Coping Strategy Utilization Among Posttraumatic Stress Disorder Symptom Severity and Substance Use Co-Occurrence Typologies: A Latent Class Analysis

Abstract

Objective:
There is a lack of research on primary prevention of posttraumatic stress disorder (PTSD) symptoms and substance use among trauma-exposed populations. To guide the development of more effective prevention efforts, the current study sought to identify underlying coping mechanisms that impact PTSD-substance use co-occurrence.

Method:
A person-centered analytic approach (latent class analysis) examined PTSD-substance use co-occurrence typologies (classes) and identified theoretically adaptive (e.g., active coping) and maladaptive (e.g., denial) coping strategies that differentiated between classes among a sample of 1,270 trauma-exposed participants (Mage = 20.71, 73.5% female, 45.7% White).

Results:
Latent class analysis identified five distinct typologies, reflective of extant epidemiological and etiological work. Generally, behavioral disengagement and self-blame coping increased the likelihood of being in more severe PTSD-illicit substance use (e.g., cocaine) comorbidity classes. Positive reframing and planning differentiated between low and moderate illicit substance typologies with moderate PTSD severity. Venting, acceptance, and self-distraction differentiated between asymptomatic and moderate PTSD severity typologies with low illicit substance use.

Conclusions:
Findings identify general coping strategies associated with increased likelihood of being in more severe comorbidity typologies, as well as several unique coping strategies associated with risk of transitioning between low/moderate PTSD and illicit substance use classes. Relevant interventions (e.g., trauma psychoeducation, guilt-reduction therapy, psychological first aid) that may be targets for future prevention-oriented work are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Full text

Psychological Trauma: Theory,
Research, Practice, and Policy
Coping Strategy Utilization Among Posttraumatic Stress
Disorder Symptom Severity and Substance Use Co-
Occurrence Typologies: A Latent Class Analysis
Nathan T. Kearns, Ateka A. Contractor, Nicole H. Weiss, and Heidemarie Blumenthal
Online First Publication, September 7, 2020. http://dx.doi.org/10.1037/tra0000964
CITATION
Kearns, N. T., Contractor, A. A., Weiss, N. H., & Blumenthal, H. (2020, September 7). Coping
Strategy Utilization Among Posttraumatic Stress Disorder Symptom Severity and Substance Use
Co- Occurrence Typologies: A Latent Class Analysis. Psychological Trauma: Theory, Research,
Practice, and Policy. Advance online publication. http://dx.doi.org/10.1037/tra0000964

Coping Strategy Utilization Among Posttraumatic Stress Disorder
Symptom Severity and Substance Use Co-Occurrence Typologies:
A Latent Class Analysis
Nathan T. Kearns
Brown University and University of North Texas
Ateka A. Contractor
University of North Texas
Nicole H. Weiss
University of Rhode Island
Heidemarie Blumenthal
University of North Texas
Objective: There is a lack of research on primary prevention of posttraumatic stress disorder (PTSD)
symptoms and substance use among trauma-exposed populations. To guide the development of more
effective prevention efforts, the current study sought to identify underlying coping mechanisms that
impact PTSD–substance use co-occurrence. Method: A person-centered analytic approach (latent class
analysis) examined PTSD–substance use co-occurrence typologies (classes) and identified theoretically
adaptive (e.g., active coping) and maladaptive (e.g., denial) coping strategies that differentiated between
classes among a sample of 1,270 trauma-exposed participants (M
age
⫽20.71, 73.5% female, 45.7%
White). Results: Latent class analysis identified five distinct typologies, reflective of extant epidemio-
logical and etiological work. Generally, behavioral disengagement and self-blame coping increased the
likelihood of being in more severe PTSD–illicit substance use (e.g., cocaine) comorbidity classes.
Positive reframing and planning differentiated between low and moderate illicit substance typologies
with moderate PTSD severity. Venting, acceptance, and self-distraction differentiated between asymp-
tomatic and moderate PTSD severity typologies with low illicit substance use. Conclusions: Findings
identify general coping strategies associated with increased likelihood of being in more severe comor-
bidity typologies, as well as several unique coping strategies associated with risk of transitioning between
low/moderate PTSD and illicit substance use classes. Relevant interventions (e.g., trauma psychoedu-
cation, guilt-reduction therapy, psychological first aid) that may be targets for future prevention-oriented
work are discussed.
Clinical Impact Statement
This study indicates that there are distinct PTSD–substance use co-occurrence typologies that utilize
unique coping strategies for distress. Identification of these differentiating strategies may facilitate
the development of more effective prevention efforts.
Keywords: trauma, prevention, substance use, coping, posttraumatic stress
Growing evidence indicates robust associations between
posttraumatic stress disorder (PTSD) symptoms and substance
use (Debell et al., 2014; Jacobsen, Southwick, & Kosten, 2001;
Kearns et al., 2018). Indeed, PTSD and substance use disorder
(SUD) comorbidity is particularly high in both clinical and
nonclinical populations, with national epidemiological studies
indicating that upward of 46.4% of individuals with PTSD also
meet criteria for a SUD (Pietrzak, Goldstein, Southwick, &
XNathan T. Kearns, Center for Alcohol and Addiction Studies, Brown
University, and Department of Psychology, University of North Texas;
Ateka A. Contractor, Department of Psychology, University of North
Texas; Nicole H. Weiss, Department of Psychology, University of Rhode
Island; Heidemarie Blumenthal, Department of Psychology, University of
North Texas.
Work on this article by Nathan T. Kearns was supported by the
National Institute on Alcohol Abuse and Alcoholism Grant
F31AA027142 and National Institute on Drug Abuse Grant
T32DA016184, Nicole H. Weiss was supported by National Institute on
Drug Abuse Grant K23DA039327, and Heidemarie Blumenthal was
supported by National Institute on Alcohol Abuse and Alcoholism
Grant R15AA026079.
Correspondence concerning this article should be addressed to Nathan T.
Kearns, Center for Alcohol and Addiction Studies, Brown University, 121
South Main Street, Providence, RI 02912. Contact: E-mail: nathantkearns@
gmail.com
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Psychological Trauma:
Theory, Research, Practice, and Policy
© 2020 American Psychological Association 2020, Vol. 2, No. 999, 000
ISSN: 1942-9681 http://dx.doi.org/10.1037/tra0000964
1

Grant, 2011). This high co-occurrence rate is problematic, with
extant work indicating that individuals with concurrent PTSD–
SUD report elevated PTSD severity, greater psychiatric comor-
bidities, and worse treatment outcomes than individuals with
PTSD alone (McCauley, Killeen, Gros, Brady, & Back, 2012;
Read, Brown, & Kahler, 2004).
