1. Trajectories of Self-Regulation Symptoms Among Child Maltreatment Survivors
Dean Lauterbach, Ph.D.1, Brian Allen, PsyD2, Stefanie Poehacker, MS1,
David Phillips, MS1
1Eastern Michigan University
2Penn State College of Medicine
Special thanks to the National Data Archive on Child Abuse and Neglect at Cornell University for providing access to the data

2. Overview Self-Regulation Behavior regulation Emotion regulation
Ability to monitor one’s affect, evaluate that affect, and modify unpleasant affect (Gratz & Roemer, 2004)
Behavior regulation
Ability to monitor one’s behaviors, identify how behaviors impact current affective states, and modify those behaviors appropriately (Beauchaine, 2012)

3. Etiology and Effect of difficulties with Self-Regulation
Etiology – May arise as a consequence of childhood maltreatment (Pollak, 2008; Shields et al., 1994).
Effect – Self-regulation mediates the relationship between childhood maltreatment and a number of later-life variables including
Child abuse potential (Smith, Cross, Winkler, Jovanovic, & Bradley, 2014)
Emotional eating (Michopoulos et al., 2015)
Likelihood of revictimization later in life (Dietrich, 2007)

4. I would go so far as to say that the daily news is replete with striking examples of difficulties with self-regulation among fans at a sporting events, actors bent on flying into a drunken rage and railing on various groups, and politicians who could be baited with a tweet.
One ongoing question pertains to the stability of these difficulties

5. Stability/Instability of Self-Regulation
Prospective study of youth with ADHD (Biederman et al. 2012)
Stability
57% of youth with dysregulated scores at baseline remained dysregulated scores at follow up
72% of the subjects in the “well regulated” group at baseline remained well regulated at follow up
Change
43% improved at follow up
28% declined at follow up
A 7-wave epidemiological study of behavioral and emotional problems (Althoff, et al., 2010)
Stability
46% of boys and 39.5% of girls were dysregulated in all waves
Change
54% of boys and 60.5% of girls were not dysregulated in all waves
Community sample of children ages 4 to 7 (Blandon et al., 2008)
Substantial unexplained inter-individual heterogeneity [Variances around the intercept and slope (random effects) were significantly different from zero].

6. Synthesis
When taken together, the previous research implies the presence of 4 trajectories of self-regulation

7. A model that looks something like this with 4 classes

8. What might determine class membership?
Maltreatment – (e.g., Kim‐Spoon, Cicchetti, & Rogosch, 2013; Kim & Cicchetti, 2010)
Severity of caregiver depression – (e.g., Blandon et al., 2008; Goodman et al., 2011)
However, the effect of exposure to maltreatment and parental depression on trajectory of self-regulation remains unclear.

9. How might class membership effect later-life psychopathology?
Difficulties with self-regulation are both coincident with and predictive of a broad range of psychopathology including:
Borderline Personality Disorder (American Psychiatric Association, 2013)
Posttraumatic Stress Disorder (Powers et al 2015; Weiss, Tull, Anestis, & Gratz, 2013)
Disruptive Mood Dysregulation Disorder (Dougherty et al., 2014)
However, the relationship between trajectory of self-regulation and later life psychopathology has not been investigated.

10. Goals of the present study
To estimate the number, size, and shape of self-regulation trajectories.
Test two predictors trajectory class membership:
Maltreatment
Maternal caregiver depression
Test the relationship between trajectory class membership and later-life PTSD symptoms.
I will be using an increasingly popular technique called Growth Mixture Modeling

11. Growth Mixture Modeling (GMM)
Person centered
Focused on identifying the minimum number of latent class trajectories that characterize a population
Slope – continuous latent variable
Intercept – continuous latent variable
Class – categorical latent variable C y0 y1 y2 y4 y3 1 3 4 2 η0 η1
Covariates
Maltreatment
Maternal depression
Distal Outcome
PTSD Sx

12. Current study
LONGSCAN – Longitudinal Studies on Child Abuse & Neglect – Multisite study examining predictors and consequences of child maltreatment.
Primary growth variable - Child Behavior Checklist Dysregulation Profile (CBCL-DP).
Sum of raw scores on three scales of the CBCL:
Anxiety/Depression + Aggression + Attention Problems (Spencer et al., 2011).
Assessed at ages 4, 6, 8, 10, 12, 14, and 16
Predictors
# of maltreatment allegations (MMCS; Barnett et al. 1993)
Assessed at ages 0 to 16
Maternal Depression (CES-D; Radloff, 1977)
Assessed at age 4
Distal outcome variables
PTSD Symptoms (Trauma Symptom Inventory; Briere, 1995)
Assessed at age 18
MMCS = Modified Maltreatment Classification System; Barnett et al. 1993

13. Analysis strategy [See (Jung & Wickrama, 2008; Wang & Bodner, 2007; Wang & Wang, 2012; Wickrama, Lee, Walker, & Lorenz, 2016)]
Viability of a one-class model was tested
Test viability of models with 2-6 classes with within-class variance on growth parameters freely estimated
Test conditional models with covariates (predictors of class membership)
Used 3-step procedure that preserves latent classes obtained in the unconditional analyses (Asparouhov & Muthén, 2014)
Test the relationship between group membership and later (age 18) PTSD symptoms
Used Lanza approach (Lanza, Tan, & Bray 2013)

