Determinants of new-onset alcohol use disorder in U.S. military veterans: Results from the National Health and Resilience in Veterans Study

Journal Pre-proofs Determinants of new-onset alcohol use disorder in U.S. military veterans: Results from the National Health and Resilience in Veterans Study Elizabeth Straus, Sonya B. Norman, Robert H. Pietrzak PII: DOI: Reference:

S0306-4603(19)30943-8 https://doi.org/10.1016/j.addbeh.2020.106313 AB 106313

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Addictive Behaviors Addictive Behaviors

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7 August 2019 9 January 2020 9 January 2020

Please cite this article as: E. Straus, S.B. Norman, R.H. Pietrzak, Determinants of new-onset alcohol use disorder in U.S. military veterans: Results from the National Health and Resilience in Veterans Study, Addictive Behaviors Addictive Behaviors (2020), doi: https://doi.org/10.1016/j.addbeh.2020.106313

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Determinants of new-onset alcohol use disorder in U.S. military veterans: Results from the National Health and Resilience in Veterans Study Elizabeth Straus, PhD ([email protected]) a,b, Sonya B. Norman, Ph.D. ([email protected]) a,b,c,d, and Robert H. Pietrzak, Ph.D. ([email protected]) e,f VA San Diego Healthcare System, San Diego, CA 92161, USAa; Department of Psychiatry, University of California, San Diego, CA 92093, USAb; VA Center of Excellence for Stress and Mental Health, San Diego, CA 92161, USAc; National Center for PTSD, White River Junction, VT 05009, USAd; National Center for PTSD, West Haven, CT 06516, USAe; Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USAf

Correspondence Address: Elizabeth Straus, Ph.D., VA San Diego Healthcare System, 3350 La Jolla Village Drive, San Diego, CA 92161, email: [email protected] Author Statement Author ES drafted primary manuscript, conceived of study aims, and contributed to interpretation of findings. SBN contributed to conceptualization of study, interpreted study findings, and provided editing of manuscript. RHP acquired the data, conceived of study aims, conducted the statistical analyses, interpreted the data, and edited the manuscript. All authors contributed and approved of the final manuscript.

Conflict of Interest The authors have no known conflicts of interest to disclose.

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Highlights Approximately 6% of veterans developed AUD over 7-year follow-up period. Adult sexual trauma and anxious arousal symptoms were associated with incident AUD. DUDs and greater alcohol use at Wave 1 explained most variance in incident AUD.

Abstract Background: Although veterans are at increased risk of alcohol use disorder (AUD) relative to civilians, few longitudinal studies have examined both risk and protective factors that influence the development of AUD. This study aimed to identify risk and protective factors that contribute to incident AUD. Methods: Data were analyzed from the National Health and Resilience in Veterans Study (NHRVS), a nationally representative, prospective cohort study of U.S. veterans. The sample included 1,770 veterans who did not meet criteria for lifetime AUD at Wave 1 and completed at least one follow-up assessment over a 7-year period. Veterans completed self-report measures to assess for risk and protective factors. A multivariable binary logistic regression analysis was conducted to examine baseline factors associated with incident AUD. Results: A total of 5.9% of veterans without AUD at Wave 1 developed AUD in the 7-year follow-up period. Adult sexual trauma exposure, greater anxious arousal symptoms of PTSD, lifetime history of drug and nicotine use disorders, and higher alcohol consumption at Wave 1 were independently associated with incident AUD. Lifetime drug use disorder (75.9%) and higher alcohol consumption (22.1%) explained the most variance in incident AUD.

