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ZuSAmmenfASSunGZiel der Studie Verletzungen bei Kindern und Jugendlichen zählen zu den dringendsten Public Health Problemen in Deutschland. Neben Unfällen im Haushalt und in der Freizeit ist insbesondere der Lebensbereich Schule von großer Bedeutung. Hinsichtlich der Einflussfaktoren auf Schulverletzungen bestehen jedoch erhebliche Forschungslücken. Hier setzt der Beitrag an und analysiert individuelle und kontextuelle Faktoren, die auf das Verletzungsgeschehen in der Schule Einfluss nehmen. Dabei werden speziell Verletzungen untersucht, die sich auf dem Schulhof ereignen.Methodik Die Datengrundlage bildet die zweite Erhebungswelle der Panelstudie "Gesundheitsverhalten und Unfallgeschehen im Schulalter" (GUS AbStR Ac tAims of the Study Injuries of children and adolescents rank among the most immediate problems in public health in Germany. Alongside accidents in the household and during leisure time, the school is of great importance. However, there are significant gaps in knowledge about the causes of school injuries. This is the starting point of this article, which analyses individual and contextual factors that influence the occurrence of injuries in schools and deals explicitly with injuries happening in the schoolyard.Method The data foundation is the second wave of the panel study "Gesundheitsverhalten und Unfallgeschehen im Schulalter" (Health Behavior and Occurrence of Accidents at School Age) (GUS). Following a random sampling of secondary general education schools in 11 German states, 10089 pupils aged 11-13 years from 138 schools were surveyed within their respective classes via a standardized electronic questionnaire. 183Stadtmüller S et al. Verletzungen auf dem Schulhof. Gesundheitswesen 2018; 80: [183][184][185][186][187][188][189][190] Originalarbeit ThiemeAlongside accidents and injuries, the questionnaire covers a wide range of topics including health and recreational behavior and also surveys information about the schools. The factors influencing schoolyard accidents are analyzed using multivariate, logistic multilevel-models. ResultsIn the second wave, 5.8 % of the pupils reported at least one injury happening in the schoolyard in the last 12 months that had to be treated by a doctor. Correlations with these schoolyard accidents were found for individual factors such as gender (female, OR = 0.53), experiences of bullying (OR = 1.68), sleeping problems (OR = 1.07) and individual athletic activity (OR = 1.03). Although the variance is primarily tied to the individual level, certain contextual variables also proved influential: Schools, in which the overall condition is rated better by the pupils, also have a lower overall risk of injuries (OR = 0.60).Conclusions According to these results, preventive measures may target the individual as well as the contextual level: prevention of bullying should be a focus in the light of these findings, but at the same time, also the condition of the schools should be taken into consideration.Einleitung

Background School injuries are an important adolescent health problem. Previous research suggests that relevant risk behaviors for school injuries, risk-taking and aggression, are highly susceptible to peer effects. Specifically, evidence suggests that the ratio of men and women in peer groups (sex ratio) affects individuals’ propensity for aggression and risk-taking. However, potential associations of classroom sex ratios with adolescent school injury risks have not been studied so far. The purpose of this paper is to investigate the association of classroom sex compositions with adolescent school injuries. Methods We investigate the association of classroom sex ratios with school injuries in a longitudinal survey dataset containing 13,131 observations from 9,204 adolescent students (ages 13-16) from secondary schools in Germany. The data also allow us to identify injuries due to aggressive behavior and analyze these injuries in detail. We use multilevel logistic regression models to analyze risks of both overall and aggression-related school injuries. Results Adolescent students’ risk for school injuries is significantly and positively associated with male-skewed classroom sex ratios (OR = 1.012, p=0.012). Specifically, the risk of sustaining a school injury increases by 33.5 percent when moving from the 10th to the 90th classroom sex ratio percentile. Moreover, we find an even stronger positive association between male-dominated classrooms and aggression-related injury risks (OR = 1.022, p=0.010). Compared to classroom sex ratios at the 10th percentile, the risk of an aggression-related injury is 78 percent higher in classrooms with a sex ratio at the 90th percentile. Finally, we find that both boys’ and girls’ injury risks equally increase with a higher proportion of male students in their classroom. Conclusions Our findings indicate that sex composition of classrooms is an important contextual factor for adolescent school injuries, in particular school injuries resulting from aggression. These findings illustrate the need to integrate a contextual perspective on school injuries among adolescent students both into research and into intervention planning.

Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.

BACKGROUND Previous research on the correlates of unintentional school injuries is based on either process or cross‐sectional data. This study aims at approaching the causal effects of risk‐seeking behavior, mental health problems, physical activity, and exposure to bullying on unintentional injuries in the school environment by relying on longitudinal survey data. METHODS The data comes from a German panel survey, including more than 10,000 students. We estimate fixed‐effects regression models that only take into account the variation within participants and are therefore most suitable for establishing causal inferences. RESULTS We find an increase in risk‐seeking behavior on the individual level to yield an increase in students' likelihood to suffer injuries during physical education and on the schoolyard or in the school building. The same holds true for an increase in mental health problems. Finally, students who expand their degree of physical activity in club sports also show a higher risk of unintentional injuries. CONCLUSIONS Interventions aimed at reducing too risky behavior and mental health problems may help prevent unintentional injuries in the school environment. Since students who increase their activities in club sports are also more prone to school injuries, preventive efforts should include this group of adolescents as well.

School injuries are an important adolescent health problem. Previous research suggests that relevant risk behaviors for school injuries, risk-taking and aggression, are highly susceptible to peer effects. Specifically, evidence suggests that the ratio of males and females in peer groups (sex ratio) affects individuals’ propensity for aggression and risk-taking. However, research so far has ignored potential associations of classroom sex ratios with adolescent school injury risks. In this paper, we investigate the association of classroom sex compositions with adolescent school injuries in a longitudinal survey dataset containing 13,131 observations from 9,204 adolescent students (ages 13-16) from secondary schools in Germany. The data also allow us to identify injuries that were due to aggressive behavior and analyze these injuries in detail. Results from multilevel logistic regression models reveal that adolescent students’ risk for school injuries is significantly and positively associated with male-skewed classroom sex ratios. Moreover, we find an even stronger positive association between male-dominated classrooms and aggression-injury risks. Finally, we find that both boys’ and girls’ injury risks equally increase with a higher proportion of males in their classroom. We discuss the implications of our findings with regard to the sex ratio literature and potential interventions.

Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data management. According to the European General Data Protection Regulation, the problem becomes much more complex. Recognizing these problems and difficulties, we have developed a Data Science Learning Platform (DSLP) that primarily targets practitioners and researchers but also the computer science students. Using our proposed tool chain together with the developed graphical user interface, data scientists and research physicians will be able to use available medical databases, apply and analyze different anonymization methods, analyze data according to the patient’s risk and quickly formulate new studies to target a disease in a complex data model. This article presents a clinical research discovery toolbox that implements and demonstrates tools for data anonymization, patient data visualization, NLP-tools for guideline search and data science learning tools.

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