Health administrative databases can be used to track disease incidence, outcomes, and care quality. Case validation is necessary to ensure accurate disease ascertainment using these databases. In this study, we aimed to validate adult-onset inflammatory bowel disease (IBD) identification algorithms.
STUDY DESIGN AND SETTING
We used two large cohorts of incident patients from Ontario, Canada to validate algorithms. We linked information extracted from charts to health administrative data and compared the accuracy of various algorithms. In addition, we validated an algorithm to distinguish patients with Crohn's from those with ulcerative colitis and assessed the adequate look-back period to distinguish incident from prevalent cases.
Over 5,000 algorithms were tested. The most accurate algorithm to identify patients 18 to 64 years at diagnosis was five physician contacts or hospitalizations within 4 years (sensitivity, 76.8%; specificity, 96.2%; positive predictive value (PPV), 81.4%; negative predictive value (NPV), 95.0%). In patients ≥65 years at diagnosis, adding a pharmacy claim for an IBD-related medication improved accuracy.
Patients with adult-onset incident IBD can be accurately identified from within health administrative data. The validated algorithms will be applied to administrative data to expand the Ontario Crohn's and Colitis Cohort to all patients with IBD in the province of Ontario.