Conclusion: Approximately 6% of veterans without AUD at Wave 1 developed AUD over a 7year period. Lifetime drug use disorder and greater alcohol consumption at baseline, as well as trauma-related characteristics were associated with increased risk of developing AUD. Future research should examine whether treatment of drug use disorder and PTSD symptoms in at-risk veterans may help mitigate risk of developing AUD in this population. Keywords: Alcohol use disorder, risk factors, protective factors, posttraumatic stress disorder, veterans 1. Introduction Alcohol use disorder (AUD) is a significant problem amongst veterans, with up to one in six U.S. veterans screening positive for AUD (Fuehrlein et al., 2016). Veterans may be at particular risk for developing AUD given the prevalence of heavy drinking in the military, high rates of trauma exposure, and readjustment stressors (Ames, Cunradi, Moore, & Stern, 2007; Bohnert et al., 2012; Hoopsick, Fillo, Vest, Homish, & Homish, 2017; Schumm & Chard, 2012; Suris & Lind, 2008). Veterans with AUD are at greater risk of experiencing a range of clinical and functional impairments, including psychiatric comorbidities, suicidal behavior, and homelessness (Dunne, Burrell, Diggins, Whitehead, & Latimer, 2015; Fuehrlein et al., 2016). Despite the prevalence and impairment associated with AUD, little is known about factors that may contribute to the development of this disorder. Identification of risk and protective factors that influence the development of AUD is critical to advancing early detection and prevention efforts for veterans and other populations at risk for AUD. To date, the literature on AUD risk has focused on demographic (e.g., younger age, male sex; Eisen et al., 2012; Green, Beckham, Youssef, & Elbogen, 2014; Jacobson et al., 2008; Vest, Homish, Fillo, & Homish, 2018), psychiatric (e.g., depression, PTSD; Capone, McGrath, Reddy,

& Shea, 2013; Kehle et al., 2012; Marshall et al., 2012), and military and trauma-related characteristics (e.g., childhood abuse, combat exposure; Anda et al., 2002; Guina, Nahhas, Goldberg, & Farnsworth, 2016; Jacobson et al., 2008) associated with AUD. While the majority of studies have been cross-sectional, recent longitudinal studies have examined risk factors associated with the development of AUD (Green et al., 2014; Kline et al., 2014; Schultz, Glickman, & Eisen, 2014). In a sample of Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) veterans, a number of psychosocial risk factors were associated with increased alcohol use at follow-up, including less unit support and being separated or divorced (Schultz et al., 2014). PTSD severity may also increase risk of developing AUD. In a study of National Guard soldiers (Kline et al., 2014), baseline PTSD symptom severity was associated with an elevated risk of new-onset AUD, with a 5% increase in risk occurring with each unit increase on the PTSD Checklist (PCL; Blanchard, Jones-Alexander, Buckley, & Forneris, 1996). Although these studies suggest that select psychosocial and clinical factors increase risk for AUD in convenience samples of veterans, no known study has examined how a more comprehensive range of risk and protective factors may influence the development of AUD in nationally representative samples of U.S. veterans. Converging evidence suggests that there may be protective factors which lower the risk of developing AUD. A limited number of studies have found that modifiable protective factors, such as perceived psychological resilience, are associated with reduced likelihood of AUD (Eisen et al., 2014; Green et al., 2014; Long et al., 2017; Schultz et al., 2014). Other psychosocial factors, including religiosity/spirituality, have also been associated with reduced likelihood of AUD (Borders, Curran, Mattox, & Booth, 2010; Sharma et al., 2017). In a prospective cohort study of at-risk drinkers, individuals without AUD at baseline and greater levels of religiosity

had reduced odds of developing AUD at six-month follow-up (Borders et al., 2010). While these studies suggest that some protective characteristics may reduce the likelihood of AUD, the majority of studies have only included six to twelve-month follow-up assessments. To our knowledge, no study has examined factors that protect against AUD in a nationally representative sample of veterans over longer follow-up periods. Prospective, population-based studies that follow veterans without a lifetime diagnosis of AUD may elucidate the key characteristics associated with incident AUD. Given that the majority of literature on risk and protective factors associated with incident AUD has utilized cross-sectional designs and included clinical samples, our primary aim was to explore how a broad range of both risk and protective factors were associated with incident AUD over a 7-year period in a nationally representative sample of U.S. veterans without lifetime or past-year AUD at Wave 1. Given that a myriad of factors likely influence vulnerability to AUD, it is critical to comprehensively examine both risk and protective factors that may contribute to risk for developing this disorder. Thus, this study utilized a comprehensive framework based on broad categories of potential risk and protective factors (see Figure 1) that have been implicated in the development of AUD in veteran samples (Eisen et al., 2014; Jakupcak et al., 2010; Kline et al., 2014; Schultz et al., 2014; Sharma et al., 2017) and also sought to identify novel predictors of AUD. Potential risk and protective factors analyzed in the study included demographic variables, psychiatric history, military and trauma characteristics, as well as individual (i.e., protective psychosocial characteristics, religiosity/spirituality) and environmental-level characteristics (i.e., social connectedness).

