Abstract: Risk Factors and Neglect Subtypes: Findings from a National Representative Data (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

Schedule:

Saturday, January 15, 2022

Mint, ML 4 (Marriott Marquis Washington, DC)

* noted as presenting author

Chien-jen Chiang, PhD, Assistant Professor, Louisiana State University at Baton Rouge, Baton Rouge, LA

Hyunil Kim, PhD, Assistant Professor, University of Illinois at Urbana-Champaign, IL

Melissa Jonson-Reid, Professor, Washington University in Saint Louis, St. Louis, MO

Miyoun Yang, PhD, Betty J.Stewart Associate Professor in Social Work Practice with Children, Louisiana State University at Baton Rouge, Baton Rouge, LA

Catherine Moon, MSW LSW PhD, Assistant Professor, Louisiana State University, Baton Rouge, LA

Patricia Kohl, PhD, Associate Professor, Washington University in Saint Louis, St. Louis, MO

Background/Purpose:

Child neglect is the most common form of reported child maltreatment in the United States. It is also a multi-dimensional concept encompassing various forms. While some studies indicate that these neglect subtypes share many common risk factors, other studies argue that certain factors are unique to specific subtypes of neglect. Although it is common to target risk factors when developing intervention programs, most studies do not distinguish between neglect subtypes. The current study sought to identify risk factors related to neglect subtypes among children reported to child protection for alleged neglect, controlling for demographic characteristics.

Methods:

This study used secondary data (National Survey of Child and Adolescent Well-Being II) from a nationally representative sample of children who had reports investigated by Child Protective Services (CPS). The sample was limited to those with a first-time neglect investigation by CPS. We assessed a wide range of factors including demographic, economic, child, caregiver, and community risk factors. We investigated the association with five neglect subtypes: failure to provide basic needs, lack of supervision, exposure to domestic violence, substance-abusing parent, and mixed neglect. Multinomial logistic regression analysis was used to examine the risk of neglect subtypes as a function of a set of multi-level risk factors.

Results:

Six demographic or risk factors were able to discriminate between subtypes of neglect with large effect sizes. For example, child’s age was associated with an increased risk of mixed neglect compared with lack of supervision. Child’s sex (i.e., being male) was associated with an increased risk of lack of supervision compared with the risk of mixed neglect (OR= 1.86; 95% CI: 1.14, 3.06). In addition, four caregiver factors were significantly associated with neglect subtypes. For example, caregiver low social support was associated with an increased likelihood of failure to provide basic needs (OR= 2.04; 95% CI: 1.09, 3.84), but with a decreased likelihood of substance-abusing parent (OR= 0.45; 95% CI: 0.21, 0.97), when compared with cases of lack of supervision. Caregiver poor parenting skills was more probable in cases of lack of supervision compared to domestic violence (OR=0.14; 95% CI: 0.06, 0.35), substance-abusing parents (OR=0.32; 95% CI: 0.16, 0.67), and failure to provide (OR=0.59; 95% CI: 0.37, 0.94).

Conclusions/Implications:

We found that among children with first-time neglect investigations, six demographic or risk factors could meaningfully distinguish between neglect subtypes. Most of these risk factors were strongly associated with a particular subtype as indicated by large effect sizes. While most intervention programs look at child neglect cases as a unitary target, we found that demographic and risk factors were not evenly distributed among subtypes. This suggests that interventions may need to be tailored to subtypes. As the use of predictive analytics is being explored to help understand how to target preventive intervention findings from studies like this one may enhance models using machine learning techniques by being able to train models for specific types that may benefit from different combinations of services.