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Explainable Machine Learning for Public Health Informatics in HEDIS Childhood Immunization Status Combo 10
1Health Care Service Corporation, 1100 E Warrenville Rd, Naperville, US.
Online Journal of Public Health Informatics
|July 21, 2026
Summary
Childhood immunization rates (CIS Combo 10) dropped significantly between 2021 and 2023. Machine learning identified influenza and rotavirus vaccines as key targets for improving pediatric immunization quality.
Area of Science:
- Public Health Informatics
- Pediatric Quality Measurement
- Machine Learning Applications
Background:
- The Childhood Immunization Status (CIS) Combination 10 is a key pediatric quality measure used by most U.S. health plans.
- HEDIS measures are vital for population health monitoring but aggregate reporting hinders identification of specific immunization gaps.
- Understanding component-level drivers of noncompletion is crucial for public health interventions.
Purpose of the Study:
- To utilize explainable machine learning for identifying specific vaccine components linked to CIS Combo 10 completion.
- To analyze immunization data for U.S. children aged 24-35 months.
Main Methods:
- Analysis of National Immunization Survey-Child data from 2021-2023.
- Application of survey-weighted logistic regression and Random Forest modeling with SHAP values.
- Identification of component-level predictors for immunization completion.
Main Results:
- A substantial decline in CIS Combo 10 completion from 53.7% in 2021 to 44.6% in 2023 was observed.
- Random Forest modeling highlighted influenza and rotavirus vaccines as having the largest impact on completion rates.
- Statistical analysis confirmed a significant annual decline in immunization completion.
Conclusions:
- Pediatric immunization rates, specifically CIS Combo 10, have decreased notably.
- Explainable machine learning identified influenza and rotavirus as critical areas for targeted quality improvement initiatives.
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