Abstract
Childhood adversity has been associated with myriad physical, emotional, and mental health symptoms across the lifespan, including higher risk for substance abuse, depression, suicidal ideation, and premature mortality. The current study evaluates the association between cumulative adverse childhood experiences and mental health distress at admission and discharge in an adolescent partial hospital program. Data were collected from 157 adolescents through clinical assessments administered during admission and discharge procedures (Youth Outcomes Questionnaire Self-Report (YOQ-SR), Treatment Support Measure (TSM), and Center for Youth Wellness Adverse Childhood Experiences Questionnaire Teen (CYW ACE-Q Teen)). Regression analyses were conducted to assess how cumulative ACEs predict admission mental health distress (Intrapersonal Distress, Critical Items, and Total Score) as well as mental health distress at discharge, above and beyond other clinically relevant factors. While ACEs significantly predicted overall distress at admission (p = .026), there were no other significant associations between ACEs and outcomes at admission, nor ACEs and any outcomes at discharge. This suggests experiences of adversity may not hinder or influence outcomes over the course of treatment in this setting. Experiences of adversity were highly endorsed in this sample; thus, further understanding of experiences of trauma and resilience in acute treatment settings is a critical area for future research to improve interventions for adolescents.
Background
Partial hospital programs for adolescents
Partial Hospitalization Programs (PHPs) are intensive day programs, which uniquely serve as a multidisciplinary approach to addressing individuals with acute psychiatric disorders and concerning levels of psychological distress. PHPs include greater intensive care than outpatient psychiatric services and are often considered a step down in level of care from inpatient hospitalization, or may be used to prevent higher levels of care. Individuals may either join PHPs as transfers from inpatient care, intensive outpatient programs, or can be referred by community providers. PHPs tend to be structured programs, which include several therapeutic groups, in addition to weekly family meetings, milieu therapies, individual therapy sessions, and medication management consultations.
Depending on the facility, PHPs may focus on targeting stabilization for specific diagnoses or dual diagnoses modified for specific demographics (i.e. adult vs. child day hospitalization). PHPs have been shown to be an effective treatment setting for adolescent mental health, as it combines medical and mental health, case management, and emphasizes the importance of the milieu (Lenz, Del Conte, Lancaster et al., 2014). Adolescent PHPs tend to address positive parent–child relationships, positive peer interactions, and building a strong therapeutic alliance, all of which have been determined to have a positive impact on treatment outcome in outpatient therapy (Hawley & Garland, 2008).
Although previous literature has examined the strengths of partial programs targeting a variety of mental disorders (Lenz, Del Conte, Lancaster et al., 2014), there is sparse literature analyzing trauma-related experiences in individuals presenting to these programs, and the research available tends to be in adult PHPs that focus on post-traumatic stress disorder (Mueser et al., 2015; Salyers et al., 2004). Aside from investigating diagnoses, past studies have failed to examine the effect of adverse childhood experiences (ACEs) on dimensional treatment outcomes, particularly for youth in PHP settings.
ACEs background
Addressing the effects of childhood trauma is imperative at both individual and societal levels, and adolescence is a crucial time to address this early life adversity. It is a critical phase of development as adolescents are beginning the transition to early adulthood (Soleimanpour et al., 2017). Childhood adversity has the potential for long-term impacts by influencing various developmental processes at critical timepoints, including emotional, cognitive, social, and neurobiological functioning (McLaughlin, 2016), which in turn impacts later psychopathology. ACEs show a graded relationship to a wide range of outcomes, with increasing number of ACEs found to increase risk of affective, somatic, memory, sexual, aggression, and substance-abuse-related concerns (Anda et al., 2006). Childhood trauma also predicts further instances of trauma in young adulthood (Ballard, 2015). ACEs measured both prospectively and retrospectively have been associated with a variety of negative adult outcomes, including subjective reports of physical, cognitive, mental, and social health, and objective measures of physical and cognitive health (Reuben et al., 2016). Even when not recalled, ACEs increase risk for poor outcomes, whether or not an individual’s perception is accurate (Reuben et al., 2016).
