valerie; Kaysen, Anne; Pavelka, Lukas; Stute, Lara; Meyers, Guilherme Ramos; Pauly, Laure; Hansen, Maxime; Pauly, Claire; Aguayo, Gloria; Perquin, Magali; Hanff, Anne-Ma﻿rie; Ghosh, Soumyabrata; Gantenbein, Manon; Huiart, Laëtitia; Ollert, Markus; Krüger, Rejko

doi:10.1101/2020.05.11.20092916

Cited by 83 publications

(136 citation statements)

References 31 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…Fourteen articles -9 preprints, 3 published studies, and 2 government reports-from 10 countries (Brazil (2), Spain (2), United States of America (USA) (2), Germany (2), and one each from Italy, Croatia, Iran, Luxembourg, Switzerland and the Channel Islands) that tested a combined total of 100,557 participants met eligibility criteria for the estimation of the primary outcome. [14][15][16][17][18][19][20][21][22][23][24][25][26][27]…”

Section: Resultsmentioning

confidence: 99%

“…The study populations included a randomly sampled general populations 17,[20][21][22][23][24][25][26][27] ; most health care workers (HCWs) of hospitals 14,16 ; most industry workers 18,19 ; and all parturient women. 15 (Table 1) Eight studies tested adults only (>16 years) [14][15][16]18,19,23,24,27 and six tested population of all ages 17,[20][21][22]25,26 -the proportion of children and young people (0-19 years) ranged from 9% to 34% and the proportion of participants aged over 70 years ranged from 0% to 14%. Ten studies tested for both anti-SARS-CoV-2 IgG and IgM, the rest tested for IgG only or IgG and IgA.…”

Section: Figure 1 Screening and Selection Of Articles For The Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: systematic review

Byambasuren

Dobler

Bell

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Accurate seroprevalence estimates of SARS-CoV-2 in different populations could help gauge the true magnitude and spread of the infection seroprevalence. Reported estimates have varied greatly, but many have derived from biased samples, and inadequate testing methods. Objective: To estimate the range of valid seroprevalence rates of SARS-CoV-2 in different populations, and compare these seroprevalence estimates with the cumulative cases seen in the same population. Methods: We searched PubMed, Embase, the Cochrane COVID-19 trials, and Europe-PMC for published studies and pre-prints from January 2020 to 25 May 2020 that reported anti-SARS-CoV-2 IgG, IgM and/or IgA antibodies for serosurveys of either the general community or of defined sub-populations, such healthcare workers and other organizations. Results: Of the 837 studies identified, 49 were assessed and 14 were includable. Included studies represented 10 countries and 100,557 subjects: 9 from randomly selected populations, 2 from healthcare workers, 2 from industry populations, and 1of parturient women. The seroprevalence proportions in 10 studies ranged between 1%-10%, and 2 study estimates under 1%, and 2 over 10% - from the notably hard-hit regions of Gangelt in Germany and from Northwest Iran. The two studies in healthcare workers, in Italy and Spain, had seroprevalence rates at higher range of estimates, with the Barcelona hospitals having a higher rate than the Spanish national survey. For only one study was the seroprevalence estimate higher than the cumulative incidence, though these were proximate for several studies. In five studies, the seroprevalence was similar to the cumulative case numbers in the same population. For seropositive cases not previously detected as COVID-19 cases, the majority had prior COVID-like symptoms. Conclusion: The seroprevalence of SARS-CoV-2 mostly less than 10% with the level of infection lower in the general community, suggesting levels well below herd immunity. The similarity of seroprevalence and reported cases is several studies, and high symptom rates in seropositive cases suggest that gaps between seroprevalence rates and reported cases are likely due to undertesting of symptomatic people.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Figure 1 Screening and Selection Of Articles For The Reviewmentioning

confidence: 99%

Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: systematic review

Byambasuren

Dobler

Bell

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The same proportion of studies validated their methods internally to report accuracy (16,21,23,24,30,32,(34)(35)(36)(37)(38)(39)45,50,52) or used sensitivity and specificity given by manufacturers or other external studies (18)(19)(20)22,36,(40)(41)(42)(44)(45)(46)(47)(48)(49)(50)(51). We also noticed that it is quite unclear if the field teams followed standardized protocols for the data collection and testing.…”

Section: Discussionmentioning

confidence: 99%

Population-based prevalence surveys during the COVID-19 pandemic: a systematic review

Franceschi

Santos

Glaeser

et al. 2020

Preprint

View full text Add to dashboard Cite

Population-based prevalence surveys of COVID-19 contribute to establish the burden and epidemiology of infection, the role of asymptomatic and mild infections in transmission, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, their reliability, and biases. The available data described 37 surveys from 19 countries, mostly from Europe and America and using antibody testing. They reached highly heterogeneous sample sizes and prevalence estimates. Disproportional prevalence was observed in minority communities. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way, and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, blood samples and prevalence >1%. Low risk of bias was related to Asian studies, RT-PCR tests and prevalence <1%.

show abstract

“…COVID-19 is caused by a betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that affects the respiratory system [1]. Despite draconian sanitary measures being applied worldwide, COVID-19 was declared a pandemic on 11 March 2020 by The World Health of Organization (WHO) [2]. By May 13th the outbreak had infected over 4 million people and caused almost 300,000 deaths worldwide (World Health of Organization, 2020).…”

Section: Introductionmentioning

confidence: 99%

The COVID-19 Pandemic: Does Our Early Life Environment, Life Trajectory and Socioeconomic Status Determine Disease Susceptibility and Severity?

Holuka

Merz

Fernandes

et al. 2020

IJMS

Self Cite

View full text Add to dashboard Cite

A poor socioeconomic environment and social adversity are fundamental determinants of human life span, well-being and health. Previous influenza pandemics showed that socioeconomic factors may determine both disease detection rates and overall outcomes, and preliminary data from the ongoing coronavirus disease (COVID-19) pandemic suggests that this is still true. Over the past years it has become clear that early-life adversity (ELA) plays a critical role biasing the immune system towards a pro-inflammatory and senescent phenotype many years later. Cytotoxic T-lymphocytes (CTL) appear to be particularly sensitive to the early life social environment. As we understand more about the immune response to SARS-CoV-2 it appears that a functional CTL (CD8+) response is required to clear the infection and COVID-19 severity is increased as the CD8+ response becomes somehow diminished or exhausted. This raises the hypothesis that the ELA-induced pro-inflammatory and senescent phenotype may play a role in determining the clinical course of COVID-19, and the convergence of ELA-induced senescence and COVID-19 induced exhaustion represents the worst-case scenario with the least effective T-cell response. If the correct data is collected, it may be possible to separate the early life elements that have made people particularly vulnerable to COVID-19 many years later. This will, naturally, then help us identify those that are most at risk from developing the severest forms of COVID-19. In order to do this, we need to recognize socioeconomic and early-life factors as genuine medically and clinically relevant data that urgently need to be collected. Finally, many biological samples have been collected in the ongoing studies. The mechanisms linking the early life environment with a defined later-life phenotype are starting to be elucidated, and perhaps hold the key to understanding inequalities and differences in the severity of COVID-19.

show abstract

Prevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study.

Cited by 83 publications

References 31 publications

Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: systematic review

Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: systematic review

Population-based prevalence surveys during the COVID-19 pandemic: a systematic review

The COVID-19 Pandemic: Does Our Early Life Environment, Life Trajectory and Socioeconomic Status Determine Disease Susceptibility and Severity?

Contact Info

Product

Resources

About