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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

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An algorithm using administrative data to measure adverse childhood experiences (ADM-ACE).

Laura E Henkhaus^1,2, Gilbert Gonzales^2,3, Melinda B Buntin²

¹Data Science Institute, Vanderbilt University, Nashville, Tennessee, USA.

Health Services Research

|March 11, 2022

Summary

A new algorithm, ADM-ACE, uses health insurance claims to identify adverse childhood experiences (ACEs) in children. The study found 19.2% of children experienced ACEs, with higher prevalence in younger, non-Hispanic white/black children, and those in rural areas.

Keywords:

Medicaid administrative data uses adverse childhood experiences child and adolescent health child welfare determinants of health observational data pediatrics

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Area of Science:

Public Health
Epidemiology
Health Services Research

Background:

Adverse Childhood Experiences (ACEs) are linked to negative health outcomes.
Measuring ACEs using administrative data can improve population-level surveillance.
Routinely collected health insurance claims offer a potential data source for ACEs measurement.

Purpose of the Study:

To develop and validate an algorithm, ADM-ACE, for measuring adverse childhood experiences using administrative health insurance claims and enrollment data.
To assess the prevalence of ACEs among children enrolled in Tennessee's Medicaid program.
To examine demographic disparities in ACEs prevalence.

Main Methods:

Utilized 2018 Tennessee Medicaid (TennCare) claims and enrollment data for children aged 0-17.
Developed the ADM-ACE algorithm using diagnosis codes, procedure codes, and prescription fills to identify five types of ACEs.
Compared ADM-ACE prevalence with child welfare records and parent survey data.

Main Results:

The ADM-ACE algorithm identified adverse childhood experiences in 19.2% of the study sample.
ACE prevalence was significantly higher in younger children, non-Hispanic white and black children, and those in rural counties.
ADM-ACE identified maltreatment prevalence (1.6%) between child welfare report rates and substantiation rates, and showed promise for measuring maternal mental illness.

Conclusions:

The ADM-ACE algorithm provides a feasible method for monitoring adverse childhood experiences using readily available health insurance claims data.
Findings highlight significant demographic disparities in ACEs exposure among children in Medicaid.
ADM-ACE can inform public health initiatives aimed at reducing ACEs and improving child health outcomes.