The Society for Social Work and Research

2013 Annual Conference

January 16-20, 2013 I Sheraton San Diego Hotel and Marina I San Diego, CA

The Pattern of Chronic Maltreatment: A Latent Growth Curve Analysis

Friday, January 18, 2013: 3:00 PM
Nautilus 4 (Sheraton San Diego Hotel & Marina)
* noted as presenting author
Saijun Zhang, PhD, Research Specialist, University of Illinois at Urbana-Champaign, Urbana, IL
Tamara Fuller, Director, University of Illinois at Urbana-Champaign, Urbana, IL

Child maltreatment recurrence indicates unresolved risk to maltreated children and the need for continuing child welfare intervention. Chronic child maltreatment, which typically refers to three and more maltreatment incidents associated with a child, has gained increasing concerns because of its persistent harm to the children and its disproportional consumption of child welfare resources (Loman, 2006). Nationally, about 11% children reported to the child protection system (CPS) would be counted as chronic maltreatment over a five-year period (Fluke, et al., 2005). However, to date child maltreat recurrence research has been nearly exclusively focusing on one single recurrence, with a few exceptions that used univariate analytical techniques such as the Survival Curve to describe chronic maltreatment patterns. This study differs from previous research by applying the Latent Growth Curve Modeling (LGCM) on consecutive observation sessions to assess the change of chronic maltreatment over time while controlling for confounding factors. 


The study used data from a state’s child welfare administrative dataset. The sample consists of 2,781 children who, in addition to an initial report in the previous year, had at least two re-reports of child abuse and neglect during the two year observational period from July 1st 2009 to June 30th 2011. The two year period was divided into four 6-month consecutive observation sessions. Maltreatment reports were summed within each session for each child, which yielded a repeated measure of maltreatment count (ranging from 0-6) every 6 months. Children who experienced foster care were excluded. LGCM of Mplus 6.1 was used for model assessment. Children’ characteristics such as age, race, gender, disability, initial maltreatment types, and caregivers’ age and their relationship with the children were controlled.


The LGCM results show that both the intercept (initial maltreatment count) and slop (change rate) of maltreatment count over time were significant (β-intercept = -.32, p<.001; β- slope = -.15, p<.001), which indicates that maltreatment count declined over time. Some covariates affect the change of intercept and slope over time. Children’ age was found to be associated with a lower initial intercept, but being a white child was associated with an increased slope. On the other hand, caregiver being a male, child having disability, and the initial report being substantiated were negatively associated with the slope, suggesting that these factors lower maltreatment recurrence over time.  Both child age and caregiver age were positively associated with the slope over time.


The study specifically targets chronic maltreatment, and is an initial effort of using growth curve modeling to assess the longitudinal pattern of chronic maltreatment while controlling confounding factors.  The findings advance the understanding of chronic maltreatment, including the change direction, influential factors, and which group of children being under escalating risk of maltreatment over time. Such knowledge is useful for policymakers and practitioners concerning effective intervention strategies for chronic maltreatment cases.