Youth receiving services in mental and behavioral health settings have high rates of risk factors, including exposure to trauma that can elevate the likelihood for long-term negative outcomes. Comprehensive screening and analyses of risks at admission can help inform early intervention and targeted treatment to increase positive outcomes. This study reports the findings from a latent class analysis (LCA) of risk factors and the differences in demographics, utilization, and treatment outcomes by class for youth admitted to a behavioral health organization. The primary objectives were to identify subgroups by risk and to determine if any differences in utilization and treatment outcomes exist by subgroup.
The study was a secondary data analysis using data from the Hillside Clinical Risk Screen (HCRS) for 1,483 youth admitted to community-based, day treatment, and residential programs within a large non-profit behavioral health organization. The Hillside Clinical Risk Screen (HCRS) is a 37-item tool that captures a variety of risks including: education problems, mental health, suicide, self-harm, trauma, violence, and substance/alcohol use. The tool is completed at admission by youth and families in partnership with staff.
The LCA was conducted using the individual scores from the HCRS for two through six classes. Chi-Square analysis and analysis of variance (ANOVA) were used to examine differences in demographics, utilization, and treatment outcomes by class.
Results from the LCA and interpretability considerations indicated a 3-class solution. The subgroup with the lowest risk on the HCRS total score had an average of 5.6 risks endorsed, the moderate subgroup had an average of 9.8, and the high risk subgroup had an average of 18.2.
Results from the analyses on demographics showed that age significantly increased from low to high subgroups [F(2, 927.31)=21.44, p<.001], that there were more males in the low and high risk subgroups, and there were significantly more Black or African-American youth in the low risk subgroup compared to the moderate and high risk subgroups [χ2(6, N=1348)=68.79, p<.001].
Youth in the low risk subgroup were more likely to be in community-based programs and youth in the high risk group were more likely to be in out-of-home placements compared to low and moderate risk subgroups [χ2(4, N=1483)=104.18, p<.001]. The average length of stay (LOS) also varied across subgroups, with LOS increasing with risk [F(2,977.42)=4.12, p=.017].
Results showed that youth in the high risk subgroup were less likely to be discharged to a lower level of care [χ2(4, N=731)=9.90, p=.041] and less likely to be discharged to a permanent living situation [χ2(2, N=766)=13.56, p=.001].
The findings from this study highlight the importance of using data to better understand clinical subgroups using methods such as LCA. The identification of subgroups by risk can have implications for referral, utilization, and discharge planning processes to ensure appropriate level of care, to reduce length of stay, and to decrease costs. Lastly, this data can be used to target early intervention, increase engagement, and guide treatment efforts, particularly for youth who may be most at-risk for negative outcomes.