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Alcohol use and treatment utilization in a national sample of veterans and nonveterans

  • Author Footnotes
    1 VA Advanced Postdoctoral Fellow in Mental Illness Research and Treatment, Sierra Pacific (VISN 21) Mental Illness Research Education and Clinical Center, VA San Francisco Health Care System, and the Department of Psychiatry and Behavioral Sciences, University of California, San Francisco School of Medicine.
    Rachel M. Ranney
    Correspondence
    Corresponding author at: San Francisco VA Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA.
    Footnotes
    1 VA Advanced Postdoctoral Fellow in Mental Illness Research and Treatment, Sierra Pacific (VISN 21) Mental Illness Research Education and Clinical Center, VA San Francisco Health Care System, and the Department of Psychiatry and Behavioral Sciences, University of California, San Francisco School of Medicine.
    Affiliations
    San Francisco VA Health Care System, 4150 Clement St, San Francisco, CA 94121, USA

    University of California – San Francisco, 401 Parnassus Ave, San Francisco, CA 94143, USA

    Sierra Pacific Mental Illness Research Education, and Clinical Center, 4150 Clement St, San Francisco, CA 94121, USA
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  • Paul A. Bernhard
    Affiliations
    Health Outcomes of Military Exposures, Epidemiology Program, Office of Patient Care Services, Veterans Health Administration, 810 Vermont Ave NW, Washington, DC 20420, USA
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  • Dawne Vogt
    Affiliations
    VA Boston Health Care System, 150 S Huntington Ave, Boston, MA 02130, USA

    Boston University School of Medicine, 72 E Concord St, Boston, MA 02118, USA
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  • John R. Blosnich
    Affiliations
    University of Southern California, 669 W 34th St, Los Angeles, CA 90089-0411, USA

    VA Pittsburgh Healthcare System, 4100 Allequippa St, Pittsburgh, PA 15240, USA
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  • Claire A. Hoffmire
    Affiliations
    VA Rocky Mountain MIRECC for Suicide Prevention, 1700 N Wheeling St, Aurora, CO 80045, USA

    University of Colorado School of Medicine, Department of Physical Medicine and Rehabilitation, 13001 E 17th Pl, Aurora, CO 80045, USA
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  • Yasmin Cypel
    Affiliations
    Health Outcomes of Military Exposures, Epidemiology Program, Office of Patient Care Services, Veterans Health Administration, 810 Vermont Ave NW, Washington, DC 20420, USA
    Search for articles by this author
  • Aaron I. Schneiderman
    Affiliations
    Health Outcomes of Military Exposures, Epidemiology Program, Office of Patient Care Services, Veterans Health Administration, 810 Vermont Ave NW, Washington, DC 20420, USA
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  • Shira Maguen
    Affiliations
    San Francisco VA Health Care System, 4150 Clement St, San Francisco, CA 94121, USA

    University of California – San Francisco, 401 Parnassus Ave, San Francisco, CA 94143, USA
    Search for articles by this author
  • Author Footnotes
    1 VA Advanced Postdoctoral Fellow in Mental Illness Research and Treatment, Sierra Pacific (VISN 21) Mental Illness Research Education and Clinical Center, VA San Francisco Health Care System, and the Department of Psychiatry and Behavioral Sciences, University of California, San Francisco School of Medicine.
Published:January 23, 2023DOI:https://doi.org/10.1016/j.josat.2023.208964

      Highlights

      • Veterans were more likely to utilize lifetime alcohol treatment than nonveterans.
      • Veterans and nonveterans did not differ in past-year alcohol treatment utilization.
      • Veterans and nonveterans did not differ in need for intensive alcohol treatment.

      Abstract

      Background

      Research comparing prevalence of alcohol use problems and alcohol treatment utilization between veterans and nonveterans is lacking. Whether predictors of alcohol use problems and alcohol treatment utilization differ in veterans vs. nonveterans is also unclear.

      Methods

      Using survey data from national samples of post-9/11 veterans and nonveterans (N = 17,298; 13,451 veterans, 3847 nonveterans), we investigated associations between veteran status and 1) alcohol consumption, 2) need for intensive alcohol treatment, and 3) past-year and lifetime alcohol treatment utilization. We also investigated associations between predictors and these three outcomes in separate models for veterans and nonveterans. Predictors included age, gender, racial/ethnic identity, sexual orientation, marital status, education, health coverage, financial difficulty, social support, adverse childhood experiences (ACEs), and adult sexual trauma.

