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Rheumatol Int. 2011 Apr;31(4):549-54. doi: 10.1007/s00296-010-1591-2. Epub 2010 Jul 28.

Surveillance of systemic autoimmune rheumatic diseases using administrative data.

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Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre, 687 Pine Avenue West, V-Building, V2.09, Montreal, QC H3A 1A1, Canada.


There is growing interest in developing tools and methods for the surveillance of chronic rheumatic diseases, using existing resources such as administrative health databases. To illustrate how this might work, we used population-based administrative data to estimate and compare the prevalence of systemic autoimmune rheumatic diseases (SARDs) across three Canadian provinces, assessing for regional differences and the effects of demographic factors. Cases of SARDs (systemic lupus erythematosus, scleroderma, primary Sjogren's, polymyositis/dermatomyositis) were ascertained from provincial physician billing and hospitalization data. We combined information from three case definitions, using hierarchical Bayesian latent class regression models that account for the imperfect nature of each case definition. Using methods that account for the imperfect nature of both billing and hospitalization databases, we estimated the over-all prevalence of SARDs to be approximately 2-3 cases per 1,000 residents. Stratified prevalence estimates suggested similar demographic trends across provinces (i.e. greater prevalence in females-versus-males, and in persons of older age). The prevalence in older females approached or exceeded 1 in 100, which may reflect the high burden of primary Sjogren's syndrome in this group. Adjusting for demographics, there was a greater prevalence in urban-versus-rural settings. In our work, prevalence estimates had good face validity and provided useful information about potential regional and demographic variations. Our results suggest that surveillance of some rheumatic diseases using administrative data may indeed be feasible. Our work highlights the usefulness of using multiple data sources, adjusting for the error in each.

[Indexed for MEDLINE]

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