How to Determine When SARS-CoV-2 Antibody Testing Is or Is Not Useful for Population Screening: A Tutorial

Extensive testing lies at the heart of any strategy to effectively combat the SARS-COV-2 pandemic. In recent months, the use of enzyme-linked immunosorbent assay–based antibody tests has gained a lot of attention. These tests can potentially be used to assess SARS-COV-2 immunity status in individuals (e.g., essential health care personnel). They can also be used as a screening tool to identify people that had COVID-19 asymptomatically, thus getting a better estimate of the true spread of the disease, gain important insights on disease severity, and to better evaluate the effectiveness of policy measures implemented to combat the pandemic. But the usefulness of these tests depends not only on the quality of the test but also, critically, on how far disease has already spread in the population. For example, when only very few people in a population are infected, a positive test result has a high chance of being a false positive. As a consequence, the spread of the disease in a population as well as individuals’ immunity status may be systematically misinterpreted. SARS-COV-2 infection rates vary greatly across both time and space. In many places, the infection rates are very low but can quickly skyrocket when the virus spreads unchecked. Here, we present two tools, natural frequency trees and positive and negative predictive value graphs, that allow one to assess the usefulness of antibody testing for a specific context at a glance. These tools should be used to support individual doctor-patient consultation for assessing individual immunity status as well as to inform policy discussions on testing initiatives.Peer Reviewe

Explaining the Mechanism Behind mRNA Vaccines Influences Perceived Vaccine Effectiveness but not Vaccination Intentions

Vaccine effectiveness and safety concerns can prevent people from receiving their first Covid-19 vaccines or boosters. Understanding the vaccine mechanism may lead people to perceive vaccine effectiveness appropriately. This study tested whether helping people understand the vaccine’s mechanism could improve their perceived vaccine safety and effectiveness. In a preregistered study, N = 1,548 unvaccinated or non-boosted participants were randomly presented with one of three communication formats: a fact box (a benefit-risk profile in tabular format; control condition), an expository text (i.e., a purely factual explanation) plus fact box, or an analogy plus fact box. Participants rated the vaccine’s effectiveness in preventing a Covid-19 disease, their perceived risk of getting vaccinated, and their intention to get vaccinated or boosted (depending on their vaccination status). Reading either additional text about the vaccines’ mechanism increased participants’ effectiveness ratings for the vaccine to prevent Covid-19 but did not affect risk ratings or vaccination intentions. The participants’ vaccine-related perceptions and intentions did not differ between the two text types. Elaborating on the vaccine’s mechanism of protection, in addition to presenting the benefit-risk profile of a vaccine, can lead people to perceive the vaccine effectiveness as slightly higher, yet it is insufficient to increase vaccination intentions

Kommunikationsempfehlungen zur Verbesserung des Verhaltens bei der Verwendung von PoC Antigen-Schnelltests und Selbsttests

Die breitere Anwendung von Antigentests zum Auffinden einer akuten Infektion mit SARS-CoV-2 beinhaltet Chancen, aber auch Risiken und Limitationen. Die Tests können als einer von mehreren Bausteinen zur Pandemiekontrolle beitragen, indem sie die Erkennung von Infektionen und somit die Unterbrechung von Infektionsketten ermöglichen - insbesondere durch wiederholte, engmaschige Testungen derselben Personen. Der Beitrag fasst die wichtigsten Aspekte zusammen und gibt evidenzbasierte Tipps zur Kommunikation rund um die PoC Antigen-Schnelltests und Selbsttests

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

International audienceThe collective control programmes (CPs) that exist for many infectious diseases of farm animals rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd-level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against bovine viral diarrhoea virus (BVDV) infection in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework, using either Stan or JAGS. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression, replacing the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive for all herds on the current month. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model implementations on parameter estimation was evaluated. The main advantage of this model is its ability to predict a probability of being status positive in a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability/presence. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (PI) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree) and can be used to carry out output-based evaluation of disease CPs

Long-Term Follow-Up of Newborns with 22q11 Deletion Syndrome and Low TRECs.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadBackground: Population-based neonatal screening using T-cell receptor excision circles (TRECs) identifies infants with profound T lymphopenia, as seen in cases of severe combined immunodeficiency, and in a subgroup of infants with 22q11 deletion syndrome (22q11DS). Purpose: To investigate the long-term prognostic value of low levels of TRECs in newborns with 22q11DS. Methods: Subjects with 22q11DS and low TRECs at birth (22q11Low, N=10), matched subjects with 22q11DS and normal TRECs (22q11Normal, N=10), and matched healthy controls (HC, N=10) were identified. At follow-up (median age 16 years), clinical and immunological characterizations, covering lymphocyte subsets, immunoglobulins, TRECs, T-cell receptor repertoires, and relative telomere length (RTL) measurements were performed. Results: At follow-up, the 22q11Low group had lower numbers of naïve T-helper cells, naïve T-regulatory cells, naïve cytotoxic T cells, and persistently lower TRECs compared to healthy controls. Receptor repertoires showed skewed V-gene usage for naïve T-helper cells, whereas for naïve cytotoxic T cells, shorter RTL and a trend towards higher clonality were found. Multivariate discriminant analysis revealed a clear distinction between the three groups and a skewing towards Th17 differentiation of T-helper cells, particularly in the 22q11Low individuals. Perturbations of B-cell subsets were found in both the 22q11Low and 22q11Normal group compared to the HC group, with larger proportions of naïve B cells and lower levels of memory B cells, including switched memory B cells. Conclusions: This long-term follow-up study shows that 22q11Low individuals have persistent immunologic aberrations and increased risk for immune dysregulation, indicating the necessity of lifelong monitoring. Clinical implications: This study elucidates the natural history of childhood immune function in newborns with 22q11DS and low TRECs, which may facilitate the development of programs for long-term monitoring and therapeutic choices. Keywords: 22q11.2 deletion syndrome; DiGeorge syndrome; T lymphopenia; TREC; long-term outcome; newborn screening; severe combined immunodeficiency.University of Gothenburg Regional research grant Region Halland Swedish Research Council European Commission Queen Silvia Jubilee Foundation Swedish Primary Immunodeficiency Organization Sparbanken Foundation Varberg Frimurare Barnhusdirektionen Foundation Gothenburg Medical Society Medical Faculty at Umea University Cancer Research Foundation in Northern Sweden Swedish government county councils, the ALF-agreement Umea University Vasterbottens County Counci

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks