Victimization and traumatic stress: Pathways to depressive symptoms among low-income, African-American girls

Child Abuse & Neglect 86 (2018) 223–234

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Child Abuse & Neglect journal homepage: www.elsevier.com/locate/chiabuneg

Victimization and traumatic stress: Pathways to depressive symptoms among low-income, African-American girls

T

⁎

Anda Gershona, , Laura Haywarda, Geri R. Donenbergb, Helen Wilsona a

Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States Center for Dissemination and Implementation Science, Department of Medicine, University of Illinois at Chicago, 1603 W. Taylor Street, Chicago, IL 60612, United States b

A R T IC LE I N F O

ABS TRA CT

Keywords: Stress Psychopathology At-risk youth Depression

Socioeconomic disadvantage is associated with increased exposure to victimization and traumatic stress. The present study evaluates longitudinal pathways linking victimization and trauma to depressive symptoms in a socioeconomically disadvantaged sample of African-American adolescent girls seeking mental health services (N = 177, 12–16 years old at baseline). Girls completed four assessments over the course of three years (T1-T4). Depressive symptoms were assessed at T1-T3 using clinical interviews and questionnaires. At T4, lifetime history of victimization and traumatic stressors was evaluated with in-person interviews. Separate structural equation models tested longitudinal pathways from stressor frequency, severity, and duration to depressive symptoms. In all three models, higher levels of victimization and traumatic stressors were associated with significantly higher levels of depressive symptoms. More frequent stressors prior to T1 directly predicted depressive symptoms at T1 and indirectly predicted depressive symptoms at T2, which, in turn, predicted depressive symptoms at T3. A similar pattern emerged in the stressor severity and duration models. Findings support the idea that victimization and traumatic stressors are associated with higher levels of depressive symptoms and that, among treatment-seeking low-income adolescent girls, these effects occur through both direct and indirect paths. Implications of these findings are discussed in the context of the stress-generation and stress proliferation models of psychopathology.

1. Introduction Early severe stress, such as childhood victimization and trauma, has long been identified as a common precipitant of depression and anxiety disorders and other negative health outcomes, and is linked with more severe and persistent forms of these problems (Green et al., 2010; Hammen, 2005; Moffitt et al., 2007; Monroe, Slavich, & Georgiades, 2009; Nanni, Uher, & Danese, 2012; Trickett, Noll, & Putnam, 2011; Wiersma et al., 2009). Given these well-established associations between childhood victimization and later psychopathology, this study aimed to examine the longitudinal pathways by which victimization confers long-term risk for psychopathology in a sample of adolescent girls who sought treatment at community mental health clinics. Findings are particularly relevant to understanding linkages between exposure to traumatic stressors and depressive symptoms as these relationships unfold over time.

⁎ Corresponding author at: Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305-5719, United States. E-mail address: [email protected] (A. Gershon).

https://doi.org/10.1016/j.chiabu.2018.10.004 Received 18 February 2018; Received in revised form 26 July 2018; Accepted 7 October 2018 0145-2134/ © 2018 Elsevier Ltd. All rights reserved.

