Accepted for/Published in: Online Journal of Public Health Informatics
Date Submitted: Dec 23, 2025
Date Accepted: Jul 21, 2026
Date Submitted to PubMed: Jul 21, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Using Explainable Machine Learning to Identify Key Predictors of HEDIS Childhood Immunization Status (CIS) Combo 10 Completion in the United States
ABSTRACT
Childhood Immunization Status (CIS) Combo 10 is a core Healthcare Effectiveness Data and Information Set (HEDIS) quality measure assessing completion of recommended vaccines by age 24 months. Using CDC National Immunization Survey–Child public-use data from 2021–2023, this study applied regression analysis and Random Forest models to examine national and state-level patterns in CIS Combo 10 completion and to identify key contributing vaccine components. Explainable machine learning using SHapley Additive exPlanations (SHAP) was used to quantify feature importance and model interpretability. Results demonstrated a consistent national decline in CIS Combo 10 completion over the study period, with influenza and rotavirus vaccination emerging as the strongest contributors to overall completion. These findings highlight the value of explainable machine learning for public health informatics and quality measurement, supporting targeted strategies to improve childhood immunization delivery and HEDIS performance.
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