Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children
Bibliographic record
Abstract
OBJECTIVE: To validate a case definition of pediatric diabetes using administrative health data and describe trends in incidence and prevalence over time in Ontario, Canada. METHODS: We sampled hospital records of 700 children from 1994 to 2003 with a prior history of at least one outpatient or hospital record for diabetes mellitus and 300 randomly selected children with no diabetes records. We defined patients as having diabetes based on diagnoses and drug utilization from chart abstraction and compared sensitivity and specificity of a number of combinations of overall health care use using administrative data to develop a highly specific definition. We used Poisson regression to test changes in incidence over time (1994-2003). RESULTS: Use of four physician claims and no hospital records over a 2-yr period yielded the most specific definition (83% sensitivity, 99% specificity). Using this definition overall age/sex standardized incidence per 100,000 was 32.3 [95% confidence intervals (CI) 30.4, 34.4] and prevalence 241.5 per 100 000 (95% CI 236.2-249.9) in 2003/2004. Overall incidence differs by age, (peaking in 10-14 yr olds) but not significantly by sex. The overall incidence has increased on average by 3.1% per year since 1994 (95% CI 1.02-1.04), with no difference in the rate of increase by age. CONCLUSIONS: Population-based surveillance of diabetes in children is possible using administrative data. This will facilitate further study of trends in incidence but also in use of health services and outcomes. Further work to differentiate type 1 and 2 diabetes will be important.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".