Forensic Age Estimation Using Computed Tomography of the Medial Clavicular Epiphysis: A Systematic Review

International audienceMedicolegal physicians are increasingly called upon to aid in determining the administrative age group affiliation of refugees with questionable unaccompanied minor claims. According to guidelines for forensic age assessment, age differentiation along the 18-year-old cut-off relies on clavicular ossification. The thin-slice computed tomography scan (TSCTs) of the medial clavicular epiphysis (MCE) is one of the methods contributing to this assessment, though it is not yet universally accepted. The aim of this systematic review was to identify scientific papers where age was assessed using TSCTs of the MCE and to observe whether this examination was reproducible and reliable in estimating a person's age relative to the 18-year-old threshold. A search algorithm was applied to several databases to identify articles in accordance with the PRISMA (Preferred Reporting Items for Systematic-Reviews and Meta-Analyses) statement. One boxplot per article was constructed, separating by stage of maturation and sex. The 13 articles selected represented a sample of 5605 individuals (3396 males, 2209 females) aged 10 to 35~years. All individuals classified as stages 4 and 5 were aged 18~years or older. The same result was obtained concerning stage 3c, except in one article. The results thus appear reliable and reproducible, in particular, with respect to the 18-year-old threshold; medicolegal physicians should be able to estimate that all individuals in stages 4 and 5 are at least 18~years old. Additional studies applied to several other populations in the world should complement the selected studies

ABO blood types and SARS-CoV-2 infection assessed using seroprevalence data in a large population-based sample: the SAPRIS-SERO multi-cohort study

International audienceAbstract ABO blood type has been reported as a potential factor influencing SARS-CoV-2 infection, but so far mostly in studies that involved small samples, selected population and/or used PCR test results. In contrast our study aimed to assess the association between ABO blood types and SARS-CoV-2 infection using seroprevalence data (independent of whether or not individuals had symptoms or sought for testing) in a large population-based sample. Our study included 67,340 French participants to the SAPRIS-SERO multi-cohort project. Anti-SARS-CoV-2 antibodies were detected using ELISA (targeting the proteins spike (S) and nucleocapsid (NP)) and seroneutralisation (SN) tests on dried blood spots collected in May–November 2020. Non-O individuals (and especially types A and AB) were more likely to bear anti SARS-CoV-2 antibodies (ELISA-S, 2964 positive cases: OR non-Ovs.O = 1.09[1.01–1.17], OR Avs.O = 1.08[1.00–1.17]; ELISA-S/ELISA-NP/SN, 678 triple positive cases: OR non-Ovs.O = 1.19 [1.02–1.39], OR Avs.O = 1.19[1.01–1.41], OR ABvs.O = 1.43[1.01–2.03]). Hence, our results provided additional insights into the dynamic of SARS-CoV-2 infection, highlighting a higher susceptibility of infection for individuals of blood types A and AB and a lesser risk for blood type O

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

International audienceThe individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a “negative” or a “positive” test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. “Indeterminate” tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available