The accuracy of burn diagnosis codes in health administrative data: A validation study |
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Authors: | Stephanie A. Mason Avery B. Nathens James P. Byrne Rob Fowler Alejandro Gonzalez Paul J. Karanicolas Rahim Moineddin Marc G. Jeschke |
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Affiliation: | 1. Sunnybrook Research Institute, Toronto, Canada;2. Division of General Surgery, Department of General Surgery, University of Toronto, Toronto, Canada;3. Institute for Health Policy, Evaluation, and Management, University of Toronto, Toronto, Canada;4. Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada;5. Institute for Clinical Evaluative Sciences, Toronto, Canada;6. Department of Family and Community Medicine, University of Toronto, Toronto, Canada;7. Ross Tilley Burn Centre, Sunnybrook Health Sciences Centre, Toronto, Canada;8. Institute of Medical Sciences, University of Toronto, Toronto, Canada |
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Abstract: | BackgroundHealth administrative databases may provide rich sources of data for the study of outcomes following burn. We aimed to determine the accuracy of International Classification of Diseases diagnoses codes for burn in a population-based administrative database.MethodsData from a regional burn center’s clinical registry of patients admitted between 2006–2013 were linked to administrative databases. Burn total body surface area (TBSA), depth, mechanism, and inhalation injury were compared between the registry and administrative records. The sensitivity, specificity, and positive and negative predictive values were determined, and coding agreement was assessed with the kappa statistic.Results1215 burn center patients were linked to administrative records. TBSA codes were highly sensitive and specific for ≥10 and ≥20% TBSA (89/93% sensitive and 95/97% specific), with excellent agreement (κ, 0.85/κ, 0.88). Codes were weakly sensitive (68%) in identifying ≥10% TBSA full-thickness burn, though highly specific (86%) with moderate agreement (κ, 0.46). Codes for inhalation injury had limited sensitivity (43%) but high specificity (99%) with moderate agreement (κ, 0.54). Burn mechanism had excellent coding agreement (κ, 0.84).ConclusionsAdministrative data diagnosis codes accurately identify burn by burn size and mechanism, while identification of inhalation injury or full-thickness burns is less sensitive but highly specific. |
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Keywords: | Burns Burn Validation Administrative data |
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