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1.1. Sustained and repeated experiences of victimization and trauma Most research on the effects of victimization and trauma on psychopathology has focused on single, discrete instances of victimization and trauma, with less attention directed to the impact that sustained or repeated experiences may have on the development of psychopathology. Yet, across both epidemiological and clinical studies, it has been shown that those who experience one form of victimization or trauma tend to experience other co-occurring adversities (Berger, 2005; Dong et al., 2004; Finkelhor, Ormrod, & Turner, 2007; Fisher et al., 2015; Romano, Bell, & Billette, 2011; Saunders, 2003). Experiences of childhood abuse or neglect are typically embedded within a context of other severe stressors, including parental unemployment and low household income (Berger, 2005), family discord or violence (Chang, Theodore, Martin, & Runyan, 2008; Dong et al., 2004; Hanson et al., 2006) and adverse neighborhood conditions that include community violence (Coulton, Korbin, Su, & Chow, 1995; Molnar, Buka, Brennan, Holton, & Earls, 2003). Moreover, longitudinal studies suggest that a single experience of victimization increases the risk for sustained and repeated victimization (Classen, Palesh, & Aggarwal, 2005; Finkelhor et al., 2007; Fisher et al., 2015; Horwitz, Widom, McLaughlin, & White, 2001; Widom, Dutton, Czaja, & DuMont, 2005), a phenomenon that has been termed “stress proliferation” (Pearlin, Schieman, Fazio, & Meersman, 2005). In a long-term prospective cohort study, Widom, Czaja, and Dutton, (2008) found that adults who had experienced court-documented abuse and neglect in childhood (n = 496) reported significantly higher rates of additional traumas by age 40 relative to a socioeconomically matched control group with no such history (n = 396) (Widom et al., 2008). Similarly, other longitudinal studies have found that individuals exposed to sexual or physical abuse early in life are significantly more likely to experience re-victimization later in life (Culatta, Clay-Warner, Boyle, & Oshri, 2017; Fisher et al., 2015; Noll, Horowitz, Bonanno, Trickett, & Putnam, 2003; Padilla Paredes & Calvete, 2014; Rich, Gidycz, Warkentin, Loh, & Weiland, 2005). Given that survivors of victimization and trauma early in life are likely to experience sustained and repeated victimization later in life, it is important for research on the effects of victimization and trauma to specifically account for the ongoing and cumulative effects of these traumatic stressors. That is, the impact of sustained or repeated victimization on mental health outcomes may be at least as high as the impact of discrete stressors (Hammen, 2005). Consistent with this idea, a dose-response relationship was found between exposure to victimization and psychopathology. Women who experienced multiple types of abuse as youth (physical, sexual, neglect) were at a higher relative risk for depression, compared to women who experienced one form of abuse, who were, in turn, at a higher risk for depression than women with no abuse history (Wise, Zierler, Krieger, & Harlow, 2001). 1.2. Stress generation In addition to stress increasing risk for depression, the stress generation model of depression (Hammen, 1991) proposes that the reverse relationship may also exist, whereby persons with depression (or who are prone to depression) may, through their own characteristics and behaviors, contribute to the occurrence of events and circumstances that perpetuate or exacerbate their symptoms (Hammen & Brennan, 2001). That is, persons with depression may play an active role in seeking out environments or social settings that are more likely to generate further stress. Evidence for this theory has accrued in studies with adults (Harkness, Monroe, Simons, & Thase, 1999) as well as with children and adolescents (Hammen & Brennan, 2001). Given this potential bidirectional relationship between stress and depression, it is critical to consider the role that depression may play in predicting future exposure to traumatic stress. 1.3. Socioeconomic disadvantage is associated with higher rates of victimization and trauma It has been documented that disadvantaged groups, such as those with low socioeconomic status, tend to be disproportionately exposed to victimization and trauma (Pearlin et al., 2005; Turner & Avison, 2003). In one study of 62 children (11–14 years old) living in a low-income community, approximately 90% had witnessed gunfire, 40% had witnessed a beating or a mugging, and 12% had witnessed a murder in their neighborhood (Trickett et al., 2011). Another study of 349 predominantly African-American youth growing up in low-income communities found that over two-thirds reported being both victims and witnesses of domestic or community violence (Howard, Feigelman, Li, Cross, & Rachuba, 2002). Taken together, these findings suggest that experiences of victimization and trauma may play a key role in the development of psychopathology among youth growing up in low-income communities. To summarize, there is now a compelling body of literature indicating that childhood victimization is associated with a higher risk for a range of psychopathology and that disadvantaged groups, such as those with low socioeconomic status, tend to be disproportionately exposed to victimization and trauma (Trickett et al., 2011). In addition, evidence from both cross-sectional and longitudinal studies indicates that those who are exposed to one instance of victimization and trauma tend to experience sustained or repeated victimization and trauma (Widom et al., 2008). However, few studies have examined the pathways by which sustained and repeated victimization are associated with risk for psychopathology among disadvantaged youth. The impact of sustained and repeated victimization on psychological health may be especially salient during adolescence. Adolescence is associated with increased vulnerability for the onset of internalizing psychopathology (Costello, Erkanli, & Angold, 2006), due in part to major biological processes (e.g., maturation of limbic and forebrain regions critical for stress responsiveness) (Naninck, Lucassen, & Bakker, 2011), and psychosocial developments (e.g., learning and developing skills for regulating emotions) (Steinberg, 2005) that occur during this time period. Adolescence is also a critical time period for the emergence of gender differences in internalizing psychopathology and, therefore, represents a particularly sensitive developmental period for girls (Hayward, 2003). 224

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Fig. 1. Study design.

1.4. The present study: aims and hypotheses The current study aims to test longitudinal pathways through which experiences of sustained and repeated victimization and traumatic stressors are associated with the development of depressive symptoms among treatment-seeking African-American adolescent girls residing in low-income communities. Testing these relationships in those who are seeking mental health treatment allows for greater generalizability to clinical practice focused on treating depression and preventing future depression. We assessed depressive symptoms across three time points (T1, T2, T3). Lifetime history of victimization and traumatic stressors was assessed retrospectively at a final time point (T4), and ages at which victimization and traumatic stressors occurred were determined from girls’ self-reports. Assessment time points included in the present analysis (T1-T4) were approximately one year apart (see Fig. 1 for study design). Our analysis tested models incorporating direct, reciprocal, and delayed relationships between traumatic stress and depression, and therefore provided a more complex picture of these dynamic relationships as they unfold over time in a high-risk sample of adolescent girls. Based on prior literature indicating a strong relationship between severe stressors and depressive symptoms, we hypothesized that higher levels of reported victimization and traumatic stress (more frequent, more severe, or longer lasting stress) at one time point would be associated with more depressive symptoms at subsequent time points. Specifically, we hypothesized that: (H1a) higher levels of victimization and traumatic stressors prior to T1 would be associated with more depressive symptoms at T1, T2, and T3; (H1b) higher levels of victimization and traumatic stressors between T1 and T2 would be associated with more depressive symptoms at T2 and T3; and (H1c) higher levels of victimization and traumatic stressors between T2 and T3 would be associated with more depressive symptoms at T3 (see Fig. 2). Based on the stress proliferation model, we further predicted that higher levels of victimization and traumatic stressors at one time point would be associated with higher levels of victimization and traumatic stressors at subsequent time points. Specifically, we hypothesized that: (H2a) higher levels of victimization and traumatic stressors prior to T1 would be associated with higher levels of victimization and traumatic stressors between T1 and T2 and between T2 and T3; and (H2b) higher levels of victimization and traumatic stressors between T1 and T2 would be associated with higher levels of victimization and traumatic stressors between T2 and T3 (see Fig. 2). Finally, drawing on the stress generation model of depression (Hammen, 1991), we hypothesized that more depressive symptoms at one time point would be associated with greater victimization and traumatic stressors at subsequent time points. Specifically, we hypothesized that: (H3a) more depressive symptoms at T1 would be associated with higher levels of victimization and traumatic stressors between T1 and T2 and between T2 and T3; and (H3b) more depressive symptoms at T2 would be associated with higher levels of victimization and traumatic stressors between T2 and T3 (see Fig. 2). 2. Method 2.1. Participants The participants in this study included 177 African-American adolescent girls who were recruited and assessed over six time points as part of a larger longitudinal study examining family relationships, peer and partner relationships, mental health, and risky behaviors in low-income, African American adolescent girls seeking mental health treatment (Wilson, Woods, Emerson, & Donenberg, 2012). Participants were recruited from eight outpatient mental health clinics serving urban, predominantly low socioeconomic status communities in Chicago, Illinois. Participants were excluded based on cognitive impairment (n = 6) or child welfare custody