      Results

      Population weighted regression models demonstrated that veterans reported modestly higher alcohol consumption than nonveterans, but were not significantly more likely to need intensive alcohol treatment. Veterans and nonveterans did not differ in past-year alcohol treatment utilization, but veterans were 2.8 times more likely to utilize lifetime treatment than nonveterans. We found several differences between veterans and nonveterans in associations between predictors and outcomes. For veterans, being male, having higher financial difficulty, and lower social support were associated with need for intensive treatment, but for nonveterans, only ACEs were associated with need for intensive treatment.

      Conclusions

      Veterans may benefit from interventions with social and financial support to reduce alcohol problems. These findings can help to identify veterans and nonveterans who are more likely to need treatment.

      Keywords

      1. Introduction

      Alcohol use disorder (AUD) is highly prevalent in both veterans and nonveterans (
      • Fuehrlein B.S.
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      The burden of alcohol use disorders in US military veterans: Results from the National Health and resilience in veterans study.
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      Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: Results from the National Epidemiologic Survey on alcohol and related conditions.
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      • Stohl M.
      • Hasin D.S.
      Correlates of mild, moderate, and severe alcohol use disorder among adults with problem substance use: Validity implications for DSM-5.
      ), psychosocial and health problems (
      • Gutkind S.
      • Fink D.S.
      • Shmulewitz D.
      • Stohl M.
      • Hasin D.
      Psychosocial and health problems associated with alcohol use disorder and cannabis use disorder in U.S. adults.
      ), psychiatric comorbidities (
      • Castillo-Carniglia A.
      • Keyes K.M.
      • Hasin D.S.
      • Cerdá M.
      Psychiatric comorbidities in alcohol use disorder.
      ), and disease burden (in terms of disability and years of life lost;
      Global Burden of Disease 2016 Alcohol and Drug Use Collaborators
      The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
      ). Although some may assume that veterans demonstrate higher alcohol use problems than nonveterans, little evidence exists to support this assumption. The few studies making direct comparisons between veterans and nonveterans on alcohol problems have found that veterans and nonveterans do not differ in prevalence of heavy episodic drinking (
      • Grossbard J.R.
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      • Hoerster K.D.
      • Jakupcak M.
      • Seal K.H.
      • Simpson T.L.
      Relationships among veteran status, gender, and key health indicators in a national young adult sample.
      ) or past-year AUD (in two all ages adult samples [
      • Boden M.T.
      • Hoggatt K.J.
      Substance use disorders among veterans in a nationally representative sample: Prevalence and associated functioning and treatment utilization.
      ;
      • Wagner T.H.
      • Harris K.M.
      • Federman B.
      • Dai L.
      • Luna Y.
      • Humphreys K.
      Prevalence of substance use disorders among veterans and comparable nonveterans from the National Survey on drug use and health.
      ] and in a sample of adults aged 21–34 [
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      ]). In contrast, one study found higher lifetime AUD among veterans than nonveterans (36 % in Veterans vs. 28 % in nonveterans;
      • Boden M.T.
      • Hoggatt K.J.
      Substance use disorders among veterans in a nationally representative sample: Prevalence and associated functioning and treatment utilization.
      ).
      Risk factors for alcohol use problems may also differ for veterans and nonveterans. Findings are mixed regarding how gender and age may be associated with alcohol problems differentially for veterans vs. nonveterans. For instance, one study found that veterans reported higher past-year AUD and heavy episodic drinking than nonveterans only in men aged 18–25 (
      • Hoggatt K.J.
      • Lehavot K.
      • Krenek M.
      • Schweizer C.A.
      • Simpson T.
      Prevalence of substance misuse among US veterans in the general population.
      ). In contrast, another study found that nonveterans demonstrate higher heavy episodic drinking than veterans only in men (
      • Bachrach R.L.
      • Blosnich J.R.
      • Williams E.C.
      Alcohol screening and brief intervention in a representative sample of veterans receiving primary care services.
      ). Yet another study found that veterans reported higher lifetime AUD than nonveterans only in women (
      • Evans E.A.
      • Upchurch D.M.
      • Simpson T.
      • Hamilton A.B.
      • Hoggatt K.J.
      Differences by veteran/civilian status and gender in associations between childhood adversity and alcohol and drug use disorders.