2. Methods

Participants and Procedure Participants were drawn from the National Health and Resilience in Veterans Study (NHRVS), a nationally representative, prospective cohort study of U.S. veterans. The NHRVS recruited veterans using KnowledgePanel®, a probability-based online survey panel by GfK Knowledge Networks, Inc. (Menlo Park, California) to complete an anonymous web-based survey. The panel includes over 50,000 households and represents approximately 98% of the U.S. adult population. In order to ensure generalizability to the general U.S. veteran population, poststratification weights were applied based on the demographic distributions (i.e., age, gender, race/ethnicity, education, Census region, and metropolitan area) in the American Community Survey (United States Census Bureau, 2010). Wave 1 included 3,157 veterans and was completed in 2011, Wave 2 included 2,157 veterans (68.3% of the Wave 1 cohort) in 2013, Wave 3 included 1,538 veterans (48.7% of the Wave 1 cohort) in 2015, and Wave 4 included 1,310 veterans (41.5% of the Wave 1 cohort) in 2018. Additional details regarding the NHRVS survey can be found elsewhere (Pietrzak & Cook, 2013; Smith et al., 2016). The current study sample was comprised of 1,770 participants who completed Wave 1 and did not meet criteria for lifetime or past-year AUD, and completed one or more follow-up assessments of AUD status over a seven-year period (mean number of follow-up assessments=2.7, SD=0.5, range=1-3). The majority of the sample (66.3%) completed 2 or 3 follow-up assessments. Relative to veterans who did not complete one or more follow-up assessments, those who did were slightly older (63.3 vs. 60.5 years, F=24.96, p<0.001), but did not differ by gender (89.7% vs. 87.3% male, X2=3.15, p=0.09). With respect to significant multivariable Wave 1 determinants of incident AUD (see Table 2), there were no differences between these groups in the prevalence of adult sexual trauma (3.4% vs. 4.0%, X2=0.44,

p=0.50), drug use disorder (10.5% vs. 11.5%, X2=0.47, p=0.49), nicotine dependence (18.2% vs. 16.3%, X2=1.31, p=0.25), or AUDIT-C scores (1.5 vs. 1.6, F=0.86, p=0.35); veterans who completed a follow-up assessment did, however, report slightly lower severity of anxious arousal symptoms (2.7 vs. 3.0, F=11.42, p=0.001; Cohen d=0.15). This study was approved by the Human Subjects Subcommittee at VA Connecticut Health Care System. Assessments Sociodemographic characteristics. A number of sociodemographic, trauma, and military characteristics were assessed (See Table 1). The following sociodemographic characteristics were coded as binary variables: sex (male vs female), race/ethnicity (Caucasian vs. nonCaucasian), education level (some college or higher education vs. less than college or higher education), marital status, (married or living with partner vs. single) and annual household income (greater than 60,000 vs. below 60,000). More detailed reference categories are shown in the Table 1 footnote. Alcohol use disorder. Veterans who did not meet criteria for lifetime or past-year AUD at Wave 1 were included in the study sample. Lifetime AUD was assessed using a self-report version of the Alcohol Use Disorder section of the DSM-IV version of the Mini Neuropsychiatric Interview (MINI; Sheehan et al., 1998) and past-year AUD was screened for using the Alcohol Use Disorder Identification Test-Consumption (Bush et al., 1998). A score of ≥5 on the AUDIT-C (range=0-12) was indicative of probable AUD given its utility in nationally representative samples (Dawson, Grant, Stinson, & Zhou, 2005; Fuehrlein et al., 2016). Individuals who were lost at a follow-up assessment (e.g., Wave 3 or 4) and had not met criteria for AUD at previous timepoint were categorized as not meeting criteria for AUD. The AUDIT-C has been demonstrated to be an effective screening measure for AUD (Crawford, Fulton,