Fig. 2. Study hypotheses. 225

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(n = 3). Two hundred and eighty-one participants (82% of the referred sample) enrolled at baseline (T1), and of these, 266 completed the T1 assessment and were eligible for follow-up. The adolescent girls and their caregivers were followed over two years during which they were invited to complete data collection every six months. Retention rates were 81% at the 6-month follow-up, 77% at 12 months, 76% at 18 months, and 81% at 24 months. During 2009–2010, participants who completed the T1 assessment and at least one follow-up assessment were invited to enroll in an additional follow-up focused on the assessment of trauma and victimization history. Among those eligible from the original study, 178 were enrolled (74%), and 26% were lost to attrition (22% were not located after multiple attempts, 3% refused, and 1% had moved out of state). Attempts to locate participants included sending letters to last known addresses, calling last known phone numbers, visiting last known addresses, and mail and phone outreach to collateral contacts. One participant was dropped from the final follow-up due to a lack of comprehension and inconsistent responses during interviews, resulting in a final sample of 177. Girls who completed T4 did not differ significantly from those lost to attrition at T1 on any key demographic characteristics or variables in the current analysis including age (14.4 versus 14.5; t = −0.63, df = 209.3, p > 0.10), SES rating (2.38 versus 2.22; t = 1.05, df = 253, p > 0.10), internalizing symptoms (15.8 versus 16.0; t = -0.18, df = 264, p > 0.10) or externalizing symptoms (15.2 versus 15.5, t = −0.24, df = 264, p > 0.10) on the Youth Self Report (Achenbach, 1991). On average, the 12-month assessment (T2) occurred 1.05 years (SD = 0.07 years) from baseline (T1), the 24-month assessment (T3) occurred 2.14 years (SD = 0.63 years) from T1, and the final assessment (T4) occurred 3.30 years (SD = 1.28 years) from T1. At T1, the adolescent girls were 12–16 years old (Mean age = 14.43 years, SD =1.21 years). At T4, the adolescent girls were 14–22 years old (Mean age = 17.72 years, SD = 1.65 years). The current study incorporates data from all four assessments (T1-T4). Participants provided written informed parental consent and child assent at T1 and again at T4. At T4, participants provided consent if 18 years or older. At each time point, the adolescent girls and their primary female caregiver completed a series of paperand-pencil and interviewer administered measures. Data used in the present study included information reported by adolescent girls on the Computerized NIMH Diagnostic Interview Schedule for children (CDISC 4.0) (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) and the Youth Self Report (Achenbach, 1991) at T1, T2, and T3. Lifetime history of victimization and traumatic stressors was assessed using the Lifetime Victimization and Trauma History (LTVH) (Widom et al., 2005) at the T4 assessment. 2.2. Measures 2.2.1. Lifetime history of victimization and traumatic stressors The Lifetime Victimization and Trauma History (LTVH) (Widom et al., 2005) is a 30-item structured interview designed to assess for lifetime history of victimization and violence exposure. The interview assesses for the occurrence of serious accidents, unsafe neighborhood conditions, physical assaults or abuse, physical threats with a weapon, sexual assaults or abuse, sexual force or pressure, crime victimization, witnessing trauma to someone else, or knowing someone who was murdered, seriously hurt, died, or committed suicide. Participants are first asked if they ever experienced a given event, using objective behavioral markers (e.g., “Has anyone ever shot at you, stabbed you, hit you, kicked you, beaten you, punched you, slapped you around, or hurt your body in some other way?”). If positively endorsed, follow-up questions are used to assess for the number of occurrences, age at each occurrence, and subjective severity of each occurrence. To assess severity, a participant was asked if she: (1) “felt afraid that she might die or get hurt really badly”, (2) “was very scared”, or (3) “felt like there was nothing she could do to stop what was happening”. Response to each of these items was scored as either 1 (yes) or 0 (no). In addition, information regarding the duration of the stressor was assessed on twelve (of 30) interview items by asking participants to provide the age at onset and offset (in years). The twelve items with duration information comprised of: Unsafe neighborhood conditions (“Have you ever lived in a war zone? This could be a place like Iraq or your neighborhood is like war zone”), physical assault or abuse (“Has anyone ever shot at you, stabbed you, hit you, kicked you, beaten you, punched you, slapped you around, or hurt your body in some other way?”), physical threats (“Has anyone ever threatened to hurt you when they were standing right in front of you?”), physical threats with a weapon (“Has anyone ever threatened to hurt you with any kind of a weapon, like a knife, a gun, a baseball bat, a frying pan, scissors, a stick, a rock, or a bottle?”), physical abuse or assault with a weapon (“Has anyone ever actually hurt you with any kind of a weapon, like a knife, a gun, a baseball bat, a frying pan, scissors, a stick, a rock, or a bottle?”), physical abuse or assault with a weapon before age 12 (“Before you turned 12 years old, did anyone ever hit you, kick you, beat you, punch you, slap you around, or hurt your body in some other way?”), physical abuse before age 12, (“Before you turned 12 (when you were in elementary school), were you ever physically abused?”), sexual abuse, assault, or coercion (“Has anyone–male or female–ever forced or pressured you into doing something sexual that you didn’t want to do?”), attempted sexual abuse, assault or coercion (“Has there ever been a time when anyone, male or female, ever tried to force or bully you into doing something sexual that you didn’t want to do, but it didn’t end up happening (for example, you stopped them or someone else stopped them)?”), unwanted touching (“Has anyone actually touched private parts of your body or made you touch theirs when you didn’t want to?”), kidnapping (“Have you ever been kidnapped or held captive?”), stalking (“Have you ever been stalked by anyone? For example, has anyone ever spied on you or followed you when you didn’t want them to?”). The LTVH was developed with a diverse sample of adults (49% women, 35% African-American) who tended toward lower income and education levels. Psychometric evaluation of the measure demonstrated convergent validity related to other self-reports and documented cases of child abuse (Widom et al., 2005). The LTVH authors modified the original measure through pilot testing and slight language modifications for youth ages 10–17 years. For each reported experience of victimization or traumatic stressor we calculated stressor frequency as the sum of occurrences reported, severity as the sum of the ratings for the three questions assessing severity (with each severity score ranging between 0–3). In addition, for the 12 items for which duration information was available, we calculated stressor duration. That is, for each reported stressor, the onset age was subtracted from the offset age to arrive at the stressor duration time (in years). Stressors with less than one 226

