Aligning a Household-Level Service Array Through a Jurisdiction-Wide Child Maltreatment Prevention Effort: Protocol for a Geospatial and Counterfactual Modeling Study.

Background: Child maltreatment is associated with multiple negative outcomes at the individual and societal levels. Children experiencing maltreatment are at greater risk of a host of negative outcomes (eg, psychological disorders, substance use, violent delinquency, suicidality, and adverse educational outcomes). Objective: This study aims to prevent and ameliorate child maltreatment by using a combination of geospatial smoothing via a risk terrain modeling (RTM) framework and counterfactual modeling to identify risky areas and determine the optimal (re)allocation of services to maximally improve maltreatment outcomes. Methods: A 3-stage process is proposed that can iteratively be applied within a collaborating jurisdiction to enable responsive and sustained achievement of identified child welfare outcomes. This process makes use of 2 analytic approaches: geospatial smoothing via an RTM framework and counterfactual modeling. RTM is a spatial analytic approach that uses spatial machine learning methods to estimate the risk of maltreatment based on previous cases of maltreatment and risk factors of the built environment provided by the participating jurisdiction. Using previously validated cases of maltreatment as our target variable (eg, substantiated claims of abuse and neglect) and violent crime data and built environment data as our primary predictor variables, we estimate a series of machine learning models to geospatially smooth the historically identified places at increased risk of child maltreatment. Areas identified as higher risk receive extensive services associated with preventing or limiting child maltreatment, such as prenatal or 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. The technique leverages a statistical model associating household-level information with maltreatment outcomes to explore combinations of services that would be predicted to achieve optimal and practical recommendations for future service allocation efforts. Constraints can be introduced to this logic, such as service availability and cost. Algorithmic fairness is also a potential consideration during aggregation, with possibilities for both measuring and balancing metrics such as "recourse fairness." Results: As of September 2025, a participating jurisdiction is being recruited. Conclusions: This protocol sets forth a novel approach for the allocation of supportive services for families at risk of child maltreatment through geospatial smoothing via an RTM framework and the maximization of service impact through a counterfactual explanation model. Child maltreatment is an unfortunate and ubiquitous issue in the United States. This proposal builds on jurisdiction-wide public health strategies to allocate services in a data-informed fashion and further align future iterations of the allocation strategy using outcomes-based counterfactual modeling at the household level. The flexibility of the proposed methodology enables its application regardless of the collaborating jurisdiction's preferences and constraints.