ArticleChild abuse & neglect2026
Estimating the impact of out-of-home placement on health risk behavior in adolescents exposed to maltreatment: An advanced causal inference approach.
Article in Child abuse & neglect, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundYouth who experience out-of-home placement (OOHP) engage in elevated health risk behaviors (e.g., substance use, unprotected sex), with risk potentially heightened for those placed during adolescence. Estimating causal effects is challenging because maltreated youth who are placed differ systematically from those who remain in-home.
objectiveThis study examined the effects of adolescent OOHP on health risk behaviors, applying causal inference methods (g-estimation and inverse probability of treatment weighting; IPTW) to address selection bias and time-varying confounders. PARTICIPANTS AND
settingData were drawn from 734 maltreated adolescents in the U.S. National Survey on Child and Adolescent Wellbeing.
methodsIPTW and g-estimation were used to estimate effects of adolescent OOHP on substance use and sexual risk behavior during adolescence and into young adulthood, adjusting for numerous confounders. Results were compared with regression analyses using traditional covariate adjustment.
resultsIn traditional regression models, OOHP was not significantly associated with health risk behaviors. However, both causal inference approaches revealed that OOHP predicted increased substance use later in adolescence. IPTW analyses also indicated greater sexual risk behavior in adolescence and increased substance use in adulthood among placed youth.
conclusionsAlthough OOHP is intended to enhance safety, adolescent OOHP may heighten risk for harmful health behaviors. Given the severe consequences of such behaviors and the risk of losing service access in adulthood, the period following OOHP is a critical window for intervention. The results demonstrate how robust causal inference techniques may lead to more accurate assessment of OOHP than traditional regression methods.
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