      ). In studies investigating only veteran samples or only nonveteran samples, lower education (
      • Bonevski B.
      • Regan T.
      • Paul C.
      • Baker A.L.
      • Bisquera A.
      Associations between alcohol, smoking, socioeconomic status and comorbidities: Evidence from the 45 and up study.
      ;
      • Gilman S.E.
      • Breslau J.
      • Conron K.J.
      • Koenen K.C.
      • Subramanian S.V.
      • Zaslavsky A.M.
      Education and race-ethnicity differences in the lifetime risk of alcohol dependence.
      ;
      • Grant J.D.
      • Scherrer J.F.
      • Lynskey M.T.
      • Agrawal A.
      • Duncan A.E.
      • Haber J.R.
      • Heath A.C.
      • Bucholz K.K.
      Associations of alcohol, nicotine, cannabis, and drug use/dependence with educational attainment: Evidence from cotwin-control analyses.
      ;
      • O'Toole B.I.
      • Gorman P.
      • Catts S.V.
      Military combat, posttraumatic stress disorder, and the course of alcohol use disorders in a cohort of Australian Vietnam war veterans.
      ), lower social support (
      • Bravo A.J.
      • Kelley M.L.
      • Hollis B.F.
      Social support, depressive symptoms, and hazardous alcohol use among navy members: An examination of social support as a protective factor across deployment.
      ;
      • Brick L.
      • Nugent N.R.
      • Kahana S.Y.
      • Bruce D.
      • Tanney M.R.
      • Fernandex M.I.
      • Bauermeister J.A.
      Interaction effects of neighborhood disadvantage and individual social support on frequency of alcohol use in youth living with HIV.
      ;
      • Groh D.R.
      • Jason L.A.
      • Davis M.I.
      • Olson B.D.
      • Ferrari J.R.
      Friends, family, and alcohol abuse: An examination of general and alcohol-specific social support.
      ;
      • McCabe C.T.
      • Mohr C.D.
      • Hammer L.B.
      • Carlson K.F.
      PTSD symptomology and motivated alcohol use among military service members: Testing a conditional indirect effect model of social support.
      ), unpartnered marital status (
      • Dash G.F.
      • Martin N.G.
      • Lynskey M.T.
      • Slutske W.S.
      Sex differences in the relative influence of marital status and parenthood on alcohol use disorder symptoms: A multilevel discordant twin design.
      ;
      • Fuehrlein B.S.
      • Mota N.
      • Arias A.J.
      • Trevisan L.A.
      • Kachadourian L.K.
      • Krystal J.H.
      • Southwick S.M.
      • Pietrzak R.H.
      The burden of alcohol use disorders in US military veterans: Results from the National Health and resilience in veterans study.
      ;
      • Kendler K.S.
      • Lönn S.L.
      • Salvatore J.
      • Sundquist J.
      • Sundquist K.
      Effect of marriage on risk for onset of alcohol use disorder: A longitudinal and co-relative analysis in a swedish national sample.
      ;
      • Kretsch N.
      • Harden K.P.
      Marriage, divorce, and alcohol use in young adulthood: A longitudinal sibling-comparison study.
      ), sexual minority status (
      • Cochran B.N.
      • Balsam K.
      • Flentje A.
      • Malte C.A.
      • Simpson T.
      Mental health characteristics of sexual minority veterans.
      ;
      • Crane P.R.
      • Swaringen K.S.
      • Foster A.M.
      • Talley A.E.
      Alcohol use disorders among sexual and gender minority populations.
      ;
      • Lehavot K.
      • Browne K.C.
      • Simpson T.L.
      Examining sexual orientation disparities in alcohol misuse among women veterans.
      ), adverse childhood experiences (ACEs;
      • Aronson K.R.
      • Perkins D.F.
      • Morgan N.R.
      • Bleser J.A.
      • Vogt D.
      • Copeland L.A.
      • Finley E.P.
      • Gilman C.L.
      The impact of adverse childhood experiences (ACEs) and combat exposure on mental health conditions among new post-9/11 veterans.
      ;
      • Hughes K.
      • Bellis M.A.
      • Hardcastle K.A.
      • Sethi D.
      • Butchart A.
      • Mikton C.
      • Jones L.
      • Dunne M.P.
      The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis.
      ;
      • Lee R.D.
      • Chen J.
      Adverse childhood experiences, mental health, and excessive alcohol use: Examination of race/ethnicity and sex differences.
      ), and adult sexual assault (
      • Caamano-Isorna F.
      • Adkins A.
      • Moure-Rodríguez L.
      • Conley A.H.
      • Dick D.
      Alcohol use and sexual and physical assault victimization among university students: Three years of follow-up.
      ;
      • Forkus S.R.
      • Rosellini A.J.
      • Monteith L.L.
      • Contractor A.A.
      • Weiss N.H.
      Military sexual trauma and alcohol misuse among military veterans: The roles of negative and positive emotion dysregulation.
      ;
      • Lindgren K.P.
      • Neighbors C.
      • Blayney J.A.
      • Mullins P.M.
      • Kaysen D.
      Do drinking motives mediate the association between sexual assault and problem drinking?.
      ;
      • Newins A.R.
      • Glenn J.J.