To ameliorate these problematic outcomes, empirically based prac-
tice guidelines for patients with comorbid PTSD–SUD have been
established, recommending integrated treatments that incorporate el-
ements of cognitive– behavioral therapy, motivational interviewing,
and/or exposure therapies for PTSD (McCauley et al., 2012). The
development of these treatments has been partially driven by extant
clinical research emphasizing the utility of bolstering adaptive coping
strategies (Najavits, 2002) to replace maladaptive coping strategies
found to be associated with PTSD–SUD comorbidity (Ford & Russo,
2006). However, despite a burgeoning literature focused on identify-
ing, understanding, and addressing coping strategies employed by
PTSD–SUD patients in clinical settings, little work has evaluated
coping mechanisms underlying PTSD and substance use co-
occurrence in nonclinical populations—individuals not currently
seeking treatment and with no history of treatment for PTSD or SUD.
Importantly, the limited existing literature focused on identification
of coping strategy utilization for PTSD–substance use in nonclinical
populations has generally indicated that adaptive coping strategies—
such as social support (e.g., Bryant-Davis et al., 2015), acceptance
(e.g., Kearns, Jackson, Elliott, Ryan, & Armstrong, 2018; Vujanovic,
Bonn-Miller, & Marlatt, 2011), and cognitive reframing (e.g., Brief,
Rubin, Enggasser, Roy, & Keane, 2011)—are associated with lesser
PTSD symptom severity and lesser substance use; conversely, mal-
adaptive coping strategies—such as avoidance (e.g., Bordieri, Tull,
McDermott, & Gratz, 2014), self-blame (e.g., Startup, Makgek-
genene, & Webster, 2007), and self-distraction (e.g., Hruska, Fallon,
Spoonster, Sledjeski, & Delahanty, 2011; Kearns et al., 2018)— have
been associated with more severe posttraumatic stress and substance
use. However, reliance on this research base may be problematic due
to at least two notable methodological limitations. More specifically,
extant work has generally (a) examined potential coping strategies in
isolation (i.e., focusing on a singular construct), disallowing for com-
prehensive comparative evaluation of the impacts and magnitudes of
influence of disparate adaptive and maladaptive coping strategies,
and/or (b) presumed that a particular construct was being utilized as a
coping strategy, without explicitly evaluating that construct as a
means of coping with posttraumatic stress or problematic substance
use (e.g., assuming a general measure of perceived social support was
indicative of the frequency with which an individual utilizes that
social support as a means of coping).
Appropriately identifying and comprehensively evaluating
these adaptive and maladaptive coping mechanisms in nonclini-
cal populations may be particular important for two reasons.
First and foremost, the prevention-oriented interventions cur-
rently being employed following trauma have not been effective
(see Roberts et al., 2019 for review). For example, a meta-
analysis indicated that psychological debriefing—a popular
technique for managing psychological distress following trau-
ma— did not prevent the onset of PTSD, nor did it reduce
general psychological morbidity, depression, or anxiety (Rose,
Bisson, Churchill, & Wessely, 2002). Second, despite the inef-
fectiveness of these interventions, there is a lack of research on
primary prevention of PTSD (see Skeffington, Rees, & Kane,
2013 for review) and broadly applicable substance use preven-
tion strategies (e.g., environmental management; DeJong &
Langford, 2002) and, subsequently, no work evaluating preven-
tative interventions for PTSD–substance use co-occurrence fol-
lowing trauma. As such, there is little information available to
justify or guide the development of more effective prevention
efforts to replace current nonefficacious interventions.
Additionally, although increasingly more common in re-
search independently evaluating PTSD (e.g., Contractor, Roley-
Roberts, Lagdon, & Armour, 2017) and substance use (e.g.,
Connor, Gullo, White, & Kelly, 2014), few studies evaluating
both PTSD and substance use have acknowledged heterogeneity
in the general population via use of appropriate person-centered
statistical approaches (e.g., latent class analysis [LCA]; Ander-
son, Hruska, Boros, Richardson, & Delahanty, 2018). These
person-centered statistical approaches provide several advan-
tages over standard analytic techniques (e.g., regression mod-
eling), which typically assess broad, linear associations be-
tween PTSD symptoms and substance use; alternatively, LCA
first identifies distinct subgroups—termed typologies or class-
es—that exist within the sample based on comorbid response
patterns, then compares those typologies based on relevant
health outcomes and/or population characteristics, such as cop-
ing strategy utilization (Kline, 2011; McCutcheon, 1987).
Given the lack of research aimed at understanding PTSD
symptom–substance use co-occurrence in nonclinical popula-
tions, identification of such typologies will be a critically
important step in pinpointing coping mechanisms underlying or
impacting PTSD–substance use comorbidity patterns within
this trauma population, which, in turn, will provide information
that may aid in the development of effective prevention
efforts.
Addressing the aforementioned limitations, the current study
aims to comprehensively examined coping strategy utilization
among PTSD–substance use co-occurrence typologies in a
large, nonclinical sample of trauma-exposed individuals using a
recommended person-centered statistical approach. More spe-
cifically, the current study examined the optimal latent class-
solution based on endorsed PTSD symptom severity and type of
substances used (e.g., alcohol, marijuana), then evaluated the
extent to which 14 unique coping strategies differentiated be-
tween identified PTSD–substance use classes. Given previous
LCA findings regarding PTSD comorbidities (Anderson et al.,
2018; Contractor et al., 2017), as well as strong associations
between PTSD symptom severity and substance use (Debell et
al., 2014; Jacobsen et al., 2001), it was hypothesized that the
current LCA analyses would, at minimum, produce three par-
allel low/medium/high PTSD–substance use co-occurrence
classes. Further, given the focus on developing adaptive coping
mechanisms in many empirically supported integrated treat-
ments for PTSD–SUD (e.g., Seeking Safety; Najavits, 2002), it
was generally hypothesized that theoretically adaptive coping
strategies (e.g., active coping, social support, positive refram-
ing) would be associated with less severe PTSD–substance use
typologies, whereas theoretically maladaptive strategies (e.g.,
self-blame, denial) would be associated with more severe
classes.
