The Great Belt train accident: the emergency medical services response

Background Major incidents (MI) are rare occurrences in Scandinavia. Literature depicting Scandinavian MI management is scarce and case reports and research is called for. In 2019, a trailer falling off a freight train struck a passing high-speed train on the Great Belt Bridge in Denmark, killing eight people instantly and injuring fifteen people. We aim to describe the emergency medical services (EMS) response to this MI and evaluate adherence to guidelines to identify areas of improvement for future MI management. Case presentation Nineteen EMS units were dispatched to the incident site. Ambulances transported fifteen patients to a trauma centre after evacuation. Deceased patients were pronounced life-extinct on-scene. Radio communication was partly compromised, since 38.9% of the radio shifts were not according to the planned radio grid and presented a potential threat to patient outcome and personnel safety. Access to the incident site was challenging and delayed due to traffic congestion and safety issues. Conclusion Despite harsh weather conditions and complex logistics, the availability of EMS units was sufficient and patient treatment and evacuation was uncomplicated. Triage was relevant, but at the physicians’ discretion. Important findings were communication challenges and the consequences of difficult access to the incident site. There is a need for an expansion of capacity in formal education in MI management in Denmark.publishedVersio

The Great Belt train accident: the emergency medical services response

Background Major incidents (MI) are rare occurrences in Scandinavia. Literature depicting Scandinavian MI management is scarce and case reports and research is called for. In 2019, a trailer falling off a freight train struck a passing high-speed train on the Great Belt Bridge in Denmark, killing eight people instantly and injuring fifteen people. We aim to describe the emergency medical services (EMS) response to this MI and evaluate adherence to guidelines to identify areas of improvement for future MI management. Case presentation Nineteen EMS units were dispatched to the incident site. Ambulances transported fifteen patients to a trauma centre after evacuation. Deceased patients were pronounced life-extinct on-scene. Radio communication was partly compromised, since 38.9% of the radio shifts were not according to the planned radio grid and presented a potential threat to patient outcome and personnel safety. Access to the incident site was challenging and delayed due to traffic congestion and safety issues. Conclusion Despite harsh weather conditions and complex logistics, the availability of EMS units was sufficient and patient treatment and evacuation was uncomplicated. Triage was relevant, but at the physicians’ discretion. Important findings were communication challenges and the consequences of difficult access to the incident site. There is a need for an expansion of capacity in formal education in MI management in Denmark

Predicting diabetes-related conditions in need of intervention: Lolland-Falster Health Study, Denmark

In the Danish population, about one-in-ten adults have prediabetes, undiagnosed, poorly or potentially sub-regulated diabetes, for short DMRC. It is important to offer these citizens relevant healthcare intervention. We therefore built a model for prediction of prevalent DMRC.Data were derived from the Lolland-Falster Health Study undertaken in a rural-provincial area of Denmark with disadvantaged health. We included variables from public registers (age, sex, age, citizenship, marital status, socioeconomic status, residency status); from self-administered questionnaires (smoking status, alcohol use, education, self-rated health, dietary habits, physical activity); and from clinical examinations (body mass index (BMI), pulse rate, blood pressure, waist-to-hip ratio). Data were divided into training/testing datasets for development and testing of the prediction model.The study included 15,801 adults; of whom 1,575 with DMRC. Statistically significant variables in the final model included age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. In the testing dataset this model had an area under the curve (AUC) = 0.77 and a sensitivity of 50% corresponding to a specificity of 84%.In a health disadvantaged Danish population, presence of prediabetes, undiagnosed, or poorly or potentially sub-regulated diabetes could be predicted from age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. Age is known from the Danish personal identification number, self-rated health and smoking status can be obtained from simple questions, and BMI, waist-to-hip ratio, and pulse rate can be measured by any person in health care and potentially by the person him/her-self. Our model might therefore be useful as a screening tool

Detailed descriptions of physical activity patterns among individuals with prediabetes and diabetes: The Lolland-Falster Health Study

Despite the importance of physical activity for type 2 diabetes, it is largely unknown to what extent physical activity patterns vary among individuals with prediabetes and diabetes. Recent developments of technological wearable devices provide new possibilities to describe detailed patterns of physical activity, physical postures, sleep characteristics, and other physiological factors over long time periods. The second-by-second continuous assessment offer many opportunities to advance research also among individuals with diabetes and other chronic conditions. No previous large-scale studies have provided a detailed description of objectively assessed habitual physical activity patterns among individuals with prediabetes and diabetes. Availability of such information would be an important resource for planning future treatment courses taking individual characteristics, needs, and preferences into account when designing a physical activity intervention. Therefore, the overall aim of this study is to describe physical activity behaviors and patterns among individuals with prediabetes and diabetes and compare these patterns with individuals with no known diabetes

Detailed descriptions of physical activity patterns among individuals with diabetes and prediabetes: the Lolland-Falster Health Study

Introduction This study aimed to describe objectively measured physical activity patterns, including daily activity according to day type (weekdays and weekend days) and the four seasons, frequency, distribution, and timing of engagement in activity during the day in individuals with diabetes and prediabetes and compared with individuals with no diabetes.Research design and methods This cross-sectional study included data from the Danish household-based, mixed rural-provincial population study, The Lolland-Falster Health Study from 2016 to 2020. Participants were categorized into diabetes, prediabetes, and no diabetes based on their glycated hemoglobin level and self-reported use of diabetes medication. Outcome was physical activity in terms of intensity (time spent in sedentary, light, moderate, vigorous, and moderate to vigorous physical activity (MVPA) intensities), adherence to recommendations, frequency and distribution of highly inactive days (<5 min MVPA/day), and timing of engagement in activity assessed with a lower-back worn accelerometer.Results Among 3157 participants, 181 (5.7 %) had diabetes and 568 (18.0 %) had prediabetes. Of participants with diabetes, 63.2% did not adhere to the WHO recommendations of weekly MVPA, while numbers of participants with prediabetes and participants with no diabetes were 59.5% and 49.6%, respectively. Around a third of participants with diabetes were highly inactive daily (<5 min MVPA/day) and had >2 consecutive days of inactivity during a 7-days period. Mean time spent physically active at any intensity (light, moderate, and vigorous) during a day was lower among participants with diabetes compared with participants with no diabetes and particularly from 12:00 to 15:00 (mean difference of −6.3 min MVPA (95% CI −10.2 to −2.4)). Following adjustments, significant differences in physical activity persisted between diabetes versus no diabetes, but between participants with prediabetes versus no diabetes, results were non-significant after adjusting for body mass index.Conclusions Inactivity was highly prevalent among individuals with diabetes and prediabetes, and distinct daily activity patterns surfaced when comparing these groups with those having no diabetes. This highlights a need to optimize current diabetes treatment and prevention to accommodate the large differences in activity engagement