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Associations of alcohol and cannabis use with change in posttraumatic stress disorder and depression symptoms over time in recently trauma-exposed individuals

Published online by Cambridge University Press:  13 June 2023

Cecilia A. Hinojosa*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
Amanda Liew
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
Xinming An
Affiliation:
Department of Anesthesiology, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jennifer S. Stevens
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
Archana Basu
Affiliation:
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
Sanne J. H. van Rooij
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
Stacey L. House
Affiliation:
Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
Francesca L. Beaudoin
Affiliation:
Department of Emergency Medicine & Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA
Donglin Zeng
Affiliation:
Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
Thomas C. Neylan
Affiliation:
Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
Gari D. Clifford
Affiliation:
Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
Tanja Jovanovic
Affiliation:
Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
Sarah D. Linnstaedt
Affiliation:
Department of Anesthesiology, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Laura T. Germine
Affiliation:
Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA The Many Brains Project, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
Scott L. Rauch
Affiliation:
Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Department of Psychiatry, McLean Hospital, Belmont, MA, USA
John P. Haran
Affiliation:
Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
Alan B. Storrow
Affiliation:
Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
Christopher Lewandowski
Affiliation:
Department of Emergency Medicine, Henry Ford Health System, Detroit, MI, USA
Paul I. Musey
Affiliation:
Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
Phyllis L. Hendry
Affiliation:
Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
Sophia Sheikh
Affiliation:
Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
Christopher W. Jones
Affiliation:
Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
Brittany E. Punches
Affiliation:
Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA Ohio State University College of Nursing, Columbus, OH, USA
Michael C. Kurz
Affiliation:
Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL, USA Department of Surgery, Division of Acute Care Surgery, University of Alabama School of Medicine, Birmingham, AL, USA Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL, USA
Robert A. Swor
Affiliation:
Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
Lauren A. Hudak
Affiliation:
Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
Jose L. Pascual
Affiliation:
Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Mark J. Seamon
Affiliation:
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
Elizabeth M. Datner
Affiliation:
Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, PA, USA Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
Anna M. Chang
Affiliation:
Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
Claire Pearson
Affiliation:
Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
David A. Peak
Affiliation:
Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
Roland C. Merchant
Affiliation:
Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
Robert M. Domeier
Affiliation:
Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
Niels K. Rathlev
Affiliation:
Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
Paulina Sergot
Affiliation:
Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, USA
Leon D. Sanchez
Affiliation:
Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
Steven E. Bruce
Affiliation:
Department of Psychological Sciences, University of Missouri, St. Louis, MO, USA
Mark W. Miller
Affiliation:
Behavioral Science Division, National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Robert H. Pietrzak
Affiliation:
Clinical Neurosciences Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
Jutta Joormann
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA
Diego A. Pizzagalli
Affiliation:
Department of Psychiatry, Harvard Medical School, Boston, MA, USA Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
John F. Sheridan
Affiliation:
Division of Biosciences, Ohio State University College of Dentistry, Columbus, OH, USA Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, USA
Steven E. Harte
Affiliation:
Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
James M. Elliott
Affiliation:
Kolling Institute, University of Sydney, St Leonards, New South Wales, Australia Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, St. Leonards NSW, New South Wales, Australia Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
Ronald C. Kessler
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Karestan C. Koenen
Affiliation:
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
Samuel A. McLean
Affiliation:
Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kerry J. Ressler
Affiliation:
Department of Psychiatry, Harvard Medical School, Boston, MA, USA Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
Negar Fani
Affiliation:
Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
*
Corresponding author: Cecilia A. Hinojosa; Email: cecilia.a.hinojosa@emory.edu
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Abstract

Background

Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

Methods

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Results

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Conclusions

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

Introduction

Alcohol, drugs, and tobacco are frequently used to cope with posttraumatic sequelae. Indeed, the prevalence of substance use disorders (SUD) among those with posttraumatic stress disorder (PTSD) is high; national estimates indicate that ~46% of people with PTSD are addicted to substances (Pietrzak, Goldstein, Southwick, & Grant, Reference Pietrzak, Goldstein, Southwick and Grant2011). Likewise, up to ~20% of individuals who have suffered from major depression in their lifetime have had an alcohol or SUD (Quello, Brady, & Sonne, Reference Quello, Brady and Sonne2005). Co-occurring SUD and PTSD predict adverse outcomes, including suicide and unemployment (Allan et al., Reference Allan, Ashrafioun, Kolnogorova, Raines, Hoge and Stecker2019; Blanco et al., Reference Blanco, Xu, Brady, Pérez-Fuentes, Okuda and Wang2013). Substances may be used to ameliorate symptom presence and severity – to avoid painful memories, fall asleep, reduce anxiety, elevate mood, and enhance pleasure in activities.

Several hypotheses attempt to explain the significance of the relationship between PTSD and SUD. The hypothesis with the most empirical evidence is the self-medication hypothesis (Khantzian, Reference Khantzian1997). According to this hypothesis, substance use temporarily relieves posttrauma symptoms (Chilcoat & Breslau, Reference Chilcoat and Breslau1998). However, this temporary relief reinforces the use of substances, leading to maladaptive use and, ultimately, symptom exacerbation (Stewart, Pihl, Conrod, & Dongier, Reference Stewart, Pihl, Conrod and Dongier1998). Epidemiological studies support this hypothesis in that PTSD and depression develop first, with subsequent onset of SUD (Abraham & Fava, Reference Abraham and Fava1999; Chilcoat & Breslau, Reference Chilcoat and Breslau1998; Kessler, Sonnega, Bromet, Hughes, & Nelson, Reference Kessler, Sonnega, Bromet, Hughes and Nelson1995; Ouimette, Read, Wade, & Tirone, Reference Ouimette, Read, Wade and Tirone2010; Wojciechowski, Reference Wojciechowski2019). Nevertheless, some studies have found the opposite sequence of disorder onset (Croughan, Miller, Wagelin, & Whitman, Reference Croughan, Miller, Wagelin and Whitman1982; Mirin, Weiss, Griffin, & Michael, Reference Mirin, Weiss, Griffin and Michael1991; Rounsaville, Weissman, Crits-Christoph, Wilber, & Kleber, Reference Rounsaville, Weissman, Crits-Christoph, Wilber and Kleber1982; Testa, Livingston, & Hoffman, Reference Testa, Livingston and Hoffman2007), suggesting a bidirectional relationship. Other hypotheses include the mutual maintenance hypothesis, which suggests that PTSD symptoms lead to substance use, and this use then exacerbates PTSD symptoms (Kaysen et al., Reference Kaysen, Atkins, Simpson, Stappenbeck, Blayney, Lee and Larimer2014; Possemato et al., Reference Possemato, Maisto, Wade, Barrie, McKenzie, Lantinga and Ouimette2015). Lastly, the shared susceptibility hypothesis suggests thatshared factors contribute to the development of co-occurring PTSD and SUD. Such factors include but are not limited to emotion regulation deficits, genetic risk, and behavioral under control (Chilcoat & Breslau, Reference Chilcoat and Breslau1998). While correlational studies have shed light on the complex relationship between PTSD, depression, and SUD, longitudinal studies allow for stronger inferences about the causal pathway between these comorbid disorders.

Only a few longitudinal studies have explored the temporal onset of the development of posttraumatic sequelae and substance use. PTSD symptoms predicted greater substance use in trauma-exposed adolescents even after controlling for factors such as pre-trauma family environment, substance use, and demographic variables (Haller & Chassin, Reference Haller and Chassin2014). New interpersonal violence exposure was associated with subsequent increases in alcohol and substance use in other longitudinal studies (Berenz et al., Reference Berenz, Cho, Overstreet, Kendler, Amstadter and Dick2016; Kaysen, Neighbors, Martell, Fossos, & Larimer, Reference Kaysen, Neighbors, Martell, Fossos and Larimer2006; Kilpatrick, Acierno, Resnick, Saunders, & Best, Reference Kilpatrick, Acierno, Resnick, Saunders and Best1997; Kline et al., Reference Kline, Weiner, Ciccone, Interian, St Hill and Losonczy2014). Further, PTSD symptoms and coping motives for using alcohol predicted worse alcohol-related consequences in college students (Read, Griffin, Wardell, & Ouimette, Reference Read, Griffin, Wardell and Ouimette2014). In a sample of recently trauma-exposed adults recruited from the emergency department (ED), increases in anhedonic symptoms of PTSD corresponded with increases in substance use over 6 months (Fani et al., Reference Fani, Jain, Hudak, Rothbaum, Ressler and Michopoulos2020).

