Focusing in on Localized Disparities in Socioeconomic Disadvantages and Child Welfare Services: Three-Dimensional Spatial Data Analysis and Structural Equation Modeling

Background/Purpose:  Spatial data analysis and latent measurements of localized disparities in socioeconomic disadvantages provide a potential opportunity for the development of prevention services aimed at reducing the impacts of difficult social and economic living conditions and exceptional intervention by the state through child welfare services. Despite increasing interest in localized policy strategies, the availability of rigorous approaches to population-based data manipulation, and associations with child welfare service concentration, is limited. This paper explores the creation of population-based social and economic composite indices, and the point at which vulnerability indices begin to predict the localized concentration of child welfare services.

Methods: Localized jurisdictions represent 166 territorial aggregations used to organize the delivery of government services in the province of Quebec, Canada. This study draws data from various sources: (1) longitudinal administrative data from Quebec's child welfare agencies; (2) 2006 and 2011 Canadian Census data; (3) intra-province health and social services spending, and utilisation data; (4) school data from the Ministry of Education; (5) social assistance payments data from the Ministry of Employment; and, (6) public transportation data. Factorial analysis was used to create six localized composite indices – weighted for child population – of well-being vulnerabilities, including: (1) child welfare service concentration; (2) economic poverty; (3) psychosocial service consultations; (4) academic functioning; (5) access to health and social services; and, (6) social prevention spending. Three-dimensional spatial data analysis in Excel 2016 was used to visualize the localized distribution of vulnerability indices, and structural equation modeling (SEM) in AMOS 24 was used to understand the extent to which socioeconomic vulnerabilities directly discriminate in favor of a high-localized concentration of child welfare services.

Results: Preliminary results suggest both direct and partial mediation effects of vulnerability indices on the increase likelihood of localized concentration of child welfare services. Specifically, as economic poverty, schooling issues and psychosocial service consultations monotonically increase, the probability of child welfare service concentration increases.

Conclusions and Implications: This study testifies to the advantages of three-dimensional spatial data analysis and SEM modeling in understanding the aggregations of families with children living in situations of social and economic vulnerability and associated child welfare service concentration. The results of this study also provide a better understanding of potential localized targets for prevention action to support the highest-need families and children.