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. Author manuscript; available in PMC: 2019 Nov 1.
Published in final edited form as: Psychol Trauma. 2017 Nov 27;10(6):652–661. doi: 10.1037/tra0000340

Service usage typologies in a clinical sample of trauma-exposed adolescents: A latent class analysis

Kristen R Choi 1, Ernestine C Briggs 2, Julia S Seng 3, Sandra A Graham-Bermann 4, Michelle L Munro-Kramer 5, Julian D Ford 6
PMCID: PMC5971119  NIHMSID: NIHMS920988  PMID: 29172561

Abstract

Objective

The purpose of this study is to describe typologies of service utilization among trauma-exposed, treatment-seeking adolescents and to examine associations between trauma history, trauma-related symptoms, demographics, and service utilization.

Method

Latent class analysis was used to derive a service utilization typologies based on ten service variables using a sample of 3,081 trauma-exposed adolescents ages 12 to 16 from the National Child Traumatic Stress Network Core Dataset. Services used 30 days prior to the initial assessment from five sectors were examined (health care, mental health, school, social services, and juvenile justice).

Results

A five-class model was selected based on statistical fit indices and substantive evaluation of classes: (1) High intensity/multi-system, 9.5%; (2) Justice-involved, 7.2%; (3) Low intensity/multi-system, 19.9%; (4) Social service and mental health, 19.9%; and (5) Low service usage/reference, 43.5%. The classes could be differentiated based on cumulative trauma, maltreatment history, PTSD, externalizing and internalizing symptoms, and age, gender, race/ethnicity and place of residence.

Conclusions

This study provides new evidence about patterns of service utilization by trauma exposed, treatment seeking adolescents. Most of these adolescents appear to be involved with at least two service systems prior to seeking trauma treatment. Higher cumulative exposure to multiple types of trauma was associated with greater service utilization intensity and complexity, but trauma symptomatology was not.

Clinical Impact Statement

Trauma exposure can lead to increased service utilization and utilization complexity among adolescents. Maltreatment trauma, cumulative trauma exposure, and behavioral problems are important factors in determining service usage intensity and complexity, more so than trauma-related disorders such as PTSD and dissociation. Given the complex service utilization patterns and frequent multi-system service utilization among this population, this study suggests a need for inter-professional collaboration and trauma-informed care in settings where trauma-exposed adolescents are seeking services.

Introduction

Child maltreatment and other adverse childhood experiences lead to chronic mental and physical health disorders and increased health risk behaviors during childhood and across the lifespan (Felitti et al., 1998). In addition to high chronic disease burden, individuals who were abused or neglected have more involvement with child welfare and juvenile justice, as children, and higher rates of sexual assault and domestic violence victimization, homelessness, and criminality, as adults (Ford et al., 2010; Hetzel & McCrane, 2005; Stein, Leslie, & Nyamathi, 2002). Considering the lifelong impact of chronic illness treatment and service utilization, the cost of abuse and neglect to society is estimated to be over $100 billion per year (Gelles & Perlman, 2012). Although the links between (1) child maltreatment and health disorders, and (2) health disorders and service utilization, are known, relationships among all three of these factors focused specifically on trauma-related disorders, such as posttraumatic stress disorder (PTSD) and dissociation, and trauma-related service utilization have not been widely studied (American Psychiatric Association [APA], 2013).

Some research reports suggest that civilian adults with PTSD have high rates of service usage and associated higher costs (Greenberg et al., 1999; Switzer et al., 1999; Tagay et al., 2005). Severity of trauma exposures and PTSD symptomatology have been found to correspond to increased service usage in both civilian and veteran populations, although there is also competing evidence suggesting that veterans with PTSD under-utilize needed services (Calhoun et al., 2002; Rosenberg et al., 2000). The literature on service usage by children and adolescents is mixed with some prior studies suggesting high rates of service usage and others suggesting under-utilization (Briggs et al., 2013; Burns et al., 2004). Between 2007 and 2010, children increased their overall usage of behavioral health services by 24% and usage of psychotropic medications by 10% (Health Care Cost Institute [HCCI], 2012), but despite this apparent overall increase in service utilization, the few existing studies of service utilization specific to trauma-exposed children suggest under-utilization. In a large prospective cohort study of mental health service needs and usage among child welfare-involved youth, approximately half of the sample had a clinically significant emotional or behavioral problem warranting mental health services (Burns et al., 2004). However, only one-quarter of those children had received mental health services during the year prior to participating in the study (Burns et al., 2004). Neglected children and youth residing at home were especially unlikely to receive needed mental health services, and other studies have found similar patterns of unmet mental health service needs among vulnerable populations of children, such as those living in rural areas and those belonging to racial or ethnic minority groups (Heflinger et al., 2015; Marrast., Himmelstein, & Woolhandler, 2016).

