Identifying Youth Problem Profiles and Predicting Remission Following Mental Health Treatment.

Abstract

This study utilized latent profile analysis to categorize youth served by a public mental health setting into homogenous classes. Then, associations between class membership and meeting clinical criteria by the latest assessment were examined. Caregiver responses to the Ohio Scales, Short Form, Problem Severity Scale for 1090 youth completed at entry into this public mental health system were subjected to latent profile analysis. This method classifies youth into categories based on mental health problem profiles, in order to determine the degree to which these groupings are related to later mental health outcomes. The classification of youth cases that emerged was then used to predict clinical remission at or nearest end of treatment, including final Ohio Scales Problem Severity scores and a measure of day-to-day functioning, the Child and Adolescent Functional Assessment Scale (CAFAS). A four-class model was identified as best representing the data, reflecting a relatively low-risk class (63.3% of the sample), an internalizing class (23.2%), a delinquency class (8.8%), and a high-risk class (4.7%). Individuals in the internalizing and high-risk classes had lower likelihoods of achieving problem remission than those in the low-risk and delinquencyclasses at the time of their last completed Ohio Scales. Additionally, youth assigned to the delinquency and high-risk classes had lower likelihoods of reaching functional impairment remission than those in theinternalizing and low-risk classes. Youth membership in a class based on initial problem scores can be utilized to predict clinical remission over the course of treatment in public mental health care. Such class-based predictions support other methods of predicting outcomes and can be used by clinicians to develop more informed treatment plans and to adjust treatment based on such classifications.