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year in duration were coded as 0.5 in order to differentiate these stressors from an absence of stressors. 2.2.2. Depressive symptoms The National Institute of Mental Health Computerized NIMH Diagnostic Interview Schedule for Children (CDISC 4.0) (Shaffer et al., 2000) and the Youth Self Report (YSR) (Achenbach, 1991) were used to assess for the presence of depressive symptoms in the sample. The CDISC is a structured diagnostic interview used to assess for past-year psychiatric disorders in children and adolescents based on DSM-IV diagnostic criteria. Disorders assessed by the CDISC include major depression, dysthymic disorder, generalized anxiety disorder, panic disorder, eating disorders, attention-deficit/hyperactivity disorder, and conduct disorder. In the current study, we focused on symptoms of major depression during the past year. The YSR is a self-report measure of emotional and behavioral problems in children and adolescents. It yields eight empirically based and DSM-oriented subscales: anxious/depressed, withdrawn/ depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behavior, and aggressive behavior. The anxious/depressed (e.g., “I am nervous or tense”) and withdrawn/depressed (e.g., “I would rather be alone than with others”) subscales were examined in the current study. Standardized T-scores were used to evaluate descriptive properties of the YSR scales, and raw scores were used in analyses. This questionnaire is considered the gold standard in self-report measures of mood, anxiety, and functioning. It has acceptable test-retest reliability (r = 0.79) and internal consistency (α = 0.83) (Achenbach, Howell, McConaughy, & Stanger, 1995; Achenbach, Howell, McConaughy, & Stanger, 1995). In the current study, internal consistency for the anxious/depressed and withdrawn/depressed scales was adequate at each time point (α = 0.77 at T1 and α = 0.76 at both T2 and T3). 2.3. Data analysis Based on reported age for each stressor, we coded the stressor for its correspondence with time point in the study (pre-T1, T1-T2, T2-T3). Summary variables were created for stressor frequency, severity, and duration. Descriptive statistics (i.e., frequencies, means, standard deviations) and preliminary bivariate analyses (i.e., variable inter-correlations) were calculated with SPSS 25.0. All hypothesized variables were included in the multivariate models, regardless of the results of bivariate analyses. Separate latent variable structural equation models (SEM) were tested using M-plus 7 (Muthén & Muthén, 2015) to examine the effects of stressor frequency, severity, and duration on latent factors reflecting depressive symptoms at each time point. The latent construct was comprised of the number of depression symptoms reported on the CDISC (Shaffer et al., 2000) and the two YSR subscales (Achenbach, 1991). Structural equation modeling proceeded in two primary stages. First, a confirmatory factor analysis (CFA) assessed the measurement model describing relationships between each of the depressive symptom indicators and the latent depressive symptoms construct. In the second stage, separate structural equation path models were tested to examine relationships between each of our chronic stress variables (frequency, severity, and duration) and the latent depressive symptoms constructs at each time point. Cross-lagged models (Andrews et al., 2015) were tested, with paths from depression at each time point to both subsequent depression and subsequent victimization, and from victimization at each time point to both victimization and depression at subsequent time points (see Fig. 2). M-plus 7 (Muthén & Muthén, 2015) uses multivariate multiple regression to determine how well an entire measurement or structural model fits the data and provides multiple indices of overall model fit. Although a non-significant χ2 statistic is desirable, a χ2 to degrees of freedom ratio of less than three indicates an adequate fit between the observed model relationships and the estimated relationships among the observed data based on this model (Barrett, 2007). Effect sizes were evaluated with standardized path coefficients (β). Exploratory analysis explored mediational associations, and the strength of these relationships was evaluated with tests of indirect effects (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002) and bias-corrected bootstrapped confidence intervals (Mackinnon, Lockwood, & Williams, 2004). Models were tested with a maximum likelihood estimator that is robust to non-normality and non-independence of observation (MLR), equivalent to the Yuan-Bentler T2* test (Muthén & Muthén, 2015). MLR uses full information maximum likelihood estimation, which includes all data available for each case (Allison, 2003; Schlomer, Bauman, & Card, 2010), and sandwich-type covariance matrices for standard errors (Cohen, Mannarino, & Knudsen, 2005). Although widely used, family-wise adjustment of alpha can be problematic and lead to inaccurate interpretation (Jensen, Holt, & Ormhaug, 2017). Instead, interpretation of results emphasized both statistical significance (p < 0.05) and the magnitude of effects (β). Power in SEM is a function of effect size, degrees of freedom, number of observed variables per latent factor, relative size of loadings within a factor, complexity of the estimated model, and how well the data meet distributional assumptions. A sample size of at least 100 and at least 5 observations per estimated parameter is recommended for SEM (Schwartz & Proctor, 2000). 3. Results 3.1. Descriptive statistics Table 1 displays descriptive statistics for all stressor and symptom severity variables. On average, severity of symptoms was below clinical thresholds on each of the subscales of the YSR. Bivariate correlations, presented in Table 2, were used for preliminary examination of the pattern of inter-correlations among the study variables collected at each time point. Spearman correlations were used because most of the stress variables had non-normal distributions. As expected, the measures of stressor frequency, severity, and duration were highly inter-correlated at each of the study’s time points. The frequency, severity, and duration of stressors occurring between T1-T2 were correlated with the number of major depression symptoms at T2, as assessed by the CDISC. This pattern was not found at other study time points. Small significant correlations were found between the frequency and severity of stressors occurring 227

