Identifying and characterizing psoriasis and psoriatic arthritis patients in Ontario administrative data: a population-based study from 1991 to 2015
Eder L, Widdifield J, Rosen CF, Alhusayen R, Cheng SY, Young J, Campbell W, Bernatsky S, Gladman DD, Paterson JM, Tu K. J Rheumatol. 2020; 47(11):1644-51. Epub 2020 Feb 15. DOI: https://doi.org/10.3899/jrheum.190659
Objective — We assessed the accuracy of case definition algorithms for psoriasis and psoriatic arthritis (PsA) in health administrative data, and used primary care electronic medical records to describe disease and treatment characteristics of these patients.
Methods — We randomly sampled 30,424 adult Ontario residents from the Electronic Medical Record Primary Care database and identified 2,215 patients with any possible psoriatic disease-related terms in their electronic medical record. The relevant patient records were chart abstracted to confirm diagnoses of psoriasis or PsA.This validation set was then linked to health administrative data to assess the performance of different algorithms of physician billing diagnosis codes, hospitalization diagnosis codes and medications for psoriatic disease. We report the performance of selected case definition algorithms and describe the disease characteristics of the validation set.
Results — Our reference standard identified 1028 patients with psoriasis and 77 patients with PsA, for an overall prevalence of 3.4% for psoriasis and 0.3% for PsA. Most patients with PsA (66%) had a rheumatology-confirmed diagnosis, while only 30% of the psoriasis patients had dermatology-confirmed diagnosis. The use of systemic medications was much more common in PsA than psoriasis. All algorithms had excellent specificity (97-100%). The sensitivity and positive predictive value were moderate and varied between different algorithms (34-72%).
Conclusion — The accuracy of case definition algorithms for psoriasis and PsA varies widely. However, selected algorithms produced population prevalence estimates which were within the expected ranges, suggesting that they may be useful for future research purposes.