493 resultados para disaster-planning


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Objective: Flood is the most common natural disaster in Australia and causes more loss of life than any other disaster. This article describes the incidence and causes of deaths directly associated with floods in contemporary Australia. ---------- Methods: The present study compiled a database of flood fatalities in Australia in the period of 1997–2008 inclusive. The data were derived from newspapers and historic accounts, as well as government and scientific reports. Assembled data include the date and location of fatalities, age and gender of victims and the circumstances of the death. ---------- Results: At least 73 persons died as a direct result of floods in Australia in the period of 1997–2008. The largest number of fatalities occurred in New South Wales and Queensland. Most fatalities occurred during February, and among men (71.2%). People between the ages of 10 and 29 and those over 70 years are overrepresented among those drowned. There is no evident decline in the number of deaths over time. 48.5% fatalities related to motor vehicle use. 26.5% fatalities occurred as a result of inappropriate or high-risk behaviour during floods. ---------- Conclusion: In modern developed countries with adequate emergency response systems and extensive resources, deaths that occur in floods are almost all eminently preventable. Over 90% of the deaths are caused by attempts to ford flooded waterways or inappropriate situational conduct. Knowledge of the leading causes of flood fatalities should inform public awareness programmes and public safety police enforcement activities.

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INTRODUCTION: Recent events have heightened awareness of disaster health issues and the need to prepare the health workforce to plan for and respond to major incidents. This has been reinforced at an international level by the World Association for Disaster and Emergency Medicine, which has proposed an international educational framework. ----------- OBJECTIVE: The aim of this paper is to outline the development of a national educational framework for disaster health in Australia. ----------- METHODS: The framework was developed on the basis of the literature and the previous experience of members of a National Collaborative for Disaster Health Education and Research. The Collaborative was brought together in a series of workshops and teleconferences, utilizing a modified Delphi technique to finalize the content at each level of the framework and to assign a value to the inclusion of that content at the various levels. ----------- FRAMEWORK: The framework identifies seven educational levels along with educational outcomes for each level. The framework also identifies the recommended contents at each level and assigns a rating of depth for each component. The framework is not intended as a detailed curriculum, but rather as a guide for educationalists to develop specific programs at each level. ----------- CONCLUSIONS: This educational framework will provide an infrastructure around which future educational programs in Disaster Health in Australia may be designed and delivered. It will permit improved articulation for students between the various levels and greater consistency between programs so that operational responders may have a consistent language and operational approach to the management of major events.

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Objective: The aim of the present study was to investigate whether parent report of family resilience predicted children’s disaster-induced post-traumatic stress disorder (PTSD) and general emotional symptoms, independent of a broad range of variables including event-related factors, previous child mental illness and social connectedness. ---------- Methods: A total of 568 children (mean age = 10.2 years, SD = 1.3) who attended public primary schools, were screened 3 months after Cyclone Larry devastated the Innisfail region of North Queensland. Measures included parent report on the Family Resilience Measure and Strengths and Difficulties Questionnaire (SDQ)–emotional subscale and child report on the PTSD Reaction Index, measures of event exposure and social connectedness. ---------- Results: Sixty-four students (11.3%) were in the severe–very severe PTSD category and 53 families (28.6%) scored in the poor family resilience range. A lower family resilience score was associated with child emotional problems on the SDQ and longer duration of previous child mental health difficulties, but not disaster-induced child PTSD or child threat perception on either bivariate analysis, or as a main or moderator variable on multivariate analysis (main effect: adjusted odds ratio (ORadj) = 0.57, 95% confidence interval (CI) = 0.13–2.44). Similarly, previous mental illness was not a significant predictor of child PTSD in the multivariate model (ORadj = 0.75, 95%CI = 0.16–3.61). ---------- Conclusion: In this post-disaster sample children with existing mental health problems and those of low-resilience families were not at elevated risk of PTSD. The possibility that the aetiological model of disaster-induced child PTSD may differ from usual child and adolescent conceptualizations is discussed.