These studies of youth with histories of adversity suggest that under-utilization may be a concern and indicate a need for system-level research to improve structures of care and facilitate access, utilization, and ultimately, outcomes. While clarifying patterns of service utilization among trauma-exposed youth generally is a needed area of research, it is important to understand developmental differences in service utilization across the lifespan, and as such, patterns should be considered separately for each developmental stage. It may be particularly useful to first consider adolescents, who are transitioning from childhood to adulthood, to seek clarity in service utilization patterns among youth and build a bridge for future study of younger children. During adolescence, children show increasing independence from their parents, experience mood fluctuations, and increase their capacity for complex thought and emotional expression (Centers for Disease Control [CDC], n.d.). These developmental characteristics are likely to influence service utilization in that adolescents have increased capacity for involvement, agency, and decision-making related to their health, but still require parental involvement and guidance. Defining and understanding patterns of service usage by adolescents in a multi-agency system of care is an important first step to illuminating dimensions of service usage and informing optimal system organization.

Even when adolescents with exposure to trauma and their families do access trauma treatment services, delivering evidence-based interventions can be challenging, and service systems do not always accommodate the complex biopsychosocial needs of the adolescent and family (Ko et al., 2008). System deficiencies often result in the placement of youth in psychiatric hospitals or residential treatment centers, which are expensive and do not advance national goals of providing professional services in the community rather than in institutions (Institute of Medicine [IOM], 2009). Examining relationships between trauma history, trauma-related symptoms, and service usage patterns has the potential to clarify how trauma-exposed adolescents are using systems of care. By understanding service utilization patterns among youth with trauma exposure and trauma-related mental illness, service delivery systems can be designed to reflect changing needs across the lifespan and ensure that individuals receive needed mental health services. The study described in this paper focuses specifically on adolescence to address the gap in knowledge of how service utilization manifests during this developmental stage. The purpose of this study was to describe typologies of service utilization that was occurring concurrently with the initiation of trauma treatment among trauma-exposed adolescents, and to examine associations between trauma, trauma-related symptoms, demographics, and service usage using a treatment-seeking sample from the National Child Traumatic Stress Network (NCTSN).

Methods

Design, Data, and Sample

This descriptive study used data from the NCTSN Core Data Set (CDS) in a secondary analysis. The NCTSN, established by Congress in 2000, is collaborative network of clinicians, researchers, and families across the United States focused on addressing child traumatic stress by raising the standard of care for child trauma and improving access to evidence-based services (National Child Traumatic Stress Network [NCTSN], 2009). From 2004 to 2010, as part of a quality improvement initiative, NCTSN complied the CDS containing 14,088 trauma-exposed children ages 0 to 21 with data on trauma history, mental health, functional status, service utilization, and treatment (NCTSN, 2009; Steinberg et al., 2014). The CDS was selected for the current study as it contains a large, diverse sample with detailed data about trauma history, mental health, and service utilization. The sample was comprised of adolescents seeking trauma treatment services at one of 57 NCTSN sites in the US. Participants were only identified if they were seeking trauma treatment at an NCTSN site. The NCTSN sites are public and private clinics that offer evidence-based trauma treatment for children in outpatient, school, multi-setting, and other types of facilities. The assessment measures used in this study, described below, were completed as part of a clinical intake prior to delivery of any treatment services. Additional information about the CDS is reported elsewhere (Steinbeg et al., 2014). A subset of the full NCTSN sample was selected to include adolescents ages 12 to 16 who had at least one trauma exposure (n= 3081). This age range was selected to capture the early and middle adolescent developmental stage in examining trauma-related symptoms and patterns of service usage. Cases missing all data on either PTSD or dissociation, the two trauma-related symptom variables of interest for this study, were listwise-deleted from the sample (926 cases; 20.2% of the sample considered for inclusion). These cases were deleted because there were not sufficient data on other variables for those cases to impute their PTSD or dissociation data.

Measures

Demographics

Standard sociodemographic variables were examined to describe the sample, including gender, race, age, place of residence, and insurance status. Gender categories were male or female. Racial categories were White, Black, Hispanic, and Other. Place of residence categories included: with parents, with other relatives, foster care, residential treatment, and other. Insurance status (public versus private) was used as a proxy variable for socioeconomic disadvantage, consistent with prior studies using the CDS (Briggs et al., 2012; Greeson et al., 2011; Kiser et al., 2014).