Ecological momentary assessment (EMA) has also been used to measure momentary change and individual-level differences contributing to the relationship between PTSD and substance use. A recent review of EMA studies examining PTSD and alcohol use, in particular, highlighted that collectively, studies showed support for the self-medication hypothesis (Lane, Waters, & Black, Reference Lane, Waters and Black2019). While EMA methods provide greater granular data on daily fluctuations in psychological symptoms and substance use, this method has some limitations including attrition and compliance rates. Nonetheless, these studies suggest that posttraumatic sequelae lead to increased substance use. However, no large-scale prospective study has measured how posttraumatic sequelae may differ based on substance use trajectories. In the context of the multisite, longitudinal AURORA (Advancing Understanding of RecOvery afteR traumA) study (McLean et al., Reference McLean, Ressler, Koenen, Neylan, Germine, Jovanovic and Kessler2020), we assessed putative relationships between trajectories of alcohol and cannabis use and PTSD and depression symptoms over time among ED patients who had experienced trauma. Alcohol and cannabis use were examined as they are among the most abused substances in trauma-exposed populations (Bhalla, Stefanovics, & Rosenheck, Reference Bhalla, Stefanovics and Rosenheck2017). Given the previous longitudinal data, we hypothesized that escalating alcohol/cannabis use over time, compared to stable or no/minimal alcohol/cannabis use, would be associated with increased PTSD and depression symptoms. We retrospectively examined PTSD and depression symptom changes 30 days before the trauma and subsequent ED admittance 3 months after trauma. This period has been frequently highlighted as a time frame during which PTSD symptom resolution or escalation occurs (Blanchard et al., Reference Blanchard, Hickling, Vollmer, Loos, Buckley and Jaccard1995; Pérez Benítez et al., Reference Pérez Benítez, Zlotnick, Dyck, Stout, Angert, Weisberg and Keller2013; Schock, Böttche, Rosner, Wenk-Ansohn, & Knaevelsrud, Reference Schock, Böttche, Rosner, Wenk-Ansohn and Knaevelsrud2016; Warren et al., Reference Warren, Foreman, Bennett, Petrey, Reynolds, Patel and Roden-Foreman2014).

Materials and methods

Participants

A total of 581 men and 1037 women with a mean age of 35.4 years (s.d. = 13) were recruited as part of a multisite study of PTSD conducted in the EDs of level 1 trauma centers (MH094757), as described prior (Harnett et al., Reference Harnett, van Rooij, Ely, Lebois, Murty, Jovanovic and Stevens2021; Kessler et al., Reference Kessler, Ressler, House, Beaudoin, An, Stevens and McLean2021; McLean et al., Reference McLean, Ressler, Koenen, Neylan, Germine, Jovanovic and Kessler2020; Steuber et al., Reference Steuber, Seligowski, Roeckner, Reda, Lebois, van Rooij and Stevens2021). Eligible patients were approached in the ED after initial medical evaluation, laboratory testing, and medical clearance. Once informed consent was obtained, trained research assistants collected demographic information and administered assessments on prior trauma, substance abuse, current and past PTSD and depression symptoms, and details concerning the presenting trauma. Patients were queried about past and current medical conditions and medications. Participants who had experienced a DSM-5 criterion A trauma in the past 24 h were eligible for the study but were excluded if they were currently suicidal or had attempted suicide in the last 3 months, were currently intoxicated, or lost consciousness due to the trauma. PTSD and depression symptoms have been examined previously within this sample (see Cakmak et al., Reference Cakmak, Alday, Da Poian, Rad, Metzler, Neylan and Clifford2021; Joormann et al., Reference Joormann, Ziobrowski, King, Gildea, Lee, Sampson and Kessler2022; Lebois et al., Reference Lebois, Harnett, van Rooij, Ely, Jovanovic, Bruce and Ressler2022; Ziobrowski et al., Reference Ziobrowski, Kennedy, Ustun, House, Beaudoin, An and van Rooij2021).

Clinical assessments

The investigation described in this manuscript used assessments to measure alcohol and cannabis use and PTSD and depression symptoms across four timepoints, baseline, weeks 2, 8, and 12. During the baseline timepoint, participants responded to assessments during their ED visit in reference to the 30 days leading up to the traumatic event. Each subsequent timepoint referenced the past 30 days (or 14 days for week 2). During the ED visit, demographic information, including biological sex, age, education, marital status, and race/ethnicity, was gathered. During the follow-up week 2 visit, demographic information, including body mass index (BMI), employment status, and income, was gathered.

The PTSD checklist for DSM-5 (PCL-5)

The PCL-5 (Blevins, Weathers, Davis, Witte, & Domino, Reference Blevins, Weathers, Davis, Witte and Domino2015; Weathers et al., Reference Weathers, Litz, Keane, Palmieri, Marx and Schnurr2013) is a 20-item measure that prompts participants to report the frequency of PTSD symptoms over the respective reference periods. Using a Likert scale ranging from 0 (‘not at all’) to 4 (‘extremely’), participants rate the degree a specific symptom disrupts their activities. A severity score is calculated as the total across all 20 items, and a score of 31–33 suggests possible PTSD. For the statistical analyses in this study, we reported the overall PCL-5 score. The PCL-5 has been shown to have strong reliability and validity (Blevins et al., Reference Blevins, Weathers, Davis, Witte and Domino2015). Cronbach's α values for the PCL-5 at each time point were as follows: ED, 0.94; week 2, 0.95; week 8, 0.96; and month 3, 0.97.

The patient-reported outcomes measurement information system (PROMIS)

For this investigation, we used the PROMIS Depression measure only. The PROMIS Depression item bank (Amtmann et al., Reference Amtmann, Kim, Chung, Bamer, Askew, Wu and Johnson2014; Cella et al., Reference Cella, Yount, Rothrock, Gershon, Cook, Reeve and Rose2007; Pilkonis et al., Reference Pilkonis, Choi, Reise, Stover, Riley and Cella2011) is a National Institutes of Health (NIH) measure that assesses self-reported negative mood, views of self, social cognition, and decreased positive affect and engagement. Participants' responses are added to create a raw score and converted to a T-score. The T-score is a standardized score with a mean of 50 and a standard deviation of 10. The higher the T-score, the more severe the depression symptoms. The PROMIS has demonstrated reliability, precision, and construct validity (Cella et al., Reference Cella, Yount, Rothrock, Gershon, Cook, Reeve and Rose2007).

PhenX toolkit

Alcohol and cannabis use were assessed using the PhenX Toolkit Alcohol and PhenX Toolkit Substance Use, respectively (Hamilton et al., Reference Hamilton, Strader, Pratt, Maiese, Hendershot, Kwok and Haines2011). For this investigation, we used the frequency of use as our variable of interest for alcohol and cannabis as we did not have a measure for quantity for alcohol and cannabis use. To assess alcohol use, we asked, ‘During the “reference period,” how many days did you have at least one drink of any kind of alcohol, not including small tastes or sips?’ Our variable of interest was the sum of days the participant consumed alcohol. To assess cannabis use, we similarly asked, ‘During the “reference period,” how many days did you use marijuana?’ Our variable of interest was the sum of days the participant used cannabis.

The childhood trauma questionnaire – short form (CTQ-SF)

The CTQ-SF (Bernstein et al., Reference Bernstein, Stein, Newcomb, Walker, Pogge, Ahluvalia and Zule2003) is a 28-item scale used to examine exposure to traumatic experiences during childhood. Five types of childhood maltreatment types were measured: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. A score for each of the five types of maltreatment is calculated by adding each item within the subtype to create a maltreatment subtype score for a range of 0–12. A total summed score was calculated and used in secondary analyses. Cronbach's α for CTQ-SF was 0.78.