Swinkels, Beckham, & Calhoun, 2013; Dawson et al., 2005; Kriston, Hölzel, Weiser, Berner, & Härter, 2008) and has performed similarly to the full-length AUDIT with regard to the detection of alcohol use disorders and/or heavy drinking in veterans (Bush et al., 1998). Although the MINI and AUDIT-C screen for alcohol abuse or dependence based on DSM-IV criteria, given that the DSM-5 criteria for AUD largely overlap with DSM-IV criteria (addition of cravings, removal of legal problems), this paper uses AUD (i.e., alcohol abuse or dependence) to utilize updated terminology. Psychiatric history. Lifetime DSM-IV PTSD diagnosis and current PTSD symptom severity were assessed using the 17-item PTSD Checklist-Specific (PCL-S; Weathers, Litz, Herman, Huska, & Keane, 1993) in relation to the veteran’s “worst” traumatic event as assessed by the Trauma History Screen (THS; Carlson et al., 2011). The THS is a self-report measure that assesses for the occurrence of 14 types of trauma exposures across the lifespan (e.g., childhood sexual abuse, military-related trauma, natural disaster) and has demonstrated strong psychometric properties, including test-retest reliability and convergent validity in veterans (Carlson et al., 2011). An additional trauma exposure type, life-threatening illness or injury, was added in the NHRVS study. Cronbach’s  with regard to the PCL-S in this sample was 0.95. Probable lifetime PTSD was operationalized as a score ≥30 (range= 17-85), which is consistent with studies of population-based, non-treatment seeking samples (McDonald & Calhoun, 2010; Terhakopian, Sinaii, Engel, Schnurr, & Hoge, 2008). Re-experiencing, avoidance, emotional numbing, dysphoric arousal (e.g., sleep difficulties), and anxious arousal (e.g., hypervigilance) symptoms were also examined as potential determinants of incident AUD (Harpaz-Rotem, Tsai, Pietrzak, & Hoff, 2014; Pietrzak, Tsai, Harpaz-Rotem, Whealin, & Southwick, 2012). Veterans were screened for lifetime major depressive disorder (MDD) using the MINI (Sheehan et al.,

1998) and current depressive symptom severity using the Patient Health Questionnaire-2 (PHQ2; Kroenke, Spitzer, & Williams, 2003). A positive MDD screen on the PHQ-2 was operationalized as a score of ≥3 (range=0-6; Kroenke et al., 2003). Cronbach’s  in this sample was 0.89. Veterans were also screened for lifetime drug and nicotine dependence using a selfreport version of the MINI (Sheehan et al., 1998). Mental health treatment history was assessed using the following item: “Have you ever received mental health treatment (e.g., prescription medication or psychotherapy for a psychiatric or emotional problem)?” Protective characteristics. The NHRVS assessed for a broad range of protective characteristics, including three different aspects of social connectedness: 1) perceived social support (i.e., score on Medical Outcomes Study Social Support Scale; range=5 to 25; α=0.90; Sherbourne & Stewart, 1991); 2) structural support (i.e., number of close friends/relatives; single item: “About how many close friends and relatives do you have [people you feel at ease with and can talk to about what is on your mind]”); and 3) loneliness (i.e., score on the Three-Item Loneliness Scale; range=0 to 9; α=0.87; Hughes, Waite, Hawkley, & Cacioppo, 2004). A range of psychosocial protective characteristics were assessed, including: 1) psychological resilience (i.e., score on Connor-Davidson Resilience-Scale-10; range=0 to 40; α=0.92; Campbell-Sills & Stein, 2007); 2) purpose in life (i.e., score on Purpose in Life TestShort Form; range=4 to 28; α=0.87; Schulenberg, Schnetzer, & Buchanan, 2010); 3) dispositional optimism (single item: “In uncertain times, I usually expect the best;” 1=strongly disagree to 7=strongly agree; Scheier, Carver, & Bridges, 1994); 4) dispositional gratitude (single item: “I have so much in life to be thankful for;” 1=strongly disagree to 7=strongly agree; McCullough et al., 2002); 5) curiosity and exploration (single item: “I frequently find myself looking for new opportunities to grow as a person [e.g., information, people, resources”];