      • Wilson L.C.
      • Wilson S.M.
      • Kimbrel N.A.
      • Beckham J.C.
      • VA Mid-Atlantic M.W.
      • Calhoun P.S.
      Psychological outcomes following sexual assault: Differences by sexual assault setting.
      ) were found to be associated with alcohol use problems. Among veterans, research has also found combat exposure to be associated with alcohol use problems (
      • Miller S.M.
      • Pedersen E.R.
      • Marshall G.N.
      Combat experience and problem drinking in veterans: Exploring the roles of PTSD, coping motives, and perceived stigma.
      ;
      • Na P.J.
      • Norman S.B.
      • Nichter B.
      • Hill M.L.
      • Rosen M.I.
      • Petrakis I.L.
      • Pietrzak R.H.
      Prevalence, risk and protective factors of alcohol use disorder during the COVID-19 pandemic in U.S. military veterans.
      ). Despite many studies investigating predictors of alcohol use problems among only veteran or only nonveteran samples, no studies to our knowledge have directly compared the strength of associations between these predictors and alcohol use problems in veteran vs. nonveterans.
      Little research exists comparing veterans' and nonveterans' alcohol treatment utilization. One study found that veterans (vs. nonveterans) were more likely to be screened for alcohol use problems, and were more likely to receive advice about alcohol's harmful effects (
      • Bachrach R.L.
      • Blosnich J.R.
      • Williams E.C.
      Alcohol screening and brief intervention in a representative sample of veterans receiving primary care services.
      ), which may lead to greater treatment utilization. Veterans (vs. nonveterans) were found to be more likely to receive substance use disorder (SUD) treatment in one study (
      • Boden M.T.
      • Hoggatt K.J.
      Substance use disorders among veterans in a nationally representative sample: Prevalence and associated functioning and treatment utilization.
      ), whereas another study found no such differences between veterans and nonveterans (
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      ). This discrepancy may be due to sample/timing differences, as
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      used data collected from 2004 to 2010 from a sample of adults aged 21–34, whereas
      • Boden M.T.
      • Hoggatt K.J.
      Substance use disorders among veterans in a nationally representative sample: Prevalence and associated functioning and treatment utilization.
      used data collected from 2012 to 2013 from a sample of adults of all ages. In a sample of men of all ages receiving mental health treatment, the study found no differences in AUD treatment utilization between veterans and nonveterans (
      • Manhapra A.
      • Stefanovics E.A.
      • Rhee T.G.
      • Rosenheck R.A.
      Who uses veterans mental health services?: A national observational study of male veteran and nonveteran mental health service users.
      ). Given these sparse and conflicting findings, more research is needed to better understand alcohol treatment differences between veterans and nonveterans in national samples, especially among post-9/11 veterans (i.e., those who served in Operation Enduring Freedom, Operation Iraqi Freedom, and/or Operation New Dawn).
      Several studies using veteran or nonveteran samples have investigated predictors of alcohol treatment utilization (with many more studies focused on nonveterans than veterans). Older age and lower income have been found to be associated with alcohol treatment utilization among veterans (
      • Halvorson M.A.
      • Ghaus S.
      • Cucciare M.A.
      Care utilization and patient characteristics of veterans who misuse alcohol.
      ) and nonveterans (
      • Cohen E.
      • Feinn R.
      • Arias A.
      • Kranzler H.R.
      Alcohol treatment utilization: Findings from the National Epidemiologic Survey on alcohol and related conditions.
      ;
      • Mowbray O.
      The moderating role of social networks in the relationship between alcohol consumption and treatment utilization for alcohol-related problems.
      ). Among veterans, combat exposure is also associated with alcohol treatment utilization (
      • Miller S.M.
      • Pedersen E.R.
      • Marshall G.N.
      Combat experience and problem drinking in veterans: Exploring the roles of PTSD, coping motives, and perceived stigma.
      ). Among nonveterans, male gender (
      • Cohen E.
      • Feinn R.
      • Arias A.
      • Kranzler H.R.
      Alcohol treatment utilization: Findings from the National Epidemiologic Survey on alcohol and related conditions.
      ;
      • Mellinger J.L.
      • Fernandez A.
      • Shedden K.
      • Winder G.S.
      • Fontana R.J.
      • Volk M.L.
      • Blow F.C.
      • Lok A.S.F.
      Gender disparities in alcohol use disorder treatment among privately insured patients with alcohol-associated cirrhosis.
      ), White race (
      • Niv N.
      • Pham R.
      • Hser Y.
