An index for assessing activity friendliness for children in urban environments of Berlin

The physical environment strongly influences physical activity in urban settings. While walkability is frequently assessed for adults, an approach for mapping the friendliness of urban environments focusing on children’s activities is not available. The aim of the presented approach was to identify supporting and limiting factors of activity friendliness in urban environments and incorporate them into a children’s physical activity index (CAI). We conducted qualitative guided interviews with nine- to ten-year-old children and parents of primary school children in Berlin to identify the factors and their importance for describing activity friendliness. Access to activity and recreational destinations, land use, traffic and road safety, and the social environment were the most prominent factors identified for the activity friendliness for children. The newly developed CAI enables a differentiation in the activity friendliness of urban neighborhoods for children

Implementation of anaphylaxis management guidelines

Anaphylaxis management guidelines recommend the use of intramuscular adrenaline in severe reactions, complemented by antihistamines and corticoids; secondary prevention includes allergen avoidance and provision of self-applicable first aid drugs. Gaps between recommendations and their implementation have been reported, but only in confined settings. Hence, we analysed nation-wide data on the management of anaphylaxis, evaluating the implementation of guidelines. Within the anaphylaxis registry, allergy referral centres across Germany, Austria and Switzerland provided data on severe anaphylaxis cases. Based on patient records, details on reaction circumstances, diagnostic workup and treatment were collected via online questionnaire. Report of anaphylaxis through emergency physicians allowed for validation of registry data. 2114 severe anaphylaxis patients from 58 centres were included. 8% received adrenaline intravenously, 4% intramuscularly; 50% antihistamines, and 51% corticoids. Validation data indicated moderate underreporting of first aid drugs in the Registry. 20% received specific instructions at the time of the reaction; 81% were provided with prophylactic first aid drugs at any time. There is a distinct discrepancy between current anaphylaxis management guidelines and their implementation. To improve patient care, a revised approach for medical education and training on the management of severe anaphylaxis is warranted

Differences in BMI z-Scores between Offspring of Smoking and Nonsmoking Mothers: A Longitudinal Study of German Children from Birth through 14 Years of Age

BACKGROUND: Children of mothers who smoked during pregnancy have a lower birth weight but have a higher chance to become overweight during childhood. OBJECTIVES: We followed children longitudinally to assess the age when higher body mass index (BMI) z-scores became evident in the children of mothers who smoked during pregnancy, and to evaluate the trajectory of changes until adolescence. METHODS: We pooled data from two German cohort studies that included repeated anthropometric measurements until 14 years of age and information on smoking during pregnancy and other risk factors for overweight. We used longitudinal quantile regression to estimate age-and sex-specific associations between maternal smoking and the 10th, 25th, 50th, 75th, and 90th quantiles of the BMI z-score distribution in study participants from birth through 14 years of age, adjusted for potential confounders. We used additive mixed models to estimate associations with mean BMI z-scores. Results: Mean and median (50th quantile) BMI z-scores at birth were smaller in the children of mothers who smoked during pregnancy compared with children of nonsmoking mothers, but BMI z-scores were significantly associated with maternal smoking beginning at the age of 4-5 years, and differences increased over time. For example, the difference in the median BMI z-score between the daughters of smokers versus nonsmokers was 0.12 (95% CI: 0.01, 0.21) at 5 years, and 0.30 (95% CI: 0.08, 0.39) at 14 years of age. For lower BMI z-score quantiles, the association with smoking was more pronounced in girls, whereas in boys the association was more pronounced for higher BMI z-score quantiles. CONCLUSIONS: A clear difference in BMI z-score (mean and median) between children of smoking and nonsmoking mothers emerged at 4-5 years of age. The shape and size of age-specific effect estimates for maternal smoking during pregnancy varied by age and sex across the BMI z-score distribution

Overweight in Adolescence Can Be Predicted at Age 6 Years: A CART Analysis in German Cohorts

Objective: To examine, whether overweight in adolescents can be predicted from the body mass index (BMI) category, at the age of 6, the mother's education level and mother's obesity and to quantify the proportion of overweight at the age of 14 that can be explained by these predictors. Method: Pooled data from three German cohorts providing anthropometric and other relevant data to a total of 1 287 children. We used a classification and regression tree (CART) approach to identify the contribution of BMI category at the age of 6 (obese: BMI>97th percentile (P97);overweight: P90P90) at the age of 14. Results: While 4.8% [95% CI: 3.2;7.0] of 651 boys and 4.1% [95% CI: 2.6;6.2] of 636 girls with a BMI= P75. The lowest prevalence was 1.9% [95% CI: 0.8;3.8] in boys with a BMI P97 (similar results for girls). BMI >= P75 at the age of 6 explained 63.5% [95% CI: 51.1;74.5]) and 72.0% [95% CI: 60.4;81.8] of overweight/obesity at the age of 14 in boys and girls, respectively. Conclusions: Overweight/obesity in adolescence can be predicted by BMI category at the age of 6 allowing for parent counselling or risk guided interventions in children with BMI >= P75, who accounted for >2/3 of overweight/obesity in adolescents