Statistical analyses

A latent class linear mixed model (LCMM) fitted by the maximum likelihood method was used to partition our sample into subgroups based on alcohol or cannabis use. To conduct these analyses, we used the heterogeneous linear mixed model function in the LCMM package in R (v3.6.1), identifying clusters of people with common use patterns. In choosing the appropriate LCMM model fit, we referenced the Bayesian information criteria (BIC) values and percent class membership. Low BIC values and percentages of no less than 5% in class membership were considered the best fit for the data (Nagin & Odgers, Reference Nagin and Odgers2010) (online Supplementary Tables S1 and S2). Given that the week 2 timepoint assessed alcohol and cannabis use within the past 14 days, for these LCMM analyses only, we divided baseline, week 8, and week 12 timepoints by 2 to achieve a more even distribution of the data. Latent class models included measurements at all four timepoints (baseline, weeks 2, 8, and 12). LCMM analyses revealed that the most optimal model for trajectory classes was a three-class alcohol use model with n = 1617 and a three-class cannabis model with n = 1617. Participants reporting alcohol use were categorized into three groups, a consistently low use (n = 1239, 76.6%) group, a consistently high use (n = 117, 7.2%) group, and an increasing use group that demonstrated increased use from week 2 to week 8, and decreased use from week 8 to week 12 (n = 261, 16.1%). Participants reporting cannabis use were categorized into groups of consistently low use (n = 1246, 77.1%), consistently high use (n = 262, 16.2%), and increasing use (n = 109, 6.7%). Figure 1a and b present the graphs of individual and mean trajectories of alcohol and cannabis use models, while Fig. 1c and d provides visuals of only the mean trajectories.

Figure 1. Observed individual (a, b) and mean (c, d) latent trajectories for both alcohol and cannabis use. Individual trajectories determined by measurements at four timepoints are represented by the thin lines, while the thick lines are the average trajectories for each designated group. Each class was identified through the chosen latent growth mixture model, based on Bayesian information criterion (BIC) values and class membership percentages.

For both alcohol and cannabis use, we used a 3 (time: baseline, week 8, and 12) by 3 (trajectory class) mixed-model repeated-measures analysis of variance (ANOVA) to test the effect of trajectory group on PTSD and depression symptom change over time. Where significant time by trajectory class interactions emerged, we conducted post-hoc analyses (univariate ANOVAs) to explicate these interactions. For these ANOVAs, because we were more interested in examining chronic change in PTSD and depression symptoms than short-term (2 week) reactivity, we examined changes in symptoms collected at baseline, week 8, and 12. Mauchly's test was used to determine whether the assumption of sphericity was violated. If violated, degrees of freedom were corrected using either Greenhouse-Geiser (ɛ < 0.75) or Huynh-Feldt (ɛ > 0.75) estimates of sphericity. Pairwise deletion was used for missing data.

Results

Participant characteristics

Sample demographic and clinical characteristics, stratified by alcohol and cannabis use class memberships, are presented in Tables 1 and 2. Given the significant differences found between trajectory classes on demographic and clinical variables, separate follow-up sensitivity analyses, including biological sex as a between-subjects factor, and age, education, CTQ-SF, BMI, marital status, and race/ethnicity as covariates, were conducted, and presented in the results section of the Supplementary material. Participants reported the following traumatic events that brought them to the ED: motor vehicle collision [1135 (70.1%)], physical assault [140 (8.7%)], sexual assault [14(0.9%)], fall ≥ 10 feet [18 (1.1%)], fall < 10 feet or from unknown height [51 (3.2%)], incident causing traumatic stress exposure to many people [e.g., plane crash, natural disaster; 3 (0.2%)], non-motorized collision [20 (1.2%)], burns [7, (0.4%)], animal-related [29 (1.8%)], other [54 (3.3%)], missing [147 (9.1%)].

Table 1. Demographics and clinical characteristics for alcohol use trajectory classes

Note. Missing data were removed pairwise.

a df = (2, 1614).

b df = (2, 1608).

c df = (2, 1249).

d df = (2, 1171).

e df = (2, 1617).

f df = (10, 1606).

g df = (6, 1610).

h df = (8, 1433).

i df = (10, 1419).

Table 2. Demographics and clinical characteristics for cannabis use trajectory classes

Note. Missing data were removed pairwise.

a df = (2, 1614).

b df = (2, 1608).

c df = (2, 1249).

d df = (2, 1171).

e df = (2, 1617).

f df = (10, 1606).

g df = (6, 1610).

h df = (8, 1433).

i df = (10, 1419).

Mixed-model repeated-measures ANOVA

Alcohol use trajectories and change in PTSD symptoms

A main effect of time, F (2, 1430) = 15.37, p < 0.001, ηp 2 = 0.02, and a significant time by alcohol use trajectory class interaction were observed F (3, 1430) = 2.83, p = 0.04, ηp 2 = 0.01 (see Table 3 for means and standard deviations). Analyses revealed the low use alcohol trajectory class had lower PTSD symptoms at baseline than the high use alcohol trajectory class, but not the increasing use trajectory class. There were no significant differences between the trajectory classes across other timepoints. For the low use trajectory class participants, their PTSD symptoms increased from baseline to week 8, reflecting moderate and clinically significant PTSD symptom severity; symptoms also decreased from week 8 to week 12 but remained clinically significant at this time. The increasing use trajectory class showed a similar pattern of change in PTSD symptoms compared to the low use trajectory class; however, no statistically significant differences between timepoints were found for this trajectory class. The high use trajectory class exhibited relatively similar PTSD symptoms at each timepoint, all within the moderate PTSD symptom severity range; no statistically significant mean differences were apparent across timepoints for this trajectory class. Figure 2a illustrates the patterns of PTSD symptom change over time for the three alcohol use trajectory classes.

Figure 2. Repeated-measures ANOVA examining interactions between levels of use and measures for PTSD (PCL) and depression (PROMIS). Analyses included three timepoints – baseline, week 8, and 12. Error bars represent 95% confidence interval; *denotes significant interaction.

Table 3. Means and standard deviation for posttraumatic stress disorder (PTSD) and depression symptoms

Alcohol use trajectories and change in depression symptoms

A statistically significant main effect of time was observed, F (2, 1807) = 53.34, p < 0.001, ηp 2 = 0.05, all trajectory classes had lower depression symptoms at baseline than at other timepoints after the ED visit (see Table 3 for means and standard deviations). A statistically significant main effect of alcohol trajectory class was found F (2, 1061) = 4.81, p = 0.01, ηp 2 = 0.01 whereby the low use alcohol trajectory class had lower depression symptoms than the other two trajectory classes at baseline; the high use and increasing use trajectory classes did not differ from each other. No statistically significant time by trajectory class interaction was observed F (3, 1807) = 2.14, p = 0.085. Figure 2c illustrates the patterns of depression symptom change over time for the three alcohol use trajectory classes.

Cannabis use trajectories and change in PTSD symptoms

A statistically significant main effect of time, F (2, 1436) = 10.72, p < 0.001, ηp 2 = 0.01, and a time by cannabis use class interaction was observed F (3, 1436) = 5.45, p = 0.001, ηp 2 = 0.01 (see Table 3 for means and standard deviations). Analyses revealed the low use cannabis trajectory class had lower PTSD symptoms at baseline than the high use and increasing use trajectory classes; the latter two trajectory classes demonstrated high levels of PTSD symptoms across all timepoints. The low use cannabis trajectory class also exhibited lower PTSD symptoms at week 8 than the high use trajectory class, unlike the increasing use trajectory class. The low use cannabis trajectory class also exhibited lower PTSD symptoms at week 12 than the high use trajectory class, unlike the increasing use trajectory class. No statistically significant differences existed between the high and increasing use trajectory classes across other timepoints. For low use trajectory class participants, PTSD symptoms significantly increased from baseline to week 8, with symptoms reaching the clinical threshold for a PTSD diagnosis at week 8, but then decreasing significantly from week 8 to week 12; however, within this trajectory class, week 12 symptoms were still greater than symptoms reported at the baseline visits and remained clinically significant at week 12. The increasing use trajectory class showed a pattern of PTSD similar to the low use trajectory class across time, with symptoms remaining high across these timepoints. No statistically significant differences across time were found within this trajectory class. The high use trajectory class exhibited relatively similar (and high) PTSD symptoms at each timepoint. Figure 2b illustrates the patterns of PTSD symptom change over time for the three cannabis use trajectory classes.