      Racial and ethnic differences in substance abuse service needs, utilization, and outcomes in California.
      ), higher social support (
      • Mowbray O.
      The moderating role of social networks in the relationship between alcohol consumption and treatment utilization for alcohol-related problems.
      ), divorced marital status, health coverage (
      • Kim J.
      • Xiang H.
      • Yang Y.
      • Lewis M.W.
      Disparities in alcohol treatment utilization by race and type of insurance.
      ), childhood maltreatment (
      • Goldstein A.L.
      • Henriksen C.A.
      • Davidov D.M.
      • Kimber M.
      • Pitre N.Y.
      • Afifi T.O.
      Childhood maltreatment, alcohol use disorders, and treatment utilization in a national sample of emerging adults.
      ), recent sexual assault (
      • Rothman E.F.
      • Cheng D.M.
      • Pedley A.
      • Samet J.H.
      • Palfai T.
      • Liebschutz J.M.
      • Saitz R.
      Interpersonal violence exposure and alcohol treatment utilization among medical inpatients with alcohol dependence.
      ), and intimate partner violence (
      • Schonbrun Y.C.
      • Orchowski L.M.
      • Spillane N.
      Intimate partner violence and use of alcohol and drug treatment services among a national sample.
      ) were all associated with alcohol treatment utilization; however, among veterans, research has not investigated these predictors. Whether education and sexual orientation may be associated with alcohol treatment utilization differentially for veterans vs. nonveterans is unclear. In veterans, one study found that education was not associated with alcohol treatment utilization (
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      ), whereas in nonveterans, there are mixed findings (
      • Cohen E.
      • Feinn R.
      • Arias A.
      • Kranzler H.R.
      Alcohol treatment utilization: Findings from the National Epidemiologic Survey on alcohol and related conditions.
      ;
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      ;
      • Grant B.F.
      Toward an alcohol treatment model: A comparison of treated and untreated respondents with DSM-IV alcohol use disorders in the general population.
      ). Sexual orientation has not been investigated as a predictor of alcohol use treatment utilization in Veterans. Among nonveterans, sexual minorities generally report greater unmet need for substance use treatment (
      • Allen J.L.
      • Mowbray O.
      Sexual orientation, treatment utilization, and barriers for alcohol related problems: Findings from a nationally representative sample.
      ;
      • Haney J.L.
      Sexual orientation, social determinants of health, and unmet substance use treatment need: Findings from a national survey.
      ); however, findings from one study of nonveterans indicate that unmet need for alcohol use treatment may only be greater among gay men (and not among bisexual men, lesbian women, or bisexual women;
      • Lehavot K.
      • Blosnich J.R.
      • Glass J.E.
      • Williams E.C.
      Alcohol use and receipt of alcohol screening and brief intervention in a representative sample of sexual minority and heterosexual adults receiving health care.
      ). Alcohol treatment utilization outcomes are measured differently across these studies, which may account for some conflicting findings in the literature; for instance, in
      • Halvorson M.A.
      • Ghaus S.
      • Cucciare M.A.
      Care utilization and patient characteristics of veterans who misuse alcohol.
      , treatment utilization is a three category outcome (“no treatment,” “non-specialty SUD-care” and “specialty SUD-care”) based on medical chart notes, whereas in
      • Cohen E.
      • Feinn R.
      • Arias A.
      • Kranzler H.R.
      Alcohol treatment utilization: Findings from the National Epidemiologic Survey on alcohol and related conditions.
      , treatment utilization was assessed with a single-item self-report question defining treatment more broadly: “Have you ever gone anywhere or seen anyone for a reason that was related in any way to your drinking: a physician, counselor, Alcoholics Anonymous, or any other community agency or professional?” In addition to these inconsistencies, one prominent gap in this prior literature is that no studies have directly compared these predictors of alcohol treatment use for veterans and nonveterans. Thus, whether these predictors of alcohol treatment may differ for veterans and nonveterans remains unclear.
      The current study aimed to fill these research gaps in direct comparisons of veterans and nonveterans on alcohol problems and treatment utilization. The study analyzed survey data from a nationally representative samples of post-9/11 veterans and nonveterans to (1) examine differences in alcohol use and alcohol treatment utilization between Veterans and nonveterans and (2) investigate whether associations between sociodemographic (and other relevant) factors and alcohol outcomes (alcohol problems and treatment utilization, specifically) differ between veterans and nonveterans.