Much previous literature surrounding ACEs and health outcomes is cross-sectional and retrospective in design. Linear associations have been found in previous research between ACEs and physical health outcomes in early childhood, particularly recent adversities (Flaherty et al., 2013). ACEs have been shown in community-based samples to have a dose-response association with emotional and behavioral problems in adolescents (Rebicova et al., 2019). The number of ACEs experienced is positively linked with the number of criteria met for borderline personality disorder in adolescents with non-suicidal self-injury (NSSI) (Hessels et al., 2018). Similar findings are present in school-based settings, with adolescents with greater number of ACEs showing increased risk of both NSSI and suicidality, particularly in girls (Wan et al., 2019). In addition to a cumulative effect, many ACEs have also shown strong associations with mental health outcomes at the individual item level during transition to adulthood (Schilling et al., 2007). Adolescence and young adulthood provide an important period of development during which youth, particularly disadvantaged youth, may have the opportunity to redirect their lives in a positive way (Schilling et al., 2007), and providing interventions that facilitate this positive redirection is key in preventing later mental health consequences. In a study conducted by the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN), recent adversities showed the greatest impact on health problems in early adolescence (Flaherty, 2013), suggesting a unique opportunity for clinical programming with youth to intervene as quickly as possible.
Various factors likely play a role in the association between ACEs and mental health outcomes. The analyses in this study include several demographic and clinically related factors that may be related to the association between ACEs and mental health. Early life adversity has been found to be negatively related with self-efficacy and may lead to harmful health behaviors in adulthood; as such, self-efficacy may play an important role in the trajectory for children who have experienced adversity (Berent et al., 2018). There is also a known connection between social support and adolescent emotional health, with studies showing that social support may be an important protective factor for teen mental health (Helsen et al., 2000; Rigby, 2000). Type of adversity has been found to differentially impact various outcomes, and gender seems to be an important variable to consider in the literature. Gender may be related to both the type of adversity experienced and the domain of mental illness, with females being more likely to report sexual assault, suicidal ideation, and symptoms of depression, while males are more likely to report exposure to violence, substance abuse, and antisocial characteristics (Ballard et al., 2015).
While the association between ACEs and various mental health symptoms has been established cross-sectionally and epidemiologically, less research has been conducted in treatment settings to examine the role of early adversity in treatment outcome and recovery. Research that has been conducted in this field suggests that childhood trauma and maltreatment are associated with higher risk of mental health conditions taking a chronic course, lower instances of remission, and higher lack of response to treatment (Nanni et al., 2012; Nelson et al., 2017). Previous research also suggests that those with a history of childhood trauma respond differentially to treatment, both psychotherapeutic and psychopharmacological, compared to those without a history of trauma (Nemeroff et al., 2005; Williams et al., 2016). In contrast, other research has found that childhood ACEs were not associated with severity of presentation or with treatment response in a sample of psychiatrically hospitalized inpatient youth (Benarous et al., 2017).
Thus, further knowledge around prior experience of adversity in clinical settings, including high-acuity settings, is needed. The purpose of the current study is to understand the prevalence of ACEs in a PHP for adolescents and to evaluate the impact of cumulative ACEs on illness severity upon admission to the PHP as well as at the end of the PHP intensive dose of treatment. We hypothesize that adolescents with a greater number of ACEs will have greater illness severity at admission, above and beyond other clinically relevant factors and known associated variables. We also hypothesize that adolescents with a greater number of ACEs will show poorer outcomes at the end of PHP treatment after controlling for baseline functioning. Lastly, due to research suggesting females are more likely to experience internalizing symptoms and suicidality following a history of ACEs compared to males (Ballard et al., 2015; Wan et al., 2019), we hypothesize that females will experience higher levels of distress on outcome variables at both admission and discharge from PHP care.
Materials and methods
Participants
The current study consists of 157 adolescents (59% female) aged 13–19 (M = 15.07, SD = 1.49) from a PHP at a youth psychiatric hospital in the New England region of the United States, who participated in the program from December 2017 through August 2020. Treatment was typically delivered in-person at the hospital, except when it was delivered in both virtual and hybrid formats from April 2020 through August 2020 due to the COVID-19 pandemic. The Adolescent Partial Hospital Program (APHP) is a clinical outpatient day program that runs Monday through Friday from 8 am to 2 pm for adolescents with high-acuity emotional and behavioral challenges. During their stay at the program, adolescents participate in individual, family, group, milieu, and medication therapies as needed, as well as occupational therapy, art therapy, and yoga. Although this program is not specifically designed for youth who have experienced trauma, trauma-informed care training is required for all staff.
