A population-based assessment of systemic lupus erythematosus incidence and prevalence results and implications of using administrative data for epidemiological studies
Bibliographic record
Abstract
OBJECTIVES: 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.
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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 | 0.054 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".