      2. Materials and methods

      2.1 Participants and survey design

      The Veterans Affairs (VA) Comparative Health Assessment Interview (CHAI) Research Study is a national, population-based survey study that examined the health and well-being of post-9/11 veterans (in Active Duty, or activated Guard/Reserve, at any point from 9/11/2001 through May 2015) and nonveterans. The response rate for the survey was 40.0 % among veterans and 56.5 % among nonveterans. This response rate is similar to response rates of other surveys (e.g.,
      • Bastian L.A.
      • Trentalange M.
      • Murphy T.E.
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      • Maisel N.C.
      • Wright S.M.
      • Gaetano V.S.
      • Allore H.
      • Skanderson M.
      • Reyes-Harvey E.
      • Yano E.M.
      • Rose D.
      • Haskell S.
      Association between women veterans’ experiences with VA outpatient health care and designation as a women’s health provider in primary care clinics.
      ;
      • Eber S.
      • Barth S.
      • Kang H.
      • Mahan C.
      • Dursa E.
      • Schneiderman A.
      The National Health Study for a new generation of United States veterans: Methods for a large-scale study on the health of recent veterans.
      ). Participants included a total of 19,820 veterans and nonveterans (n = 15,166 Veterans, n = 4654 nonveterans). A population-based sample of veteran participants were randomly selected from the U.S. Veterans Eligibility Trends and Statistics sampling frame (not limited to veterans seeking care at VA). GfK Group's KnowledgePanel applied a complex sampling frame to recruit nonveteran participants (). The study collected data from 4/18/2018 to 8/10/2018 through an online survey or computer-assisted telephone interview. All participants provided informed consent and received $50 for survey completion. Previous published studies include additional detail on sampling and recruitment methods (
      • Blosnich J.R.
      • Garfin D.R.
      • Maguen S.
      • Vogt D.
      • Dichter M.E.
      • Hoffmire C.A.
      • Bernhard P.A.
      • Schneiderman A.
      Differences in childhood adversity, suicidal ideation, and suicide attempt among veterans and nonveterans.
      ;
      • Hoffmire C.A.
      • Monteith L.L.
      • Forster J.E.
      • Bernhard P.A.
      • Blosnich J.R.
      • Vogt D.
      • Maguen S.
      • Smith A.A.
      • Schneiderman A.I.
      Gender differences in lifetime prevalence and onset timing of suicidal ideation and suicide attempt among post-9/11 veterans and nonveterans.
      ). The VA Central Institutional Review Board approved the CHAI study protocols.
      For the current study, we included 17,298 respondents (87 % of the total sample; n = 13,451 veterans, n = 3847 nonveterans) who had complete data on key variables.

