Abstract: Child Problematic Behavior Trajectories Among Children Who Have Experienced Maltreatment: A Latent Class Analysis (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

306P Child Problematic Behavior Trajectories Among Children Who Have Experienced Maltreatment: A Latent Class Analysis

Friday, January 14, 2022
Marquis BR Salon 6, ML 2 (Marriott Marquis Washington, DC)
* noted as presenting author
Richard Alboroto, PhD, LMSW, Assistant Professor, Texas Tech University, Lubbock
Background/Purpose: In 2018, an estimated 678,000 children were victims of abuse or neglect nationwide. The consequences of child maltreatment range from physical and mental health issues, at the micro-level, to increased child welfare worker caseloads and overcrowding of residential facilities at the mezzo-level, to increased costs and policy implications at the macro-level. These children involved with child welfare services are also at high risk for behavior problems. This study aims to identify internalizing and externalizing behavior paths that this high-risk population follows over four years. This current study will also identify the predictors of membership in normative and problematic pathways.

Methods: Using data from the National Study of Child and Adolescent Well-being II (NSCAW II). The sample included 5,872 children and youth who remained home after the investigation. Latent class growth analysis (LCGA) was used to estimate the number, size, and shape of subgroups of children and youth following distinct behavioral pathways. Raw scores from the CBCL internalizing and externalizing behaviors subscales measured at the 3-time points were entered into a series of unconditional LCGA model using Mplus Version 8.6 with the mixture add-on. Bayesian Information Criterion (BIC) and bootstrap likelihood ratio test (BLRT) were given the most credence when selecting the best model fit. Once the best-fitting model was identified, bivariate analyses were conducted to examine each latent trajectory class's characteristics and identify correlations between class memberships and theoretically relevant covariates. Multinomial logistic regression was performed to identify factors predicting membership in the behavior trajectory groups.

Results: In estimating mixed models of internalizing and externalizing behavior patterns, both showed a 4-class LCGA model fit the data best as indicated by both the BIC and BLRT tests (BICint=5938.619, BLRTint=21.410, p<.0005; BICext=5969.875, BLRText=37.218, p<.005). The 4-class linear LCGA model includes the following subgroups: (1) persistently high problematic behavior; (2) improving group; (3) worsening group; and (4) low problem group. Some child level and caregiver level variables predicted membership in the trajectory groups. Child level: gender (b=0.26, t=-1.53, p<.001); social skills (b=0.31, t=-0.28, p<.001); exposure to violence (b=0.19, t=0.19, p<.001); and physical health (b=0.18, t=-1.56, p<.001). Caregiver level: age (b=0.19, t=-0.32, p<.001); number of children in the home (b=0.14, t=-0.21, p<.01); physical health (b=0.18, t=-0.71, p<.001); domestic violence (b=0.11, t=0.14, p<.001); social support (b=0.15, t=-0.96, p<.001); and perception of neighborhood (b=0.11, t=0.19, p<.001). None of the environmental level variables predicted membership in any of the trajectory groups.

Conclusions: Children and youth differ regarding how they respond to maltreatment and other life events or situations. Interventions must be individualized to target specific issues and reduce particular behavior problems. Results indicated that increasing caregiver social support and improving child social skills may be vital in reducing child behavior problems. Furthermore, identifying early indicators of internalizing and externalizing behavior problems and addressing them with evidence-based interventions to reduce negative behaviors may avert long-term adverse outcomes. Both practice and policy implications are discussed as well as recommendations for future research.