Trauma History

The CDS General Trauma Information Form contains data on trauma exposure history and characteristics. This form was created for the CDS based on the Trauma History Profile (THP) in the UCLA Posttraumatic Stress Disorder Reaction Index (UCLA PTSD-RI) (Steinberg et al., 2004). Clinicians interviewed the child and his or her caregiver to assess for 20 possible trauma exposures and the age when the trauma exposure occurred. In addition to calculating the overall number of types of trauma exposures (1–20), four maltreatment variables (physical abuse—actual or attempted infliction of physical pain or bodily injury by a caregiver; sexual abuse—actual or attempted sexual molestation, exploitation or coercion by a caregiver; emotional abuse—emotional abuse, verbal abuse, excessive demands, emotional neglect; and neglect—physical, medical, or educational neglect) were used to calculate a count of number of maltreatment trauma exposures (0–4) and to identify the age when maltreatment exposure occurred (before age 6 or after age 6)..

Past 30-day service utilization

Current service utilization was measured using the Baseline Assessment Form from the CDS. This form asks clinicians to indicate which of nineteen different services the child has received in the past 30 days specifically for trauma-related concerns, including at the center where the child is presenting for services and other services with the responses ‘Yes,’ ‘No,’ or ‘Unknown.’ The services include inpatient psychiatric unit or hospital for a mental health problem, residential treatment center, detention center/training school/jail/prison, group home, treatment foster care, probation officer/court counselor, day treatment program, case management or care coordination, in-home counseling, outpatient therapy, outpatient treatment from a psychiatrist, primary care physician/pediatrician for symptoms related to trauma or emotional/behavioral problems, school counselor/school psychologist/school social worker, special class/special school, child welfare/Department of Social Services, foster care, therapeutic recreation services/mentor, hospital emergency room, and self-help groups. For improved model parsimony, these service variables were collapsed into 10 variables reflecting service intensity and service sector (Table 1).

Table 1.

Service Usage Variables

Variable Name Services Included
Mental Health (High intensity) Inpatient psychiatric unit, Residential treatment center, Day treatment program, In-home counseling, Group Home
Mental Health (Low intensity) Outpatient therapy, Outpatient psychiatrist, treatment Case management, care coordination
Juvenile Justice (High intensity) Detention center, training school, jail, prison
Juvenile Justice (Low intensity) Probation officer, court counselor
Social services (High intensity) Treatment foster care
Social services (Low intensity) Foster Care, Child welfare, Department of Social Services
School (High intensity) Special class or special school
School (Low intensity) School counselor, psychologist, social worker
Healthcare (High intensity) Hospital emergency room
Healthcare (Low intensity) Primary care provider for symptoms related to trauma, emotional, behavioral problem
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PTSD Symptoms

The ULCA PTSD-RI for DSM-IV was used to measure PTSD symptoms (Elhai et al., 2013; Steinberg et al., 2004; Steinberg et al., 2013). This 48-item measure can be administered in self-report or interview format. The portion used in the CDS includes 22 items assessing three DSM-IV symptom clusters (intrusive re-experiencing, avoidance/numbing, and hyper-arousal) and two associated features of PTSD, trauma-related guilt and fear of trauma recurrence (Elhai et al., 2013; Steinberg et al., 2013), in the past month. Symptoms were considered present for scores of 2 (some) or greater on a scale of 0 (none) to four (most). The internal consistency reliability of the UCLA PTSD-RI symptom items for this sample was 0.93.

Dissociation Symptoms

Dissociation symptoms were measured using the dissociation scales of the Trauma Symptom Checklist for Children-Alternate Version (TSCC-A), a 44-item measure of lifetime traumatic stress symptoms designed for children ages 8 to 16 years (Briere, 1996). The dissociation scale has ten items and two subscales, overt dissociation and fantasy. Items were considered present for scores of 2 or higher on a 0 (never) to 3 (almost all of the time) scale. The internal consistency reliability for the TSCC-A was 0.97.

Behavioral symptoms

The Child Behavior Checklist (CBCL) is a 112-item measure of emotional and behavioral problems among children ages 6 to 18 (Achenbach, 1991; Achenbach & Rescorla, 2001). Items are reported on 3-point Likert scales (0/Not true, 2/Very true or often true). This study used t-scores from the internalizing and externalizing behavior broadband scales of the CBCL. The t-scores are standardized scores based on normative samples by age and gender, where 50 is the mean score with a standard deviation of 10 for each age group (6–10 years, 11–18 years) and gender (girls, boys) group (Achenbach, 1991). Scores higher than 63 indicate clinically significant levels of behavioral problems (normal scores are below 60; scores of 60 to 63 are considered borderline) (Achenbach & Rescorla, 2001). The internal consistency reliability in this sample was 0.90 for the internalizing subscale and 0.92 for the externalizing subscale.

