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Objectives: To estimate the future direct cost of OA in Canada using a population-based health microsimulation model of osteoarthritis (POHEM-OA).Methods: We 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 populate a microsimulation model. Cost components associated with pharmacological and non-pharmacological treatments, total joint replacement (TJR) surgery, as well as use of hospital resources and management of complications arising from the treatment of osteoarthritis (OA) were included. Future costs were then simulated using the POHEM-OA model to construct profiles for each adult Canadian.Results: From 2010 to 2031, as the prevalence of OA is projected to increase from 13.8% to 18.6%, the total direct cost of OA is projected to increase from $2.9 billion to $7.6 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 ($2.9 billion), outpatient services ($1.2 billion), alternative care and out-of-pocket cost categories ($1.2 billion), drugs ($1 billion), rehabilitation ($0.7 billion) and side-effect of drugs ($0.6 billion).Conclusions: Projecting the future trends in the cost of OA enables policy makers to anticipate the significant shifts in its distribution of burden in the future. [ABSTRACT FROM AUTHOR]

Summary: Objective: To evaluate the measurement properties of an Internet-based self-administered questionnaire in ascertaining cases of hip and knee osteoarthritis (OA). Methods: Questionnaire data from 4269 Canadian subjects aged 45–85 were collected on hip and knee joint health including self-reported items on medically-diagnosed hip and knee OA and joint replacement. A sub-cohort of 100 subjects was recruited for clinical examination. The self-reported outcomes were evaluated using the American College of Rheumatology clinical classification criteria for hip and knee OA as the gold standard for clinical verification. Analysis was at the joint level (200 knees, 200 hips). Validity was examined using sensitivity, specificity, and predictive values; to account for correlated joints of the same subject, bootstrapping was performed to yield valid 95% confidence interval (CI''s). Results: Self-reported measures for a medical diagnosis of knee OA had a positive predictive value of 86%, negative predictive value 91%, sensitivity 73% and specificity 96% for correctly identifying clinical knee OA. For hip OA, the values were 61%, 98%, 81% and 94% respectively. Conclusion: Internet self-report of medically-diagnosed hip and knee OA in metro Vancouver residents correctly identified most cases and non-cases of clinical OA when compared with the ACR clinical classification criteria gold standard. In particular, specificity was very high, important in risk factor studies due to the profound effect of even small losses in specificity on the measure of association. The findings provide evidence that these questionnaire case definitions have utility for identifying hip and knee OA in community and population-based studies when the purpose is to link potential risk factors with knee and hip health. [Copyright &y& Elsevier]

Background and Objective: This study is based on secondary analysis of Western Ontario McMaster Osteoarthritis Index (WOMAC) data from a community sample over 55 years and total hip or knee arthroplasty samples presurgery and 1-year postoperative.Methods: The WOMAC data were evaluated by Rasch analysis. Data were considered to fit the Rasch mathematical model for the pain and physical dimensions of the WOMAC if unidimensionality was confirmed by principle component analysis of the subscale and the residuals from the Rasch analysis, infit and outfit statistics were in the range of 0.80 to 1.20; if there was no differential item functioning based on gender or hip vs. knee subjects; and, if there was stability of the item logits across the three data samples.Results: A three-item pain dimension (excluding night pain and pain on standing) and a 14-item physical dimension (excluding heavy domestic duties, getting in and out of the bath and getting on and off the toilet) fit the Rasch model based on these criteria.Conclusion: In evaluating existing health status questionnaires using Rasch methodology, it is important to evaluate relevant patient samples and longitudinal data when the measure is intended to evaluate change in status. By these criteria, a modified WOMAC questionnaire fits the Rasch model and has interval-level scaling properties. [ABSTRACT FROM AUTHOR]