      2.2 Measures

      2.2.1 Predictor variables

      2.2.1.1 Sociodemographic variables

      Participants self-reported their age, gender identity, race/ethnicity, sexual orientation, marital status, education, current healthcare coverage, and current financial difficulty. The study restricted gender identity to participants who self-identified as a man or woman, with transgender participants categorized based on their self-identified gender. We assessed financial difficulty in one item: “Which of the following best describes your financial condition over the past 4 months?” Options included 1) “Very comfortable and secure,” 2) “Able to make ends meet without much difficulty,” 3) “Occasionally have some difficulty making ends meet,” 4) “Tough to make ends meet but keeping your head above water,” and 5) “In over your head.” Current health care coverage categories included VA, Tricare, other government coverage (e.g., Medicare), employer coverage, other private coverage, or no coverage.

      2.2.1.2 Social support

      We used the Multidimensional Scale of Perceived Social Support (MSPSS;
      • Zimet G.D.
      • Dahlem N.W.
      • Zimet S.G.
      • Farley G.K.
      The multidimensional scale of perceived social support.
      ) to assess perceived social support from friends, family, and significant others. The MSPSS includes 12 items, such as “I can count on my friends when things go wrong,” with response options from “very strongly disagree (1)” to “very strongly agree (7).” Items are summed with resulting scale scores ranging from 12 to 84. The MSPSS has demonstrated good psychometric properties (
      • Zimet G.D.
      • Powell S.S.
      • Farley G.K.
      • Werkman S.
      • Berkoff K.
      Psychometric characteristics of the multidimensional scale of perceived social support.
      ).

      2.2.1.3 Adverse childhood experiences (ACEs)

      The measure of ACEs comprised items from the Life Stressors Checklist–Revised (LSC-R;
      • Wolfe J.
      • Kimerling R.
      Gender issues in the assessment of posttraumatic stress disorder.
      ) and the Life Events Checklist (LEC) for DSM–5—Extended (
      • Weathers F.W.
      • Blake D.D.
      • Schnurr P.P.
      • Kaloupek D.G.
      • Marx B.P.
      • Keane T.M.
      B. P.MarxT. M.Keane (2013). The life events checklist for DSM–5 (LEC-5) - extended [measurement instrument].
      ). The LSC-R and LEC-5 have demonstrated good psychometric properties (
      • Blevins C.A.
      • Weathers F.W.
      • Davis M.T.
      • Witte T.K.
      • Domino J.L.
      The posttraumatic stress disorder checklist for DSM–5 (PCL-5): Development and initial psychometric evaluation.
      ;
      • Gray M.J.
      • Litz B.T.
      • Hsu J.L.
      • Lombardo T.W.
      Psychometric properties of the life events checklist.
      ). For each response endorsing a specific type of adverse event, the assessment asked participants if the event happened “before age 18,” “age 18 or older,” or both. We assessed 24 individual potentially traumatic events that occurred before the age of 18 (yes = 1 or no = 0). As in prior research, we transformed these 24 variables into a 5-category ordinal variable representing the cumulative frequency of ACEs (0, 1–2, 3–4, 5–6, >6 ACEs;
      • Maguen S.
      • Griffin B.J.
      • Vogt D.
      • Hoffmire C.A.
      • Blosnich J.R.
      • Bernhard P.A.
      • Akhtar F.Z.
      • Cypel Y.S.
      • Schneiderman A.I.
      Moral injury and peri- and post-military suicide attempts among post-9/11 veterans.
      ).

      2.2.1.4 Adult sexual assault

      We used two items from The Life Stressor Checklist-Revised (LSC-R;
      • Wolfe J.
      • Kimerling R.
      Gender issues in the assessment of posttraumatic stress disorder.
      ) to assess adult sexual assault: 1) “Did you ever have sex (oral, anal, genital) when you didn't want to because someone forced you in some way or threatened to hurt you if you didn't?”) and 2) “Were you ever touched or made to touch someone else in a sexual way because he/she forced you in some way or threatened to harm you if you didn't?” Participants who responded yes to at least one item, and specified that this occurred when they were 18 or older, were categorized as experiencing adult sexual assault.

