Objectives: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big datadriven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case.Methods: This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics.Results: There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0. Conclusions:In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that

Older people are among the most vulnerable in major disasters. In their aftermath, it is crucial to institute efforts that will maintain a high level of elders' quality of life (QoL). This paper presents QoL assessments of elderly survivors five years after the Bam earthquake in Iran, and evaluates the determinants. A cross-sectional analysis of 210 randomly-selected survivors was carried out in 2008 using the WHOQOL-BREF questionnaire. A comparison of the results with data on the general population showed that experiencing the earthquake may adversely affect psychological dimensions of QoL even five years after, but paradoxically the earthquake resulted in better social relationships in affected communities than in the general population. Lower QoL associated with female gender, higher age, living alone, severe earthquake-related injury, poor quality of living conditions, increased dependency in the activities of daily living, living in an urban area, and being temporarily housed. Recovery experts and donors should carry out long-term monitoring of health status and QoL in disaster-affected communities, with a focus on psychological wellbeing. Intervention programmes that emphasise post-disaster quality of care and satisfactory housing may lead to better QoL of the victims and may shorten the recovery phase.

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Displacement is a hallmark of modern humanitarian emergencies. Displacement itself is a traumatic event that can result in illness or death. Survivors face challenges including lack of adequate shelter, decreased access to health services, food insecurity, loss of livelihoods, social marginalisation as well as economic and sexual exploitation. Displacement takes many forms in the Middle East and the Arab World. Historical conflicts have resulted in long-term displacement of Palestinians. Internal conflicts have driven millions of Somalis and Sudanese from their homes. Iraqis have been displaced throughout the region by invasion and civil strife. In addition, large numbers of migrants transit Middle Eastern countries or live there illegally and suffer similar conditions as forcibly displaced people. Displacement in the Middle East is an urban phenomenon. Many displaced people live hidden among host country populations in poor urban neighbourhoods - often without legal status. This represents a challenge for groups attempting to access displaced populations. Furthermore, health information systems in host countries often do not collect data on displaced people, making it difficult to gather data needed to target interventions towards these vulnerable populations. The following is a discussion of the health impacts of conflict and displacement in the Middle East. A review was conducted of published literature on migration and displacement in the region. Different cases are discussed with an emphasis on the recent, large-scale and urban displacement of Iraqis to illustrate aspects of displacement in this region.

Chronic pain is a highly prevalent disease with poorly understood pathophysiology. In particular, the brain mechanisms mediating the transition from acute to chronic pain remain largely unknown. Here, we identify a subcortical signature of back pain. Specifically, subacute back pain patients who are at risk for developing chronic pain exhibit a smaller nucleus accumbens volume, which persists in the chronic phase, compared to healthy controls. The smaller accumbens volume was also observed in a separate cohort of chronic low-back pain patients and was associated with dynamic changes in functional connectivity. At baseline, subacute back pain patients showed altered local nucleus accumbens connectivity between putative shell and core, irrespective of the risk of transition to chronic pain. At follow-up, connectivity changes were observed between nucleus accumbens and rostral anterior cingulate cortex in the patients with persistent pain. Analysis of the power spectral density of nucleus accumbens resting-state activity in the subacute and chronic back pain patients revealed loss of power in the slow-5 frequency band (0.01 to 0.027 Hz) which developed only in the chronic phase of pain. This loss of power was reproducible across two cohorts of chronic low-back pain patients obtained from different sites and accurately classified chronic low-back pain patients in two additional independent datasets. Our results provide evidence that lower nucleus accumbens volume confers risk for developing chronic pain and altered nucleus accumbens activity is a signature of the state of chronic pain.

The chief complaint is a patient's self-reported primary reason for presenting for medical care. The clinical utility and analytical importance of recording chief complaints have been widely accepted in highly developed emergency care systems, but this practice is far from universal in global emergency care, especially in limited-resource areas. It is precisely in these settings, however, that the use of chief complaints may have particular benefit. Chief complaints may be used to quantify, analyze, and plan for emergency care and provide valuable information on acute care needs where there are crucial data gaps. Globally, much work has been done to establish local practices around chief complaint collection and use, but no standards have been established and little work has been done to identify minimum effective sets of chief complaints that may be used in limited-resource settings. As part of the Academic Emergency Medicine consensus conference, "Global Health and Emergency Care: A Research Agenda," the breakout group on data management identified the lack of research on emergency chief complaints globally-especially in low-income countries where the highest proportion of the world's population resides-as a major gap in global emergency care research. This article reviews global research on emergency chief complaints in high-income countries with developed emergency care systems and sets forth an agenda for future research on chief complaints in limited-resource settings.ACADEMIC EMERGENCY MEDICINE 2013; 20:1241-1245 by the Society for Academic Emergency Medicine A ny analysis of emergency care must take account of a fundamental aspect of the presentation itself: the chief complaint. Despite the fact that the chief complaint is "the patient's reason for seeking care or attention, expressed in terms as close as possible to those used by [the] patient or responsible

An educational intervention administered through the PHC system effectively improved disaster awareness and readiness at a community level. For sustainability, community disaster reduction programs must be integrated into routine public health service delivery.

Despite the fact that the 15 leading causes of global deaths and disability-adjusted life years are from conditions amenable to emergency care, and that this burden is highest in low-income and middle-income countries (LMICs), there is a paucity of research on LMIC emergency care to guide policy making, resource allocation and service provision. A literature review of the 550 articles on LMIC emergency care published in the 10-year period from 2007 to 2016 yielded 106 articles for LMIC emergency care surveillance and registry research. Few articles were from established longitudinal surveillance or registries and primarily composed of short-term data collection. Using these articles, a working group was convened by the US National Institutes of Health Fogarty International Center to discuss challenges and potential solutions for established systems to better understand global emergency care in LMICs. The working group focused on potential uses for emergency care surveillance and registry data to improve the quality of services provided to patients. Challenges included a lack of dedicated resources for such research in LMIC settings as well as over-reliance on facility-based data collection without known correlation to the overall burden of emergency conditions in the broader community. The group outlined potential solutions including incorporating data from sources beyond traditional health records, use of standard clinical forms that embed data needed for research and policy making and structured population-based research to establish clear linkages between what is seen in emergency units and the wider community. The group then identified current gaps in LMIC emergency care surveillance and registry research to form a research agenda for the future.