Procedure

The NCTSN Publication Review Committee (PRC) reviewed and approved the study. The University of Michigan Institutional Review Board approved this study. A data use agreement was arranged between the University of Michigan and Duke University School of Medicine. The analysis was conducted at the National Center for Child Traumatic Stress at the Duke University. All analyses were conducted in R, version 3.2.3, and Mplus. Cases missing all data on either PTSD or dissociation were omitted from the sample. There were no distinguishable patterns of missingness for other variables and item-level data were missing in low proportions (less than 10%), and these missing data were coded as ‘no’ responses (i.e., if the symptom or trauma exposure was not recorded, it was assumed to be absent) (Low, Seng, & Miller, 2008).

Latent class analysis (LCA) was used to derive service usage typologies. Latent class analysis is a statistical technique used to identify unobserved (latent) heterogeneity in a population from categorical data (McCutcheon, 1987). The 10 service usage variables reflecting service sector and service intensity were entered into the model. Initially, a 2-class model was estimated. Then, the number of latent classes was incrementally increased, comparing the fit of each new model to the previous model. Several statistical fit indices were used to compare models and select the most parsimonious model, including Bayesian information criterion (BIC), sample-size adjusted Bayesian information criterion (SSABIC) Akaike information criterion (AIC), and the Vuong-Lo-Mendel-Rubin (VLMR) likelihood ratio test (Nylund, Asparouhov, & Muthén, 2007). Lower BIC or AIC values indicate that the model fit is improved by adding a class. The VLMR likelihood ratio test compares a model with k classes to a model with k + 1 classes. It generates a test statistic and p-value, and if the p-value is less than .05, the model fit is improved by adding a class. To determine the distinctness of the latent classes, entropy values and substantively meaningful characteristics of the classes were assessed by the investigators. Entropy values range from 0 to 1, and values closer to 1 indicate better differentiation and separation between classes (Asparouhov & Muthén, 2014).

After selecting the best-fit latent class model and assigning cases in the sample to their most likely latent classes, a multinomial logistic regression model was estimated with service usage class as the outcome variable and demographic and trauma history characteristics as the predictor variables. The lowest intensity service usage class identified served as the reference group. Then, ANOVA tests with pairwise follow up tests were used to examine differences between latent classes on (1) overall trauma count, (2) maltreatment count, (3) PTSD symptom count, (4) dissociation symptom count, (5) internalizing behavior symptom count, and (6) externalizing behavior symptom count. For these tests, the p-value was set at 0.008 to adjust for multiple comparisons.

Results

Descriptive Statistics

The mean age of the sample was 14.5 years (SD= 1.45). The sample was 60.5% female and 39.5% male. The ethnic/racial proportions of the sample were 32.4% white, 22.9% black, 36.9% Hispanic, and 6.0% other. Public insurance status was considered a proxy variable for socioeconomic risk, and 61.2% of the sample had public insurance. Sixty-two percent of the sample resided with their parents, while 11.8% were living with other relatives, 8.9% were in foster care, 7.1% were in residential treatment, and 4.0% had another living situation. The sample had a mean of 3.9 types of trauma exposures (SD= 2.42) and a mean of 1.1 types of maltreatment exposures (SD= 1.28).

Model Selection

After evaluating several fit indices and model quality both statistically and substantively, a 5-class model was selected as the best fit for the data (Supplement Table 1). This model was favored by the BIC, the VLMR likelihood ratio test, and the adjusted LMR likelihood ratio test. The entropy values were slightly low for the 5-class model. However, considering the fit indices overall and that the entropy values did not substantially differ between models, this did not change the model selection in the end. Additionally and most importantly, the 5-class model was substantively meaningful. The 5-class model classified 9.5% of the sample in group 1 (M class membership probability= 0.77), 7.2% in group 2 (M class membership probability= 0.73), 19.9% in group 3 (M class membership probability= 0.66), 19.9% in group 4 (M class membership probability= 0.70), and 43.5% in group 5 (M class membership probability= 0.82).

Description of Latent Classes

Figure 1 shows the profiles of the 5 latent classes. Class 1 (High intensity/multi-system) was characterized by usage of intensive mental health services and school services, as well as lower-intensity social service and healthcare services. Class 2 (Justice-involved) had the highest probabilities of both high- and low-intensity justice service usage of any group, and these probabilities were higher than other types of service usage within the class. Class 3 (Low intensity/multi-system) used low-intensity services across multiple systems, including mental health, school, and healthcare. Class 4 (Social service and mental health) was similar to class 3 in using multiple low-intensity services, but this class was characterized by high probability of mental health and social service usage. Class 5 (Low service usage/Reference) had very low probabilities of service usage across all systems.

Figure 1.