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Executive summary Objective: The aims of this study were to identify the impact of Pandemic (H1N1) 2009 Influenza on Australian Emergency Departments (EDs) and their staff, and to inform planning, preparedness, and response management arrangements for future pandemics, as well as managing infectious patients presenting to EDs in everyday practice. Methods This study involved three elements: 1. The first element of the study was an examination of published material including published statistics. Standard literature research methods were used to identify relevant published articles. In addition, data about ED demand was obtained from Australian Government Department of Health and Ageing (DoHA) publications, with several state health departments providing more detailed data. 2. The second element of the study was a survey of Directors of Emergency Medicine identified with the assistance of the Australasian College for Emergency Medicine (ACEM). This survey retrieved data about demand for ED services and elicited qualitative comments on the impact of the pandemic on ED management. 3. The third element of the study was a survey of ED staff. A questionnaire was emailed to members of three professional colleges—the ACEM; the Australian College of Emergency Nursing (ACEN); and the College of Emergency Nursing Australasia (CENA). The overall response rate for the survey was 18.4%, with 618 usable responses from 3355 distributed questionnaires. Topics covered by the survey included ED conditions during the (H1N1) 2009 influenza pandemic; information received about Pandemic (H1N1) 2009 Influenza; pandemic plans; the impact of the pandemic on ED staff with respect to stress; illness prevention measures; support received from others in work role; staff and others’ illness during the pandemic; other factors causing ED staff to miss work during the pandemic; and vaccination against Pandemic (H1N1) 2009 Influenza. Both qualitative and quantitative data were collected and analysed. Results: The results obtained from Directors of Emergency Medicine quantifying the impact of the pandemic were too limited for interpretation. Data sourced from health departments and published sources demonstrated an increase in influenza-like illness (ILI) presentations of between one and a half and three times the normal level of presentations of ILIs. Directors of Emergency Medicine reported a reasonable level of preparation for the pandemic, with most reporting the use of pandemic plans that translated into relatively effective operational infection control responses. Directors reported a highly significant impact on EDs and their staff from the pandemic. Growth in demand and related ED congestion were highly significant factors causing distress within the departments. Most (64%) respondents established a ‘flu clinic’ either as part of Pandemic (H1N1) 2009 Influenza Outbreak in Australia: Impact on Emergency Departments. the ED operations or external to it. They did not note a significantly higher rate of sick leave than usual. Responses relating to the impact on staff were proportional to the size of the colleges. Most respondents felt strongly that Pandemic (H1N1) 2009 Influenza had a significant impact on demand in their ED, with most patients having low levels of clinical urgency. Most respondents felt that the pandemic had a negative impact on the care of other patients, and 94% revealed some increase in stress due to lack of space for patients, increased demand, and filling staff deficits. Levels of concern about themselves or their family members contracting the illness were less significant than expected. Nurses displayed significantly higher levels of stress overall, particularly in relation to skill-mix requirements, lack of supplies and equipment, and patient and patients’ family aggression. More than one-third of respondents became ill with an ILI. Whilst respondents themselves reported taking low levels of sick leave, respondents cited difficulties with replacing absent staff. Ranked from highest to lowest, respondents gained useful support from ED colleagues, ED administration, their hospital occupational health department, hospital administration, professional colleges, state health department, and their unions. Respondents were generally positive about the information they received overall; however, the volume of information was considered excessive and sometimes inconsistent. The media was criticised as scaremongering and sensationalist and as being the cause of many unnecessary presentations to EDs. Of concern to the investigators was that a large proportion (43%) of respondents did not know whether a pandemic plan existed for their department or hospital. A small number of staff reported being redeployed from their usual workplace for personal risk factors or operational reasons. As at the time of survey (29 October –18 December 2009), 26% of ED staff reported being vaccinated against Pandemic (H1N1) 2009 Influenza. Of those not vaccinated, half indicated they would ‘definitely’ or ‘probably’ not get vaccinated, with the main reasons being the vaccine was ‘rushed into production’, ‘not properly tested’, ‘came out too late’, or not needed due to prior infection or exposure, or due to the mildness of the disease. Conclusion: Pandemic (H1N1) 2009 Influenza had a significant impact on Australian Emergency Departments. The pandemic exposed problems in existing plans, particularly a lack of guidelines, general information overload, and confusion due to the lack of a single authoritative information source. Of concern was the high proportion of respondents who did not know if their hospital or department had a pandemic plan. Nationally, the pandemic communication strategy needs a detailed review, with more engagement with media networks to encourage responsible and consistent reporting. Also of concern was the low level of immunisation, and the low level of intention to accept vaccination. This is a problem seen in many previous studies relating to seasonal influenza and health care workers. The design of EDs needs to be addressed to better manage infectious patients. Significant workforce issues were confronted in this pandemic, including maintaining appropriate staffing levels; staff exposure to illness; access to, and appropriate use of, personal protective equipment (PPE); and the difficulties associated with working in PPE for prolonged periods. An administrative issue of note was the reporting requirement, which created considerable additional stress for staff within EDs. Peer and local support strategies helped ensure staff felt their needs were provided for, creating resilience, dependability, and stability in the ED workforce. Policies regarding the establishment of flu clinics need to be reviewed. The ability to create surge capacity within EDs by considering staffing, equipment, physical space, and stores is of primary importance for future pandemics.

