Abstract: The Impact of Structural Disadvantage on Child Protective Service Involvement and Recurrence (Society for Social Work and Research 25th Annual Conference - Social Work Science for Social Change)

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The Impact of Structural Disadvantage on Child Protective Service Involvement and Recurrence

Schedule:
Wednesday, January 20, 2021
* noted as presenting author
Patricia Carlson, PhD, Research Associate, University of Connecticut, Hartford, CT
Megan Feely, PhD, Assistant Professor, University of Connecticut, Hartford, CT
Brenda Kurz, PhD, Associate Professor and MSW Program Director, University of Connecticut, Hartford, CT
Joshua Pierce, BA, Research Assistant, University of Connecticut, Hartford, CT
Emily Loveland, MSW, Research Assistant, University of Connecticut, Hartford, CT
Background

Reports of child abuse/neglect cluster in densely populated and socioeconomically disadvantaged areas. Similarly, medical literature has documented the inverse relationship between this disadvantage, disease occurrence, and rehospitalization, suggesting that socioeconomic factors may be “fundamental causes” of disease as well as proxies for lack of access to critical resources. The effect of community-related factors on maltreatment recurrence is less-well studied. Such an understanding may provide guidance for distributing services and programming. In this study, the role of community factors in a relatively service-rich environment is assessed. The state in this study has a state-run and well-resourced child welfare system, extensive family support services, and widely available adult and child mental and behavioral health services.

This study aimed to assess the relationship between structural and community factors and family recurrence in child welfare. The study contributes to the growing literature on the relationship between poverty and child abuse/neglect by using family-level reports to analyze this relationship in a well-resourced state child welfare system.

Methods

The sample consisted of accepted reports of child maltreatment to a state child welfare agency between 2014-2018 to families with no prior child welfare history and with a 12-month follow-up. Data were analyzed at the family-level (n=46,900). The family’s home address was geocoded to the census tract level and matched to the Area Deprivation index (ADI), calculated from the 2015 ACS which allowed for rankings of neighborhoods by socioeconomic disadvantage in deciles (1="Least Disadvantaged", 10="Most Disadvantaged). The ADI incorporated factors for the domains income, education, employment, and housing quality and has been used in other studies.

Results

The sample was racially/ethnically diverse (44.1% White, 19.2% Black, 30.1% Latinx, and 6.5% other. Most families (74.3%) were reported for neglect and 23.6% had a child under the age of two. Initial maltreatment reports rates ranged from 0.5% in the least to 2.9% in the most disadvantaged tracts. Subsequent report rates ranged from 14.2% to 21.0% from least to most disadvantaged. Logistic and hierarchical logistic models were run. One unit increase in ADI was associated with a 6.0% increase in likelihood of 12 month subsequent report of child maltreatment and an 8.1% increase in likelihood of 12 month substantiated subsequent report of child maltreatment.

Conclusions & Implications

This study highlights the association of income, education, employment, and housing quality to the likelihood of child maltreatment recurrence. The child welfare system in this study is state run so there are not large regional disparities in resource allocation. As such, factors such as these structural characteristics may account for the difference in the recurrence of child maltreatment. Addressing such characteristics is generally not in the standard purview of child welfare services. The growing literature in this area, however, suggests that child welfare systems should consider and address these basic social conditions as primary contributors to recurrence along with the more traditional individual and family-based risk factors.