Systematic SARS-CoV-2 testing is a valuable tool for infection control and surveillance. However, broad application of high sensitive RT-qPCR testing in children is often hampered due to unpleasant sample collection, limited RT-qPCR capacities and high costs. Here, we developed a high-throughput approach (‘Lolli-Method’) for SARS-CoV-2 detection in children, combining non-invasive sample collection with an RT-qPCR-pool testing strategy. SARS-CoV-2 infections were diagnosed with sensitivities of 100% and 93.9% when viral loads were >106 copies/ml and >103 copies/ml in corresponding Naso-/Oropharyngeal-swabs, respectively. For effective application of the Lolli-Method in schools and daycare facilities, SEIR-modeling indicated a preferred frequency of two tests per week. The developed test strategy was implemented in 3,700 schools and 698 daycare facilities in Germany, screening over 800,000 individuals twice per week. In a period of 3 months, 6,364 pool-RT-qPCRs tested positive (0.64%), ranging from 0.05% to 2.61% per week. Notably, infections correlated with local SARS-CoV-2 incidences and with a school social deprivation index. Moreover, in comparison with the alpha variant, statistical modeling revealed a 36.8% increase for multiple (≥2 children) infections per class following infections with the delta variant. We conclude that the Lolli-Method is a powerful tool for SARS-CoV-2 surveillance and can support infection control in schools and daycare facilities.

Many validation studies deal with item nonresponse and measurement error in earnings data. In this article, the author explores respondents’ motives for failing to reveal earnings using the German Socio-Economic Panel (SOEP). The SOEP collects socioeconomic information from private households in the Federal Republic of Germany. The author explains the evolution of income nonresponse in the SOEP and demonstrates the importance of discriminating between refusing to state income and responses of “don’t know.”

Eine Vielzahl von Studien zur raumbezogenen Bildungsforschung zeigen, dass die schulische Bildungsbeteiligung sozialräumlich ungleich verteilt ist. Im Hinblick auf diese Heterogenität wurde schon seit Längerem in Nordrhein-Westfalen der Ruf nach einem schulscharfen Sozialindex laut, der die spezifischen Gegebenheiten vor Ort berücksichtigt und eine gezielte Förderung von Schulen ermöglicht, um zu einer Verringerung von Chancenunterschieden beizutragen. Die Autoren wurden durch das Ministerium für Schule und Bildung des Landes NRW mit der Konstruktion eines solchen Sozialindex für Schulen beauftragt. Der vorliegende Text beschreibt, auf welchen Indikatoren der Sozialindex basiert, welche statistischen Verfahren zur Indexkonstruktion genutzt wurden und auf welcher Datengrundlage er berechnet wird. Abschließend wird der Schulsozialindex mit Daten zu den zentralen Abschlussprüfungen nach Klasse 10 und den Vergleichsarbeiten (VERA 3 und 8) evaluiert und es werden Möglichkeiten zur Bildung von Sozialindexstufen diskutiert.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This series presents research findings based either directly on data from the German SocioEconomic Panel Study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science. Terms of use: Documents in EconStor mayThe decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly.Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin.Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions. 1 We thank the fieldwork organization TNS Infratest Sozialforschung, Munich that is carrying out the SOEP survey and managing the implementation of microgeographic data and interviewer data for their generous support. In particular, we would like to thank Nico A. Siegel (TNS Infratest Sozialforschung) and Bernhard Schimpl-Neimanns (GESIS, Mannheim) for helpful suggestions and comments. The usual disclaimer applies. 1 AbstractThis study examines the phenomenon of nonresponse in the first wave of a refresher sample (subsample H) of the German Socio-Economic Panel Study (SOEP). Our first step is to link additional (commercial) microgeographic data on the immediate neighborhoods of the households visited by interviewers. These additional data (paradata) provide valuable information on respondents and nonrespondents, including milieu or lifestyle, dominant household structure, desire for anonymity, frequency of moves, and other important microgeographic information. This linked information is then used to analyze nonresponse. In a second step, we also use demographic variables for the interviewer f...