Objective: Our objective was to calculate rheumatoid arthritis (RA) point prevalence estimates in the CARTaGENE cohort, as well as to estimate the sensitivity and specificity of our ascertainment approach, using physician billing data. We investigated the effects of using varying observation windows in the Régie de l'assurance maladie du Québec (RAMQ) health services administrative databases, alone or in combination with self-reported diagnoses and drugs.
Methods: We studied subjects enrolled in the CARTaGENE cohort, which recruited 19,995 participants from 4 metropolitan regions in Québec from August 2009 to October 2010. A series of Bayesian latent class models were developed to assess the effects of 3 factors: the number of years of billing data, the addition of self-reported information on RA diagnoses and drugs, and the adjustment for misclassification error.
Results: The 3-year 2010 point prevalence estimate among cohort members aged 40-69 years, using physician billing plus self-report, adjusting for misclassification error in each source, was 0.9% [95% credible interval (CrI) 0.7-1.2] with RAMQ sensitivity of 84.0% (95% CrI 74.0-93.7) and a specificity of 99.8% (95% CrI 99.6-100.0). Our results show variations in the prevalence point estimates related to all 3 factors investigated.
Conclusion: Our study illustrates that multiple data sources identify more RA cases and thus a higher prevalence estimate. RA point prevalence estimates using billing data are lower if fewer years of data are used.
Keywords: BAYESIAN LATENT CLASS MODELS; CANADIAN PROVINCIAL HEALTH ADMINISTRATIVE DATA; PREVALENCE; QUEBEC; RHEUMATOID ARTHRITIS; SELF-REPORT DATA.