Figure 1

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Latent Class Model of Service Usage Typologies

Comparison of Latent Classes

Demographics

In the multinomial regression, older age increased odds of being in the high intensity/multi-system class and justice-involved class (Table 2). The justice-involved class was the oldest (M age= 15.34, SD=1.17) and all other classes were on average between 14 and 15 years of age. Girls had lower odds of being in the justice-involved group than boys (OR= 0.57). The classes differed with respect to race/ethnicity; notably, there were large proportions of Hispanic adolescents in the low service usage/reference class (50.5%) and Hispanic youth had lower odds of being in all four other service usage classes than White youth. The effect sizes (Cohen’s w) for membership in the low service utilization class related to demographic characteristics were as follows: white race, 0.27 (small), black race, 0.13 (small), public insurance status, 0.18 (small), and Hispanic race, 0.34 (medium). Additionally, Black adolescents had lower odds of being in the justice-involved class and social service and mental health class than White youth.

Table 2.

Multinomial Logistic Regression Model

Outcome (reference class=low utilization) High intensity/multi-system Justice-involved Low intensity/multi-system Social service and mental health
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Trauma Exposures
 Number 1.11* 1.04, 1.20 1.08* 1.00, 1.17 1.03 0.98, 1.10 1.00 0.94, 1.06
Age of trauma (reference= After 6 years old)
 Onset before 6 years old 0.98 0.69, 1.41 0.94 0.61, 1.46 0.99 0.74, 1.31 0.86 0.66, 1.12
Maltreatment Exposures
 Sexual abuse 1.47* 1.05, 2.07 0.80 0.52, 1.23 1.07 0.82, 1.39 1.38* 1.08, .176
 Physical abuse 1.28 0.89, 1.83 1.35 0.90, 2.03 1.29 0.98, 1.69 1.66* 1.29, 2.15
 Emotional abuse 1.35 0.93, 1.95 0.69 0.47, 1.04 0.88 0.68, 1.13 1.17 0.90, 1.51
 Neglect 1.96* 1.36, 2.8 1.17 0.74, 1.31 1.26 0.93, 1.72 2.17* 1.65, 2.84
Gender (reference= Boys)
 Girls 0.75 0.55, 1.03 0.57* 0.41, 0.80 0.92 0.75, 1.15 1.17 0.94, 1.47
Age
 Number 1.16* 1.04, 1.29 1.61* 1.04, 1.29 1.06 0.99, 1.14 1.06 0.99, 1.83
Race (reference= White)
 Black 1.25 0.85, 1.84 0.56* 0.36, 0.88 0.91 0.6, 1.21 0.73* 0.55, 0.97
 Hispanic 0.39* 0.27, 0.59 0.44* 0.30, 0.65 0.57* 0.44, 0.73 0.42* 0.32, 0.55
 Other 0.99 0.57, 1.73 0.60 0.31, 1.18 1.03 0.69, 1.54 0.93 0.63, 1.38
Insurance (reference= None)
 Private 0.79 0.42, 1.45 0.88 0.48, 1.62 1.01 0.72, 1.42 1.16 0.78, 1.72
 Public 1.05 0.73, 1.51 1.36 0.92, 2.01 0.94 0.74, 1.19 1.36* 1.05, 1.75
 Both 0.67 0.15, 3.01 <.001 <.001, >999 1.42 0.58, 3.50 1.13 0.42, 3.09
Residence (reference= Parents)
 Relatives 0.64 0.37, 1.16 1.14 0.68, 1.92 0.83 0.59, 1.16 1.87* 1.38, 2.54
 Foster care 5.68* 3.35, 9.63 1.77 0.80, 3.91 0.58 0.29, 1.17 9.91* 6.59, 14.90
 Residential treatment 38.59* 20.99, 70.95 17.50* 9.19, 22.31 1.04 0.42, 2.56 11.72* 6.52, 21.06
 Other 1.50 0.91, 2.46 0.87 0.48, 1.60 0.41* 0.26, 0.63 1.44* 1.02, 2.04
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Note.

*

p < 0.05

Adolescents living in residential treatment centers had very high odds (i.e., OR > 10) of being in the high intensity/multi-system class justice-involved class, and social service and mental health class. Those living in foster care also had high odds of being in the social service and mental health class and high intensity/multi-system class Adolescents living with relatives or some other living situation also had higher odds of being in the social service and mental health class than those living with their parents. Class membership was unrelated to access to health insurance except that adolescents with public insurance had higher odds of being in the social service and mental health class than youths with no insurance.

Trauma History, Trauma Symptoms, and Behavioral Symptoms

In the multinomial regression, cumulative trauma history was associated with membership in the high intensity/multi- system class and the justice-involved class (Table 2). The high intensity/multi-system group had the highest number of types of trauma and maltreatment, followed by the social service and mental health class and justice-involved class (see Table 3). On the cumulative trauma history variable, the high intensity/multi-system class had a mean of 0.941 more trauma exposures than the justice-involved class (p < .001) and 0.651 more trauma exposures than the social service and mental health class (p= .001), but the justice-involved group did not differ from the social service and mental health class. On the cumulative maltreatment history variable, the difference between the high intensity/multi-system and justice-involved classes was statistically significant (M difference= 0.801, p < .001) as was the difference between the justice-involved and social service and mental health classes (M difference= 0.594, p < .001), but the high intensity/multi-system and social service and mental health classes did not differ significantly from each other.