14. Participants N = 1354 – 48.5% boys
Distal Outcomes
Predictors of Class Membership
# Maltreatment allegations
M = 5.28, SD = 7.1,
Range = 46
Maternal Depression
M = 12.48, SD = 10.93
Range = 59
30.1% having scores of 16 or more, the cutoff for possible depression
Anxious Arousal
Sexual Concerns
M = 44, SD = 8.8
M = 46, SD = 7.8
Depression
Dysfunctional Sexual Behavior
M = 47, SD = 9.0
Anger/Irritability
M = 50, SD = 10.6
Impaired Self-Reference
M = 47, SD = 10.3
Intrusive Experiences
M = 49, SD = 10.4
M = 48, SD = 9.5
Defensive Avoidant
Tension Reduction Behavior
M = 50, SD = 10.4
Dissociation
M = 49, SD = 10.1
M = 48, SD = 10.3
There was considerable heterogeneity in the number of allegations of child maltreatment. The average age of first CPS referral was about 2 ½ years of age but ranged from birth to 17 years of age. The total number of maltreatment allegations  ranged from 0 to 46 with an average of about 5 ¼ allegations.
Maternal depression scores ranged from 0 to 59 (M = 12.48, SD = 10.93, N = 1163) with 30.1% having scores of 16 or more, the cutoff for possible depression.
Mean T-Scaled scores for the Trauma Symptom Inventory were all below 50. The percent with T-Scaled scores > 65T ranged from 3.5% (Sexual Concerns) to 11.1% (Defensive Avoidant).

15. Figure 1. Unconditional 1-class model
Reasonably well-fitting model (CFI = 0.93, TLI = 0.94)
As children age, frequency of dysregulated behaviors/emotions decreases
Initially a 1-class latent growth model was tested. The fit indices suggest that this is a reasonably well fitting model. Figure 1 illustrates this 1-class model. This figure indicates that symptoms begin low and there is a modest improvement in self-regulation skills across the next assessment points.

16. If we stopped the analyses here we would arrive at the wrong conclusion

17. Tests of models with 2, 3, 4, and 5 classes
Entropy
AIC
A-BIC
LMR-LRT
BLRT
posterior probabilities 1 –
54708
54732
CFI=.93
TLI=.94
RMSEA=.09 2
.80
54494
54524
p <.00005
p < 3
.75
54422
54459 NS 4
.82
54364
54407
p <.08 5
.79
54304
54352 6
Negative variance for slope term
*p < .05, **p < .01, *** p < .001, **** p < .0001
In subsequent analyses we tested the viability of 2,3,4,and 5 class models. Findings support the relative superiority of the 4 class model.
The resulting model looked like this

18. Unconditional 4-class Linear GMM4%
10% 7%
80%
Note – low scores reflect better self-regulation skills
we have a low group composed of 80% of the sample with relatively consistent low symptoms
A second group composed of about 7% of the sample with high symptoms that decrease sharply over time
A third group composed of 10% of the sample with symptoms that start low and increase sharply
Lastly, a group composed of about 4% of the sample with symptoms that remain high throughout the evaluation period

19. Predictors of Latent Class Membership
Maternal Depression
predicted membership in the consistently high and decreasing classes relative to the consistently low class

20. Predictors of Latent Class Membership
Maternal Depression
predicted membership in the consistently high and decreasing classes relative to the increasing class

21. Predictors of Latent Class Membership
# of Maltreatment Allegations
predicted membership in the increasing class relative to the consistently low class

22. Prediction of distal outcomes
Low vs. High
Low vs. Decreasing
Low vs. Increasing
Anxious Arousal
12.36****
6.59**
14.82****
Depression
4.36*
12.23****
14.40****
Anger/Irritability
9.93***
4.45*
21.31****
Intrusive Experiences
3.30
13.09****
22.65****
Defensive Avoidant
0.32
12.39****
27.49****
Dissociation
4.20*
10.31****
11.63***
Sexual Concerns
9.60***
13.37****
12.33****
Dysfunctional Sexual Behavior
5.23*
15.65****
9.34***
Impaired Self-Reference
9.47**
12.83****
17.40****
Tension-Reduction Behavior
9.89**
16.77****
13.07****
*p < .01. **p < .01. ***p < ****p < .0001

23. Conclusions
There is clearly heterogeneity in self- regulation over time.
Efforts to support caregivers and ameliorate depressive symptoms may have multiple down stream effects including the improvement in self-regulation skills in their children and reduction in later-life PTSD symptoms.
4-class model best fit the data
There is evidence to support claims of both
stability in self-regulation over time (low and high classes)
changes in self-regulation (increasing and decreasing classes)
The most consistent predictor of membership in one of the more symptomatic self- regulation classes was maternal depression.
Membership in the more symptomatic self- regulation classes was predictive of virtually all PTSD symptoms relative to those in the low class.