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Table 1 Descriptive statistics for study participants. N

Min

Max

M

SD

Frequency of stressors Pre-T1 T1-T2 T2-T3

177 177 177

0 0 0

54 100 57

3.6 1.7 1.6

6.4 8.8 4.9

Severity of stressors Pre-T1 T1-T2 T2-T3

177 177 177

0 0 0

20 24 18

3.1 1 1.4

4.3 2.6 2.6

Duration of stressors (in yrs.) Pre-T1 T1-T2 T2-T3

177 177 177

0 0 0

24 9 5

1.1 1.1 0.3

2.7 5.3 0.8

Number of depression symptoms in past year (CDISC) T1 177 T2 154 T3 171

0 0 0

19 20 16

7.5 4.9 4.8

4.7 4.5 4.3

T-Score Anxious-Depressed subscale symptoms (YSR) T1 177 T2 154 T3 171

50 50 50

92 75 66

55.5 52.3 51.7

7.2 4.2 3.4

T-Score Withdrawn-Depressed subscale symptoms (YSR) T1 177 T2 154 T3 171

50 50 50

91 78 73

59.3 56.3 55.1

8.5 6.8 5.5

Note. Severity of victimization and traumatic stressors was rated on a 0–3 scale. CDISC = The National Institute of Mental Health Computerized NIMH Diagnostic Interview Schedule for Children; YSR = Youth Self Report. Twenty-three participants were missing CDISC and YSR symptoms at T2. Six participants were missing CDISC and YSR symptoms at T3. Table 2 Correlations among stressors and symptoms in the sample by time point.

Pre-T1 stressors and T1 symptoms 1. Frequency of stressors 2. Severity of stressors 3. Duration of stressors 4. CDISC no. depression symptoms 5. YSR anxious-depressed symptoms 6. YSR withdrawn-depressed symptoms T1-T2 stressors and T2 symptoms 1. Frequency of stressors 2. Severity of stressors 3. Duration of stressors 4. CDISC no. depression symptoms 5. YSR anxious-depressed symptoms 6. YSR withdrawn-depressed symptoms T2-T3 stressors and T3 symptoms 1. Frequency of stressors 2. Severity of stressors 3. Duration of stressors 4. CDISC no. depression symptoms 5. YSR anxious-depressed symptoms 6. YSR withdrawn-depressed symptoms

1

2

3

4

5

6

―

.81*** ―

.80*** .58*** ―

.11 .14† .16* ―

.15* .17* .11 .43*** ―

.04 .07 .05 .40*** .59*** ―

―

.81*** ―

.55*** .47*** ―

.32*** .30*** .29*** ―

.09 .05 .24** .41*** ―

−.07 −.05 .09 .29*** .52*** ―

―

.83*** ―

.66*** .55*** ―

.18* .14† .12 ―

.11 .17* .11 .50*** ―

.17* .10* .19* .50*** .57*** ―

Note. * p < .05, ** p < .01, *** p < .001. CDISC = The National Institute of Mental Health Computerized NIMH Diagnostic Interview Schedule for Children; YSR = Youth Self Report.

Pre-T1 and the YSR anxious/depressed subscale score at T1 and between the severity of stressors occurring between T2-T3 and the YSR anxious/depressed subscale score at T3 (rs ranging from 0.15–0.17, ps < 0.05). The duration of stressors occurring between T1T2 was significantly correlated with the YSR anxious/depressed subscale score at T2 (r = 0.24, p < 0.01). Small significant correlations were found between the frequency, severity, and duration of stressors occurring between T2-T3 and the YSR withdrawn/ depressed subscale score at T3 (rs ranging from 0.10–0.19, ps < 0.05). The four symptom measures were highly inter-correlated at 228

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Fig. 3. A structural path model for the relationships between frequency of victimization and traumatic stressors and depressive symptoms in the sample (N = 177). Numbers above the paths are standardized linear regression coefficients.

