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http://hdl.handle.net/1974/7331
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| Title: | A VALIDATION STUDY OF COMPUTER-BASED DIAGNOSTIC ALGORITHMS FOR CHRONIC DISEASE SURVEILLANCE |
| Authors: | Kadhim-Saleh, AMJED |
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| Keywords: | Health Informatics
Chronic Diseases
Surveillance
Practice-based research network |
| Issue Date: | 24-Jul-2012 |
| Series/Report no.: | Canadian theses |
| Abstract: | Background: 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. |
| Description: | Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2012-07-23 17:58:11.302 |
| URI: | http://hdl.handle.net/1974/7331 |
| Appears in Collections: | Queen's Theses & Dissertations
Community Health & Epidemiology Graduate Theses
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