The current sample consists of patients with both admission and discharge data from clinical assessments administered at the APHP as part of standard care. Data for this study were obtained through a retrospective chart review. Formal informed consent was not required for this study, as a waiver of consent approval was obtained from the Institutional Review Board of the Hospital. Data were collected via Research Electronic Data Capture (REDCap; Harris et al., 2009) and the OQ-Analyst system. All data were cleaned and analyzed in IBM Statistical Package for the Social Sciences 26 (SPSS 26).
Measures
Demographics
Demographic information was collected at admission from patient medical charts, including age, biological sex, previous mental health service use, and length of stay.
Primary measures
Cumulative ACEs were measured using the Adverse Childhood Experience Questionnaire Teen (CYW ACE-Q Teen; Burke Harris & Renschler, 2015). Adolescents reported the number of adverse events they had experienced in their lifetime based on the Adverse Childhood Experiences questionnaire at admission.
Several dimensional domains of mental health were measured using the Youth Outcomes Questionnaire-Self-Report (YOQ-SR). This measure consists of 64 Likert-type items (never or almost never, rarely, sometimes, frequently, almost always or always) (Burlingame et al., 1996, 2004; Wells et al., 2003). The present study includes the Total Score as well as the Interpersonal Distress and Critical Items subscales to explore a broad range of psychiatric functioning. The Total Score can range from −16 to 240, with higher scores indicating a greater level of distress in general mental health functioning. The Intrapersonal Distress subscale includes scores ranging from −4 to 68 and assesses emotional distress including anxiety, depression, fearfulness, hopelessness, and self-harm. The Critical Items subscale ranges from 0 to 36, which assesses symptoms of paranoia, obsessive-compulsive behaviors, delusions, hallucinations, suicidal ideation, mania, and eating disorders.
Control variables
Analyses in the current study control for age, sex, past mental health service use, and length of stay in the APHP. Any history of past mental health service use was obtained through chart review, and coded as a binary variable (i.e. “yes” or “no”). The number of calendar days admitted to the program (including weekends) was also obtained through chart review. Lastly, youth self-efficacy and social support were included as control variables based on previous literature and were measured using the Youth Self-Efficacy and Social Support subscales of the Treatment Support Measure (TSM; Warren et al., 2017). Scores on both subscales are summed and range from 15 to 75, with scores on the Social Support subscale at or below 42 indicating steps should be taken to improve social support networks, and scores at or below 43 indicating need to explore and improve youth self-efficacy. The TSM is scored on a 5-point Likert scale ranging from strongly disagree to strongly agree (Warren & Lambert, 2013; Warren et al., 2017).
Data analysis
Descriptive statistics were conducted for demographic data. Correlations were conducted between ACEs, mental health symptom domains at admission and discharge, and other relevant clinical factors. Independent samples t-tests were conducted to examine sex differences on ACEs and the mental health measures (Intrapersonal Distress, Critical Items, and Total Score) at baseline and discharge.
A series of hierarchical regression models were then conducted to probe the relation between ACEs and baseline mental health symptoms. Models were also conducted between ACEs and discharge mental health symptom domains, adjusting for respective baseline symptoms. Hierarchical regression is a theory-driven model-building technique in which additional predictors are added in successive steps. Hierarchical regression allows for the evaluation of the contributions of predictors above and beyond previously entered predictors. R square (R2) indicates the variation in the dependent variable that is accounted for by the independent variable(s) included in each step of the model. Thus, R2 change across steps and corresponding significance tests reflect the improvement in R2 when additional predictors are added in successive steps. For all models, age and sex were included in step 1. Length of stay, past mental health services use, self-efficacy, and social support were added in step 2. ACEs (for models in which baseline mental health was the dependent variable) or ACEs and the respective baseline mental health score (for models in which discharge functioning was the dependent variable) were included in step 3. A priori confounders were entered into the model regardless of univariable association, based on previous literature of factors affecting clinical outcomes (Berent et al., 2018; Helsen et al., 2000; Rigby, 2000). Age is often included as a control variable because it is expected that older adolescents may endorse greater numbers of ACEs. Past service use and length of stay were included because of a likely association with treatment gains. Regression diagnostics were conducted to ensure suitability of multiple regression models and variance inflation factors were ascertained to test for multicollinearity in the models.
