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Pediatric asthma surveillance using Medicaid claims
Kevin J Dombkowski1, Elizabeth A Wasilevich, Sarah K Lyon-Callo
1Child Health Evaluation and Research Unit, Division of General Pediatrics, University of Michigan, Ann Arbor, MI 48109-0456, USA. kjd@med.umich.edu
Asthma prevalence in children varies by definition, with inconsistent year-to-year classification. Using four or more medication events offers the most stable asthma case identification.
Area of Science:
- Pediatric Health
- Epidemiology
- Health Services Research
Background:
- Administrative claims data are frequently used to identify pediatric asthma cases.
- Varying case definitions can lead to significant differences in estimated asthma prevalence.
- Consistent classification of asthma cases over time is crucial for accurate outcome assessment.
Purpose of the Study:
- To determine pediatric asthma prevalence using multiple case definitions in administrative claims data.
- To evaluate the year-to-year consistency of asthma case classification.
- To compare the concordance of different asthma case definitions.
Main Methods:
- Retrospective cohort analysis of 357,729 children (0-18 years) using Michigan Medicaid claims (2001-2002).
- Six alternative asthma case definitions were applied, including Health Plan Employer Data Information Set (HEDIS) criteria.
- Definitions incorporated outpatient, emergency department, inpatient, and pharmacy claims.
Main Results:
- Asthma prevalence ranged from 3.7% (≥4 medication events) to 14.9% (any asthma utilization).
- 55.5% of cases meeting HEDIS criteria in 2001 also met them in 2002.
- The definition requiring ≥4 medication dispensing events showed the best year-to-year classification concordance.
Conclusions:
- Pediatric asthma case classification can be inconsistent year-to-year, irrespective of the definition used.
- Inconsistent classification may introduce bias in longitudinal studies assessing asthma outcomes.
- Classifying asthma cases by requiring four or more medication dispensing events appears to minimize misclassification bias.
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