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2KEARNS, CONTRACTOR, WEISS, AND BLUMENTHAL

Method
Participants and Procedure
The current study sample comprised 1,270 undergraduates
(M
age
⫽20.71, 73.5% female; 45.7% White) attending a large
university in the southwestern United States. Data were drawn
from a larger study evaluating psychological well-being and sub-
stance use from December 2016 through January 2019. Partici-
pants were recruited via the university online research study pool
and completed an online questionnaire battery via Qualtrics—an
online data management software that complies with Health In-
surance Portability and Accountability Act and Family Educa-
tional Rights and Privacy Act regulations. Eligibility criteria in-
cluded (a) being above the age of 18 and (b) experiencing at least
one Diagnostic and Statistical Manual of Mental Disorders
(DSM)–5-defined PTSD Criterion A traumatic event (American
Psychiatric Association, 2013) measured by the Life Events
Checklist for DSM–5 (LEC-5; Weathers et al., 2013a). Participants
were compensated via course credit. All procedures were approved
by the institutional review board at the University of North Texas.
See Table 1 for full demographic characteristics of the sample.
Measures
Traumatic experiences. The LEC-5 (Weathers et al., 2013a)
assessed the presence of DSM–5 Criterion A traumatic events
(American Psychiatric Association, 2013). The LEC-5 consists of
16 specified traumatic events (e.g., physical and sexual assault)
and an option for an unspecified traumatic event. Only participants
Table 1
Demographics and Class Comparisons for Full Sample and Five Classes
Variable
Full sample
(N⫽1,270)
Class 1
(n⫽546)
Class 2
(n⫽139)
Class 3
(n⫽285)
Class 4
(n⫽210)
Class 5
(n⫽90)
Age
a
20.71 ⫾3.19 20.35 ⫾2.43 22.20 ⫾4.27 20.54 ⫾3.05 20.74 ⫾3.35 21.09 ⫾4.48
Biological sex (Female)
a
868 (73.5%) 379 (69.4%) 91 (65.5%) 230 (80.7%) 161 (76.7%) 73 (81.1%)
Race/Ethnicity
b
Asian 99 (7.8%) 46 (8.4%) 6 (4.3%) 24 (8.4%) 21 (10.0%) 2 (2.2%)
African American 175 (13.8%) 80 (14.7%) 11 (7.9%) 40 (14.0%) 33 (15.7%) 11 (12.2%)
White/Caucasian 580 (45.7%) 246 (45.1%) 75 (54.0%) 123 (43.2%) 92 (43.8%) 44 (48.9%)
Hispanic/Latino 253 (19.9%) 114 (20.9%) 27 (19.4%) 61 (21.4%) 32 (15.2%) 19 (21.1%)
Other 17 (1.3%) 11 (2.0%) 1 (0.7%) 1 (0.4%) 2 (1.0%) 2 (2.2%)
Multiracial 144 (11.3%) 48 (8.8%) 19 (13.7%) 36 (12.6%) 29 (13.8%) 12 (13.3%)
Socioeconomic status
a
Less than $25,000 169 (13.3%) 68 (12.5%) 13 (9.4%) 33 (11.6%) 39 (18.6%) 16 (17.8%)
$25,000 to $50,000 333 (26.2%) ‘145 (26.6%) 35 (25.2%) 80 (28.1%) 49 (23.3%) 24 (26.7%)
$50,000 to $75,000 294 (23.1%) 134 (24.5%) 30 (21.6%) 61 (21.4%) 48 (22.9%) 21 (23.3%)
More than $75,000 473 (37.2%) 199 (36.6%) 61 (43.9%) 111 (38.9%) 73 (34.8%) 29 (32.2%)
Substance use endorsement
Alcohol 1,020 (80.3%) 406 (74.9%) 138 (99.3%) 229 (80.34%) 172 (81.9%) 72 (80.0%)
AmED 547 (43.1%) 177 (32.4%) 115 (82.7%) 112 (39.3%) 100 (47.6%) 43 (47.8%)
Cannabis 693 (54.6%) 237 (43.4%) 137 (98.6%) 142 (49.8%) 126 (60.0%) 51 (56.7%)
Cocaine 157 (12.4%) 1 (0.2%) 80 (57.6%) 21 (7.4%) 38 (18.1%) 17 (18.9%)
Prescription stimulants 171 (13.5%) 3 (0.5%) 81 (58.3%) 31 (10.9%) 37 (17.6%) 19 (21.1%)
Schedule I/II hallucinogens 182 (14.3%) 2 (0.4%) 100 (71.9%) 22 (7.7%) 42 (20.0%) 16 (17.8%)
Schedule III hallucinogens 144 (11.3%) 4 (0.7%) 71 (51.1%) 17 (6.0%) 40 (19.0%) 12 (13.3%)
Trauma type (worst)
c
Natural disaster 67 (5.3%) 52 (9.5%) 1 (0.7%) 5 (1.8%) 8 (3.8%) 1 (1.1%)
Fire or explosion 22 (1.7%) 14 (2.6%) 5 (3.6%) 2 (0.7%) 1 (0.5%) —
Transportation accident 217 (17.1%) 140 (25.6%) 26 (18.7%) 39 (13.7%) 10 (4.8%) 2 (2.2%)
Serious accident during activity 42 (3.3%) 29 (5.3%) 2 (1.4%) 7 (2.5%) 4 (1.9%) —
Exposure to toxic substance 3 (0.2%) 1 (0.2%) — — 1 (0.5%) 1 (1.1%)
Physical assault 83 (6.5%) 29 (5.3%) 9 (6.5%) 19 (6.7%) 17 (8.1%) 9 (10.0%)
Assault with a weapon 27 (2.1%) 11 (2.0%) 1 (0.7) 11 (3.9%) 4 (1.9%) —
Sexual assault 209 (16.5%) 49 (9.0%) 22 (15.8%) 60 (21.1%) 52 (24.8%) 26 (28.9%)
Other unwanted sexual experience 120 (9.4%) 42 (7.7%) 15 (10.8%) 30 (10.5%) 25 (11.9%) 8 (52.2%)
Combat or war exposure 8 (0.6%) 2 (0.4%) 1 (0.7%) 1 (0.4%) 2 (1.0%) 2 (54.4%)
Captivity 7 (0.6%) 2 (0.4%) 1 (0.7%) 4 (1.4%) — —
Life-threatening illness/injury 85 (6.7%) 46 (8.4%) 6 (4.3%) 17 (6.0%) 13 (6.2%) 3 (3.3.%)
Severe human suffering 32 (2.5%) 9 (1.6%) 2 (1.4%) 9 (3.2%) 6 (2.9%) 6 (6.7%)
Sudden violent death of loved one 71 (5.6%) 28 (5.1%) 3 (2.2%) 18 (6.3%) 13 (6.2%) 9 (10.0%)
Sudden accidental death of loved one 86 (6.8%) 36 (6.6%) 19 (13.7%) 15 (5.3%) 13 (6.2%) 3 (3.3%)
Serious injury to someone else 10 (0.8%) 1 (0.2%) 3 (2.2%) 2 (0.7%) 1 (0.5) 3 (3.3%)
Unspecified traumatic experience 160 (12.6%) 39 (7.1%) 18 (12.9%) 46 (16.1%) 40 (19.0%) 17 (18.9%)
Note. Class 1 ⫽asymptomatic PTSD–low illicit substance; Class 2 ⫽asymptomatic PTSD– high illicit substance; Class 3 ⫽low NACM/AAR–low illicit
substance; Class 4 ⫽moderate PTSD–moderate illicit substance; Class 5 ⫽high PTSD–moderate illicit substance; AmED ⫽alcohol mixed with energy
drinks.