Table 3.

Service Usage Group Comparison on Cumulative Trauma/Maltreatment History, Trauma Symptoms, and Behavioral Symptoms

Variable Overall High Intensity/multi-system Justice-involved Low intensity/multi-system Social service and mental health Low service usage/reference F
Sample size N(%) 3081 292 223 612 613 1341 NA
(100.0) (9.5) (7.2) (19.9) (19.9) (43.5)
Trauma count M(SD) 3.85 5.09 4.15 3.67 4.44 3.39 43.90**
(2.42) (2.85) (2.66) (2.19) (2.48) (2.2)
Maltreatment count M(SD) 1.14 1.86 1.06 0.98 1.66 0.85 75.99**
(1.28) (1.48) (1.25) (1.18) (1.4) (1.08)
PTSD Symptoms M(SD) 8.41 9.5 8.3 8.69 8.55 8.08 4.19**
(5.39) (5.31) (5.28) (5.39) (5.48) (5.36)
Dissociation symptoms M(SD) 2.28 2.39 1.98 2.49 2.21 2.25 2.07
(2.38) (2.59) (2.14) (2.45) (2.34) (2.37)
Externalizing behavior M(SD) 62.4 66.18 65.33 64.38 62.31 60.58 19.71**
(11.94) (11.23) (10.49) (11.24) (12.39) (11.83)
Internalizing behavior M(SD) 62.4 63.91 61.99 64.18 61.62 61.84 4.772**
(12.21) (10.27) (11.67) (11.8) (12.72) (12.47)
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Note. PTSD= Posttraumatic stress disorder;

**

p < 0.008.

In the multinomial regression, two maltreatment types were significantly associated with complex service utilization. Adolescents who had experienced sexual abuse and those who had experienced neglect had higher odds of being in the high intensity/multi-system class and the social service and mental health class than in the low utilization class (see Table 2). Youth in the high intensity/multi-system class and the social service and mental health class also had significantly higher cumulative maltreatment history than youth in the three other utilization classes (Table 3).

The only statistically significant difference in PTSD symptom count was between the high intensity/multi-system class and the low utilization/reference group (M difference= 1.423, p= .002). The high intensity/multi-system class had the highest mean number of PTSD symptoms present, and the reference class had the lowest mean number of PTSD symptoms (although still averaging 8 PTSD symptoms, see Table 3). There were no statistically significant group differences on dissociation symptom count between any of the utilization classes. For externalizing behavioral symptoms, the high intensity/multi-system class had a mean higher t-score of 3.9 than the social service and mental health class (Table 3). The justice-involved and low intensity/multi-system classes also had higher externalizing T-scores than the social service and mental health class. The high intensity/multi-system and low intensity/multi-system classes had the highest mean internalizing behavior symptoms t-scores, significantly higher than the other three classes (see Table 2 and Supplement 3).

Discussion

This study identified patterns of service utilization in a sample of trauma-exposed adolescents seeking trauma-related treatment, and examined associations between trauma/maltreatment history and PTSD, dissociation, externalizing and internalizing symptoms, with service utilization patterns. This study provides new evidence about patterns and characteristics of service utilization by trauma exposed adolescents and builds on the existing literature about service utilization by trauma-exposed youth. Previous studies have described under-utilization or over-utilization, and this study provides more detailed information about the service utilization itself beyond its intensity or frequency (Ford et al., 2005; Heflinger et al., 2015).

Most adolescents in trauma treatment reported receiving services in at least two service systems in the 30 days prior to receiving trauma treatment services. The high intensity/multi-system group, justice-involved group, and social service/mental health group had the highest numbers of overall trauma exposures, maltreatment exposures, and maltreatment exposure before age 6., and the highest number of PTSD symptoms and both externalizing and internalizing T-scores. Even in the specialized population of adolescents who are identified as traumatized and are receiving treatment for post-traumatic sequelae, there appear to be distinct sub-groups who are more likely than other traumatized youths to be receiving services from multiple systems (e.g., behavioral health, family/social services, school services, juvenile justice) (Ko et al., 2008). These multi-system-involved youths are more likely than other traumatized youths to be polyvictims and to have experienced maltreatment (D’Andrea et al., 2012; Finkelhor, Ormrod, & Turner., 2007; Ford et al., 2010). They also are more likely to come from economically disadvantaged families, consistent with the adverse effects of socioeconomic disadvantage (Yoshikawa, Abor, & Bearsless, 2012). Some—particularly, but not exclusively, males—have become involved with juvenile justice, which may be a source of therapeutic and social services but which also can subject the youth and their families to additional adversities and potentially places them on a lifetime trajectory of problematic legal involvement (Feierman & Ford, 2016). Others may receive highly intensive—and often restrictive—mental health and school-based services (i.e., high-intensity/multi-system group), which appears to be associated with especially high levels of externalizing behavior problems. They may also receive primarily only low-intensity services (i.e., social service mental health group) when their levels of externalizing and internalizing problems are relatively low, despite warranting treatment for posttraumatic stress.