Cannabis use trajectories and change in depression symptoms

A statistically significant main effect of time, F (2, 1811) = 46.16, p < 0.001, ηp 2 = 0.04, and a time by cannabis use trajectory class interaction were observed F (3, 1811) = 3.68, p = 0.01, ηp 2 = 0.01 (see Table 3 for means and standard deviations). Analyses revealed that the low use cannabis trajectory class had significantly lower depression symptoms at baseline than the high use and increasing use trajectory classes. The low use cannabis trajectory class also showed lower depression symptoms at week 8 compared to the high use trajectory class and increasing use trajectory class. The low use cannabis trajectory class also showed lower depression symptoms at week 12 than the high use trajectory class and increasing use trajectory class. No statistically significant differences existed between the high and increasing use trajectory classes across timepoints. Within the low use trajectory classes, participants' depression symptoms increased from baseline to week 8 but decreased significantly from week 8 to week 12; however, week 12 symptoms were greater than those reported during the baseline recording. For the high and increasing use trajectory classes participants, depression symptoms significantly increased from baseline to week 8 but were not statistically significantly different between week 8 and week 12. Figure 2d illustrates the patterns of depression symptom change over time for the three cannabis use trajectory classes.

Discussion

We examined alcohol and cannabis use trajectories among ED patients who recently suffered trauma and whether these trajectories were associated with changes in overall PTSD and depression symptoms over the 3 months following that trauma. We found that alcohol and cannabis use exhibited similar yet functionally different trajectory classes that included low, high, and increasing use trajectory classes. Specifically, the high and increasing cannabis use trajectory classes exhibited similarly high PTSD and depression symptoms at week 8, unlike the low use group. Conversely, only the increasing alcohol use trajectory class exhibited higher PTSD and depression symptoms at week 8. These findings suggest that increasing cannabis intake after trauma may be linked to increased post-trauma sequelae. They also indicate that high cannabis use at the time of trauma may contribute to a vulnerability for developing post-trauma sequelae.

Contrary to our hypothesis, for both alcohol and cannabis, the increasing use trajectory classes did not show a statistically significant increase in PTSD symptoms over the 3-month posttrauma period. Instead, we observed the following by trajectory class. For the low use alcohol and cannabis classes: (1) PTSD symptoms were lower than the high use trajectory classes measured at the baseline timepoint collected in the ED; (2) PTSD symptoms increased from baseline to week 8, then decreased from week 8 to week 12; and (3) PTSD symptoms increased overall and reached a clinically significant severity threshold by week 12. The increasing cannabis trajectory classes reported higher PTSD symptoms at baseline that increased slightly to week 8 and decreased slightly from week 8 to week 12. In contrast, the high use alcohol and cannabis trajectory classes maintained similar high, clinically significant levels of PTSD symptoms at each timepoint.

Regarding depression symptoms, our results partly support our hypothesis. The low cannabis use trajectory class had lower depression symptoms recorded at baseline than the other two trajectory classes. Furthermore, all cannabis trajectory classes showed similar patterns of increased depression symptoms from baseline to week 8, yet only the low use trajectory class exhibited decreased symptoms from week 8 to week 12. In contrast, for the alcohol use trajectory classes, there was a statistically significant pattern with depression symptoms related to time over the 3-month follow-up period, but no significant interaction. The lack of interaction suggests that the three patterns of alcohol use did not differ in the occurrence of depression symptoms across time.

Our findings reveal two important characteristics of the participants in the low use trajectory classes for alcohol and cannabis use. First, low alcohol and cannabis users exhibited a greater initial increase in clinical symptoms between baseline and week 8 and subsequently a greater decrease from week 8 to week 12. While noteworthy, it is important to highlight that week 12 PTSD and depression symptoms in the alcohol and cannabis low use trajectory classes were greater than at baseline and that these participants' symptoms never returned to baseline levels. This latter finding, in part, provides some evidence that individuals who rarely consume substances could also be at risk for trauma and stress-related psychopathology compared to those who occasionally or frequently use alcohol or cannabis.

Individuals already using alcohol or cannabis exhibited clinically significant PTSD symptoms at baseline that did not significantly change over time. These findings align with other research showing that individuals with substance dependence exhibited greater rates of PTSD than controls (Gielen, Havermans, Tekelenburg, & Jansen, Reference Gielen, Havermans, Tekelenburg and Jansen2012). There could be several explanations for this pattern. The susceptibility hypothesis (Acierno, Resnick, Kilpatrick, Saunders, & Best, Reference Acierno, Resnick, Kilpatrick, Saunders and Best1999; Chilcoat & Breslau, Reference Chilcoat and Breslau1998; Cottler, Compton, Mager, Spitznagel, & Janca, Reference Cottler, Compton, Mager, Spitznagel and Janca1992) suggests that individuals who use substances might place themselves in more dangerous situations and experience more traumatic events, increasing the likelihood of developing posttraumatic symptoms after trauma. Indeed, research suggests that being subjected to more traumatic events in one's lifetime could increase PTSD symptoms (Breslau, Peterson, & Schultz, Reference Breslau, Peterson and Schultz2008). Such populations show a lifetime prevalence of trauma exposure up to 95% (Dansky, Saladin, Coffey, & Brady, Reference Dansky, Saladin, Coffey and Brady1997; Farley, Golding, Young, Mulligan, & Minkoff, Reference Farley, Golding, Young, Mulligan and Minkoff2004; Read et al., Reference Read, Griffin, Wardell and Ouimette2014; Reynolds et al., Reference Reynolds, Mezey, Chapman, Wheeler, Drummond and Baldacchino2005). Thus, it may be that participants in the alcohol and cannabis high use and increasing groups had been subjected to more traumatic experiences, explaining the high PTSD symptoms reported in the 30 days following the trauma.

Furthermore, alcohol and cannabis use may make an individual more susceptible to developing posttraumatic stress symptoms due to an inability to successfully regulate stress response to a traumatic event. Both alcohol and cannabis use has been associated with dysregulation of hypothalamic-pituitary axis function. Alcohol consumption has been associated with excessive cortisol response (Richardson, Lee, O'Dell, Koob, & Rivier, Reference Richardson, Lee, O'Dell, Koob and Rivier2008), whereas cannabis users show blunted cortisol reactivity in response to stress (Cservenka, Lahanas, & Dotson-Bossert, Reference Cservenka, Lahanas and Dotson-Bossert2018). Heavy cannabis use has been associated with increased sympathetic arousal and fluctuations in heart rate variability [reviewed in (Wemm & Sinha, Reference Wemm and Sinha2019)]. While we did not include biomarkers, future studies should examine differences in biological functions to determine whether the changes associated with alcohol and substance use affect biological processes that may make an individual susceptible to developing posttrauma psychopathology.

Alcohol consumption might exacerbate PTSD symptoms (Ouimette, Finney, & Moos, Reference Ouimette, Finney and Moos1999; Volpicelli, Balaraman, Hahn, Wallace, & Bux, Reference Volpicelli, Balaraman, Hahn, Wallace and Bux1999), and may explain the greater baseline symptoms in the high use trajectory class than the low use trajectory class at baseline. Indeed, alcohol use might exacerbate posttraumatic stress over time by promoting negative coping strategies (Read et al., Reference Read, Griffin, Wardell and Ouimette2014). Read et al. (Reference Read, Griffin, Wardell and Ouimette2014) used a cross-lagged panel model to test how coping may influence the associations between PTSD symptoms and alcohol use over time in trauma-exposed young adults entering college. Specifically, the authors showed an indirect association between problems caused by alcohol use and PTSD symptoms through negative coping.