a
Data were not available for one participant (0.1%).
b
Data were not available for two participants (0.2%).
c
Designated by the participant as their “most
stressful or traumatic” event; all participants endorsed that at least one of the 16 specified traumatic events “happened to them.”
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3
COPING IN PTSD-SUBSTANCE USE TYPOLOGIES

who endorsed that at least one of the 16 specified traumatic events
“happened to me” (cf. “witnessed it”) were included in the anal-
yses, consistent with the conservative approach used in trauma
research (e.g., Kearns, Cloutier, Carey, Contractor, & Blumenthal,
2019; Paulus, Vujanovic, & Wardle, 2016; Thornley, Vorsten-
bosch, & Frewen, 2016).
PTSD symptom severity. The PTSD Checklist for DSM–5
(PCL-5; Weathers et al., 2013b) is a 20-item self-report measure
assessing past-month PTSD symptom severity. Participants were
asked to complete the PCL-5 in response to their most stressful
event from the LEC-5. The PCL-5 includes four subscales of
Intrusions (Items 1–5), Avoidance (Items 6 –7), Negative Altera-
tions in Cognition and Mood (NACM; Items 8 –14), and Altera-
tions in Arousal and Reactivity (AAR; Items 15–20). Responses
range from 0 (not at all)to4(extremely). The PCL-5 is a psycho-
metrically sound measure (Blevins, Weathers, Davis, Witte, &
Domino, 2015) and evidenced good reliability for the PTSD sub-
scales in the current study (Cronbach’s ␣s⫽.89 to .92).
Substance use endorsement. Past-month substance use was
assessed via a series of single-item questions derived from Barrett,
Darredeau, and Pihl (2006). Specifically, participants were asked,
“In the past month, how many times have you used [insert sub-
stance] for the purpose of getting high, drunk, stoned, buzzed, or
intoxicated?” for eight substances or categories of substances with
similar neurological effects: alcohol, alcohol mixed with energy
drinks (AmED), marijuana, cocaine, prescription stimulants (e.g.,
dextroamphetamine [Adderall], methylphenidate [Ritalin]), Sched-
ule I/II hallucinogens (e.g., methylenedioxy-methamphetamine
[MDMA], psilocybin [mushrooms]; Drug Enforcement Adminis-
tration, 2017), and Schedule III hallucinogens (e.g., lysergic acid
diethylamide [LSD] and ketamine; Drug Enforcement Adminis-
tration, 2017). Responses to each substance use question ranged
from zero occasions to 20 or more occasions. Participants report-
ing zero occasions were coded as 0 (no), indicating no past-month
use of that category of substances; conversely, participants report-
ing one to two occasions or higher were coded as 1 (yes), indicat-
ing past-month use of that category of substances (Kearns et al.,
2019).
Coping strategies. The Brief COPE (Carver, 1997) is a 28-item
self-report measure that evaluates 14 strategies (two questions for
each strategy) for coping with stress: self-distraction, active coping,
denial, substance use, emotional support, instrumental support, accep-
tance, behavioral disengagement, venting, positive reframing, plan-
ning, humor, religion, and self-blame. Participants rated the frequency
of using each strategy on a 4-point scale from 1 (I haven’t been doing
this at all)to4(I’ve been doing this a lot). Brief COPE subscales
evidence good psychometric properties (Carver, 1997) and adequate
internal consistency: self-distraction (␣⫽.62), active coping (␣⫽
.76), denial (␣⫽.77), substance use (␣⫽.93), emotional support
(␣⫽.83), instrumental support (␣⫽.85), acceptance (␣⫽.77),
behavioral disengagement (␣⫽.75), venting (␣⫽.66), positive
reframing (␣⫽.81), planning (␣⫽.79), humor (␣⫽.84), religion
(␣⫽.89), and self-blame (␣⫽.80).
Data Analytic Plan
Latent class analysis. An LCA was conducted to categorize
participants into latent subgroups based on their past-month en-
dorsement patterns of 20 PTSD symptoms (continuous indicators)
and eight categories of substances (categorical indicators). Maxi-
mum Likelihood estimation with robust standard errors as the
estimator was used to address nonnormality and estimate missing
data in Mplus. Missing data were minimal (i.e., ⬎4% on any given
item) and completely at random (␹
2
⫽681.19, p⫽.090). To
determine the optimal model, Akaike information criterion, Bayes-
ian information criterion (BIC), and sample-size-adjusted BIC
(SSABIC) values were evaluated; lower values indicate better
model fit (Nylund, Bellmore, Nishina, & Graham, 2007). Further,
adjusted Lo–Mendell–Rubin likelihood ratio test and bootstrapped
likelihood ratio test values were examined; a statistically nonsig-
nificant finding for the k– 1 class indicates a better fit for the
k-class solution (Nylund et al., 2007). In terms of nonstatistical
criteria, interpretability, parsimony, and size of latent classes as-
sociated within each model were considered (Cloitre, Garvert,
Weiss, Carlson, & Bryant, 2014; Lanza & Rhoades, 2013).