There were few differences between any of the groups on PTSD or dissociation symptoms. This finding is somewhat surprising; not all individuals who are exposed to trauma develop PTSD or dissociation, and it follows that the subset of trauma-exposed individuals who do develop pathological responses to trauma might be experiencing it as the most distressing and therefore would be the most likely to seek treatment. One possible explanation for this finding is that a PTSD diagnosis derived from the DSM-IV or DSM-5 may not fully capture the symptoms and functional effects of the prolonged, severe interpersonal trauma exposure of maltreatment explored in this study (APA, 2000; APA, 2013; D’Andrea et al., 2012). Early childhood maltreatment can lead to dysregulation and impairment in affect, behavior, relationships, stress response systems, and somatic systems, some of which are not captured by the DSM-5 symptom clusters. This may explain why there were high rates of early childhood maltreatment exposure and subsequent complex service utilization patterns, but no differences in PTSD or dissociation symptoms (D’Andrea et al., 2012). While trauma-exposed youth often show elevations on PTSD symptoms, they may fall short of the diagnostic threshold for PTSD (Scheeringa, Wright, Hunt, & Zeanah, 2006; Scheeringa, Zeanah, & Cohen, 2011). In this case, service utilization by adolescents experiencing functional impairment and trauma-related distress as a result of maltreatment might be better predicted by maltreatment itself or age of maltreatment rather than PTSD or dissociation. In the current study, it is also important to note that while there were no differences in PTSD or dissociation symptom count, each of these three highly trauma-exposed groups had high, clinically significant levels of PTSD symptoms. It may also be the case that there is not any one distinct service usage pattern for the most highly trauma-affected youth and that multiple patterns of utilization can occur depending on the adolescent’s circumstances.

Girls were less likely to be involved in juvenile justice than boys. This may reflect the generally greater propensity for boys to present with externalizing behavior problems or girls to present with dissociative problems (Zona & Milan, 2011). Those problems tend to be used as markers for intensive or restrictive services, and they could lead to gender-based decisions about services that stigmatize youth perceived to be more troubled or troublesome and place them in restrictive settings (James et al., 2006). This also could potentially lead to insufficient intensity of services for girls or boys who have lower levels of externalizing and internalizing (including dissociative) symptoms, despite being comparably likely to have clinically significant PTSD symptoms as other traumatized youths, including those who receive intensive services. Approximately one-third of adolescents in the social service and mental health group were residing in a foster care placement, and this finding highlights a potential area of need for trauma-specific treatment and services for foster care-involved youth. Foster care youth have documented high rates of mental and physical health service utilization, but those services are not necessarily targeted for trauma treatment (Halfon, Berkowitz, & Klee, 1992).

This study revealed some apparent racial disparities in service utilization. Hispanic adolescents were less likely to be in any of the four service usage classes identified in this study than the low service usage/reference class. This finding of lower service utilization is consistent with previous studies of Hispanic youth (Bridges et al., 2010; Kataoka, Zhang, & Wells, 2002). Factors associated with Hispanic culture and values and factors associated with immigration may explain this finding. Hispanics may be more likely to conceptualize symptoms of mental illness as somatic rather than psychological in origin, and some studies have found lower rates of mental illness among Hispanic youth compared with other ethnoracial groups (Peifer, Hu, & Vega, 2000). Hispanic families may be more likely to seek spiritual support, traditional or folk healers, or other informal providers for mental disorder (Higginbotham, Trevino, & Ray, 1990). A final possible reason is that there may be real or perceived social and economic consequences to seeking mental health services for Hispanic families (e.g., social stigma, distrust of service providers, lack of insurance, fear of deportation or law enforcement involvement) (Lewis et al., 2005). Another unexpected finding related to race was that African American youth were under-represented in the justice-user class, which is inconsistent with prior studies that have found racial disparities in the juvenile justice system (Bishop & Frazier, 1996). This discrepancy may be because service utilization was only measured for the past 30 days, and it is possible that many of these youths had a history of juvenile justice involvement but just not recently. It also may indicate that justice-involved African American youth are at risk for being under-identified as in need of services for traumatic stress when compared to White or Hispanic youth. A final possible explanation for this finding is that recent juvenile justice involvement increases the need for trauma-specific service engagement.