Results
Demographic characteristics are reported in Table 1. Eighty-six percent of participants had previous mental health service use and 36% of participants endorsed prior substance abuse. Descriptive statistics for the focal study variables are reported in Table 2. The average length of time spent in the APHP was 24.1 days (SD = 7.88), including weekends. Adolescents reported a mean of 4.61 (SD = 3.21) ACEs.
Table 1.
Demographic characteristics (N = 157).
| Sociodemographic characteristics at admission | n | % |
|---|---|---|
| Ethnicity | ||
| White | 122 | 77.7 |
| African American/Black | 9 | 5.7 |
| Asian/Asian American | 3 | 1.9 |
| Hispanic/Latino | 15 | 9.6 |
| Other | 6 | 3.8 |
| Unknown | 2 | 1.3 |
| Gender | ||
| Male | 58 | 36.9 |
| Female | 90 | 57.3 |
| Transgender Male | 7 | 4.5 |
| Genderqueer/Gender non-conforming | 2 | 1.3 |
| Biological sex | ||
| Male | 65 | 41.4 |
| Female | 92 | 58.6 |
| Receiving special services in school | 73 | *61.9 (73/118) |
| Past mental health service use | 135 | 86 |
| Prior substance abuse | 45 | *36 (45/125) |
Note = Due to missing data, these percentages are based on a smaller N than the overall sample.
Table 2.
Means (standard deviations) and correlations among study variables.
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.Age | 15.07 (1.49) | ||||||||||
| 2.Length of stay | −.07 | 24.10 (7.88) | |||||||||
| 3.Self-efficacy | .17* | −.21* | 48.96 (11.18) | ||||||||
| 4.Social support | −.01 | −.25** | .66** | 55.45 (12.53) | |||||||
| 5.Lifetime ACEs | −.04 | −.09 | −.13 | −.19 | 4.61 (3.21) | ||||||
| 6.lntrapersonal distress (admission) | −.05 | .11 | −.51** | −.37** | .04 | 36.24 (13.63) | |||||
| 7.Critical items (admission) | −.13 | .08 | −.39** | −.28** | .19* | .66** | 11.08 (6.04) | ||||
| 8. Total score (admission) | −.08 | .14 | −.54** | −.40** | .23** | .86** | .79** | 81.91 (31.23) | |||
| 9.lntrapersonal distress (discharge) | −.01 | .18* | −.40** | −.28** | −.04 | .65** | .44** | .52** | 26.33 (13.32) | ||
| 10.Critical items (discharge) | −.03 | .09 | −.004 | .00 | .09 | .07 | .22** | .10 | .24** | 8.82 (10.45) | |
| 11. Total score (discharge) | −.001 | .18 | −.43** | −.28** | .11 | .56** | .52** | .61** | .90** | .29** | 60.10 (29.64) |
Note: ACEs = adverse childhood experiences;
p < .05,
p < .01 (two-tailed).
As seen in Table 2, the number of ACEs was significantly positively correlated with Critical Items and Total Score at admission. Scores on each of the mental health scales (Intrapersonal Distress, Critical Items, and Total Score) were significantly and negatively associated with self-efficacy and social support. Additionally, there was a negative correlation between ACES and social support, such that as number of ACEs increased, less social support was endorsed by adolescents. Notably, length of stay in the treatment program was negatively correlated with both self-efficacy and social support at admission. Mental health scores at admission were significantly positively correlated with discharge scores; effect sizes were large for Interpersonal Distress and Total Scores and small for Critical Items (Cohen’s d [Cohen, 1988] = .88 [interpersonal distress], .81 [total score], and .21 [critical items]).