Three-step approach. Multinomial logistic regression (i.e., the
three-step approach) was conducted in Mplus 8 to evaluate the con-
struct validity of the optimal class solution. More specifically, the
three-step approach, which accounts for misspecification bias (Asp-
arouhov & Muthén, 2014; Vermunt, 2010), evaluated which coping
strategies impacted likelihood of specific group classification (e.g.,
Classes 1 vs. Class 2). Minimal missing data were accounted for via
listwise deletion in the secondary analyses (n⫽1,173).
Results
Latent Class Analysis (See Table 2)
Model selection. The five-class solution was selected as the
optimal LCA model for two primary reasons. First, regarding the
statistical criteria, the five-class solution produced the lowest SS-
ABIC and BIC values of all the compared models with the de-
Table 2
Fit Indices From Analyses of One to Five Latent Classes
Model AIC BIC SSABIC Entropy LMR (p) BLRT (p)
1 Class 92,290.19 92,532.09 92,382.79 ⬍.001
2 Class 79,485.34 79,871.35 79,633.12 .97 12,796.90 (⬍.001) ⬍.001
3 Class 76,159.173 76,689.29 76,362.11 .96 3,365.35 (⬍.001) ⬍.001
4 Class 74,672.60 75,346.83 74,930.71 .95 1,534.91 (.138) ⬍.001
5 Class 73,862.03 74,680.36 74,175.30 .94 862.264 (.053) ⬍.001
Note. AIC ⫽Akaike information criterion; BIC ⫽Bayesian information criterion; SSABIC ⫽sample-size-adjusted BIC; LMR ⫽Lo–Mendell–Rubin
adjusted likelihood ratio test; BLRT ⫽bootstrap likelihood ratio test.
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4KEARNS, CONTRACTOR, WEISS, AND BLUMENTHAL

crease in SSABIC and BIC values being minimal between the
four- and five-class solutions. Further, the five-class solution con-
tinued to have a significant bootstrapped likelihood ratio test value,
indicating improved fit over preceding models. Additionally, the
five-class solution had an entropy value similar to the more par-
simonious models. Thus, aside from a nonsignificant Lo–Mendell–
Rubin pvalue, which is not considered the most robust statistical
indicator for LCA class selection (Nylund-Gibson & Masyn,
2016;), the majority of recommended statistical indicators sup-
ported a five-class solution as the optimal model (DiStefano &
Kamphaus, 2006). Second, regarding the nonstatistical criteria, the
five-class solution evidenced adequate sample sizes (i.e., n⫽90 in
smallest class) and produced theoretically consistent and interpre-
table classes (described below), consistent with the larger epide-
miological literature on PTSD and substance use (Kilpatrick et al.,
2013). Further, although limited, much of the existing literature on
latent classes of PTSD comorbidities indicate more complex/
expansive solutions (Cloitre et al., 2014) than the standard low/
medium/high classes. Figure 1 provides a graphical depiction of
the five-class model.
Classification. Generally, all classes evidenced similar prob-
ability of endorsing alcohol, AmED, and marijuana/cannabis use.
As such, for ease of interpretation, classes were defined as low,
moderate, or high “illicit substance” use, indicating increased
probability of endorsing past-month cocaine, prescription stimu-
lant, and Schedule I/II and Schedule III hallucinogens use. Given
these results, Class 1 (n⫽546), which was relatively asymptom-
atic with regard to PTSD severity and was generally characterized
by lesser substance use, was labeled “asymptomatic PTSD–low
illicit substance.” Comparatively, Class 2 (n⫽139), which was
also PTSD asymptomatic, but endorsed the highest levels of sub-
stance use, was labeled “asymptomatic PTSD– high illicit sub-
stance.” Although more subtle, Classes 3 (n⫽285) and 4 (n⫽
210) both had moderate PTSD intrusion and avoidance severity;
however, Class 3 had lower PTSD NACM and AAR severity,
relative to Class 4. Further, Class 3 evidenced less illicit substance
use than Class 4. As such, Class 3 was labeled “low NACM/AAR–
low illicit substance” and Class 4 was labeled “moderate PTSD–
moderate illicit substance.” Lastly, Class 5 (n⫽90) was charac-
terized by the highest overall PTSD severity, as well as moderate
substance use endorsement patterns, similar to Class 4. As such,
Class 5 was labeled “high PTSD–moderate illicit substance.”
Three-Step Approach
Relative to the asymptomatic PTSD–low illicit substance use
class, greater frequency of substance use coping (p⬍.001) and
lesser behavioral disengagement (p⫽.031) and religious coping
(p⫽.001) increased odds of being in the asymptomatic PTSD–
high illicit substance class; greater frequency of self-distraction
(p⫽.004), substance use (p⫽.016), venting (p⫽.017), accep-
tance (p⫽.003), and self-blame coping (p⫽.011) increased odds
of being in the low NACM/AAR–low illicit substance class;
greater frequency of substance use (p⬍.001), behavioral disen-
gagement (p⫽.020), venting (p⫽.040), and self-blame coping
(p⬍.001) and lesser positive reframing coping (p⫽.004)
increased odds of being in the moderate PTSD–moderate illicit
substance class; and greater frequency of substance use (p⫽.001),
behavioral disengagement (p⬍.001), and self-blame coping (p⬍
.001) increased odds of being in the high PTSD–moderate illicit
substance class. See Table 3 for full class comparison information
(three-step approach).
0
0.5
1
1.5
2
2.5
3
3.5
4
Intrusive thoughts
Recurrent Nightmares
Flashbacks
Emotional reactivity
Physiological reactivity
Avoidance of thoughts
Avoidance of reminders
Memory impairment
Negative beliefs
Blame of self or others
Negative trauma emotions
Loss of interest
Detachment
Restricted range of affect
Irritability/anger
Rreckless behavior
Hypervigilance
Exaggerated startle response
Difficulty concentrating
Difficulty sleeping
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Alcohol
Alcohol mixed Energy
Drinks
Marijuana/Cannabis
Cocaine
Prescription Stimulants
Schedule I/II Hallucinogen
Schedule III Hallucinogen
Class 1
Class 2
Class 3
Class 4
Class 5
Figure 1. Latent profile of participants based on posttraumatic stress disorder (PTSD) symptom severity and
endorsement of substances. The indicators are the 20 items on the PTSD Checklist for DSM–5 and probability
of past-month endorsement of substances. Class 1 ⫽asymptomatic PTSD–low illicit substance; Class 2 ⫽
asymptomatic PTSD– high illicit substance; Class 3 ⫽low NACM/AAR–low illicit substance; Class 4 ⫽
moderate PTSD–moderate illicit substance; Class 5 ⫽high PTSD–moderate illicit substance. The data on the
y-axis for PTSD symptoms are assessed on symptom severity (0 – 4; higher values indicate greater severity);
substance use is assessed on probability of endorsement in the past month (0.00 –1.00).
