Identifying persons with treated asthma using administrative data via latent class modelling.

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.