Tests of sex differences on ACEs and mental health measures at admission revealed no sex differences in lifetime numbers of ACEs (p = .77). However, compared to males, females reported significantly poorer Intrapersonal Distress (t(155) = −3.73, p < .001), Critical Items (t(155) = −2.30, p = .02), and Total Score (t(155) = −2.28, p = .02) scales at admission. At discharge, females continued to report significantly poorer Intrapersonal Distress (t (155) = −2.49, p = .01), but there were no longer sex differences on Critical Items (p = .76), or Total Score (p = .15).
Tables 3–5 report the hierarchical regression analyses for each of the outcomes at admission. For Intrapersonal Distress (see Table 3), in step 1, sex was a significant predictor of baseline Intrapersonal Distress, such that female patients endorsed greater Intrapersonal Distress at admission. In step 2, sex and self-efficacy were significant, such that those with greater self-efficacy endorsed less Intrapersonal Distress. There was a significant change in R2 (ΔR2 = .24,p < .001) from step 1 to step 2, indicating that the addition of these covariates improved prediction of Interpersonal Distress. In step 3, inclusion of ACEs did not improve prediction of intrapersonal distress above and beyond the other covariates. The final model accounted for 30% of the variance in Intrapersonal Distress scores at admission. A similar pattern was observed for Critical Items (see Table 4), such that females reported higher Critical Items scores in step 1. Those with higher self-efficacy reported lower Critical Items in step 2. ACEs did not predict critical items above and beyond the other covariates in step 3. The variables included in step 3 accounted for 20% of the variance in Critical Items scores at admission. Consistent with the previous models, females had higher Total Scores at admission (step 1) and those with greater self-efficacy reported lower total scores (step 2). In step 3 of the Total Score model (see Table 5), self-efficacy was negatively associated with Total Scores, and ACEs predicted Total Scores at admission above and beyond the other covariates in the model, such that greater number of ACEs predicted higher mental health distress. Inclusion of ACEs in step 3 resulted in a significant ΔR2 (.02,p < .001) relative to step 2. Step 3 accounted for 34% of the variance on the Total Score subscale at admission.
Table 3.
Hierarchical linear regression model of the associations between ACEs and intrapersonal distress (Admission).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.085 | |||
| Age | −0.474 | 0.707 | 0.504 | |
| Sex | 7.905 | 2.127 | 0.000 | |
| Step 2 | 0.326 | |||
| Age | 0.230 | 0.635 | 0.718 | |
| Sex | 6.531 | 1.876 | 0.001 | |
| Length of stay | −0.008 | 0.120 | 0.944 | |
| Past mental health service use | −3.552 | 2.648 | 0.182 | |
| Self-efficacy | −0.545 | 0.114 | 0.000 | |
| Social support | −0.069 | 0.101 | 0.495 | |
| Step 3 | 0.327 | |||
| Age | 0.216 | 0.638 | 0.735 | |
| Sex | 6.545 | 1.881 | 0.001 | |
| Length of stay | −0.016 | 0.122 | 0.898 | |
| Past mental health service use | −3.436 | 2.670 | 0.200 | |
| Self-efficacy | −0.544 | 0.114 | 0.000 | |
| Social support | −0.077 | 0.103 | 0.459 | |
| ACEs | −0.121 | 0.296 | 0.682 |
Note: ACEs = adverse childhood experiences.
Table 5.
Hierarchical linear regression model of the associations between ACEs and total score (Admission).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.038 | |||
| Age | −1.620 | 1.666 | 0.332 | |
| Sex | 11.390 | 5.008 | 0.024 | |
| Step 2 | 0.314 | |||
| Age | 0.027 | 1.472 | 0.985 | |
| Sex | 7.358 | 4.346 | 0.093 | |
| Length of stay | 0.061 | 0.278 | 0.827 | |
| Past mental health service use | 3.743 | 6.134 | 0.543 | |
| Self-efficacy | −1.328 | 0.264 | 0.000 | |
| Social support | −0.198 | 0.234 | 0.398 | |
| Step 3 | 0.337 | |||
| Age | 0.197 | 1.454 | 0.892 | |
| Sex | 7.185 | 4.289 | 0.096 | |
| Length of stay | 0.152 | 0.277 | 0.585 | |
| Past mental health service use | 2.298 | 6.086 | 0.706 | |
| Self-efficacy | −1.332 | 0.261 | 0.000 | |
| Social support | −0.106 | 0.235 | 0.651 | |
| ACEs | 1.521 | 0.675 | 0.026 |
Note: ACEs = adverse childhood experiences.