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5
COPING IN PTSD-SUBSTANCE USE TYPOLOGIES

Relative to the asymptomatic PTSD– high illicit substance class,
greater frequency of self-distraction (p⫽.005), behavioral disen-
gagement (p⫽.007), acceptance (p⫽.019), and religion coping
(p⬍.001) and lesser substance use coping (p⬍.001) increased
odds of being in the low NACM/AAR–low illicit substance class;
greater frequency of in self-distraction (p⫽.042), behavioral
disengagement (p⫽.001), religion (p⫽.001), and self-blame
(p⫽.007) and lesser substance use (p⫽.010) and use of
instrumental support coping (p⫽.025) increased odds of being in
the moderate PTSD–moderate illicit substance class; and greater
frequency of behavioral disengagement (p⬍.001), religion (p⫽
.033), and self-blame coping (p⫽.001) and lesser substance use
coping (p⬍.001) increased odds of being in the high PTSD–
moderate illicit substance class.
Relative to the low NACM/AAR–low illicit substance class,
greater frequency of substance use (p⫽.046), planning (p⫽
.004), and self-blame coping (p⫽.002) and lesser positive re-
framing coping (p⫽.025) increased odds of being the moderate
PTSD–moderate illicit substance class; alternatively, greater fre-
quency of behavioral disengagement (p⬍.001) and self-blame
coping (p⬍.001) increased odds of being in the high PTSD–
moderate illicit substance class. Last, relative to the moderate
PTSD–moderate illicit substance class, only greater frequency of
behavioral disengagement coping (p⫽.001) increased odds of
being the high PTSD–moderate illicit substance class.
Discussion
Characterization of PTSD-Substance Use Typologies
Five distinct classes were identified in the present data, each
uniquely characterized by variations in PTSD symptom severity
and substance use endorsement. Extant epidemiological work in-
dicates that (a) the majority of individuals exposed to a potentially
traumatic event do not sustain PTSD symptomatology or develop
PTSD over time (Kilpatrick et al., 2013; Santiago et al., 2013) and
(b) the majority of individuals do not endorse current use of
psychoactive substances, beyond alcohol and marijuana (Grant et
al., 2004; Johnston, O’Malley, Miech, Bachman, & Schulenberg,
2017). Consistent with these findings, the largest of the identified
classes were individuals who were asymptomatic and endorsed
limited illicit substance use (i.e., 42.7% in Class 1 [asymptomatic
PTSD–low illicit substance]). Similarly, only 7.1% of the sample
were categorized in the high PTSD severity class (i.e., Class 5
[high PTSD–moderate illicit substance])—a proportion that
closely aligns with prevalence rates of lifetime PTSD in the
general population (⬃7%; Kilpatrick et al., 2013). The identifica-
tion of these theoretically consistent typologies that comprise
appropriate proportions of the sample provide support for the
selection and validity of the model.
Further, consistent with other LCA findings (e.g., Anderson et
al., 2018; Contractor et al., 2017), the data also produced a low/
medium/high parallel pattern of PTSD severity classes (e.g.,
Classes 1, 4, and 5). However, notably, individuals in the medium
and high PTSD severity classes (i.e., Classes 4 and 5, respectively)
did not meaningful differ in co-occurring substance use endorse-
ment. This runs counter to extant literature— drawing primarily
from self-medication (Khantzian, 1997) and mutual maintenance
models (Simpson, Stappenbeck, Luterek, Lehavot, & Kaysen,
2014)—which indicate greater PTSD severity is typically associ-
ated with greater substance use. Our findings indicate a possible
“ceiling” or plateauing effect, by which PTSD–substance use
co-occurrence becomes less strongly linked as PTSD symptoms
exceed moderate severity. Given that much of the extant literature
in epidemiological and nonclinical samples presumes a linear
association between PTSD severity and substance use (Debell et
al., 2014)—and utilize analytic approaches that assume linearity—
this potential inversely exponential pattern of co-occurrence may
have simply gone undetected in the literature. As such, future work
incorporating analytic approaches that can evaluate nonlinear
Table 3
Results for Three-Step Approach Among Classes 1 Through 5 by Coping Strategies
Coping strategy
Three-step approach (multinomial logistical regression)
Class 1 Class 2 Class 3 Class 4
1
a
vs. 2 1
a
vs. 3 1
a
vs. 4 1
a
vs. 5 2
a
vs. 3 2
a
vs. 4 2
a
vs. 5 3
a
vs. 4 3
a
vs. 5 4
a
vs. 5
Self-distraction 0.93 1.18
ⴱⴱ
1.12 1.11 1.27
ⴱⴱ
1.20
ⴱ
1.19 0.95 0.94 0.99
Active coping 1.06 0.97 0.94 1.06 0.92 0.89 1.01 0.97 1.10 1.13
Denial 0.82 1.09 1.08 1.21 1.33 1.31 1.47 0.99 1.11 1.12
Substance use 1.65
ⴱⴱⴱ
1.20
ⴱ
1.36
ⴱⴱⴱ
1.34
ⴱⴱ
0.72
ⴱⴱⴱ
0.82
ⴱⴱ
0.81
ⴱ
1.14
ⴱ
1.12 0.98
Use of emotional support 1.03 1.02 1.05 1.05 0.99 1.02 1.02 1.03 1.03 1.00
Use of instrumental support 1.12 0.94 0.85 0.82 0.84 0.76
ⴱ
0.73 0.91 0.88 0.96
Behavioral disengagement 0.72
ⴱ
1.08 1.22
ⴱ
1.74
ⴱⴱⴱ
1.51
ⴱⴱ
1.70
ⴱⴱⴱ
2.42
ⴱⴱⴱ
1.13 1.61
ⴱⴱⴱ
1.43
ⴱⴱⴱ
Venting 1.07 1.18
ⴱ
1.17
ⴱ
1.28 1.11 1.09 1.20 0.99 1.09 1.10
Positive reframing 0.95 0.96 0.80
ⴱⴱ
0.81 1.01 0.85 0.85 0.84
ⴱ
0.85 1.01
Planning 1.06 0.90 1.17 1.00 0.85 1.11 0.94 1.30
ⴱⴱ
1.11 0.85
Humor 0.96 0.96 1.03 0.99 1.00 1.07 1.02 1.07 1.02 0.96
Acceptance 0.95 1.20
ⴱⴱ
1.14 1.03 1.26
ⴱ
1.19 1.08 0.95 0.86 0.90
Religion 0.80
ⴱⴱ
1.04 1.01 0.99 1.30
ⴱⴱⴱ
1.26
ⴱⴱⴱ
1.23
ⴱ
0.97 0.95 0.97
Self-blame 1.08 1.16
ⴱ
1.42
ⴱⴱⴱ
1.65
ⴱⴱⴱ
1.07 1.31
ⴱⴱ
1.52
ⴱⴱⴱ
1.23
ⴱⴱ
1.43
ⴱⴱⴱ
1.16
Note. Class 1 ⫽asymptomatic PTSD–low illicit substance; Class 2 ⫽asymptomatic PTSD– high illicit substance; Class 3 ⫽low NACM/AAR–low illicit
substance; Class 4 ⫽moderate PTSD–moderate illicit substance; Class 5 ⫽high PTSD–moderate illicit substance.
