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Accuracy of Ontario Health Administrative Databases in Identifying Patients with Rheumatoid Arthritis

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Accuracy of Ontario Health Administrative Databases in Identifying Patients with Rheumatoid Arthritis ACCURACY OF ONTARIO HEALTH ADMINISTRATIVE DATABASES IN IDENTIFYING PATIENTS WITH RHEUMATOID ARTHRITIS by Jessica Widdifield A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy in Health Services Research Institute of Health Policy, Management & Evaluation University of Toronto © Copyright by Jessica Widdifield 2013 Accuracy of Ontario Health Administrative Databases in Identifying Patients with Rheumatoid Arthritis (RA): Creation of the Ontario RA administrative Database (ORAD) Jessica Widdifield Doctor of Philosophy Institute of Health Policy Management and Evaluation University of Toronto 2013 Abstract Rheumatoid arthritis (RA) is a chronic, destructive, inflammatory arthritis that places significant burden on the individual and society. This thesis represents the most comprehensive effort to date to determine the accuracy of administrative data for detecting RA patients; and describes the development and validation of an administrative data algorithm to establish a province-wide RA database. Beginning with a systematic review to guide the conduct of this research, two independent, multicentre, retrospective chart abstraction studies were performed amongst two random samples of patients from rheumatology and primary care family physician practices, respectively. While a diagnosis by a rheumatologist remains the gold standard for establishing a RA diagnosis, the high prevalence of RA in rheumatology clinics can falsely elevate positive predictive values. It was therefore important we also perform a validation study in a primary care setting where prevalence of RA would more closely approximate that observed in the general population. The algorithm of [1 hospitalization RA code] OR [3 physician RA diagnosis codes (claims) with !1 by a specialist in a 2 year period)] demonstrated a ii high degree of accuracy in terms of minimizing both the number of false positives (moderately good PPV; 78%) and true negatives (high specificity: 100%). Moreover, this algorithm has excellent sensitivity at capturing contemporary RA patients under active rheumatology care (>96%). Application of this algorithm to Ontario health administrative data to establish the Ontario RA administrative Database (ORAD) identified 97,499 Ontarians with RA as of 2010, yielding a cumulative prevalence of (0.9%). Age/sex-standardized RA prevalence has doubled from 473 per 100,000 in 1996 to 784 per 100,000 in 2010, with approximately 50 new cases of RA emerging per 100,000 Ontarians each year. Our findings will inform future population-based research and will serve to improve arthritis surveillance activities across Canada and abroad. iii Acknowledgments I am tremendously appreciative of the support of my thesis committee for the myriad of ways in which they have all actively supported me in my determination to find and realize my potential. To Claire Bombardier, my thesis supervisor, for providing financial support and stimulating my interest in pursuing post-graduate training in health services research, clinical epidemiology and the field of rheumatology. To Sasha Bernatsky, thesis committee member, for your methodological expertise, sharing your valuable time, and friendship. My interest in administrative data research stems from the joy of being able to work with you. To Michael Paterson, thesis committee member, for your sound advice, expertise, careful guidance, and patience. Under your careful guidance and trust, the Ontario Rheumatoid Arthritis administrative Database (ORAD) is in excellent hands at the Chronic Disease and Pharmacotherapy Program at the Institute for Clinical Evaluative Science (ICES). To Karen Tu, thesis committee member, I am profoundly grateful for inviting me to be a part of your research team, enabling access to your data, and opening up the world of primary care research to me. To Carter Thorne for your clinical expertise; If only all patients had a champion like you. Thank you to Janet Pope and George Tomlinson for your expertise and critique of this work; Vandana Ahluwalia for disseminating the results; and all rheumatologists who provided data. To ICES staff and scientists (Simon Hollands, Ryan Ng, Alexander Kopp, Peter Gozdyra, Nadia Gunraj, Ping Li, Nelson Chong) and to the EMRALD research team [Liisa Jaakkimainen, Noah Ivers, Debra Butt, Myra Wang, Jacqueline Young, Diane Green, Robert Turner, William Oud] and to EMRALD chart abstractors and contracted staff (Nancy Cooper, Abayomi Fowora, Diane Kerbel, Anne Marie Mior and Barbara Thompson). To the Canadian Institutes of Health Research (CIHR) for financial support; members of the Ontario Biologics Research Initiative/Ontario Best Practices Research Initiative (OBRI), particularly Annette Wilkins, Angela Cesta, Xiuying Li and Chris Sammut (for data applications development); the CANRAD (Canadian Rheumatology Administrative Data) Network (especially Lisa Lix and Jeremy Labrecque) and support of: the Public Health Agency of Canada (PHAC), particularly Siobhan O’Donnell; the Ontario Rheumatology Association (ORA), the Canadian Rheumatology Association (CRA), The Arthritis Society (TAS), the Arthritis Alliance of Canada (AAC), the Canadian Arthritis Patient Alliance (CAPA), particularly Anne Lyddiatt, Catherine Hofstetter; and the Canadian Arthritis Network Consumer Committee – all who supported this work. I also wish to thank the external reviewers of this thesis, Drs. Jeffrey Curtis and Susan Jaglal. Finally, to my friends, family, peers, and personal champions, who pushed me at every stage. This thesis is dedicated to all individuals with Rheumatoid Arthritis. My hope is that this research (and all future research arising from it) will improve their lives. iv Table of Contents !"#$%&'()*+($,-........................................................................................................................................../0! 123'(4%546%$,($,-............................................................................................................................................. 0! 7/-,4%54123'(- ..................................................................................................................................................../8! 7/-,4%549/*:;(-.................................................................................................................................................... 8! 7/-,4%54!<<($)/"(- ..........................................................................................................................................8/! 4 6=2<,(;4>4?$,;%):",/%$..................................................................................................................................>! >.>41=(-/-4@0(;0/(& ...................................................................................................................................................A! >.A4BC<%,=(-(-42$)4D(-(2;"=4E:(-,/%$- ............................................................................................................F! 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