Table 4.
Hierarchical linear regression model of the associations between ACEs and critical items (Admission).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.050 | |||
| Age | −0.526 | 0.319 | 0.102 | |
| Sex | 2.209 | 0.960 | 0.023 | |
| Step 2 | 0.182 | |||
| Age | −0.301 | 0.310 | 0.333 | |
| Sex | 1.754 | 0.915 | 0.057 | |
| Length of stay | −0.006 | 0.059 | 0.912 | |
| Past mental health service use | −0.970 | 1.291 | 0.454 | |
| Self-efficacy | −0.176 | 0.056 | 0.002 | |
| Social support | −0.028 | 0.049 | 0.577 | |
| Step 3 | 0.201 | |||
| Age | −0.272 | 0.308 | 0.379 | |
| Sex | 1.724 | 0.908 | 0.059 | |
| Length of stay | 0.009 | 0.059 | 0.875 | |
| Past mental health service use | −1.220 | 1.288 | 0.345 | |
| Self-efficacy | −0.177 | 0.055 | 0.002 | |
| Social support | −0.012 | 0.050 | 0.815 | |
| ACEs | 0.263 | 0.143 | 0.067 |
Note: ACEs = adverse childhood experiences.
Tables 6–8 report the hierarchical regression results for each of the outcomes at discharge, controlling for respective baseline symptoms in step 3. Baseline scores predicted discharge scores in step 3 of each model. In step 3 of the Intrapersonal Distress model (see Table 6), number of ACEs did not predict Intrapersonal Distress. This model accounted for 45% of the variance in Intrapersonal Distress scores at discharge. In the Critical Items step 3 model (see Table 7), number of ACEs did not predict Critical Items. The final model accounted for 7% of the variance in Critical Items scores at discharge. In step 3 of the Total Score model (see Table 8), number of ACEs did not predict total mental health distress at discharge. Higher self-efficacy predicted higher Total Scores at discharge. The step 3 model accounted for 40% of the variance on the Total Score subscale at discharge.
Table 6.
Hierarchical linear regression model of the associations between ACEs and intrapersonal distress (Discharge).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.039 | |||
| Age | −0.073 | 0.708 | 0.918 | |
| Sex | 5.290 | 2.129 | 0.014 | |
| Step 2 | 0.444 | |||
| Age | 0.455 | 0.565 | 0.422 | |
| Sex | 0.474 | 1.735 | 0.785 | |
| Length of stay | 0.177 | 0.107 | 0.099 | |
| Past mental health service use | −3.330 | 2.369 | 0.162 | |
| Self-efficacy | −0.132 | 0.109 | 0.229 | |
| Social support | 0.050 | 0.090 | 0.577 | |
| Intrapersonal distress (Admission) | 0.574 | 0.073 | 0.000 | |
| Step 3 | 0.447 | |||
| Age | 0.430 | 0.567 | 0.449 | |
| Sex | 0.513 | 1.737 | 0.768 | |
| Length of stay | 0.164 | 0.108 | 0.132 | |
| Past mental health service use | −3.122 | 2.384 | 0.192 | |
| Self-efficacy | −0.132 | 0.109 | 0.227 | |
| Social support | 0.036 | 0.092 | 0.691 | |
| Intrapersonal distress (Admission) | 0.572 | 0.073 | 0.000 | |
| ACEs | −0.227 | 0.263 | 0.389 |
Note: ACEs = adverse childhood experiences.
Table 8.