a
The reference class for interpretation of adjusted odds ratios.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
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6KEARNS, CONTRACTOR, WEISS, AND BLUMENTHAL

trends at varying gradients of PTSD severity is needed to replicate
and evaluate these potential differential PTSD–substance use pat-
terns.
Alternatively, there were distinct differences in substance use
co-occurrence patterns in the moderate PTSD severity classes (i.e.,
Classes 3 and 4). More specifically, although both classes evi-
denced relatively similar intrusion and avoidance severity, there
were differences in NACM and AAR severity. Coincidingly, the
class with high NACM and AAR symptoms reported generally
higher illicit substance use co-occurrence. These findings gener-
ally support extant literature indicated that NACM and AAR
symptoms are most strongly and consistently associated with in-
creases in substance use (e.g., Jacobsen et al., 2001; Kearns et al.,
2018; Walton et al., 2018). However, the current findings expand
on that extant work, indicating that NACM and AAR symptoms
may be particularly important in understanding PTSD–illicit sub-
stance use co-occurrence among individuals evidencing moderate
symptom severity.
Last, approximately 10% of the trauma-exposed sample en-
dorsed limited PTSD symptom severity, but reported generally
high illicit substance use (i.e., Class 2 [asymptomatic PTSD– high
illicit substance]). This additional asymptomatic class (i.e., 54% of
the total sample when combined with Class 1 [asymptomatic
PTSD–low illicit substance]) is consistent with extant work indi-
cating that the majority of individuals exposed to a potentially
traumatic event do not express PTSD symptoms (Kilpatrick et al.,
2013; Santiago et al., 2013). “Lifestyle-risk” or “high-risk” theo-
retical models of trauma–substance use interplay may support the
identification of this class, purporting that illicit substance use—
particularly initial substance use during young adulthood— often
occurs in the context of other potential risky situations or behav-
iors (e.g., partying), which may increase the likelihood of being
exposed to a potentially traumatic event (e.g., unwanted sexual
experience; Cottler, Compton, Mager, Spitznagel, & Janca, 1992;
Breslau, 2009). As such, it is likely that many individuals engaging
in these riskier behaviors, including illicit substance use would
have experienced a traumatic event, but, in accordance with typical
patterns of symptom progression (Kilpatrick et al., 2013), did
not sustain or develop PTSD symptoms over time and, as such,
present as generally asymptomatic. The identification of a high
illicit substance use class may also be indicative of the sample,
given that young adults evidence the highest overall prevalence of
illicit substance use (Center for Behavioral Health Statistics &
Quality, 2015), which may be partially explained by unique de-
velopmental characteristics (e.g., experimentation; Dworkin,
2005), increased frequency of substance use initiation (i.e., first
time use; Arria et al., 2017), and availability of illicit substances
(Lipari & Jean-Francois, 2016) during this time period, particu-
larly among college students.
Interestingly, PTSD–substance use co-occurrence classes did
not statistically differ on alcohol, AmED, and marijuana use en-
dorsement. These findings may be also be indicative of the sample,
given that young adults typically report higher rates of current
alcohol and marijuana use than adolescent and broader adult
populations (Johnston et al., 2017); this high use prevalence may
make classes less distinguishable (McLaughlin et al., 2017). Future
work is needed to replicate these findings in other contextually and
developmentally distinct trauma populations. Alternatively, it may
be that “legal”— or more socially acceptable—substances are not
salient indicators of PTSD–substance use co-occurrence patterns
when other illicit substances are considered in the statistical model
(e.g., Johnston et al., 2017). This highlights the need to meaning-
fully assess other— often less prevalent—substance use (e.g., co-
caine) when evaluating PTSD–substance use co-occurrence in
nonclinical populations (Kearns et al., 2019).
Coping Strategies in Relation to PTSD-Substance
Use Typologies
Intuitively, increased use of substance use coping increased
likelihood of being in more severe PTSD–substance use comor-
bidity classes. Further, two theoretically negatively valenced, mal-
adaptive coping strategies— behavioral disengagement and self-
blame—most strongly and consistently differentiated between
classes. Specifically, increased frequency of behavioral disengage-
ment and self-blame generally increased likelihood of being in
more severe PTSD–substance use comorbidity classes. These find-
ings support previous work in contextually specific, nonclinical
trauma populations. For example, constructs with similar opera-
tional definitions of behavioral disengagement coping (e.g.,
learned helplessness, mental defeat) have long been implicated as
primary underlying mechanisms related to the development of
PTSD (Foa & Rothbaum, 1992), and have been linked to increased
risk of PTSD and PTSD comorbidities (e.g., substance use; Bargai,
Ben-Shakhar, & Shalev, 2007). Further, an extensive literature has
identified self-blame as a robust indicator of PTSD severity
(Startup et al., 2007), as well as a risk factor for comorbid PTSD–
substance use among sexual assault survivors (Ullman, Filipas,
Townsend, & Starzynski, 2006) and military personnel (Friedman,
2006).