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Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

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At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant (p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

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The Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 mandated the consideration of safety in the regional transportation planning process. As part of National Cooperative Highway Research Program Project 8-44, "Incorporating Safety into the Transportation Planning Process," we conducted a telephone survey to assess safety-related activities and expertise at Governors Highway Safety Associations (GHSAs), and GHSA relationships with metropolitan planning organizations (MPOs) and state departments of transportation (DOTs). The survey results were combined with statewide crash data to enable exploratory modeling of the relationship between GHSA policies and programs and statewide safety. The modeling objective was to illuminate current hurdles to ISTEA implementation, so that appropriate institutional, analytical, and personnel improvements can be made. The study revealed that coordination of transportation safety across DOTs, MPOs, GHSAs, and departments of public safety is generally beneficial to the implementation of safety. In addition, better coordination is characterized by more positive and constructive attitudes toward incorporating safety into planning.

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Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

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The field of collaborative health planning faces significant challenges due to the lack of effective information, systems and the absence of a framework to make informed decisions. These challenges have been magnified by the rise of the healthy cities movement, consequently, there have been more frequent calls for localised, collaborative and evidence-driven decision-making. Some studies in the past have reported that the use of decision support systems (DSS) for planning healthy cities may lead to: increase collaboration between stakeholders and the general public, improve the accuracy and quality of the decision-making processes and improve the availability of data and information for health decision-makers. These links have not yet been fully tested and only a handful of studies have evaluated the impact of DSS on stakeholders, policy-makers and health planners. This study suggests a framework for developing healthy cities and introduces an online Geographic Information Systems (GIS)-based DSS for improving the collaborative health planning. It also presents preliminary findings of an ongoing case study conducted in the Logan-Beaudesert region of Queensland, Australia. These findings highlight the perceptions of decision-making prior to the implementation of the DSS intervention. Further, the findings help us to understand the potential role of the DSS to improve collaborative health planning practice.

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The field of collaborative health planning faces significant challenges posed by the lack of effective information, systems and a framework to organise that information. Such a framework is critical in order to make accessible and informed decisions for planning healthy cities. The challenges have been exaggerated by the rise of the healthy cities movement, as a result of which, there have been more frequent calls for localised, collaborative and evidence-based decision-making. Some studies suggest that the use of ICT-based tools in health planning may lead to: increased collaboration between stakeholder sand the community; improve the accuracy and quality of the decision making process; and, improve the availability of data and information for health decision-makers as well as health service planners. Research has justified the use of decision support systems (DSS) in planning for healthy cities as these systems have been found to improve the planning process. DSS are information communication technology (ICT) tools including geographic information systems (GIS) that provide the mechanisms to help decision-makers and related stake holders assess complex problems and solve these in a meaningful way. Consequently, it is now more possible than ever before to make use of ICT-based tools in health planning. However, knowledge about the nature and use of DSS within collaborative health planning is relatively limited. In particular, little research has been conducted in terms of evaluating the impact of adopting these tools upon stakeholders, policy-makers and decision-makers within the health planning field. This paper presents an integrated method that has been developed to facilitate an informed decision-making process to assist in the health planning process. Specifically, the paper describes the participatory process that has been adopted to develop an online GIS-based DSS for health planners. The literature states that the overall aim of DSS is to improve the efficiency of the decisions made by stakeholders, optimising their overall performance and minimizing judgmental biases. For this reason, the paper examines the effectiveness and impact of an innovative online GIS-based DSS on health planners. The case study of the online DSS is set within a unique settings-based initiative designed to plan for and improve the health capacity of Logan-Beaudesert area, Australia. This unique setting-based initiative is named the Logan-Beaudesert Health Coalition (LBHC).The paper outlines the impact occurred by implementing the ICT-based DSS. In conclusion, the paper emphasizes upon the need for the proposed tool for enhancing health planning.

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Groundwater is increasingly recognised as an important yet vulnerable natural resource, and a key consideration in water cycle management. However, communication of sub-surface water system behaviour, as an important part of encouraging better water management, is visually difficult. Modern 3D visualisation techniques can be used to effectively communicate these complex behaviours to engage and inform community stakeholders. Most software developed for this purpose is expensive and requires specialist skills. The Groundwater Visualisation System (GVS) developed by QUT integrates a wide range of surface and sub-surface data, to produce a 3D visualisation of the behaviour, structure and connectivity of groundwater/surface water systems. Surface data (elevation, surface water, land use, vegetation and geology) and data collected from boreholes (bore locations and subsurface geology) are combined to visualise the nature, structure and connectivity of groundwater/surface water systems. Time-series data (water levels, groundwater quality, rainfall, stream flow and groundwater abstraction) is displayed as an animation within the 3D framework, or graphically, to show water system condition changes over time. GVS delivers an interactive, stand-alone 3D Visualisation product that can be used in a standard PC environment. No specialised training or modelling skills are required. The software has been used extensively in the SEQ region to inform and engage both water managers and the community alike. Examples will be given of GVS visualisations developed in areas where there have been community concerns around groundwater over-use and contamination.