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dc.contributor.authorKadhim-Saleh, Amjed
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2012-07-23 17:58:11.302en
dc.date.accessioned2012-07-24T18:32:15Z
dc.date.available2012-07-24T18:32:15Z
dc.date.issued2012-07-24
dc.identifier.urihttp://hdl.handle.net/1974/7331
dc.descriptionThesis (Master, Community Health & Epidemiology) -- Queen's University, 2012-07-23 17:58:11.302en
dc.description.abstractBackground: Chronic conditions comprise a significant amount of healthcare utilization. For example, people with chronic diseases account for 51% of family physician encounters. Therefore, diagnostic algorithms based on comprehensive clinical records could be a rich resource for clinicians, researchers and policy-makers. However, limitations such as misclassification warrant the need for examining the accuracy of these algorithms. Purpose: To investigate and enhance the accuracy of the diagnostic algorithms for five chronic diseases in the Canadian Primary Care Sentinel Surveillance Network. Methods: DESIGN: A validation study using primary chart abstraction. SETTING: A stratified random sample of 350 patient charts from Kingston practice-based research network. OUTCOME MEASURES: Sensitivity and specificity for the diagnostic algorithms. ANALYSIS: A multiple logistic regression model along with the receiver operating characteristic curve was employed to identify the algorithm that maximized accuracy measures. Results: The sensitivities for diagnostic algorithms were 100% (diabetes), 83% (hypertension), 45% (Osteoarthritis), 41% (COPD), and 39% (Depression). The lowest specificity was 97% for depression. A data-driven logistic model and receiver-operating characteristic curve improved sensitivity for identifying hypertension patients from 83% to 88% and for osteoarthritis patients from 45% to 81% with areas under the curve of 92.8% and 89.8% for hypertension and osteoarthritis, respectively. Conclusion: The diagnostic algorithms for diabetes and hypertension demonstrate adequate accuracy, thus allowing their use for research and policy-making purposes. A multivariate logistic model for predicting osteoarthritis diagnosis enhanced sensitivity while maintaining high specificity. This approach can be used towards further refining the diagnostic algorithms for other chronic conditions.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectHealth Informaticsen_US
dc.subjectChronic Diseasesen_US
dc.subjectSurveillanceen_US
dc.subjectPractice-based research networken_US
dc.titleA VALIDATION STUDY OF COMPUTER-BASED DIAGNOSTIC ALGORITHMS FOR CHRONIC DISEASE SURVEILLANCEen_US
dc.typethesisen_US
dc.description.degreeMasteren
dc.contributor.supervisorGreen, Michaelen
dc.contributor.supervisorHunter, Duncanen
dc.contributor.departmentCommunity Health and Epidemiologyen


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