Hierarchical linear regression model of the associations between ACEs and total score (Discharge).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.014 | |||
| Age | −0.020 | 1.597 | 0.990 | |
| Sex | 7.000 | 4.801 | 0.147 | |
| Step 2 | 0.400 | |||
| Age | 1.606 | 1.308 | 0.222 | |
| Sex | −0.001 | 3.898 | 1.000 | |
| Length of stay | 0.350 | 0.247 | 0.159 | |
| Past mental health service use | −5.067 | 5.457 | 0.355 | |
| Self-efficacy | −0.499 | 0.254 | 0.051 | |
| Social support | 0.196 | 0.209 | 0.350 | |
| Total score (Admission) | 0.505 | 0.073 | 0.000 | |
| Step 3 | 0.400 | |||
| Age | 1.600 | 1.314 | 0.225 | |
| Sex | −0.003 | 3.911 | 0.999 | |
| Length of stay | 0.347 | 0.251 | 0.169 | |
| Past mental health service use | −5.020 | 5.502 | 0.363 | |
| Self-efficacy | −5.020 | 0.255 | 0.054 | |
| Social support | −0.497 | 0.212 | 0.365 | |
| Total score (Admission) | 0.193 | 0.074 | 0.000 | |
| ACEs | −0.054 | 0.620 | 0.931 |
Note: ACEs = adverse childhood experiences.
Table 7.
Hierarchical linear regression model of the associations between ACEs and critical items (Discharge).
| Variable | B | SE | P | R 2 |
|---|---|---|---|---|
| Step 1 | 0.002 | |||
| Age | −0.224 | 0.567 | 0.693 | |
| Sex | −0.517 | 1.703 | 0.762 | |
| Step 2 | 0.067 | |||
| Age | −0.041 | 0.577 | 0.943 | |
| Sex | −1.361 | 1.718 | 0.430 | |
| Length of stay | 0.127 | 0.109 | 0.243 | |
| Past mental health service use | 0.084 | 2.401 | 0.972 | |
| Self-efficacy | 0.082 | 0.107 | 0.443 | |
| Social support | 0.031 | 0.092 | 0.735 | |
| Critical items (Admission) | 0.457 | 0.152 | 0.003 | |
| Step 3 | 0.072 | |||
| Age | −0.021 | 0.578 | 0.972 | |
| Sex | −1.353 | 1.720 | 0.433 | |
| Length of stay | 0.142 | 0.110 | 0.199 | |
| Past mental health service use | −0.167 | 2.418 | 0.945 | |
| Self-efficacy | 0.078 | 0.107 | 0.468 | |
| Social support | 0.045 | 0.093 | 0.628 | |
| Critical items (Admission) | 0.437 | 0.153 | 0.005 | |
| ACEs | 0.243 | 0.270 | 0.371 |
Note: ACEs = adverse childhood experiences.
Discussion
The current study highlights the need to incorporate trauma informed care and intervention in this acute level of care, with over 90% of the adolescents in this sample endorsing at least one ACE and nearly 70% endorsing three or more ACEs. Results showed ACEs predicted overall distress upon admission to the APHP above and beyond relevant demographic and clinical factors. No significant associations were found between ACEs and admission Intrapersonal Distress or Critical Items, or between ACEs and any of our outcome variables at time of discharge. These findings partially support the initial study hypotheses and support previous research suggesting an association between ACEs and mental distress (Anda et al., 2006; Flaherty et al., 2013; Hessels et al., 2018; Rebicova et al., 2019; Reuben et al., 2016; Wan et al., 2019). However, the lack of association between ACEs and functioning at discharge may contradict previous research that suggests poorer treatment response for individuals who have experienced childhood adversity (Nanni et al., 2012; Nelson et al., 2017; Nemeroff et al., 2003; Williams et al., 2016). Results of the current study also partially support the hypothesis that females would experience greater levels of mental health distress at admission and discharge, noting that the current study found females reported higher levels of distress across all three mental health variables at admission, but only reported poorer outcomes compared to males on the Intrapersonal Distress scale at discharge. The significant finding that ACEs predict overall mental distress at time of admission to partial hospitalization beyond other factors examined in this study confirms what many mental health professionals already believe based on clinical experience; that a history of trauma and adverse experiences in childhood contribute to complex clinical presentations and often require an intensive level of mental healthcare. Partial hospitalization level of care is a prime opportunity to provide such acute care to youth with a history of ACEs and potentially prevent the need for inpatient psychiatric hospitalization by working collaboratively with the adolescent and family utilizing a trauma-informed approach.
