27 research outputs found

    Health care productivity.

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    A meta-analysis of variables that predict significant intracranial injury in minor head trauma.

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    BACKGROUND: Previous studies have presented conflicting results regarding the predictive effect of various clinical symptoms, signs, and plain imaging for intracranial pathology in children with minor head injury. AIMS: To perform a meta-analysis of the literature in order to assess the significance of these factors and intracranial haemorrhage (ICH) in the paediatric population. METHODS: The literature was searched using Medline, Embase, Experts, and the grey literature. Reference lists of major guidelines were crosschecked. Control or nested case-control studies of children with head injury who had skull radiography, recording of common symptoms and signs, and head computed tomography (CT) were selected. OUTCOME VARIABLE: CT presence or absence of ICH. RESULTS: Sixteen papers were identified as satisfying criteria for inclusion in the meta-analysis, although not every paper contained data on every correlate. Available evidence gave pooled patient numbers from 1136 to 22 420. Skull fracture gave a relative risk ratio of 6.13 (95% CI 3.35 to 11.2), headache 1.02 (95% CI 0.62 to 1.69), vomiting 0.88 (95% CI 0.67 to 1.15), focal neurology 9.43 (2.89 to 30.8), seizures 2.82 (95% CI 0.89 to 9.00), LOC 2.23 (95% CI 1.20 to 4.16), and Glasgow Coma Scale (GCS) <15 of 5.51 (95% CI 1.59 to 19.0). CONCLUSIONS: There was a statistically significant correlation between intracranial haemorrhage and skull fracture, focal neurology, loss of consciousness, and GCS abnormality. Headache and vomiting were not found to be predictive and there was great variability in the predictive ability of seizures. More information is required about the current predictor variables so that more refined guidelines can be developed. Further research is currently underway by three large study groups

    Using patient-based measures to evaluate surgical outcomes: myths and realities

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    Re-introducing Survey Data: References / Resources

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    These resources are produced to help social scientists conduct quantitative longitudinal research using large-scale survey datasets. It has been our experience that an intrinsic part of such training necessarily concerns recapping and revising some introductory issues in quantitative data analysis. The references below are intended to provide background materials which relate to a range of applications which are commonly applied in quantitative social science research. More specific guidance on longitudinal quantitative research is found in the corresponding file 'Quantitative Longitudinal Research: References / Resources'

    The influence of demographic characteristics, living conditions, and trauma exposure on the overall health of a conflict-affected population in Southern Sudan

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    BACKGROUND: There remains limited evidence on how armed conflict affects overall physical and mental well-being rather than specific physical or mental health conditions. The aim of this study was to investigate the influence of demographic characteristics, living conditions, and violent and traumatic events on general physical and mental health in Southern Sudan which is emerging from 20 years of armed conflict. METHODS: A cross-sectional survey of 1228 adults was conducted in November 2007 in the town of Juba, the capital of Southern Sudan. Multivariate linear regression analysis was used to investigate the associations and relative influence of variables in three models of demographic characteristics, living conditions, and trauma exposure, on general physical and mental health status. These models were run separately and also as a combined model. Data quality and the internal consistency of the health status instrument (SF-8) were assessed. RESULTS: The variables in the multivariate analysis (combined model) with negative coefficients of association with general physical health and mental health (i.e. worse health), respectively, were being female (coef. -2.47; -2.63), higher age (coef.-0.16; -0.17), absence of soap in the household (physical health coef. -2.24), and experiencing within the past 12 months a lack of food and/or water (coef. -1.46; -2.27) and lack of medical care (coef.-3.51; -3.17). A number of trauma variables and cumulative exposure to trauma showed an association with physical and mental health (see main text for data). There was limited variance in results when each of the three models were run separately and when they were combined, suggesting the pervasive influence of these variables. The SF-8 showed good data quality and internal consistency. CONCLUSIONS: This study provides evidence on the pervasive influence of demographic characteristics, living conditions, and violent and traumatic events on the general physical and mental health of a conflict-affected population in Southern Sudan, and highlights the importance of addressing all these influences on overall health

    Statistics in Telemedicine

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