1=strongly disagree to 7=strongly agree; Kashdan et al., 2009); and 6) community integration (single item: “I feel well integrated in my community;” 1=strongly disagree to 7=strongly agree). Three aspects of religiosity/spirituality were assessed using the Duke University Religion Index (DUREL; Koenig & Büssing, 2010): 1) frequency of religious service attendance (i.e., “How often do you attend church or other religious meetings;” 1=never to 6=more than once/week); 2) frequency of non-organizational religious activities (i.e., “How often do you spend in private religious activities, such as prayer, meditation or study;” 1=rarely or never to 6=more than once a day); and 3) intrinsic religiosity (e.g., “My religious beliefs are what really lie behind my whole approach to life;” 1=definitely not true to 6=definitely true of me; α=0.93). Data Analysis Data analyses included three steps. First, independent-samples t-tests and chi-square analyses were conducted to examine sociodemographic, psychiatric, and psychosocial characteristics by incident AUD status. Second, variables that were significantly associated with incident AUD (p<0.05) were entered into a multivariable binary logistic regression analysis to identify which factors were independently associated with incident AUD. Third, a relative importance analysis (Tonidandel & LeBreton, 2009, 2011) was conducted to compute the relative variance in incident AUD that was explained by independent variables that were significantly associated with incident AUD in the multivariable regression model.

3. Results A total of 5.9% of veterans who did not endorse AUD at Wave 1 screened positive for AUD in the 7-year follow-up period. Table 1 shows the characteristics at Wave 1 of veterans who did and did not go on to screen positive for AUD over the follow-up period. Veterans who

developed AUD were more likely to be younger, report exposure to childhood physical trauma and adult physical and sexual trauma, endorse greater severity of PTSD anxious arousal symptoms (i.e., hypervigilance, exaggerated startle response), screen positive for a lifetime drug use and nicotine disorder, report greater alcohol consumption, and score lower on measures of religious service attendance and intrinsic religiosity. Multivariable logistic regression results (see Table 2) revealed that exposure to adult sexual trauma, greater anxious arousal symptoms, lifetime history of drug use disorder (DUD), and nicotine dependence, and higher AUDIT-C scores at baseline were independently associated with incident AUD (total R2=0.20; ps<.05). A post-hoc analysis revealed that greater severity of hypervigilance (p=0.001), but not exaggerated startle response (p=0.19), drove the association between anxious arousal symptoms and incident AUD. Relative importance analysis results revealed that lifetime drug use disorder (75.9% relative variance explained [RVE]) and higher AUDIT-C scores at baseline (22.1% RVE) explained the vast majority of variance in incident AUD, with adult sexual trauma (1.2% RVE), anxious arousal symptoms (0.5% RVE), and nicotine use disorder (0.3%), explaining significantly less variance in incident AUD. To examine whether time-varying changes in alcohol consumption severity and PTSD symptoms related to incident AUD, we conducted a binary logistic regression analysis that additionally included trajectories of alcohol consumption (rare, moderate, excessive, and recovering drinkers; Fuehrlein et al., 2018) and PTSD symptoms (no/low, moderate, severe; Mota et al., 2019). Results of this analysis revealed that neither trajectories of alcohol consumption (Wald X2=0.01, p=0.99) nor PTSD symptoms (Wald X2=0.77, p=0.68) were associated with incident AUD.

4.

Discussion This study examined a comprehensive range of risk and protective factors for developing

