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

• Published in: Health services research Vol. 57; no. 4; pp. 963 - 972
• Main Authors: Henkhaus, Laura E., Gonzales, Gilbert, Buntin, Melinda B.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.08.2022; Health Research and Educational Trust
• Subjects: administrative data uses; Adverse childhood experiences; Aggression; Algorithm; Algorithms; child and adolescent health; Child welfare; Childhood; Childhood experiences; Childhood factors; Children; Claims; Clinical outcomes; Data collection; Demography; determinants of health; Disruption; Drug abuse; Drugs; Enrollments; Extraction; Forecasts and trends; Foster care; Government regulation; Health insurance; Health status; Illnesses; Insurance; Insurance claims; Laws, regulations and rules; Market trend/market analysis; Medicaid; Medical diagnosis; Mental disorders; Methods; Methods Corner; Mothers; observational data; pediatrics; Polls & surveys; Prescribing; Prescription drugs; Prescriptions (Drugs); Prevalence; Risk factors; Substance abuse; Substance use; Substance use disorder; Surveys; Tennessee; United States > US; administrative data uses; child and adolescent health; pediatrics; child welfare; determinants of health; Medicaid; observational data; adverse childhood experiences
• ISSN: 0017-9124; 1475-6773; 1475-6773
• DOI: 10.1111/1475-6773.13972

Objective To develop an algorithm using administrative data to measure adverse childhood experiences (ADM‐ACE) within routinely collected health insurance claims and enrollment data. Data Sources We used claims and enrollment data from Tennessee's Medicaid program (TennCare) in 2018. Study Design We studied five types of ACEs: maltreatment and peer violence, foster care and family disruption, maternal mental illness, maternal substance use disorder, and abuse of the mother. We used diagnosis and procedure codes, prescription drug fills, and enrollment files to develop the ADM‐ACE, which we applied to measure the prevalence of ACEs and to examine prevalence by demographic characteristics among our sample of children in TennCare. We compared ADM‐ACE prevalence to child welfare records and survey results from Tennessee. Data Collection/Extraction Methods Our study sample included children aged 0–17 years who were linked to their mothers if also enrolled in TennCare in 2018 (N = 763,836 children). Principal Findings Approximately 19.2% of children in TennCare had indicators for ADM‐ACEs. The prevalence of ACEs was higher among children who were younger (p < 0.001), non‐Hispanic white or black (compared to Hispanic) (p < 0.001), and children residing in rural versus urban counties (p < 0.001). The prevalence of maltreatment identified through the ADM‐ACE (1.6%) falls between the percent of children in Tennessee who were reported to child welfare authorities and the percent for whom reports of maltreatment were substantiated. Comparison with survey reports from Tennessee parents suggests an advantage in measuring maternal mental illness with the ADM‐ACE using health insurance claims data. Conclusions The ADM‐ACE can be applied to health encounter data to study and monitor the prevalence of certain ACEs, their association with health conditions, and the effects of policies on reducing exposure to ACEs or improving health outcomes for children with ACEs.