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J Rheumatol. 2019 Mar 15. pii: jrheum.181121. doi: 10.3899/jrheum.181121. [Epub ahead of print]

Identifying Rheumatoid Arthritis Cases within the Quebec Health Administrative Database.

Author information

1
From the Department of Epidemiology, Biostatistics and Occupational Health, McGill University; Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre; Department of Medicine, McGill University, Montreal, Quebec, Canada. Z.F. Slim, PhD, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, and Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre; C. Soares de Moura, PhD, Research Associate, Centre for Outcomes Research and Evaluation, Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre; S. Bernatsky, MD, PhD, Professor of Medicine, Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre, and Department of Medicine, McGill University; E. Rahme, PhD, Associate Professor of Medicine, Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre, and Department of Medicine, McGill University. Address correspondence to Dr. S. Bernatsky, Centre for Outcomes Research and Evaluation of Research Institute of the McGill University Health Centre, 5252 Boul. de Maisonneuve Ouest, Office 3F.51, Montreal, Quebec H4A 3S5, Canada. E-mail: sasha.bernatsky@mcgill.ca. Accepted for publication February 13, 2019.

Abstract

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.

PMID:
30877218
DOI:
10.3899/jrheum.181121

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