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dc.contributor.authorMorriss, Richard K.
dc.contributor.authorLindson, N.
dc.date.accessioned2017-08-24T14:57:02Z
dc.date.available2017-08-24T14:57:02Z
dc.date.issued2012
dc.identifier.citationMorriss, R. K., Lindson, N., Coupland, C., Dex, G. & Avery, A. (2012). Estimating the prevalence of medically unexplained symptoms from primary care records. Public Health, 126 (10), pp.846-854.
dc.identifier.other10.1016/j.puhe.2012.05.008
dc.identifier.urihttp://hdl.handle.net/20.500.12904/2276
dc.description.abstractOBJECTIVES: To develop models to estimate the likely prevalence of medically unexplained symptoms (MUS) and severe MUS in a primary care practice from existing patient electronic records collected in the previous 2 years for secondary prevention and commissioning of psychological treatment.
dc.description.abstractSTUDY DESIGN: Cross-sectional survey comparing general practitioners' (GPs) assessment of the presence or absence of MUS and severe MUS with clinical, demographic and service use variables associated with MUS or functional somatic syndromes from previous research in the patient's routine electronic record over the previous 2 years.
dc.description.abstractMETHODS: Seventeen GPs from eight practices identified cases of MUS and severe MUS in 828 consecutive consulters in primary care. Models of variables associated with MUS and severe MUS were constructed using multivariate multilevel logistic regression. The predictive validity of the final models was tested, comparing predicted with observed data and expected prevalence rates from the literature.
dc.description.abstractRESULTS: Models to predict MUS and severe MUS had areas under the receiver operating characteristic curve of 0.70 [95% confidence interval (CI) 0.65-0.74] and 0.76 (95% CI 0.70-0.82), respectively. Both models showed adequate goodness of fit with observed data, and had good predictive validity compared with the expected prevalence of MUS, severe MUS, and anxiety or depression.
dc.description.abstractCONCLUSION: Models to predict the prevalence of MUS and severe MUS from routine practice records for commissioning purposes were successfully developed, but they require independent validation before general use. The sensitivity of these models was too low for use in clinical screening.Copyright © 2012 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
dc.description.urihttp://www.sciencedirect.com/science/article/pii/S0033350612001801
dc.subjectElectronic health records
dc.subjectSomatoform disorders
dc.subjectPrimary health care
dc.subjectTheoretical model
dc.subjectMedically unexplained symptoms
dc.titleEstimating the prevalence of medically unexplained symptoms from primary care records
dc.typeArticle


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