Risk terrain modeling predicts child maltreatment. (December 2016)

• Record Type: Journal Article
• Authors: Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A.; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye
• Is Part Of: Child abuse & neglect. Volume 62(2016)
• Page Start: 29
• Page End: 38
• Publication Date: 2016-12
• Subjects: Risk terrain modeling -- Geospatial -- Predictive analytics -- Child maltreatment -- Prevention -- Risk factors; Child abuse -- Periodicals; 362.76
• DOI: 10.1016/j.chiabu.2016.09.014
• Languages: English
• ISSNs: 0145-2134

Abstract

Abstract: As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.