Data-driven prediction of COVID-19 cases in Germany for decision making

The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation

Prognosemodelle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten in Deutschland

Hintergrund: Zeitdynamische Prognosemodelle spielen eine zentrale Rolle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten im Pandemiegeschehen. Ein wichtiger Vorhersagewert (Prädiktor) für die zukünftige intensivmedizinische (ITS-)COVID-19-Bettenbelegungen ist die Anzahl der SARS-CoV-2-Neuinfektionen in der Bevölkerung, die wiederum stark von Schwankungen im Wochenverlauf, Meldeverzug, regionalen Unterschieden, Dunkelziffer, zeitabhängiger Ansteckungsrate, Impfungen, SARS-CoV-2-Virusvarianten sowie von nichtpharmazeutischen Eindämmungsmaßnahmen abhängt. Darüber hinaus wird die aktuelle und auch zukünftige COVID-ITS-Belegung maßgeblich von den intensivmedizinischen Entlassungs- und Sterberaten beeinflusst. Methode: Sowohl die Anzahl der SARS-CoV-2-Neuinfektionen in der Bevölkerung als auch die intensivmedizinischen COVID-19-Bettenbelegungen werden bundesweit flächendeckend erfasst. Diese Daten werden tagesaktuell mit epidemischen SEIR-Modellen aus gewöhnlichen Differenzialgleichungen und multiplen Regressionsmodellen statistisch analysiert. Ergebnisse: Die Prognoseergebnisse der unmittelbaren Entwicklung (20-Tage-Vorhersage) der ITS-Belegung durch COVID-19-Patienten*innen werden Entscheidungsträgern auf verschiedenen überregionalen Ebenen zur Verfügung gestellt. Schlussfolgerung: Die Prognosen werden der Entwicklung von betreibbaren intensivmedizinischen Bettenkapazitäten gegenübergestellt, um frühzeitig Kapazitätsengpässe zu erkennen und kurzfristig reaktive Handlungssteuerungen, wie etwa überregionale Verlegungen, zu ermöglichen.Background: Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV‑2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates. Methods: Both the number of new SARS-CoV‑2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models. Results: Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country. Conclusion: The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.Peer Reviewe

Retrospektive Evaluation eines Prognosemodells für die Bettenbelegung durch COVID-19-Patientinnen und -Patienten auf deutschen Intensivstationen

Während der COVID-19-Pandemie waren bundesweite Daten zu den Kapazitäten der Gesundheits-versorgung und Krankenhäuser eine wichtige Orientierungshilfe für ressourcenstrategische und politische Entscheidungen. Im vorliegenden Beitrag wird die Leistungsfähigkeit eines Prognosemodells für die Bettenbelegung durch COVID-19-Patienten und -Patientinnen auf deut-schen Intensivstationen am Beispiel der fünf „Kleeblatt“-Regionen Deutschlands (Nord, Ost, Süd, Süd-West, West) im Zeitraum vom 1.7.2021 bis zum 2.1.2023 retrospektiv evaluiert. Außerdem werden Zeiträume mit außergewöhnlichen Abweichungen von prognostizierten und tatsächlichen Werten visualisiert und diskutiert.Peer Reviewe

A Federated and Distributed Data Management Infrastructure to Enable Public Health Surveillance from Intensive Care Unit Data

The Robert Koch Institute (RKI) monitors the actual number of COVID-19 patients requiring intensive care from aggregated data reported by hospitals in Germany. So far, there is no infrastructure to make use of individual patient-level data from intensive care units for public health surveillance. Adopting concepts and components of the already established AKTIN Emergency Department Data registry, we implemented the prototype of a federated and distributed research infrastructure giving the RKI access to patient-level intensive care data.Peer Reviewe

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

Introduction Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population. Methods We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used. Results Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53–1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53–3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03–2.62). Conclusion We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.Peer Reviewe

Routinedaten aus der medizinischen Versorgung für die Notaufnahme-Surveillance: 1,5 Jahre Notaufnahme-Situationsreport

SUMO ist ein am Robert Koch-Institut entwickeltes und betriebenes System, welches Gesundheitsdaten für Public Health-Surveillance verarbeitet und bereitstellt. Der Notaufnahme-Situationsreport enthält Daten der Routinedokumentation aus einer Auswahl deutscher Notaufnahmen und bildet die aktuelle Inanspruchnahme dieser Notaufnahmen ab.SUMO is a system that has been developed and implemented at the Robert Koch Institute. It processes and provides health data for surveillance and public health research. The Emergency Department Situation Report presents data from the routine documentation of selected emergency departments in Germany, and shows the current utilisation of those emergency departments