1600 The Journal of Rheumatology 2014; 41:8; doi:3899/jrheum.131449 Personal non-commercial use only. The Journal of Rheumatology Copyright © 2014. All rights reserved. Effect of Remission Definition on Healthcare Cost Savings Estimates for Patients with Rheumatoid Arthritis Treated with Biologic Therapies Cheryl Barnabe, Nguyen Xuan Thanh, Arto Ohinmaa, Joanne Homik, Susan G. Barr, Liam Martin, and Walter P. Maksymowych ABSTRACT. Objective. Sustained remission in rheumatoid arthritis (RA) results in healthcare utilization cost savings. We evaluated the variation in estimates of savings when different definitions of remission [2011 American College of Rheumatology/European League Against Rheumatism Boolean Definition, Simplified Disease Activity Index (SDAI) ≤ 3.3, Clinical Disease Activity Index (CDAI) ≤ 2.8, and Disease Activity Score-28 (DAS28) ≤ 2.6] are applied. Methods. The annual mean healthcare service utilization costs were estimated from provincial physician billing claims, outpatient visits, and hospitalizations, with linkage to clinical data from the Alberta Biologics Pharmacosurveillance Program (ABioPharm). Cost savings in patients who had a 1-year continuous period of remission were compared to those who did not, using 4 definitions of remission. Results. In 1086 patients, sustained remission rates were 16.1% for DAS28, 8.8% for Boolean, 5.5% for CDAI, and 4.2% for SDAI. The estimated mean annual healthcare cost savings per patient achieving remission (relative to not) were SDAI $1928 (95% CI 592, 3264), DAS28 $1676 (95% CI 987, 2365), and Boolean $1259 (95% CI 417, 2100). The annual savings by CDAI remission per patient were not significant at $423 (95% CI –1757, 2602). For patients in DAS28, Boolean, and SDAI remission, savings were seen both in costs directly related to RA and its comorbidities, and in costs for non-RA-related conditions. Conclusion. The magnitude of the healthcare cost savings varies according to the remission definition used in classifying patient disease status. The highest point estimate for cost savings was observed in patients attaining SDAI remission and the least with the CDAI; confidence intervals for these estimates do overlap. Future pharmacoeconomic analyses should employ all response defini- tions in assessing the influence of treatment. (First Release July 15 2014; J Rheumatol 2014;41: 1600–606; doi:10.3899/jrheum131449) Key Indexing Terms: RHEUMATOID ARTHRITIS BIOLOGICS HEALTHCARE COSTS OUTCOME ASSESSMENT REMISSION DEFINITIONS From the Department of Medicine, University of Calgary, Calgary; Department of Community Health Sciences, University of Calgary, Calgary; Institute of Health Economics, Edmonton; School of Public Health, University of Alberta, Edmonton; and Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. The Alberta Biologics Pharmacosurveillance Program (ABioPharm) was supported by a grant from Alberta Health and Wellness for the study period reported. C. Barnabe, MD, MSc, FRCPC, Department of Medicine and Department of Community Health Sciences, University of Calgary; N.X. Thanh, MD, PhD, MPH, Institute of Health Economics and School of Public Health, University of Alberta; A. Ohinmaa, PhD, Institute of Health Economics and School of Public Health, University of Alberta; J. Homik, MD, MSc, FRCPC, Department of Medicine, University of Alberta; S.G. Barr, MD, MSc, FRCPC, Department of Medicine, and Department of Community Health Sciences, University of Calgary; L. Martin, MB, ChB, FRCPC, Department of Medicine, University of Calgary; W.P. Maksymowych, MB, ChB, FRCPC, Department of Medicine, University of Alberta. Address correspondence to Dr. C. Barnabe, Department of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1. E-mail: ccbarnab@ucalgary.ca Accepted for publication April 17, 2014. The cost of biologic therapies to control disease-modifying antirheumatic drug (DMARD)-refractory rheumatoid arthritis (RA) has driven the development and refinement of cost-effectiveness modeling using both clinical trial and registry data. These analyses incorporate the cost of the new treatment offset by improvements in work productivity and future reduced health resource utilization 1 . Cost-effec- tiveness models are recognized to vary greatly in their inputs, namely in the assumptions made around patient disease characteristics, disability progression, treatment sequences, cycle length, medication dosing and wastage, risk of adverse effects, disease complications and mortality, comorbidity, and fluctuations in Health Assessment Questionnaire (HAQ) scores that are used as a surrogate to estimate health utility for the estimates 2 . Another source of variation is in the choice of effec- tiveness data for the modeling, specifically the use of Disease Activity Scores (DAS) 3 or American College of www.jrheum.org Downloaded on January 13, 2022 from