each of the study’s time points, which supported use of latent factors reflecting depressive symptoms in structural equation modeling. 3.2. Structural equation modeling 3.2.1. Measurement model The measurement model included three latent variables that represented depressive symptoms at T1, T2, and T3. Each latent variable was comprised of three observed indicators – number of major depression symptoms as assessed by the CDISC, the YSR anxious/depressed subscale score, and the YSR withdrawn/depressed subscale score – measured at the given time point. The model also included covariances among the residuals (i.e., error variances) of the same variables measured at different time points. Overall fit of the model was acceptable (χ2 = 24.30, df = 12, p = 0.02; RMSEA = 0.08; CFI = 0.98; TLI = 0.93; SRMR = 0.03). Depressive symptoms at T1 were significantly correlated with depressive symptoms at T2 (r = 0.80, p < 0.001) and at T3 (r = 0.71, p < 0.001). Depressive symptoms at T2 were also significantly correlated depressive symptoms at T3 (r = 0.77, p < 0.001). 3.3. Structural models 3.3.1. Stressor frequency The stressor frequency model demonstrated an acceptable fit, although RMSEA was slightly high and TLI was slightly low (χ2 = 78.49, df = 30, p < 0.001; RMSEA = 0.10; CFI = 0.91; TLI = 0.81; SRMR = 0.04). Fig. 3 displays the stressor frequency model. Significant paths are shown using bolded lines. Non-significant paths are shown using dashed lines. As shown in Fig. 3, the estimated path coefficients (above the lines) indicated that a higher frequency of victimization and traumatic stressors occurring PreT1 predicted greater depressive symptoms at T1 (β = 0.22, p = 0.03, CI = 0.02, 0.39). Greater T1 depressive symptoms predicted greater T2 depressive symptoms (β = 0.78, p < 0.001, CI = 0.12, 1.29), which, in turn, predicted greater T3 depressive symptoms (β = 0.71, p = 0.02, CI = 0.17, 1.09). In addition, greater depressive symptoms at T1 predicted a higher frequency of victimization and traumatic stressors occurring between T2-T3 (β = 0.67, p = 0.01, CI = 0.17, 1.09). By contrast, greater depressive symptoms at T2 predicted a lower frequency of victimization and traumatic stressors occurring between T2-T3 (β = −0.76, p < 0.001, CI = −1.27, −0.32). A higher frequency of victimization and traumatic stressors occurring between T1-T2 predicted a higher frequency of victimization and traumatic stressors occurring between T2-T3 (β = 0.48, p < 0.001, CI = 0.20, 0.72). Given that a higher frequency of victimization and traumatic stressors occurring Pre-T1 predicted greater depressive symptoms at T1, and that greater depressive symptoms at T1 predicted greater depressive symptoms at T2, we ran an exploratory analysis to test for an indirect path in which a higher frequency of victimization and traumatic stressors occurring Pre-T1 predict greater depressive symptoms at T2 indirectly through greater depressive symptoms at T1. Results indicated a significant indirect effect (β = 0.17, CI = 0.02, 0.30). 3.3.2. Stressor severity The severity model fit the observed data adequately (χ2 = 66.73, df = 30, p < 0.001; RMSEA = 0.08; CFI = 0.95; TLI = 0.88; SRMR = 0.05). Fig. 4 displays the stressor severity model. Significant paths are shown using bolded lines. Non-significant paths are shown using dashed lines. As shown in Fig. 4, the estimated path coefficients (above the lines) indicated that greater severity of victimization and traumatic stressors occurring Pre-T1 directly predicted depressive symptoms at T1 (β = 0.22, p = 0.02, CI = 0.04, 0.40) and T2 (β = 0.20, p = 0.02, CI = 0.03, 0.36). Greater depressive symptoms at T1 predicted greater depressive symptoms at T2 (β = 0.77, p < 0.001, CI = 0.64, 0.91), which, in turn, predicted greater depressive symptoms at T3 (β = 0.63, p = 0.01, CI = 0.23, 1.03). Greater severity of victimization and traumatic stressors occurring Pre-T1 directly predicted greater severity of victimization and traumatic stressors occurring between T1-T2 (β = 0.39, p < 0.001, CI = 0.26, 0.52) and between T2-T3 (β = 0.24, p = 0.02, 229

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Fig. 4. A structural path model for the relationships between severity of victimization and traumatic stressors and depressive symptoms in the sample (N = 177). Numbers above the paths are standardized linear regression coefficients.

CI = 0.09, 0.39). In addition, the severity of victimization and traumatic stressors occurring between T1-T2 had a direct predictive effect on the severity of victimization and traumatic stressors occurring between T2-T3 (β = 0.42, p = 0.001, CI = 0.28, 0.55). In this model, depressive symptoms did not predict the severity of victimization and traumatic stressors. Given that a greater severity of victimization and traumatic stressors occurring Pre-T1 predicted greater depressive symptoms at T1 and that greater depressive symptoms at T1 predicted greater depressive symptoms at T2, we ran an exploratory analysis to test for an indirect path in which a greater severity of victimization and traumatic stressors occurring Pre-T1 predict greater depressive symptoms at T2 indirectly through depressive symptoms at T1. Results indicated a significant indirect effect (β = 0.17, CI = 0.01, 0.31). Results also supported an indirect path in which greater severity of victimization and traumatic stressors occurring Pre-T1 indirectly predict a greater severity of victimization and traumatic stressors occurring between T2-T3 via the severity of victimization and traumatic stressors occurring between T1-T2 (β = 0.16, CI = 0.01, 0.31).

3.3.3. Stressor duration The duration model fit the observed data adequately (χ2 = 50.70, df = 30, p = 0.01; RMSEA = 0.07; CFI = 0.96; TLI = 0.92; SRMR = 0.04). Fig. 5 displays the stressor duration model. Significant paths are shown using bolded lines. Non-significant paths are shown using dashed lines. As shown in Fig. 5, the estimated path coefficients (above the lines) indicated that a longer duration of victimization and traumatic stressors occurring Pre-T1 directly predicted greater depressive symptoms at T1 (β = 0.18, p = 0.02, CI = 0.03, 0.33) and at T2 (β = 0.21, p = 0.003, CI = 0.07, 0.35). Greater depressive symptoms at T1 predicted greater depressive symptoms at T2 (β = 0.73, p < 0.001, CI = 0.59, 0.88), which, in turn, predicted greater depressive symptoms at T3 (β = 0.62, p = 0.01, CI = 0.16, 1.08). A longer duration of victimization and traumatic stressors occurring Pre-T1 had a direct predictive effect on greater T2 depressive symptoms (β = 0.21, p < 0.001, CI = 0.16, 1.08). The indirect path from a longer duration of victimization and traumatic stressors occurring Pre-T1 to greater depressive symptoms at T2, via depressive symptoms at T1, was also significant (β = 0.13, p = 0.02, CI = 0.01, 0.25).

