Identifying Persons with Treated Asthma Using Administrative Data via Latent Class Modelling
Bibliographic record
Abstract
OBJECTIVE: To develop a parsimonious model of the respiratory patient population in British Columbia (BC), Canada through latent class modelling (LCM), using administrative data records and to assess conventional case definitions for asthma in relation to model-based case selection. DATA SOURCES: 1996-2001 data from linked provincial databases containing fee-for-service physician billing records, hospital inpatient separation abstracts, and prescription drug purchase records for 1.9 million BC respiratory patients. STUDY DESIGN: This is a retrospective methodological/descriptive study that assesses case definitions for asthma in terms of sensitivity and specificity using a model fitted to seven physician, hospital and medication utilization markers in place of a conventional gold standard. DATA COLLECTION: We computed values of the treatment markers for each of the 5 years for each patient aged 5-55 years who had had at least one occurrence of a respiratory diagnosis code. PRINCIPAL FINDINGS: The marker for prescription of short-acting beta agonists (SABAs) consistently had the highest sensitivity. Markers' specificities ranged from 0.97 to 1.0. The conventional case definitions' sensitivities were 0.41-0.87; specificities ranged from 0.98 to 0.997. Model-based estimates of asthma prevalence increased from 827/10,000 in 1996 to 992/10,000 in 2001. Conventional case definitions' estimates were consistently lower. CONCLUSIONS: The linkage between utilization and case status is more complex than conventional case definitions allow for. LCM-based case classification was consistent over time and tends to lead to larger prevalence estimates than conventional definitions. The estimated increases in asthma prevalence are reliable. LCM provides health services planners with a useful probability-based approach for developing and assessing case definitions and estimating case prevalence.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".