Interestingly, few of the theoretically positively valenced, adap-
tive coping strategies consistently differentiated PTSD–substance
use typologies; in fact, active coping, emotional support, and
humor did not significantly differentiate between any of the iden-
tified classes. These findings are inconsistent with literature im-
plicating social support as a protective factor against the develop-
ment of PTSD symptoms after trauma (Agaibi & Wilson, 2005),
and as a mediator in the associations between trauma, PTSD
severity, and substance use (Kilpatrick et al., 2013). These findings
are also inconsistent with the limited literature that has evaluated
coping strategies among PTSD comorbidity subgroups using
person-centered statistical approaches (e.g., Contractor et al.,
2017). Generally, these findings indicate that prevention-oriented
work targeting PTSD–substance use co-occurrence may need to
focus on modifying or replacing maladaptive coping strategies
(e.g., self-blame), as opposed to reinforcing certain adaptive strat-
egies that individuals are already utilizing following trauma (e.g.,
active coping).
Current study findings also highlight unique coping strategies
differentiating between more nuanced PTSD–substance use
classes. For example, the two moderate PTSD severity classes (i.e.,
Classes 3 and 4) were most strongly differentiated by two theo-
retically positively valenced coping strategies. In particular, in-
creased frequency of “planning” and decreased “positive refram-
ing” were associated with greater likelihood of being in the class
with greater NAMC/AAR severity and higher illicit substance use
(i.e., Class 4). One possible explanation is that frequent rumination
over developing a “plan” for symptom relief, without professional
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7
COPING IN PTSD-SUBSTANCE USE TYPOLOGIES

guidance on the appropriate steps, may manifest into trauma-
related anxiety and uncertainty, leading to greater NAMC/AAR
severity and substance misuse. Indeed, “uncertainty” has been
linked to increased NAMC/AAR severity in nonclinical trauma
samples (Boelen, 2010), as well as increased risk for co-occurring
PTSD–SUD in clinical samples (Banducci, Bujarski, Bonn-Miller,
Patel, & Connolly, 2016). Conversely, positive reframing— often
characterized by greater optimism and openness to positive out-
comes—may be “protective” against more severe NAMC and
AAR symptoms. Indeed, constructs like optimism and openness
have garnered increasing attention in the positive psychology
literature and are effective in reducing PTSD severity and related
negative outcomes, such as problematic substance use (e.g.,
Knaevelsrud, Liedl, & Maercker, 2010).
Last, the current findings highlighted a number of distinct cop-
ing strategies that differentiated the asymptomatic class with min-
imal illicit substance use (Class 1 [asymptomatic PTSD–low illicit
substance]), from the moderate PTSD symptom class with minimal
illicit substance use (Class 3 [low NACM/AAR–low illicit sub-
stance]). More specifically, increased frequency of venting, accep-
tance, and self-distraction coping strategies increased likelihood of
being in the more severe PTSD symptom severity class. Although
often considered positively valenced, venting without appropriate
therapeutic context may be maladaptive—a concept often dis-
cussed in social support deterioration models following trauma
exposure (Gjesfjeld, Greeno, Kim, & Anderson, 2010). Further, as
previously discussed, acceptance of PTSD symptomatology may
be manifesting as another form of learned helplessness, which is
associated with both increased symptom severity and risk of sub-
stance use (Mikulincer, 2013). Finally, self-distraction is often
considered a form of avoidance coping—a construct that is fun-
damental to the development and maintenance of PTSD (Asmund-
son, Stapleton, & Taylor, 2004), and is a robust risk factor for the
development of posttrauma substance use (e.g., Hruska et al.,
2011).
Implications
Several implications for future prevention-oriented work might
be gleaned from these collective results. Drawing from the PTSD
treatment literature, the general finding that behavioral disengage-
ment and self-blame appear to be the most robust indicators of
transitioning into more severe PTSD–substance use classes sug-
gests that trauma psychoeducation—which systematically engage
trauma survivors by providing information about the nature of
traumatic stress, what to expect, and what to do about their
symptoms (Pratt et al., 2005; Wessely et al., 2008)— combined
with components of guilt-reduction therapy to reduce self-
blame (Norman, Wilkins, Myers, & Allard, 2014) may be an
appropriate “next step” for efforts targeting prevention of PTSD–
substance use. The unique coping strategies (e.g., planning and
positive reframing) that characterized more nuanced class differ-
entiations further reinforce the potential utility of trauma psychoe-
ducation/interventions as a direction for prevention efforts, as
these interventions aim to provide trauma survivors with knowl-
edge to understand, adequately “plan” for, and manage their trau-
matic stress reactions (Wessely et al., 2008), thus potentially
reducing trauma-related uncertainty. Notably, these findings also
support many of the evidenced-informed strategies outlined in
psychological first aid (Brymer et al., 2006)—a systematic set of
eight core “helping actions,” which include contact and engage-
ment, information on coping support, and information gathering—
and provide additional information that may be useful in the
development, validation, expansion, and adaptation of psycholog-
ical first aid and other preventative intervention tools targeting
nonclinical populations at risk for PTSD–substance use.
Limitations
The current study findings should be interpreted with consider-
ation of some limitations. First, the cross-sectional nature of the
data does not allow for evaluation of temporal precedence. As
such, it is plausible that varying levels of PTSD–substance use
co-occurrence may be impacting the development of coping strat-
egies. Future work involving longitudinal designs are needed to
better understand the (bi-)directionality of coping, PTSD, and
substance use. Second, PTSD severity, substance use history, and
coping strategy utilization were all assessed via self-report, which
may inherently introduce response bias. Future work should con-
sider use of structured interviews for PTSD and coping strategies,
as well as established timeline follow-back procedures for sub-
stance use. Third, given the focus of the current study on compre-
hensively evaluating coping strategy utilization, other variables
that may impact PTSD symptom–substance use patterns were not
examined (e.g., biological sex, race/ethnicity; Dansky et al., 1996;
Stewart, Ouimette, & Brown, 2002). Future work should consider
evaluating of these sociodemographic constructs in their modeling.
Last, the nonclinical sample for this study was comprised entirely
of undergraduates. Although this subpopulation may be ideal for
preliminary evaluation of PTSD–substance use co-occurrence—
given elevated rates of trauma exposure (Read, Ouimette, White,
Colder, & Farrow, 2011), and lifetime peaks for substance use
(Johnston et al., 2017)—replication and extension are needed to
evaluate if the findings generalize to other developmentally dis-
tinct (e.g., adolescent) and contextually distinct (e.g., trauma-
specific) nonclinical populations.
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Received August 29, 2019
Revision received February 10, 2020
Accepted July 20, 2020 䡲
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11
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