Fig. 5. A structural path model for the relationships between duration of victimization and traumatic stressors and depressive symptoms in the sample (N = 177). Numbers above the paths are standardized linear regression coefficients. 230

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4. Discussion The present longitudinal study sought to examine a set of pathways through which experiences of sustained or repeated victimization and traumatic stress are associated with the development of depressive symptoms among low-income, African-American adolescent girls who were seeking outpatient mental health services. Across our three indices of victimization and traumatic stress – frequency, severity, and duration – stressors experienced at the earliest time point (prior to T1), when the girls were 12–16 years old, directly predicted depressive symptoms at the first assessment time point (T1). Depressive symptoms at T1 directly predicted depressive symptoms at T2 which, in turn, directly predicted depressive symptoms at T3. Across the stressor frequency and severity models (but not the stressor duration model), we also identified indirect paths whereby victimization and traumatic stressors occurring prior to T1 indirectly predicted depressive symptoms at T2 via depressive symptoms at T1. In addition, delayed patterns emerged in the stressor severity and duration models (but not the stressor frequency model), such that victimization and traumatic occurring prior to T1 directly predicted depressive symptoms at T2. Our findings of shared pathways from early experiences of victimization and trauma to continuing depressive symptoms across adolescence, regardless of the index used to assess victimization and trauma, illustrates the long-lasting impact of such experiences on depressive outcomes and underscores the need for prevention and intervention efforts to be implemented at the earliest time points (prior to adolescence) among low-income, African-American girls with histories of repeated trauma and victimization. Although the adolescent girls in this sample were recruited from mental health clinics at a time when they were seeking treatment, our findings suggest that treatment should be implemented even before adolescence to prevent the cascade of depressive symptoms associated with victimization and trauma in childhood. Trauma-focused cognitive behavioral interventions have been successfully used to treat depression symptoms in victimized children as young as 8 years old (Cohen et al., 2005) with promising long-term benefits (Jensen et al., 2017). The persistence of depressive symptoms across adolescence also suggests that treatments which incorporate booster sessions or ongoing mental health services may be effective in protecting adolescents with an early history of depression. Although the mechanisms by which early victimization and trauma confer risk for depression remain unknown, studies have explored the biological, cognitive, affective, and behavioral changes that are associated with experiences of victimization and trauma. Some studies have focused on biological changes such as reduced sensitivity to positive or rewarding stimuli, a process that has been implicated in the etiology of depression (Guyer et al., 2006). Other studies suggest that early experiences of victimization and trauma leads to maladaptive cognitive schemas that include expectations that violence or aggression is necessary to achieve one's goals (Schwartz & Proctor, 2000). In a study of homeless children conducted in Africa, high levels of maltreatment were associated with a bias towards over-attribution of anger to negative facial expressions, suggesting that victimization experiences may alter a child's emotional processing and regulation (Ardizzi et al., 2013). We obtained some support for the stress proliferation model (Pearlin et al., 2005). That is, in both the stressor frequency and the severity models (but not in the stressor duration model), victimization and traumatic stressors occurring between T1 and T2 directly predicted victimization and traumatic stressors between T2 and T3. In the stressor severity model, two additional findings consistent with stress proliferation were found. Victimization and traumatic stressors occurring prior to T1 directly predicted victimization and traumatic stressors occurring between T1 and T2 and both directly and indirectly predicted stressors occurring between T2 and T3. The idea that victimization and trauma leads to further victimization and trauma is consistent with previous research (Classen et al., 2005; Finkelhor et al., 2007; Fisher et al., 2015; Horwitz et al., 2001; Widom et al., 2005) and highlight the need for early interventions aimed at preventing initial victimization and trauma in childhood, as well as re-victimization of those who experience early trauma. In our sample, early exposure leads not only to a cascade of depressive symptoms across adolescence, but also to continued victimization and trauma. Targeted treatments that address victimization trauma in culturally diverse populations, such as traumafocused cognitive behavioral therapy (Cohen, Mannarino, & Deblinger, 2006) or cue centered treatment (Carrion, Kletter, Weems, Rialon Berry, & Rettger, 2013) may be effective for reducing depressive symptoms in girls with early histories of victimization. We found mixed support for the stress generation model of depression (Hammen, 1991). This model emphasizes the transactional relations between a person and her environment and proposes that depressed or depression-prone individuals may contribute to their symptoms by generating stressful circumstances that perpetuate or exacerbate their symptoms. Consistent with stress generation, our findings show that greater depressive symptoms at T1 were related to a greater frequency of victimization and traumatic stressors at the subsequent time period, between T1 and T2. On the other hand, greater depressive symptoms at T2 were related to a lower frequency of victimization and traumatic stressors at the subsequent time period, between T2 and T3. Furthermore, we did not find support for the stress generation hypothesis in the stressor severity nor the stressor duration model. As such, in our sample, we found limited support for the idea that girls who experience depressive symptoms contribute to further stressors in their lives, and in fact, as girls aged depressive symptoms were associated with decreased vulnerability to victimization and traumatic stressors. These results may be attributable in part to the relatively low depressive symptoms in the sample; that is, symptom ratings in our sample fell below the clinical range. The limited support we obtained for the stress generation hypothesis may also be due to the types of stressors we assessed. The Lifetime Victimization and Trauma History (Widom et al., 2005) interview focuses on trauma and violence exposure. Some of the events assessed by this interview, such as experiencing a natural disaster, a robbery, or a serious accident, are considered “independent” events, or events that are outside of the individual’s control (Brown & Harris, 1978). The stress generation model of depression refers to events that are at least, in part, influenced by the individual's behavior. Such events often involve a mutual interpersonal transaction (e.g., breakup of a romantic relationship) (Hammen, 1991). Thus, the interview’s inclusion of stressors that tend to fall outside the person’s control likely weakened our ability to assess for stress generation processes in this sample. Because our sample was recruited directly from mental health clinics, it is also possible that mental health treatment, and presumably learning coping mechanisms for managing symptoms and regulating stress, reduced girls’ vulnerability to self-generated forms of stress. 231

