To estimate the prevalence of systemic sclerosis (SSc) using population-based administrative data, and to assess the sensitivity of case ascertainment approaches.


We ascertained SSc cases from Quebec physician billing and hospitalization databases (covering ∼7.5 million individuals). Three case definition algorithms were compared, and statistical methods accounting for imperfect case ascertainment were used to estimate SSc prevalence and case ascertainment sensitivity. A hierarchical Bayesian latent class regression model that accounted for possible between-test dependence conditional on disease status estimated the effect of patient characteristics on SSc prevalence and the sensitivity of the 3 ascertainment algorithms.


Accounting for error inherent in both the billing and the hospitalization data, we estimated SSc prevalence in 2003 at 74.4 cases per 100,000 women (95% credible interval [95% CrI] 69.3–79.7) and 13.3 cases per 100,000 men (95% CrI 11.1–16.1). Prevalence was higher for older individuals, particularly in urban women (161.2 cases per 100,000, 95% CrI 148.6–175.0). Prevalence was lowest in young men (in rural areas, as low as 2.8 cases per 100,000, 95% CrI 1.4–4.8). In general, no single algorithm was very sensitive, with point estimates for sensitivity ranging from 20–73%.


We found marked differences in SSc prevalence according to age, sex, and region. In general, no single case ascertainment approach was very sensitive for SSc. Therefore, using data from multiple sources, with adjustment for the imperfect nature of each, is an important strategy in population-based studies of SSc and similar conditions.