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Updated: May 23, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Using Medicaid claims to identify children with asthma
Kevin J Dombkowski1, Kara Lamarand, Shiming Dong
1Child Health Evaluation and Research (CHEAR) Unit, Division of General Pediatrics, University of Michigan, Ann Arbor, MI, USA. kjd@med.umich.edu
Objective:
To assess the accuracy of using administrative data from state-managed programs to identify children with asthma in a statewide immunization information system. We wished to understand the degree to which alternative asthma case definitions applied to administrative data influence the accuracy of cases identified in an immunization information system.
Design & Setting:
Children aged 2 to 18 years were sequentially classified into 3-case definition groups on the basis of Michigan Department of Community Health administrative data (2005-2006): (1) children with a Children's Special Health Care Services (CSHCS) Program qualifying diagnosis of asthma (CSHCS cases); (2) those having 1 or more asthma medication claims (Rx cases); or (3) those without asthma medications having 1 or more health services claim reporting an asthma diagnosis code (Dx cases).
Participants:
Children were randomly selected from each asthma case definition group; parents were invited to participate in a telephone interview to document physician diagnosis of asthma, symptoms, activity limitations, medications, and asthma health services use.
Main Outcome Measures:
The positive predictive value of parent report of a physician diagnosis of asthma; asthma severity, based on National Asthma Education and Prevention Program criteria.
Results:
: Of 440 completed interviews, 89% of parents confirmed the child's high-risk status, reporting physician diagnosis of asthma (83%), wheezy-cough (5%), or reactive airway disease (1%). The positive predictive value varied for CSHCS cases (100%), Rx cases (91%) and Dx cases (73%, P < .0001). Although reported asthma severity levels were similar among CSHCS and Rx cases (P = .9100), asthma severity was lower among Dx cases (P = .0218).
Conclusions:
Medicaid administrative data can be used to accurately identify children with asthma and represents a feasible approach for Medicaid programs and health plans to identify priority groups for targeted influenza vaccination reminders.
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