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A few additional limitations should be considered when interpreting our findings. First, the initial assessment (T1) occurred when girls were adolescents (12–16 years old), and as such, we do not have information about the girls’ depression symptoms prior to T1. Second, this study focused on African-American adolescent girls from socioeconomically disadvantaged communities limiting generalizability to other disadvantaged groups. Future studies should include samples that represent a wider range of ages, genders, and ethnicities. It should be noted, however, that our sample represents a particularly vulnerable population beyond community samples, in that all girls were seeking mental health services. Furthermore, our focus on low-income youth is supported by literature showing that the effect of victimization on depressive outcomes is higher among low-income youth relative to high-income youth (Andrews et al., 2015). Third, our study relied on girls’ reports of their own symptoms and stressors, which may have resulted in informant bias. It is important to note, however, that the use of structured interviews to assess for stress has been shown to enhance recall and minimize potential reporting biases relative to use of questionnaires (McQuaid, Monroe, Roberts, Kupfer, & Frank, 2000). Nevertheless, it is possible that information gathered during the interviews was biased by the presence of psychiatric symptoms. The use of multiple informants would help reduce this bias (De Los Reyes & Kazdin, 2005). Fourth, larger samples of at least 200–300 are most appropriate for testing structural equation models, and therefore interpretation of findings is limited by the relatively small sample, which may affect the stability of findings. Replication with larger samples is required for increased confidence in these findings. Fifth, girls in this sample showed higher levels of symptoms at the initial time point (T1) relative to subsequent time points. One possible explanation for this observation may be that the girls were recruited from outpatient mental health clinics at a time when they were seeking treatment and presumably at high levels of distress. In addition, future research with larger samples might examine the interactive effects of stress and depression in predicting future depression or stress exposure. Finally, victimization and traumatic stressors were assessed retrospectively at the final study time point (T4). Although this assessment was for lifetime exposure, and each stressor was carefully dated using adolescents’ reports of stressor onset and duration, our design is limited in providing the exact temporal sequence between stressors and symptom onset. A prospective longitudinal design in which girls are assessed prior to the onset of (any) symptoms or stressors, and re-assessed with frequent follow-ups, would provide greater precision in delineating stress and psychopathology pathways over time and would make a stronger case for temporal directionality of effects. In addition, an interview method such as the Bedford College Life Event and Difficulty Schedule (LEDS) (Brown & Harris, 1978) would be helpful in teasing apart the stressors that may have set symptoms in motion from those that were triggered by the symptoms. In the LEDS method, independent raters provide consensus ratings of whether each stressor appeared to be caused by the depression symptoms. Stressors that appear to have been triggered by symptoms can be excluded from analyses or examined separately. Despite the fact that we could not test a causal model, we view our findings as valuable first steps from which to generate further hypotheses about the types of stress that may place young low-income girls at risk for depressive onset, and to motivate future studies specifically designed to test a causal model of this process. In addition, this study assessed detailed information about the ages that various stressors occurred, which offers an important strength over most existing research. Despite these limitations, the current study is unique in using diagnostic and structured victimization interviews to examine pathways to depression via exposure to traumatic stress. As such, findings highlight that victimization and traumatic stressors experienced early in childhood and adolescence may have long-standing effects on risk for psychopathology across adolescence and into emerging adulthood. Each stress model (stressor frequency, severity, and duration) showed a direct path from early victimization and traumatic stressors to later depressive symptoms, and in some cases, this effect was also lagged. In addition, two of the three models showed a direct path from victimization and trauma at one time point to victimization and trauma at the subsequent time point. Given that victimization and traumatic stressors occur within a larger social and cultural context, effective prevention in this population requires community-level interventions that target the contextual factors that give rise to victimization and trauma exposure, such as poverty, sustained unemployment, unsafe neighborhoods, and a lack of community resources. Acknowledgements This research was supported by funding from the National Institute of Mental Health and the Eunice Kennedy Shriver National Institute of Health and Human Development (R03MH086361; R01MH065155; R01HD067511). Preparation of this manuscript was supported by the National Institute of Mental Health Research Scientist Development Award (K01MH100433) to A. Gershon. We thank the mothers and daughters who participated in the study and gratefully acknowledge the administrators and clinical staff at the outpatient mental health clinics who identified eligible families. We also thank Gloria Coleman, the study recruiter at UIC, and for their invaluable assistance in conducting interviews and entering data, graduate students at Rosalind Franklin University of Medicine and Science Bola Animashaun, Tiffany Brakefield, Neha Darji, and Mary Beth Tull, and an undergraduate intern, Paige Saltzberg. Data collected include self-reported behaviors that place girls at risk for sexually transmitted infections, including HIV/AIDS, and may not represent girls’ willingness to engage in the behavior. References Achenbach, T. (1991). 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