AUD in a nationally representative sample of veterans. Amongst veterans without AUD at Wave 1, 5.9% developed AUD over a 7-year follow-up period. This rate is greater than the new-onset incident rate of AUD in a nationally representative sample of civilians (1.6%; Grant et al., 2009), but less than rates of new-onset AUD in recently deployed veterans (11.7%; Orr et al., 2014). The fact that our study found lower rates of incident AUD relative to recently deployed veterans may be attributable to the older age of our sample, as younger age has consistently been associated with greater risk of AUD (Fleury, Grenier, Bamvita, Perreault, & Caron, 2014; Green et al., 2014; Seal et al., 2011). Multiple risk factors emerged as determinants of incident AUD, with lifetime drug use disorders (DUDs) and alcohol consumption explaining the majority of variance. Our findings align with results from other nationally representative samples of U.S. adults, which have found that AUD frequently co-occurs with other DUDs (Brook, Brook, Zhang, Cohen, & Whiteman, 2002; Compton, Thomas, Stinson, & Grant, 2007; Hasin, Stinson, Ogburn, & Grant, 2007). Our findings may be explained by the syndrome model of addiction (Shaffer et al., 2004), which posits that the various forms of addiction should not be conceptualized as distinct disorders, but rather as different expressions of one syndrome. Thus, it may be that the relationship between lifetime DUDs and AUD are the result of shared neurobiological (e.g., similar reward pathways) and psychosocial determinants (e.g., early environmental stressors; Adinoff, 2004; Dube et al., 2003; Khoury, Tang, Bradley, Cubells, & Ressler, 2010; Konkoly Thege et al., 2017). Further, the relationship between alcohol consumption at baseline and incident AUD suggests that higher

alcohol use is a significant risk factor for developing AUD. Given that lifetime DUDs and higher alcohol consumption are associated with incident AUD, future research should examine the efficacy of routinely providing preventative interventions for substance misuse, such as relapse prevention (Witkiewitz & Marlatt, 2004), to individuals with lifetime histories of DUDs and higher alcohol consumption. Trauma-related characteristics (i.e., adult sexual trauma and greater PTSD anxious arousal symptoms) were also associated with incident AUD, although explained relatively less variance. These results align with prior studies, which have found that PTSD increases the risk of developing AUD. For example, in a longitudinal study of National Guard members, PTSD symptom severity was associated with an increased risk of developing AUD when controlling for other risk and protective factors (Kline et al., 2014). The relationship between PTSD and incident AUD suggests that treatment of PTSD symptoms may help mitigate risk of AUD in atrisk individuals, although this must be directly examined in future studies. The finding that adult sexual trauma exposure and greater severity of anxious arousal symptoms of PTSD were associated with incident AUD provides additional support for the selfmedication hypothesis (Khantzian, 2003). The self-medication hypothesis posits that individuals with trauma exposure may drink to cope with PTSD symptoms and, through a cycle of negative reinforcement, are at greater risk for developing AUD. Thus, it may be that veterans who experience PTSD secondary to sexual trauma that is characterized by heightened physiological reactivity (i.e., hypervigilance) are at particularly high risk to self-medicate with alcohol. However, prior findings regarding PTSD symptom clusters and alcohol use outcomes have been mixed, with other studies failing to find an association between physiological symptoms of PTSD and AUD symptoms, such as alcohol craving (Simpson, Stappenbeck, Varra, Moore, &

Kaysen, 2012; Trautmann et al., 2015), and other cross-sectional findings revealing a stronger association between emotional numbing symptoms and AUD (Jakupcak et al., 2010). Thus, further longitudinal research is needed to clarify the relationship between trauma type, PTSD symptom clusters, and incident AUD, and whether these associations may be moderated by age, sex, and other demographic variables. Several limitations should be noted in order to guide future research. First, the sample was predominately male, Caucasian, and older; thus the results may not be generalizable to diverse veteran samples. It will be important to examine whether similar risk and protective factors emerge in other at-risk populations, such as active duty and National Guard members (Bray, Brown, & Williams, 2013) and with regard to the development of other types of substance use disorders. Second, when interpreting the study results, it is important to note the estimated incidence of AUD in the study sample may have been underestimated given sample attrition, and that if individuals were misclassified with regard to their incident AUD status, the resulting findings may be biased. Nevertheless, the majority of the sample (66.3%) completed 2 or 3 follow-up AUD assessments, and veterans who did and did not complete one or more follow-up surveys did not differ with respect to most Wave 1 characteristics, most notably AUDIT-C scores and most multivariable determinants of incident AUD, though those who did complete a follow-up survey were 2.8 years older on average and reported slightly lower severity of anxious arousal symptoms. Third, this study used self-report measures to assess for multidimensional risk and protective factors. Although this allowed for an assessment of a range of factors, it may be that certain aspects of the factors were not detected. Further, this study utilized the AUDIT-C, which is a screening measure that focuses on alcohol consumption, to assess for probable AUD. While several studies have found that the AUDIT-C may detect alcohol use disorders as well as