There are both strengths and limitations to this study. A large and diverse sample of trauma-exposed adolescents was used for the study, and detailed information about trauma history and service utilization was available in the dataset. The measures used in the study have good validity and reliability established in previous studies and performed well in the current study. Limitations to the study include using some self-report measures, using a DSM-IV measure of PTSD, and the age range of 12 to 16 that does not capture older adolescence. The use of the TSCC-A in the present study which can be extended to 17 year olds only resulted in 39 adolescents meeting inclusion criteria for the study. Service utilization data were only available for the past 30 days prior to seeking services at an NCTSN site, and it is possible that there could have been different service utilization patterns if the timeframe had been extended. The sample was treatment-seeking in nature, and it is possible that the levels and patterns of service utilization identified in this study are unique to high utilizers who seek treatment. The results of this study are not generalizable to trauma-exposed adolescents in general because of the clinical, treatment-seeking nature of the sample used to identify service usage classes. Usage of health services for physical conditions was not captured, but emerging research on the adverse impact of trauma and PTSD on physical health suggests this is an area to measure in future studies of service usage by trauma-affected youth. Because the study was descriptive and exploratory in nature, it is possible that the service usage patterns identified were due to chance or endogenous to the NCTSN CDS.

This study demonstrated relationships between trauma history, trauma-related symptoms (PTSD and dissociation), and patterns of service utilization. Future studies in this area should investigate how service utilization patterns are related to clinical and psychosocial outcomes, as well as how service utilization changes over time. It would also be useful to investigate other dimensions of service utilization and how they relate to outcomes. Prior studies of mental health service delivery have found that merely accessing services is not always enough to affect outcomes (Becker et al., 2015; Dawson & Berry, 2001). Additional dimensions such as accessibility, collaboration, cooperation, cognitive engagement, and relationships with providers might illuminate areas for system-level interventions to improve treatment for trauma survivors and provide treatment in ways that will maximize the likelihood of improved outcomes. Along with these dimensions of service utilization and treatment engagement, assessing the extent to which services and service organizations are trauma-informed might be another important service delivery domain for this population given the high frequency of contact between trauma-exposed youth and service systems (Substance Abuse and Mental Health Services Administration [SAMHSA], n.d.).

Conclusion

Service utilization related to traumatic stress is an understudied topic among adolescents, and expanding the evidence base in this area is important given the high costs and burden of chronic illness for individuals with adverse childhood experiences like abuse and neglect. By identifying patterns of utilization, this study clarifies how trauma and trauma-related distress affect adolescent functioning and help-seeking. Understanding relationships between trauma and service utilization among adolescents may illuminate opportunities for intervention and system improvements that maladaptive patterns of help seeking from developing or continuing into adulthood. For adolescents exposed to maltreatment trauma, the trauma itself, rather than its subsequent trauma-related disorders, is the most important factor in determining service usage intensity and complexity. Given the patterns complexity and frequency of multi-system service usage among this population, this study also suggests that comprehensive trauma treatment will necessarily involve inter-professional collaboration, intentional trauma-informed care in all service sectors explored in this study, and better care coordination for traumatic stress related to child maltreatment trauma.

Supplementary Material

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Acknowledgments

This research project was supported by the Rita & Alex Hillman Foundation Hillman Scholars Program in Nursing Innovation and the Eunice Kennedy Shriver National Institutes of Child Health and Human Development (NICHD) at the National Institutes of Health (NIH) through an individual National Research Service Award (NRSA) (1F31HD088091-01). This project was developed (in part) under grant number 2U79SM054284 from the Center for Mental Health Services (CMHS), Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS). The views, policies, and opinions expressed are those of the authors and do not necessarily reflect those of the above funding sources. We would like to acknowledge the 56 sites within the NCTSN that have contributed data to the Core Data Set as well as the children and families that have contributed to our growing understanding of child traumatic stress. Tracy Bethel, Robert Lee, Carrie Purbeck-Trunzo, and Courtney Fleck at The National Center for Child Traumatic Stress (NCCTS) at Duke University School of Medicine facilitated access to the dataset used in this project and provided technical assistance throughout the study.

Contributor Information

Kristen R. Choi, National Clinician Scholars Program Fellow, University of California Los Angeles, School of Medicine, (517) 582-6280.

Ernestine C. Briggs, Assistant Professor, Duke University, School of Medicine, (919) 419-3474.

Julia S. Seng, Professor, University of Michigan, School of Nursing, (734) 647-0152.

Sandra A. Graham-Bermann, Professor, University of Michigan, College of Literature, Science, & the Arts, (734) 615-7082.

Michelle L. Munro-Kramer, Assistant Professor, University of Michigan, School of Nursing, (734) 647-0154.

Julian D. Ford, Professor, University of Connecticut, School of Medicine, (860) 679-8778.

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