Clinical Epidemiology, 2018. 10: pp. 671-681.
Friedman MY, Leventer-Roberts M, Rosenblum J, Zigman N, Goren I, Mourad V, Lederman N, Cohen N, Matz E, Dushnitzky DZ, Borovsky N, Hoshen MB, Focht G, Avitzour M, Shachar Y, Chowers Y, Eliakim R, Ben-Horin S, Odes S, Schwartz D, Dotan I, Israeli E, Levi Z, Benchimol EI, Balicer RD, Turner D.
Background: Before embarking on administrative research, validated case ascertainment algorithms must be developed. We aimed at developing algorithms for identifying inflammatory bowel disease (IBD) patients, date of disease onset, and IBD type (Crohn’s disease [CD] vs ulcerative colitis [UC]) in the databases of the four Israeli Health Maintenance Organizations (HMOs) covering 98% of the population.
Methods: Algorithms were developed on 5,131 IBD patients and 2,072 controls, following independent chart review (60% CD and 39% UC). We reviewed 942 different combinations of clinical parameters aided by mathematical modeling. The algorithms were validated on an independent cohort of 160,000 random subjects.
Results: The combination of the following variables achieved the highest diagnostic accuracy: IBD-related codes, alone if more than five to six codes or combined with purchases of IBD-related medications (at least three purchases or ≥3 months from the first to last purchase) (sensitivity 89%, specificity 99%, positive predictive value [PPV] 92%, negative predictive value [NPV] 99%). A look-back period of 2–5 years (depending on the HMO) without IBD-related codes or medications best determined the date of diagnosis (sensitivity 83%, specificity 68%, PPV 82%, NPV 70%). IBD type was determined by the majority of CD/UC codes of the three recent contacts or the most recent when less than three contacts were recorded (sensitivity 92%, specificity 97%, PPV 97%, NPV 92%). Applying these algorithms, a total of 38,291 IBD patients were residing in Israel, corresponding to a prevalence rate of 459/100,000 (0.46%).
Conclusion: The application of the validated algorithms to Israel’s administrative databases will now create a large and accurate ongoing population-based cohort of IBD patients for future administrative studies.