Regarding depression symptoms, our study results suggest that for the cannabis trajectory classes, the high and increasing use classes exhibited relatively high symptoms at baseline in the ED that increased by week 8 and remained higher than baseline levels by week 12. One review found a similar association in heavy cannabis use with increasing depression symptoms (Degenhardt, Hall, & Lynskey, Reference Degenhardt, Hall and Lynskey2003). Cannabis use (Hser et al., Reference Hser, Mooney, Huang, Zhu, Tomko, McClure and Gray2017; Lev-Ran et al., Reference Lev-Ran, Roerecke, Le Foll, George, McKenzie and Rehm2014; Moore et al., Reference Moore, Zammit, Lingford-Hughes, Barnes, Jones, Burke and Lewis2007) and alcohol consumption have been associated with an increased risk for depression (McEachin, Keller, Saunders, & McInnis, Reference McEachin, Keller, Saunders and McInnis2008; Paljärvi et al., Reference Paljärvi, Koskenvuo, Poikolainen, Kauhanen, Sillanmäki and Mäkelä2009; Sihvola et al., Reference Sihvola, Rose, Dick, Pulkkinen, Marttunen and Kaprio2008) in some studies, though these findings have not been consistent (Rosenthal et al., Reference Rosenthal, Clark, Marshall, Buka, Carey, Shepardson and Carey2018).

This study is not without limitations. First, the clinical information acquired was measured on a continuous scale. Therefore, we did not focus on only those who had a clinical diagnosis of PTSD, depression, and alcohol or SUD. Therefore, we cannot generalize the results to samples suffering from the dual clinical diagnosis of PTSD or depression and alcohol or SUD. Though, this also allows the exploration of sub-threshold populations. Second, our clinical measures' assessment was based only on self-report questionnaires and not clinician-based diagnostic assessments. Thus, the data may be a poor representation of the general population. Lastly, for the baseline timepoint, participants were asked to report symptoms 30 days before the ED visit. Nevertheless, these responses were collected immediately after a traumatic event, potentially leading to inaccurate recall ability.

These limitations do not diminish the many strengths of this study. For one, our findings reveal that, shortly after trauma exposure, there appears to be a window of time wherein frequent substance users or those who increase their substance use may be more sensitive to their effects, potentially leading to the development of PTSD and depression symptoms. This is illustrated by increasing and high use groups having higher PTSD and depression symptoms at baseline, which increased and remained clinically significant over time. This points to the value of interventions or educational materials targeted to these sub-groups of trauma survivors shortly after the event to help prevent an increase in post-trauma sequelae. Furthermore, our data also highlight differences in trajectories and associations between clinical symptoms between substances. For example, while we did not find a significant time by substance use class interaction with depression symptoms for alcohol use, we did for cannabis. This suggests different mechanisms through which substances may negatively affect the development of post-trauma sequelae. Lastly, our data shed light on the importance of assessing PTSD and depression symptoms in substance-using individuals in the aftermath of trauma. Increasingly, substance use clinicals are incorporating trauma-informed care; this study highlights the importance of measuring trauma exposure and substance use in these clinical settings to provide more targeted services to patients. Future studies should examine these associations across even longer periods using more ecologically valid methods such as EMA.

To conclude, three alcohol and cannabis use trajectory classes demonstrated different relationships with changes in PTSD and depression symptoms across 3 months posttrauma. Our results illustrate an interaction between alcohol and cannabis use and change in clinical symptoms soon after trauma exposure. The findings highlight the importance of developing and implementing preventive measures for alcohol and cannabis use in recently trauma-exposed individuals to inhibit the development and continuation of severe post-traumatic sequelae.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291723001642

Acknowledgments

The investigators wish to thank the trauma survivors participating in the AURORA Study. Their time and effort during a challenging period of their lives make our efforts to improve recovery for future trauma survivors possible.

Financial support

This project was supported by NIMH under U01MH110925, the US Army MRMC, One Mind, and The Mayday Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funders. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. Data and/or research tools used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Dataset identifier(s): NIMH Data Archive Digital Object Identifier (DOI) 10.15154/1527790. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the submitters submitting original data to NDA. Dr Hinojosa reported no biomedical financial interests or potential conflicts of interest. Liew reported no biomedical financial interests or potential conflicts of interest. Dr An reported no biomedical financial interests or potential conflicts of interest. Dr Stevens reported no biomedical financial interests but is an NPP Board Member. Dr Basu reported no biomedical financial interests or potential conflicts of interest. Dr van Rooij reported no biomedical financial interests or potential conflicts of interest. Dr House reported no biomedical financial interests or potential conflicts of interest. Dr Beaudoin reported no biomedical financial interests or potential conflicts of interest. Dr Zeng reported no biomedical financial interests or potential conflicts of interest. Dr Neylan has received research support from NIH, VA, and Rainwater Charitable Foundation, and consulting income from Jazz Pharmaceuticals. In the last 3 years Dr Clifford has received research funding from the NSF, NIH, Nextsense Inc., LifeBell AI, Otsuka UA, and unrestricted donations from AliveCor Inc, Amazon Research, the Center for Discovery, the Gates Foundation, Google, the Gordon and Betty Moore Foundation, MathWorks, Microsoft Research, One Mind Foundation, the Rett Research Foundation, and Samsung Research. Dr Clifford has financial interest in AliveCor Inc and Nextsense Inc. He also is the CTO of MindChild Medical and CSO of LifeBell AI and has ownership in both companies. These relationships are unconnected to the current work. Dr Jovanovic has NIH funding (MH111682, MH122867, HD099178). Dr Linnstaedt reported no biomedical financial interests or potential conflicts of interest. Dr Germine reported no biomedical financial interests or potential conflicts of interest. Dr Rauch reports grants from NIH during the conduct of the study; personal fees from SOBP (Society of Biological Psychiatry) paid role as secretary, other from Oxford University Press royalties, other from APP (American Psychiatric Publishing Inc.) royalties, other from VA (Veterans Administration) per diem for oversight committee, and other from Community Psychiatry/Mindpath Health paid board service, including equity outside the submitted work; other from the National Association of Behavioral Healthcare for paid Board service; and Leadership roles on Board or Council for SOBP, ADAA (Anxiety and Depression Association of America), and NNDC (National Network of Depression Centers). Dr Haran reported no biomedical financial interests or potential conflicts of interest. Dr Storrow reported no biomedical financial interests or potential conflicts of interest. Dr Lewandowski reported no biomedical financial interests or potential conflicts of interest. Dr Musey reported no biomedical financial interests or potential conflicts of interest. Dr Hendry reported no biomedical financial interests or potential conflicts of interest. Dr Sheikh has received funding from the Florida Medical Malpractice Joint Underwriter's Association Dr Alvin E. Smith Safety of Healthcare Services Grant; Allergan Foundation; the NIH/NIA-funded Jacksonville Aging Studies Center (JAX-ASCENT; R33AG05654); and the Substance Abuse and Mental Health Services Administration (1H79TI083101-01); and the Florida Blue Foundation. Dr Jones has no competing interests related to this work, though he has been an investigator on studies funded by AstraZeneca, Vapotherm, Abbott, and Ophirex. Dr Punches reported no biomedical financial interests or potential conflicts of interest. Dr Kurz reported no biomedical financial interests or potential conflicts of interest. Dr Swor reported no biomedical financial interests or potential conflicts of interest. Dr Hudak reported no biomedical financial interests or potential conflicts of interest. Dr Pascual reported no biomedical financial interests or potential conflicts of interest. Dr Seamon reported no biomedical financial interests or potential conflicts of interest. Dr Datner serves as Medical Advisor for Cayaba Care. Dr Chang reported no biomedical financial interests or potential conflicts of interest. Dr Pearson reported no biomedical financial interests or potential conflicts of interest. Dr Peak reported no biomedical financial interests or potential conflicts of interest. Dr Merchant reported no biomedical financial interests or potential conflicts of interest. Dr Domeier reported no biomedical financial interests or potential conflicts of interest. Dr Rathlev reported no biomedical financial interests or potential conflicts of interest. Dr Sergot reported no biomedical financial interests or potential conflicts of interest. Dr Sánchez reported no biomedical financial interests or potential conflicts of interest. Dr Bruce reported no biomedical financial interests or potential conflicts of interest. Dr Miller reported no biomedical financial interests or potential conflicts of interest. Dr Pietrzak reported no biomedical financial interests or potential conflicts of interest. Dr Joormann receives consulting payments from Janssen Pharmaceuticals. Over the past 3 years, Dr Pizzagalli has received consulting fees from Albright Stonebridge Group, Boehringer Ingelheim, Compass Pathways, Concert Pharmaceuticals, Engrail Therapeutics, Neumora Therapeutics (former BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka Pharmaceuticals, and Takeda Pharmaceuticals; honoraria from the Psychonomic Society (for editorial work) and Alkermes, and research funding from NIMH, Dana Foundation, Brain and Behavior Research Foundation, and Millennium Pharmaceuticals. In addition, he has received stock options from Neumora Therapeutics (former BlackThorn Therapeutics), Compass Pathways, Engrail Therapeutics, and Neuroscience Software. Lastly, Dr Pizzagalli is an NPP Board Member. Dr Sheridan reported no biomedical financial interests or potential conflicts of interest. Dr Harte has no competing interests related to this work, though in the last 3 years he has received research funding from Aptinyx and Arbor Medical Innovations, and consulting payments from Aptinyx, Heron Therapeutics, and Eli Lilly. Dr Elliott reports support from the National Institutes of Health (NIH) through Grant Numbers R01HD079076 and R03HD094577: Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Center for Medical Rehabilitation Research. He also reports funding from New South Wales Health, Spinal Cord Injury Award (2020–2025) and consulting fees (<$15000 per annum) from Orofacial Therapeutics, LLC. In the past 3 years, Dr Kessler was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., and Sage Pharmaceuticals. He has stock options in Mirah, PYM, and Roga Sciences. Dr Koenen's research has been supported by the Robert Wood Johnson Foundation, the Kaiser Family Foundation, the Harvard Center on the Developing Child, Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the National Institutes of Health, One Mind, the Anonymous Foundation, and Cohen Veterans Bioscience. She has been a paid consultant for Baker Hostetler, Discovery Vitality, and the Department of Justice. She has been a paid external reviewer for the Chan Zuckerberg Foundation, the University of Cape Town, and Capita Ireland. She has had paid speaking engagements in the last 3 years with the American Psychological Association, European Central Bank. Sigmund Freud University – Milan, Cambridge Health Alliance, and Coverys. She receives royalties from Guilford Press and Oxford University Press. Dr McLean reported no biomedical financial interests or potential conflicts of interest. Dr Ressler has performed scientific consultation for Bioxcel, Bionomics, Acer, Takeda, and Jazz Pharma; serves on Scientific Advisory Boards for Sage and the Brain Research Foundation, and he has received sponsored research support from Takeda, Brainsway, and Alto Neuroscience. Lastly, Dr Ressler is an NPP Board Member. Dr Fani reported no biomedical financial interests or potential conflicts of interest.

