title: A population-based assessment of systemic lupus erythematosus incidence and prevalence--results and implications of using administrative data for epidemiological studies.
abstract: OBJECTIVE
  To estimate (i) systemic lupus erythematosus (SLE) incidence and prevalence using multiple sources of population-based administrative data; (ii) the sensitivity and specificity of case ascertainment methods; and (iii) variation in performance of each ascertainment approach, according to patient and physician characteristics.
  
  
  METHODS
  We examined the physician billing and hospitalization databases of the province of Quebec (1994-2003) covering all health care beneficiaries (approximately 7.5 million). We compared various approaches to ascertain SLE cases, using information from each database separately or combining sources; we then estimated the sensitivity and specificity of these alternative approaches. We used regression models to determine if sensitivity was independently influenced by patient or physician characteristics.
  
  
  RESULTS
  Using billing data, we calculated SLE incidence at 3.0/100,000 person-years [95% confidence interval (CI) 2.6-3.4]; prevalence was 32.8/100,000 persons, in 2003. Results were similar using hospitalization data. However, only a proportion of prevalent cases were identified as having SLE by both methods. Combining cases from billing and hospitalization data, we found a prevalence of 51/100,000 in 2003. Our latent class regression model estimated a prevalence of 44.7/100,000 (95% CI 37.4-54.7). We found high specificity for SLE diagnoses across all strategies and data sources; sensitivity ranged from 42.1% to 67.6%, and was independently influenced by both patient and physician characteristics.
  
  
  CONCLUSIONS
  In observational studies, particularly with administrative databases, SLE incidence and prevalence estimates differ considerably, according to the approach for case ascertainment. In the absence of gold standards, statistical modelling can provide sensitivity and specificity estimates for different approaches.
authors.affiliation: Department of Clinical Epidemiology, Research Institute of the McGill University Health Centre, 687 Pine Avenue West, V-Building, Montreal, Quebec, H3A 1A1, Canada. sasha.bernatsky@mail.mcgill.ca
authors.forename: S
authors.initials: S
authors.lastname: Bernatsky
authors.forename: L
authors.initials: L
authors.lastname: Joseph
authors.forename: C A
authors.initials: CA
authors.lastname: Pineau
authors.forename: R
authors.initials: R
authors.lastname: Tamblyn
authors.forename: D E
authors.initials: DE
authors.lastname: Feldman
authors.forename: A E
authors.initials: AE
authors.lastname: Clarke
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D001499:Bayes Theorem; D016001:Confidence Intervals; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D015994:Incidence; D008180:Lupus Erythematosus, Systemic / Q000175:diagnosis* / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance; D015995:Prevalence; D011792:Quebec / Q000453:epidemiology; D012042:Registries; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D017678:Sex Distribution