BackgroundPeople with intellectual disabilities (ID) have a lower life expectancy than their peers without ID. A contributing factor to the lower life expectancy and early mortality could be sarcopenia: low muscle mass and low muscle function. In the general population, sarcopenia strongly predicts early mortality, but this association is unknown in people with ID. Therefore, this study aims to explore the association between sarcopenia and 5-year mortality in older adults with ID.MethodsIn the Healthy Ageing and Intellectual Disabilities (HA-ID) study, the prevalence of sarcopenia was measured at baseline among 884 older adults (≥50 years) with ID. All-cause mortality was measured over a 5-year follow-up period. Univariable and multivariable Cox proportional hazard models were applied to determine the association between sarcopenia (no sarcopenia, pre-sarcopenia, sarcopenia, severe sarcopenia) and early mortality, adjusted for age, sex, level of ID, presence of Down syndrome, and co-morbidity (chronic obstructive pulmonary disease, diabetes type 2 and metabolic syndrome).ResultsThe unadjusted hazard ratio (HR) for sarcopenia was 2.28 [95% confidence interval (CI) 1.48–3.42], P < 0.001), and 2.40 (95% CI 1.40–4.10, P = 0.001) for severe sarcopenia. When adjusted for age, sex, level of ID, and Down syndrome, sarcopenia (HR = 1.72, 95% CI 1.08–2.75, P = 0.022) and severe sarcopenia (HR = 1.86, 95% CI 1.07–3.23, P = 0.028) were significantly associated with early mortality. When additionally adjusted for co-morbidity, the adjusted HR decreased to 1.62 (95% CI 1.02–2.59, P = 0.043) and 1.81 (95% CI 1.04–3.15, P = 0.035) for sarcopenia and severe sarcopenia, respectively.ConclusionSarcopenia is an independent risk factor for early mortality in older adults with ID over a 5-year follow-up period. Our results stress the need to delay the incidence and development of sarcopenia in older adults with ID.
The objective of the study described in this paper is to define safety metrics that are based on the effectiveness of risk controls. Service providers define and implement such risk controls in order to prevent hazards developing into an accident. The background of this research is a specific need of the aviation industry where small and medium-sized enterprises lack large amounts of safety-related data to measure and demonstrate their safety performance proactively. The research department of the Aviation Academy has initiated a 4-year study, which will test the possibility to develop new safety indicators that will be able to represent safety levels proactively without the benefit of large data sets. As part of the development of alternative safety metrics, safety performance indicators were defined that are based on the effectiveness of risk controls. ICAO (2013) defines a risk control as “a defence with specific mitigation actions, preventive controls or recovery measures put in place to prevent the realization of a hazard or its escalation into an undesirable consequence”. Examples of risk controls are procedures, education and training, a piece of equipment etc. It is crucial for service providers to determine whether the introduced risk controls are indeed effective in reducing the targeted risk. ICAO (2013) describes the effectiveness of risk control as "the extent to which the risk control reduces or eliminates the safety risks”, but does not provide guidance on how to measure the effectiveness of risk control. In this study, a generic metrics for the effectiveness of risk controls based on their effectiveness was developed. The definition of the indicators allows, for each risk control, derivation of specific indicators based on the generic metrics. The suitability of the metrics will subsequently be tested in pilot studies within the aviation industry.
Most violence risk assessment tools have been validated predominantly in males. In this multicenter study, the Historical, Clinical, Risk Management–20 (HCR-20), Historical, Clinical, Risk Management–20 Version 3 (HCR-20V3), Female Additional Manual (FAM), Short-Term Assessment of Risk and Treatability (START), Structured Assessment of Protective Factors for violence risk (SAPROF), and Psychopathy Checklist–Revised (PCL-R) were coded on file information of 78 female forensic psychiatric patients discharged between 1993 and 2012 with a mean follow-up period of 11.8 years from one of four Dutch forensic psychiatric hospitals. Notable was the high rate of mortality (17.9%) and readmission to psychiatric settings (11.5%) after discharge. Official reconviction data could be retrieved from the Ministry of Justice and Security for 71 women. Twenty-four women (33.8%) were reconvicted after discharge, including 13 for violent offenses (18.3%). Overall, predictive validity was moderate for all types of recidivism, but low for violence. The START Vulnerability scores, HCR-20V3, and FAM showed the highest predictive accuracy for all recidivism. With respect to violent recidivism, only the START Vulnerability scores and the Clinical scale of the HCR-20V3 demonstrated significant predictive accuracy.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