References

Abraham, H. D., & Fava, M. (1999). Order of onset of substance abuse and depression in a sample of depressed outpatients. Comprehensive Psychiatry, 40(1), 4450. doi: 10.1016/S0010-440X(99)90076-7CrossRefGoogle Scholar
Acierno, R., Resnick, H., Kilpatrick, D. G., Saunders, B., & Best, C. L. (1999). Risk factors for rape, physical assault, and posttraumatic stress disorder in women: Examination of differential multivariate relationships. Journal of Anxiety Disorders, 13(6), 541563.CrossRefGoogle ScholarPubMed
Allan, N. P., Ashrafioun, L., Kolnogorova, K., Raines, A. M., Hoge, C. W., & Stecker, T. (2019). Interactive effects of PTSD and substance use on suicidal ideation and behavior in military personnel: Increased risk from marijuana use. Depression & Anxiety, 36(11), 10721079. doi: 10.1002/da.22954CrossRefGoogle ScholarPubMed
Amtmann, D., Kim, J., Chung, H., Bamer, A. M., Askew, R. L., Wu, S., … Johnson, K. L. (2014). Comparing CESD-10, PHQ-9, and PROMIS depression instruments in individuals with multiple sclerosis. Rehabilitation Psychology, 59(2), 220229. doi: 10.1037/a0035919CrossRefGoogle ScholarPubMed
Berenz, E. C., Cho, S. B., Overstreet, C., Kendler, K., Amstadter, A. B., & Dick, D. M. (2016). Longitudinal investigation of interpersonal trauma exposure and alcohol use trajectories. Addictive Behaviors, 53, 6773. doi: 10.1016/j.addbeh.2015.09.014CrossRefGoogle ScholarPubMed
Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., … Zule, W. (2003). Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse & Neglect, 27(2), 169190. doi: 10.1016/s0145-2134(02)00541-0CrossRefGoogle ScholarPubMed
Bhalla, I. P., Stefanovics, E. A., & Rosenheck, R. A. (2017). Clinical epidemiology of single versus multiple substance use disorders: Polysubstance use disorder. Medical Care, 55(Suppl 9 Suppl 2), S24S32. doi: 10.1097/mlr.0000000000000731CrossRefGoogle ScholarPubMed
Blanchard, E. B., Hickling, E. J., Vollmer, A. J., Loos, W. R., Buckley, T. C., & Jaccard, J. (1995). Short-term follow-up of post-traumatic stress symptoms in motor vehicle accident victims. Behaviour Research and Therapy, 33(4), 369377. doi: 10.1016/0005-7967(94)00067-tCrossRefGoogle ScholarPubMed
Blanco, C., Xu, Y., Brady, K., Pérez-Fuentes, G., Okuda, M., & Wang, S. (2013). Comorbidity of posttraumatic stress disorder with alcohol dependence among US adults: Results from National Epidemiological Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence, 132(3), 630638. doi: 10.1016/j.drugalcdep.2013.04.016CrossRefGoogle ScholarPubMed
Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. Journal of Traumatic Stress, 28(6), 489498. doi: 10.1002/jts.22059CrossRefGoogle ScholarPubMed
Breslau, N., Peterson, E. L., & Schultz, L. R. (2008). A second look at prior trauma and the posttraumatic stress disorder effects of subsequent trauma: A prospective epidemiological study. Archives of General Psychiatry, 65(4), 431437. doi: 10.1001/archpsyc.65.4.431CrossRefGoogle ScholarPubMed
Cakmak, A. S., Alday, E. A. P., Da Poian, G., Rad, A. B., Metzler, T. J., Neylan, T. C., … Clifford, G. D. (2021). Classification and prediction of post-trauma outcomes related to PTSD using circadian rhythm changes measured via wrist-worn research watch in a large longitudinal cohort. Institute of Electrical and Electronics Engineers Journal of Biomedical and Health Informatics, 25(8), 28662876. doi: 10.1109/jbhi.2021.3053909Google Scholar
Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., … Rose, M. (2007). The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45(5 Suppl 1), S3S11. doi: 10.1097/01.mlr.0000258615.42478.55.CrossRefGoogle ScholarPubMed
Chilcoat, H. D., & Breslau, N. (1998). Investigations of causal pathways between PTSD and drug use disorders. Addictive Behaviors, 23(6), 827840.CrossRefGoogle ScholarPubMed
Cottler, L. B., Compton, W. M III.., Mager, D., Spitznagel, E. L., & Janca, A. (1992). Posttraumatic stress disorder among substance users from the general population. American Journal of Psychiatry, 149(5), 664670. doi: 10.1176/ajp.149.5.664Google ScholarPubMed
Croughan, J. L., Miller, J. P., Wagelin, D., & Whitman, B. Y. (1982). Psychiatric illness in male and female narcotic addicts. Journal of Clinical Psychiatry, 43(6), 225228.Google ScholarPubMed
Cservenka, A., Lahanas, S., & Dotson-Bossert, J. (2018). Marijuana use and hypothalamic-pituitary-adrenal axis functioning in humans. Frontiers in Psychiatry, 9, 472. doi: 10.3389/fpsyt.2018.00472CrossRefGoogle ScholarPubMed
Dansky, B. S., Saladin, M. E., Coffey, S. F., & Brady, K. T. (1997). Use of self-report measures of crime-related posttraumatic stress disorder with substance use disordered patients. Journal of Substance Abuse Treatment, 14(5), 431437. doi: 10.1016/s0740-5472(97)00120-7CrossRefGoogle ScholarPubMed
Degenhardt, L., Hall, W., & Lynskey, M. (2003). Exploring the association between cannabis use and depression. Addiction, 98(11), 14931504. doi: 10.1046/j.1360-0443.2003.00437.xCrossRefGoogle ScholarPubMed
Fani, N., Jain, J., Hudak, L. A., Rothbaum, B. O., Ressler, K. J., & Michopoulos, V. (2020). Post-trauma anhedonia is associated with increased substance use in a recently-traumatized population. Journal of Psychiatry Research, 285, 112777. doi: 10.1016/j.psychres.2020.112777CrossRefGoogle Scholar
Farley, M., Golding, J. M., Young, G., Mulligan, M., & Minkoff, J. R. (2004). Trauma history and relapse probability among patients seeking substance abuse treatment. Journal of Substance Abuse Treatment, 27(2), 161167. doi: 10.1016/j.jsat.2004.06.006CrossRefGoogle ScholarPubMed
Gielen, N., Havermans, R. C., Tekelenburg, M., & Jansen, A. (2012). Prevalence of post-traumatic stress disorder among patients with substance use disorder: It is higher than clinicians think it is. European Journal of Psychotraumatology, 3, 17734. 10.3402/ejpt.v3i0.17734.CrossRefGoogle ScholarPubMed
Haller, M., & Chassin, L. (2014). Risk pathways among traumatic stress, posttraumatic stress disorder symptoms, and alcohol and drug problems: A test of four hypotheses. Psychology of Addictive Behaviors, 28(3), 841851. doi: 10.1037/a0035878CrossRefGoogle ScholarPubMed
Hamilton, C. M., Strader, L. C., Pratt, J. G., Maiese, D., Hendershot, T., Kwok, R. K., … Haines, J. (2011). The PhenX toolkit: Get the most from your measures. American Journal of Epidemiology, 174(3), 253260. doi: 10.1093/aje/kwr193CrossRefGoogle ScholarPubMed