the lengthier AUDIT across diverse veteran samples (Bradley et al., 2007; Crawford et al., 2013) and has been shown to be an efficient screener of AUD in a large U.S. general sample (Dawson, Grant, Stinson, & Zhou, 2006), future research should attempt to replicate these results using gold-standard clinical interviews, such as the Structured Clinical Interview for DSM-5 (SCID-5). Although the design of the National Health and Resilience in Veterans Study did not allow for the administration of a full-length clinical interview, this will be an important future direction. Lastly, given sample size constraints, we were not able to examine factors associated with fluctuations in AUD, such as remission and relapse, although incorporation of trajectories of alcohol consumption into the logistic regression model suggested that time-varying changes in alcohol consumption frequency were unrelated to incident AUD. Relatedly, given that certain baseline factors, such as social support, likely fluctuate over the follow-up period, it will be important for future research to investigate the relationship between time-varying changes in baseline factors and incident AUD. Despite these limitations, this is one of the first studies to comprehensively examine risk and protective factors associated with incident AUD in a nationally representative sample of veterans. Results suggest that, in addition to the high prevalence and burden of AUD in U.S. veterans (Fuehrlein et al., 2016), this population has elevated risk of developing new-onset AUD relative to their civilian counterparts (Grant et al., 2009). They further underscore the importance of prior DUD history and alcohol consumption, as well as trauma-related factors, particularly adult sexual trauma and anxious arousal symptoms, as determinants of incident AUD in this population. Further research is needed to evaluate risk and protective factors for AUD in other at-risk populations, to prospectively evaluate factors associated with fluctuations in AUD (e.g., relapse and remission), and to examine the efficacy of providing preventative substance use

interventions to individuals with lifetime histories of DUDs and high levels of alcohol consumption.

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Table 1. Baseline determinants of incident alcohol use disorder over 7-year study period in U.S. military veterans (N=1,770) No incident Incident AUD AUD N=103 N= 1,667 weighted weighted 5.9% 94.1%

Demographic characteristics Age Male sex Caucasian race Some college or higher education Married or living with partner Annual household income >60K Military and trauma characteristics Enlisted into military Combat veteran Years spent in military Number of lifetime traumas Child physical trauma Child sexual trauma Adult physical trauma Adult sexual trauma Psychiatric history Lifetime major depressive disorder Current depressive symptom severity

Test of difference

P value

N (weighted %) or Mean (SD)

N (weighted %) or Mean (SD)

62.8 (14.0) 1,489 (90.2%) 1,406 (78.8%) 1,438 (69.9%) 1,344 (76.4%) 882 (45.1%)

55.6 (15.9) 178 (92.6%) 86 (70.5%) 96 (75.8%) 83 (85.1%) 63 (51.6%)

4.81 0.60 3.59 1.50 3.79 1.51

<0.001 0.44 0.058 0.22 0.052 0.22

1,403 (84.5%) 539 (31.1%) 6.9 (7.7) 3.0 (2.5) 214 (12.1%) 101 (6.4%) 163 (8.9%) 45 (3.0%)

91 (90.4%) 35 (29.8%) 6.9 (7.2) 3.5 (3.3) 15 (20.2%) 6 (7.6%) 16 (20.2%) 6 (7.5%)

2.40 0.08 0.01 1.73 5.31 0.21 13.17 5.60

0.12 0.78 0.99 0.084 0.021 0.65 <0.001 0.018

190 (11.3%) 0.4 (1.2)

9 (13.7%) 0.3 (0.9)

