Identifying Rheumatoid Arthritis Cases within the Quebec Health Administrative Database
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".