Comparing Health Administrative and Clinical Registry Data: Trends in Incidence and Prevalence of Pediatric Inflammatory Bowel Disease in British Columbia
Bibliographic record
Abstract
PURPOSE: Canada maintains robust health administrative databases and British Columbia Children's Hospital (BCCH), as the only tertiary care pediatric hospital in British Columbia (BC), maintains a comprehensive clinical inflammatory bowel disease (IBD) registry. To evaluate the strengths and weaknesses of utilizing health administrative and clinical registry data to study the epidemiology of IBD in BC, we conducted a population-based retrospective cohort study of all children <18 years of age who were diagnosed with IBD between 1996 and 2008 in BC. METHODS: IBD cases from health administrative data were identified using a combination of IBD-coded physician encounters and hospitalizations while a separate IBD cohort was identified from the BCCH clinical registry data. Age and gender standardized incidence and prevalence rates were fitted to Poisson regression models. RESULTS: The overall incidence of pediatric IBD identified in health administrative data increased from 7.1 (95% CI 5.5-9.2) in 1996 to 10.3 (95% CI 8.2-12.7) per 100,000 children in 2008. Similarly, the incidence of the BCCH cohort increased from 4.3 (95% CI 3.0-6.0) to 9.7 (95% CI 7.6-12.1) per 100,000. Children aged 10-17 had the highest rise in incidence in both data sources; however, the administrative data identified significantly more 10-17-year-olds and significantly less 6-9-year-olds (p<0.05) compared to clinical registry data. CONCLUSION: While the application of both health administrative and clinical registry data demonstrates that the incidence of IBD is increasing in BC, we identify strengths and limitations to both and suggest that the utilization of either data source requires unique considerations that mitigate misclassification biases.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".