0.50 0.98

0.48 0.33

Lifetime PTSD (PCL score >=30) Current PTSD symptom severity Re-experiencing symptoms Avoidance symptoms Emotional numbing symptoms Dysphoric arousal symptoms Anxious arousal symptoms Ever received mental health treatment Protective psychosocial characteristics Psychological resilience Community integration Dispositional gratitude Dispositional optimism Purpose in life Curiosity and exploration Social connectedness Loneliness Perceived social support Structural social support Religiosity/spirituality Frequency religious service Frequency non-org. religious activities per week+religiosity Intrinsic Substance use disorder history Lifetime drug use disorder (DUD) Disorder nicotine depen dependence Lifetime dependence Baseline AUDIT-C score

363 (21.9%) 22.6 (9.9) 6.2 (3.1) 2.6 (1.4) 6.7 (3.2) 4.4 (2.2) 2.7 (1.5) 295 (17.7%)

18 (25.5%) 22.8 (8.5) 6.4 (3.3) 2.9 (1.9) 6.1 (2.2) 4.3 (1.8) 3.1 (1.8) 15 (12.6%)

0.68 0.69 0.60 1.75 1.60 0.59 2.63 1.61

0.41 0.85 0.55 0.079 0.11 0.56 0.009 0.20

29.8 (6.8) 4.4 (1.7) 6.2 (1.1) 4.8 (1.4) 21.7 (4.3) 5.2 (1.3)

29.8 (7.4) 4.2 (1.7) 6.1 (1.3) 5.1 (1.4) 21.7 (4.0) 5.3 (1.4)

0.05 0.95 0.94 1.48 0.16 0.27

0.96 0.34 0.35 0.14 0.87 0.78

4.3 (1.7) 19.8 (4.9) 8.6 (9.0)

4.1 (1.4) 19.6 (4.5) 8.2 (10.6)

0.89 0.38 0.45

0.37 0.70 0.65

3.2 (1.8) 3.5 (2.2) 10.4 (3.9)

2.5 (1.6) 3.1 (2.1) 8.6 (4.1)

3.38 1.62 4.48

0.001 0.10 <0.001

123 (6.9%) 241 (15.8%) 1.4 (1.3)

12 (17.9%) 20 (28.4%) 2.9 (1.5)

15.38 10.39 10.08

<0.001 0.001 <0.001

Note. Weighted prevalence estimates are within the NHRVS subsample of veterans without a history of alcohol use disorder, AUDIT-C score < 5, and who completed at least one follow-up survey. Abbreviations: SD =standard deviation; PCL=PTSD Checklist; AUDIT-C= Alcohol Use Disorders Identification Test-Consumption. Bivariate correlations are presented and statistically significant (p<.05) determinants of incident AUD are highlighted in bold. Reference categories for race were Black, non-Hispanic, Hispanic, Other, or Mixed Race; for education were Less than High School or High School; for martial status were Never married, Divorced, Separated, or Widowed; and for income were <$30,000 or $30,000-59,999. Table 2. Results of multivariable binary logistic regression analysis of Wave 1 determinants of incident alcohol use disorder over 7-year period in U.S. military veterans Wald X2

P value

Odds ratio (95% confidence interval)

Age Child physical trauma Adult physical trauma Adult sexual trauma Anxious arousal symptoms Frequency religious service Intrinsic religiosity Lifetime drug use disorder Lifetime nicotine dependence AUDIT-C score

2.98 0.28 1.12 11.62 6.19 0.50 1.09 5.83 4.91 53.77

0.084 0.59 0.29 0.001 0.013 0.48 0.30 0.016 0.027 <0.001

0.98 (0.97-1.00) 1.21 (0.60-2.45) 1.53 (0.70-3.34) 6.21 (2.17-17.75) 1.23 (1.04-1.45) 0.93 (0.77-1.13) 0.96 (0.89-1.04) 2.37 (1.18-4.77) 1.95 (1.08-3.51) 1.99 (1.65-2.39)

Note. Statistically significant (p<0.05) determinants of incident AUD are highlighted in bold. Abbreviations: AUDIT-C= Alcohol Use Disorders Identification Test-Consumption.

Substance Use Disorder History Social Connectedness

Psychiatric History

Military and Trauma Characteristics

Demographic characteristics

Religiosity/ Spirituality

Incident Alcohol Use Disorder

Protective Psychosocial Characteristics

Figure 1. Broad categories of baseline factors which may be associated with incident alcohol use disorder.