The development of a quick-running prediction tool for the assessment of human injury owing to terrorist attack within crowded metropolitan environments

In the aftermath of the London ‘7/7’ attacks in 2005, UK government agencies required the development of a quick-running tool to predict the weapon and injury effects caused by the initiation of a person borne improvised explosive device (PBIED) within crowded metropolitan environments. This prediction tool, termed the HIP (human injury predictor) code, was intended to: — assist the security services to encourage favourable crowd distributions and densities within scenarios of ‘sensitivity’;— provide guidance to security engineers concerning the most effective location for protection systems;— inform rescue services as to where, in the case of such an event, individuals with particular injuries will be located;— assist in training medical personnel concerning the scope and types of injuries that would be sustained as a consequence of a particular attack;— assist response planners in determining the types of medical specialists (burns, traumatic amputations, lungs, etc.) required and thus identify the appropriate hospitals to receive the various casualty types.This document describes the algorithms used in the development of this tool, together with the pertinent underpinning physical processes. From its rudimentary beginnings as a simple spreadsheet, the HIP code now has a graphical user interface (GUI) that allows three-dimensional visualization of results and intuitive scenario set-up. The code is underpinned by algorithms that predict the pressure and momentum outputs produced by PBIEDs within open and confined environments, as well as the trajectories of shrapnel deliberately placed within the device to increase injurious effects. Further logic has been implemented to transpose these weapon effects into forms of human injury depending on where individuals are located relative to the PBIED. Each crowd member is subdivided into representative body parts, each of which is assigned an abbreviated injury score after a particular calculation cycle. The injury levels of each affected body part are then summated and a triage state assigned for each individual crowd member based on the criteria specified within the ‘injury scoring system’. To attain a comprehensive picture of a particular event, it is important that a number of simulations, using what is substantively the same scenario, are undertaken with natural variation being applied to the crowd distributions and the PBIED output. Accurate mathematical representation of such complex phenomena is challenging, particularly as the code must be quick-running to be of use to the stakeholder community. In addition to discussing the background and motivation for the algorithm and GUI development, this document also discusses the steps taken to validate the tool and the plans for further functionality implementation

Falls and fall risk among nursing home residents

Artikkel som beskriver en studie hvor hensikten var å kartlegge risikofaktorer for fall blant eldre i svenske sykehjem.The aim of this study was to identify risk factors for falls in older people living in nursing homes. Impaired cognitive function and a poor sense of orientation could lead to an increase in falls among those with impaired freedom of movement. Many accidents occur while an older person is walking or being moved. The study was carried out over four years (2000–2003) and 21 nursing home units in five municipal homes for older people in Stockholm, Sweden, participated. A questionnaire was sent to staff nurses, including questions on fall risk assessments, falls, fractures, medication and freedom‐restricting measures, such as wheelchairs with belts and bed rails. The data were aggregated and not patient‐bound. The study covered 2343 reported incidents. There was a significant correlation between falls and fractures (r=0·365, p=0·004), fall risk and use of wheelchairs (r=0·406, p=0·001, safety belts (r=0·403, p=0·001 and bed rails (r=0·446, p=0·000) and between the occurrence of fractures and the use of sleeping pills with benzodiazepines (r=0·352, p=0·005). Associations were also found between fall risk and the use of anti‐depressants (r=0·412, p=0·001). In clinical practice, patient safety is very important. Preventative measures should focus on risk factors associated with individuals, including their environment. Wheelchairs with safety belts and bed rails did not eliminate falls but our results support the hypothesis that they might be protective when used selectively with less anti‐depressants and sleeping pills, especially benzodiazepines