Surveillance of ischemic heart disease should include physician billing claims: population-based evidence from administrative health data across seven Canadian provinces
Bibliographic record
Abstract
BACKGROUND: Canadian provinces and territories routinely collect health information for administrative purposes. This study used Canadian medical and hospital administrative data for population-based surveillance of diagnosed ischemic heart disease (IHD). METHODS: Hospital discharge abstracts and physician billing claims data from seven provinces were analyzed to estimate prevalence and incidence of IHD using three validated algorithms: a) one hospital discharge abstract with an IHD diagnosis or procedure code (1H); b) 1H or at least three physician claims within a one-year period (1H3P) and c) 1H or at least two physician claims within a one-year period (1H2P). Crude and age-standardized prevalence and incidence rates were calculated for Canadian adults aged 20 +. RESULTS: IHD prevalence and incidence varied by province, were consistently higher among males than females, and increased with age. Prevalence and incidence were lower using the 1H method compared to using the 1H2P or 1H3P methods in all provinces studied for all age groups. For instance, in 2006/07, crude prevalence by province ranged from 3.4%-5.5% (1H), from 4.9%-7.7% (1H3P) and from 6.0%-9.2% (1H2P). Similarly, crude incidence by province ranged from 3.7-5.9 per 1,000 (1H), from 5.0-6.9 per 1,000 (1H3P) and from 6.1-7.9 per 1,000 (1H2P). CONCLUSIONS: Study findings show that incidence and prevalence of diagnosed IHD will be underestimated by as much as 50% using inpatient data alone. The addition of physician claims data are needed to better assess the burden of IHD in Canada.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".