Objective: To estimate the prevalence of polymyositis and dermatomyositis using population-based administrative data, the sensitivity of case ascertainment approaches and patient demographics and these parameters.
Methods: Cases were ascertained from Quebec physician billing and hospitalisation databases (approximately 7.5 million beneficiaries). Three different case definition algorithms were compared, and statistical methods were also used that account for imperfect case ascertainment, to generate estimates of disease prevalence and case ascertainment sensitivity. A hierarchical Bayesian latent class regression model was developed to assess patient characteristics with respect to these parameter estimates.
Results: Using methods that account for the imperfect nature of both billing and hospitalisation databases, the 2003 prevalence of polymyositis and dermatomyositis was estimated to be 21.5/100 000 (95% credible interval (CrI) 19.4 to 23.9). Prevalence was higher for women and for older individuals, with a tendency for higher prevalence in urban areas. Prevalence estimates were lowest in young rural men (2.7/100 000, 95% CrI 1.6 to 4.1) and highest in older urban women (70/100 000, 95% CrI 61.3 to 79.3). Sensitivity of case ascertainment tended to be lower for older versus younger individuals, particularly for rheumatology billing data. Billing data appeared more sensitive in ascertaining cases in urban (vs rural) regions, whereas hospitalisation data seemed most useful in rural areas.
Conclusions: Marked variations were found in the prevalence of polymyositis and dermatomyositis according to age, sex and region. These methods allow adjustment for the imperfect nature of multiple data sources and estimation of the sensitivity of different case ascertainment approaches.
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Funding: This study was funded by the Canadian Institutes of Health Research (CIHR). SB is a Canadian Arthritis Network scholar and is supported by the CIHR, the Fonds de la Recherche en Santé du Québec (FRSQ) and the McGill University Health Centre (MUHC) Research Institute and Department of Medicine. CAP is supported by the MUHC Research Institute and Department of Medicine. AEC and LJ are FRSQ national scholars.
Competing interests: None.
Ethics approval: This research was approved by the McGill University ethics review board.
↵i The definition of credible intervals actually corresponds to how many interpret frequentist confidence intervals, but a 95% confidence interval in fact represents the concept that 95% of the confidence intervals generated with a large number of repeated samples would include the true value of the parameter.