Childhood Asthma Surveillance using Administrative Data: Consistency between Medical Billing and Hospital Discharge Diagnoses
Bibliographic record
Abstract
BACKGROUND: The absence of ongoing surveillance for childhood asthma in Montreal, Quebec, prompted the present investigation to assess the validity and practicality of administrative databases as a foundation for surveillance. OBJECTIVE: To explore the consistency between cases of asthma identified through physician billings compared with hospital discharge summaries. METHODS: Rates of service use for asthma in 1998 among Montreal children aged one, four and eight years were estimated. Correspondence between the two databases (physician billing claims versus medical billing claims) were explored during three different time periods: the first day of hospitalization, during the entire hospital stay, and during the hospital stay plus a one-day margin before admission and after discharge ('hospital stay +/- 1 day'). RESULTS: During 1998, 7.6% of Montreal children consulted a physician for asthma at least once and 0.6% were hospitalized with a principal diagnosis of asthma. There were no contemporaneous physician billings for asthma 'in hospital' during hospital stay +/- 1 day for 22% of hospitalizations in which asthma was the primary diagnosis recorded at discharge. Conversely, among children with a physician billing for asthma 'in hospital', 66% were found to have a contemporaneous in-hospital record of a stay for 'asthma'. CONCLUSIONS: Both databases of hospital and medical billing claims are useful for estimating rates of hospitalization for asthma in children. The potential for diagnostic imprecision is of concern, especially if capturing the exact number of uses is more important than establishing patterns of use.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".