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Abstract

Introduction: While OA is a debilitating disease with an immense economic burden on the Canadian society, there is a lack of understanding about OA’s direct costs and its future trend in Canada. Objectives: The overall goal of this thesis is to illustrate the application of population-based disease microsimulation (PDMS) modeling in estimating the economic burden of a disease by performing the direct cost analyses for osteoarthritis (OA) using Population Health Microsimulation Model for OA (POHEM-OA). Specific objectives were: 1) To estimate the average direct costs of OA from 2003 to 2010 in Canada; 2) To estimate the future direct cost of OA from 2010 to 2031 in Canada; 3) to estimate the uncertainty around the prevalence and total cost of OA in future years. Methods: I used administrative health data from the province of British Columbia (BC), Canada, a survey of a random sample of BC residents diagnosed with OA (Ministry of Health of BC data), Canadian Institute of Health Information (CIHI) cost data and literature estimates to perform a bottom-up cost of illness (COI) study for OA. I then implemented the results of the COI study into POHEM-OA and constructed cost profiles for each individual. Finally, I developed a framework and adapted an ANOVA-based approach for performing uncertainty analysis (UA) for OA outcomes. Results: I showed that the average cost increased from $735 to $811 between 2003 and 2010 (in 2010 $CAD). From 2010 to 2031, while the prevalence of OA increases from 13.8% to 18.6%, the total direct cost of OA is projected to increase from $2.9 billion (95% uncertainty interval (UI): $2.4-$3.1 billion), to $7.6 billion ($6.2-$9.1 billion), an almost 2.6-fold increase (in 2010 $CAD). From the highest to the lowest, the cost components that will constitute the total direct cost of OA in 2031 are hospitalization cost, outpatient services, drugs, and out-of-pocket cost categories. Conclusions: By further developing a PDMS model of OA, I were able to project trends in the cost of OA and identify the key cost drivers, while predicting significant shifts in distribution of cost in the future.