Harnett, N. G., van Rooij, S. J. H., Ely, T. D., Lebois, L. A. M., Murty, V. P., Jovanovic, T., … Stevens, J. S. (2021). Prognostic neuroimaging biomarkers of trauma-related psychopathology: Resting-state fMRI shortly after trauma predicts future PTSD and depression symptoms in the AURORA study. Neuropsychopharmacology, 46(7), 12631271. doi: 10.1038/s41386-020-00946-8CrossRefGoogle ScholarPubMed
Hser, Y. I., Mooney, L. J., Huang, D., Zhu, Y., Tomko, R. L., McClure, E., … Gray, K. M. (2017). Reductions in cannabis use are associated with improvements in anxiety, depression, and sleep quality, but not quality of life. Journal of Substance Abuse Treatment, 81, 5358. doi: 10.1016/j.jsat.2017.07.012CrossRefGoogle Scholar
Joormann, J., Ziobrowski, H. N., King, A. J., Gildea, S. M., Lee, S., Sampson, N. A., … Kessler, R. C. (2022). Prior histories of posttraumatic stress disorder and major depression and their onset and course in the three months after a motor vehicle collision in the AURORA study. Depression & Anxiety, 39(1), 5670. doi: 10.1002/da.23223CrossRefGoogle ScholarPubMed
Kaysen, D., Atkins, D. C., Simpson, T. L., Stappenbeck, C. A., Blayney, J. A., Lee, C. M., & Larimer, M. E. (2014). Proximal relationships between PTSD symptoms and drinking among female college students: Results from a daily monitoring study. Psychology of Addictive Behaviors, 28(1), 62.CrossRefGoogle ScholarPubMed
Kaysen, D., Neighbors, C., Martell, J., Fossos, N., & Larimer, M. E. (2006). Incapacitated rape and alcohol use: A prospective analysis. Addictive Behaviors, 31(10), 18201832. doi: 10.1016/j.addbeh.2005.12.025CrossRefGoogle ScholarPubMed
Kessler, , Sonnega, A., Bromet, E., Hughes, M., & Nelson, C. B. (1995). Posttraumatic stress disorder in the National Comorbidity Survey. Archives of General Psychiatry, 52(12), 10481060.CrossRefGoogle ScholarPubMed
Kessler, R. C., Ressler, K. J., House, S. L., Beaudoin, F. L., An, X., Stevens, J. S., … McLean, S. A. (2021). Socio-demographic and trauma-related predictors of PTSD within 8 weeks of a motor vehicle collision in the AURORA study. Molecular Psychiatry, 26(7), 31083121. doi: 10.1038/s41380-020-00911-3CrossRefGoogle ScholarPubMed
Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231244. doi: 10.3109/10673229709030550CrossRefGoogle ScholarPubMed
Kilpatrick, D. G., Acierno, R., Resnick, H. S., Saunders, B. E., & Best, C. L. (1997). A 2-year longitudinal analysis of the relationships between violent assault and substance use in women. Journal of Consulting and Clinical Psychology, 65(5), 834847. doi: 10.1037//0022-006x.65.5.834CrossRefGoogle ScholarPubMed
Kline, A., Weiner, M. D., Ciccone, D. S., Interian, A., St Hill, L., & Losonczy, M. (2014). Increased risk of alcohol dependency in a cohort of National Guard troops with PTSD: A longitudinal study. Journal of Psychiatric Research, 50, 1825. doi: 10.1016/j.jpsychires.2013.11.007CrossRefGoogle Scholar
Lane, A. R., Waters, A. J., & Black, A. C. (2019). Ecological momentary assessment studies of comorbid PTSD and alcohol use: A narrative review. Addictive Behaviors Reports, 10, 100205. doi: 10.1016/j.abrep.2019.100205CrossRefGoogle ScholarPubMed
Lebois, L. A. M., Harnett, N. G., van Rooij, S. J. H., Ely, T. D., Jovanovic, T., Bruce, S. E., … Ressler, K. J. (2022). Persistent dissociation and its neural correlates in predicting outcomes after trauma exposure. American Journal of Psychiatry, 179(9), 661672. doi: 10.1176/appi.ajp.21090911CrossRefGoogle ScholarPubMed
Lev-Ran, S., Roerecke, M., Le Foll, B., George, T. P., McKenzie, K., & Rehm, J. (2014). The association between cannabis use and depression: A systematic review and meta-analysis of longitudinal studies. Psychological Medicine, 44(4), 797810. doi: 10.1017/s0033291713001438CrossRefGoogle ScholarPubMed
McEachin, R. C., Keller, B. J., Saunders, E. F. H., & McInnis, M. G. (2008). Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach. BioData Mining, 1(1), 2. doi: 10.1186/1756-0381-1-2CrossRefGoogle ScholarPubMed
McLean, S. A., Ressler, K., Koenen, K. C., Neylan, T., Germine, L., Jovanovic, T., … Kessler, R. (2020). The AURORA study: A longitudinal, multimodal library of brain biology and function after traumatic stress exposure. Molecular Psychiatry, 25(2), 283296. doi: 10.1038/s41380-019-0581-3CrossRefGoogle ScholarPubMed
Mirin, S. M., Weiss, R. D., Griffin, M. L., & Michael, J. L. (1991). Psychopathology in drug abusers and their families. Comprehensive Psychiatry, 32(1), 3651.CrossRefGoogle ScholarPubMed
Moore, T. H., Zammit, S., Lingford-Hughes, A., Barnes, T. R., Jones, P. B., Burke, M., & Lewis, G. (2007). Cannabis use and risk of psychotic or affective mental health outcomes: A systematic review. Lancet, 370(9584), 319328. doi: 10.1016/s0140-6736(07)61162-3CrossRefGoogle ScholarPubMed
Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling (nearly) two decades later. Journal of Quantitative Criminology, 26(4), 445453. doi: 10.1007/s10940-010-9113-7CrossRefGoogle ScholarPubMed
Ouimette, P. C., Finney, J. W., & Moos, R. H. (1999). Two-year posttreatment functioning and coping of substance abuse patients with posttraumatic stress disorder. Psychology of Addictive Behaviors, 13(2), 105114. doi: 10.1037/0893-164X.13.2.105CrossRefGoogle Scholar
Ouimette, P. C., Read, J. P., Wade, M., & Tirone, V. (2010). Modeling associations between posttraumatic stress symptoms and substance use. Addictive Behaviors, 35(1), 6467. doi: 10.1016/j.addbeh.2009.08.009CrossRefGoogle ScholarPubMed
Paljärvi, T., Koskenvuo, M., Poikolainen, K., Kauhanen, J., Sillanmäki, L., & Mäkelä, P. (2009). Binge drinking and depressive symptoms: A 5-year population-based cohort study. Addiction, 104(7), 11681178. doi: 10.1111/j.1360-0443.2009.02577.xCrossRefGoogle ScholarPubMed
Pérez Benítez, C. I., Zlotnick, C., Dyck, I., Stout, R., Angert, E., Weisberg, R., & Keller, M. (2013). Predictors of the long-term course of comorbid PTSD: A naturalistic prospective study. International Journal of Psychiatry Clinical Practice, 17(3), 232237. doi: 10.3109/13651501.2012.667113CrossRefGoogle ScholarPubMed
Pietrzak, R. H., Goldstein, R. B., Southwick, S. M., & Grant, B. F. (2011). Prevalence and axis I comorbidity of full and partial posttraumatic stress disorder in the United States: Results from wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Anxiety Disorders, 25(3), 456465. doi: 10.1016/j.janxdis.2010.11.010CrossRefGoogle ScholarPubMed
Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263283. doi: 10.1177/1073191111411667CrossRefGoogle ScholarPubMed
Possemato, K., Maisto, S. A., Wade, M., Barrie, K., McKenzie, S., Lantinga, L. J., & Ouimette, P. (2015). Ecological momentary assessment of PTSD symptoms and alcohol use in combat veterans. Psychology of Addictive Behaviors, 29(4), 894.CrossRefGoogle ScholarPubMed
Quello, S. B., Brady, K. T., & Sonne, S. C. (2005). Mood disorders and substance use disorder: A complex comorbidity. Science & Practice Perspectives, 3(1), 1321. doi: 10.1151/spp053113CrossRefGoogle ScholarPubMed
Read, J. P., Griffin, M. J., Wardell, J. D., & Ouimette, P. C. (2014). Coping, PTSD symptoms, and alcohol involvement in trauma-exposed college students in the first three years of college. Psychology of Addictive Behaviors, 28(4), 10521064. doi: 10.1037/a0038348CrossRefGoogle ScholarPubMed
Reynolds, M., Mezey, G., Chapman, M., Wheeler, M., Drummond, C., & Baldacchino, A. (2005). Co-morbid post-traumatic stress disorder in a substance misusing clinical population. Drug and Alcohol Dependence, 77(3), 251258.CrossRefGoogle Scholar
Richardson, H. N., Lee, S. Y., O'Dell, L. E., Koob, G. F., & Rivier, C. L. (2008). Alcohol self-administration acutely stimulates the hypothalamic-pituitary-adrenal axis, but alcohol dependence leads to a dampened neuroendocrine state. European Journal of Neuroscience, 28(8), 16411653. doi: 10.1111/j.1460-9568.2008.06455.xCrossRefGoogle ScholarPubMed
Rosenthal, S. R., Clark, M. A., Marshall, B. D. L., Buka, S. L., Carey, K. B., Shepardson, R. L., & Carey, M. P. (2018). Alcohol consequences, not quantity, predict major depression onset among first-year female college students. Addictive Behaviors, 85, 7076. doi: 10.1016/j.addbeh.2018.05.021CrossRefGoogle Scholar
Rounsaville, B. J., Weissman, M. M., Crits-Christoph, K., Wilber, C., & Kleber, H. (1982). Diagnosis and symptoms of depression in opiate addicts. Course and relationship to treatment outcome. Archives of General Psychiatry, 39(2), 151156. doi: 10.1001/archpsyc.1982.04290020021004CrossRefGoogle ScholarPubMed
Schock, K., Böttche, M., Rosner, R., Wenk-Ansohn, M., & Knaevelsrud, C. (2016). Impact of new traumatic or stressful life events on pre-existing PTSD in traumatized refugees: Results of a longitudinal study. European Journal of Psychotraumatology, 7, 32106. doi: 10.3402/ejpt.v7.32106CrossRefGoogle ScholarPubMed
Sihvola, E., Rose, R. J., Dick, D. M., Pulkkinen, L., Marttunen, M., & Kaprio, J. (2008). Early-onset depressive disorders predict the use of addictive substances in adolescence: A prospective study of adolescent Finnish twins. Addiction, 103(12), 20452053. doi: 10.1111/j.1360-0443.2008.02363.xCrossRefGoogle ScholarPubMed
Steuber, E. R., Seligowski, A. V., Roeckner, A. R., Reda, M., Lebois, L. A. M., van Rooij, S. J. H., … Stevens, J. S. (2021). Thalamic volume and fear extinction interact to predict acute posttraumatic stress severity. Journal of Psychiatric Research, 141, 325332. doi: 10.1016/j.jpsychires.2021.07.023CrossRefGoogle ScholarPubMed
Stewart, S. H., Pihl, R. O., Conrod, P. J., & Dongier, M. (1998). Functional associations among trauma, PTSD, and substance-related disorders. Addictive Behaviors, 23(6), 797812.CrossRefGoogle ScholarPubMed
Testa, M., Livingston, J. A., & Hoffman, J. H. (2007). Does sexual victimization predict subsequent alcohol consumption? A prospective study among a community sample of women. Addictive Behaviors, 32(12), 29262939. doi: 10.1016/j.addbeh.2007.05.017CrossRefGoogle ScholarPubMed
Volpicelli, J., Balaraman, G., Hahn, J., Wallace, H., & Bux, D. (1999). The role of uncontrollable trauma in the development of PTSD and alcohol addiction. Alcohol Research & Health, 23(4), 256.Google ScholarPubMed
Warren, A. M., Foreman, M. L., Bennett, M. M., Petrey, L. B., Reynolds, M., Patel, S., & Roden-Foreman, K. (2014). Posttraumatic stress disorder following traumatic injury at 6 months: Associations with alcohol use and depression. Journal of Trauma and Acute Care Surgery, 76(2), 517522. doi: 10.1097/ta.0000000000000110CrossRefGoogle ScholarPubMed
Weathers, F. W., Litz, B. T., Keane, T. M., Palmieri, P. A., Marx, B. P., & Schnurr, P. P. (2013). The PTSD checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov.Google Scholar
Wemm, S. E., & Sinha, R. (2019). Drug-induced stress responses and addiction risk and relapse. Neurobiology of Stress, 10, 100148.CrossRefGoogle ScholarPubMed
Wojciechowski, T. W. (2019). Developmental trajectories of opioid use among juvenile offenders: An epidemiological examination of group characteristics and criminological risk factors. Substance Use & Misuse, 54(7), 12031213. doi: 10.1080/10826084.2019.1573837CrossRefGoogle ScholarPubMed
Ziobrowski, H. N., Kennedy, C. J., Ustun, B., House, S. L., Beaudoin, F. L., An, X., … van Rooij, S. J. H. (2021). Development and validation of a model to predict posttraumatic stress disorder and major depression after a motor vehicle collision. JAMA Psychiatry, 78(11), 12281237. doi: 10.1001/jamapsychiatry.2021.2427CrossRefGoogle Scholar
Figure 0

Figure 1. Observed individual (a, b) and mean (c, d) latent trajectories for both alcohol and cannabis use. Individual trajectories determined by measurements at four timepoints are represented by the thin lines, while the thick lines are the average trajectories for each designated group. Each class was identified through the chosen latent growth mixture model, based on Bayesian information criterion (BIC) values and class membership percentages.

Figure 1

Table 1. Demographics and clinical characteristics for alcohol use trajectory classes

Figure 2

Table 2. Demographics and clinical characteristics for cannabis use trajectory classes

Figure 3

Figure 2. Repeated-measures ANOVA examining interactions between levels of use and measures for PTSD (PCL) and depression (PROMIS). Analyses included three timepoints – baseline, week 8, and 12. Error bars represent 95% confidence interval; *denotes significant interaction.

Figure 4

Table 3. Means and standard deviation for posttraumatic stress disorder (PTSD) and depression symptoms

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