There are several possibilities for the lack of significant associations between ACEs and discharge functioning in this study. First, the short-term nature of the APHP (two to three weeks) may limit the amount of statistically significant change that can occur on mental health variables. Notably, admission scores were the greatest predictor of discharge scores in this study, other than the additional significant predictor of self-efficacy on overall distress. This supports the theory that a few weeks in this level of care may not be long enough to make a significant statistical impact. However, this does not mean that there is not an important clinical impact of the PHP treatment on adolescent functioning. It is also possible that number of lifetime ACEs did not predict functioning at discharge due to the APHP interventions being just as advantageous for adolescents who have experienced trauma compared to those who have no history of trauma.
Lastly, several of the variables included in this study could be considered factors of resilience (i.e. self-efficacy and the ability to build a social support system). Self-efficacy, in particular, was significantly associated with outcomes across all admission regression models. It was also the only additional significant predictor for Total Score at discharge, other than admission scores. It is possible that resilience may play an important mitigating role in the relationship between trauma exposure and mental health distress (Bethell et al., 2014). Perhaps focusing on building resilience and healthy coping strategies through PHP programming could facilitate improvement in emotional and behavioral functioning. Resilience at the family level may also play an important role, with factors such as parenting stress and family resilience having been shown to mediate the impact of trauma exposure on child mental health (Uddin et al., 2020). All of these hypothetical explanations are important questions for future research in this population and may inform targeted treatments in adolescent PHPs, particularly for youth who have experienced ACEs.
Limitations
Study results should be interpreted within the context of the methodological limitations of this study. Most of the measures were based on adolescent self-report, and social desirability response may have biased results (van de Mortel, 2008). Further, since this study was conducted via chart review in a clinical care setting, there is no control group with which to compare results. Future studies with a healthy control sample from the community would serve to strengthen findings and would allow for comparison of resilience characteristics between clinical and non-clinical samples. The lack of a control group also limits generalizability of results to non-clinical populations. Generalization of results may also be influenced by the predominately Caucasian sample in the current study. Lastly, while results showed no sex differences on lifetime number of ACEs, out data was limited to this quantitative variable. Future research that breaks down types of ACEs by sex may find more nuanced differences that could inform assessment and intervention for youth. It will also likely be important to broaden investigations of sex and gender differences to include gender minority youth and examine the relationships between ACEs and mental health in these populations as well.
Conclusion
The current investigation revealed childhood adversity significantly predicts overall mental distress at the time of admission to PHP care, above and beyond relevant demographic and clinical factors, with adolescents with greater number of ACEs exhibiting greater distress and poorer general mental health. However, childhood adversity was not associated with any mental health outcome measures at discharge, suggesting no association with level of distress following intensive PHP treatment. These findings suggest that adolescents are not limited by their trauma in a PHP setting approached from a multidisciplinary team framework of developmental, attachment, and systemic approaches. This study also highlights the importance of targeting resilience factors such as self-efficacy as part of PHP programming for adolescents, Overall, this study provides insight into the trauma experiences of adolescents presenting to a high-acuity PHP treatment setting and allows for an understanding of how that adversity relates to symptoms of psychiatric distress in this population. It also adds to the body of literature surrounding treatment outcomes in partial hospital settings, particularly for adolescents, where there is a dearth of literature. Further understanding of experiences of trauma and resilience in psychiatric day treatment settings and the impact on clinical outcomes is a critical area for future research to improve acute care interventions for these patients.
Acknowledgments
We would like to thank the adolescents and their families who participated in the APHP, as well as our dedicated clinical team for all that they do for our patients.
Funding
This study was conducted without external grant funding. Dr. Micalizzi is supported by NIH grant [K01DA048135].
Abbreviations:
- ACE(s)
Adverse Childhood Experience(s)
- ACE-Q
Adverse Childhood Experience Questionnaire
- APHP
Adolescent Partial Hospitalization Program
- LOS
Length of Stay
- NSSI
Non-Suicidal Self-Injury
- PHP
Partial Hospitalization Program
- Y-OQ
Youth Outcome Questionnaire
- Y-OQ-SR
Youth Outcome Questionnaire Self-Report
- TSM
Treatment Support Measure
Footnotes
Disclosure statement
The authors declare no competing interests.
Ethical approval
Institutional Review Board approval was obtained through Lifespan-Rhode Island Hospital IRB [ref 001717:45CFR46.110 (5)].
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