JMIR Publications

ABSTRACT

Introduction: Child maltreatment is associated with multiple negative outcomes at the individual and societal level. Children sufferers of maltreatment are at greater risk of a host of negative outcomes (e.g., psychological disorders, substance use, violent delinquency, suicidality, educational outcomes). In order to prevent and to ameliorate child maltreatment a combination of geospatial smoothing via a risk terrain modeling framework and counterfactual modeling are proffered here to identify risky areas and to determine optimal (re-)allocation of services to maximally improve maltreatment outcomes. Methods and Analysis: A three stage process is proposed which can iteratively be applied within a collaborating jurisdiction to enable responsive and sustained achievement of identified child welfare outcomes. This process makes use of two analytic approaches: geospatial smooth- ing via a risk terrain framework and counterfactual modeling. Risk terrain modeling (RTM) is a spatial analytic approach that uses spatial machine learning methods to estimate the risk of maltreatment based on prior cases of maltreatment and risk factors of the built environment. Using prior validated cases of maltreatment, violent crime data and built environment data we estimate a series of machine learning models to geospatially smooth the historically identitied places at increased risk of child maltreatment. Areas identified as higher risk receive exten- sive services associated with preventing or limiting child maltreatment such as pre/postnatal care, subsidized daycare and parental counseling. We make use of counterfactual explanation modeling to optimally align service allocation to maximally improve maltreatment outcomes for future service allocations within a collaborating jurisdiction. This technique leverages a statistical model associating household-level information with maltreatment outcomes in order to explore combinations of services which would be predicted to achieve optimal and practical recommendations for future service allocation efforts.