      2.2.1.5 Combat exposure

      The study asked participants to self-report whether they had experienced combat or exposure to a warzone on one item from the LEC.

      2.2.2 Outcome variables

      2.2.2.1 Alcohol Use Disorders Identification Test – Consumption (AUDIT-C)

      The AUDIT-C includes three questions assessing alcohol consumption. The first question asks, “How often did you have a drink containing alcohol in the past year?” The second question asks, “How many drinks containing alcohol did you have on a typical day when you were drinking in the past year?” The third question asks, “How often did you have six or more drinks on one occasion in the past year?” Items are rated on a 0–4 scale and summed to a total score of 0–12. Research has shown the AUDIT-C to be valid in veteran and nonveteran samples (
      • Aalto M.
      • Alho H.
      • Halme J.T.
      • Seppä K.
      AUDIT and its abbreviated versions in detecting heavy and binge drinking in a general population survey.
      ;
      • Bradley K.A.
      • Bush K.R.
      • Epler A.J.
      • Dobie D.J.
      • Davis T.M.
      • Sporleder J.L.
      • Maynard C.
      • Burman M.L.
      • Kivlahan D.R.
      Two brief alcohol-screening tests from the alcohol use disorders identification test (AUDIT): Validation in a female veterans affairs patient population.
      ;
      • Bush K.
      • Kivlahan D.R.
      • McDonell M.B.
      • Fihn S.D.
      • Bradley K.A.
      The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Ambulatory care quality improvement project (ACQUIP).
      ;
      • Crawford E.F.
      • Fulton J.J.
      • Swinkels C.M.
      • Beckham J.C.
      • Calhoun P.S.
      Diagnostic efficiency of the AUDIT-C in U.S. veterans with military service since September 11, 2001.
      ). In the current study, we used the total score as an outcome variable; we also used cutoff scores recommended by
      • Aalto M.
      • Alho H.
      • Halme J.T.
      • Seppä K.
      AUDIT and its abbreviated versions in detecting heavy and binge drinking in a general population survey.
      ,
      • Aalto M.
      • Tuunanen M.
      • Sillanaukee P.
      • Seppä K.
      Effectiveness of structured questionnaires for screening heavy drinking in middle-aged women.
      for the general population (>4 for women and >5 for men) to restrict the sample when investigating need for treatment as an outcome variable. The study administered the ASSIST (assessing need for treatment; described below) only to participants who met these cutoff scores.

      2.2.2.2 Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST)

      We used the alcohol involvement subscale (comprising six items) from the ASSIST to assess need for intensive alcohol treatment (
      • Ali R.
      • Awwad E.
      • Babor T.
      • Bradley F.
      • Butau T.
      • Farrell M.
      • Formigoni M.L.O.S.
      • Isralowitz R.
      • Boerngen de Lacerda R.
      • Marsden J.
      • McRee B.
      • Monteiro M.
      • Pal H.
      • Rubio-Stipec M.
      • Vendetti J.
      The alcohol, smoking and substance involvement screening test (ASSIST): Development, reliability and feasibility.
      ;
      • Humeniuk R.
      • Ali R.
      • Babor T.F.
      • Farrell M.
      • Formigoni M.L.
      • Jittiwutikarn J.
      • de Lacerda R.B.
      • Ling W.
      • Marsden J.
      • Monteiro M.
      • Nhiwatiwa S.
      • Pal H.
      • Poznyak V.
      • Simon S.
      Validation of the alcohol, smoking and substance involvement screening test (ASSIST).
      ;
      • Humeniuk R.E.
      • Henry-Edwards S.
      • Ali R.L.
      • Poznyak V.
      • Monteiro M.
      The alcohol, smoking and substance involvement screening test (ASSIST): Manual for use in primary care.
      ). The alcohol involvement subscale measures alcohol use problems (e.g., “During the past three months, how often has your use of alcohol led to health, social, legal, or financial problems?”). Item responses are summed to a total alcohol involvement score of 0–39. Alcohol involvement scores fall into three categories: 1) no treatment recommended (score 0–10), 2) brief treatment recommended (score 11–26), and 3) intensive treatment recommended (score 27 or more). A brief intervention is defined as a one-time discussion about problem alcohol use with a provider; intensive treatment is any targeted treatment for problem alcohol use beyond a one-time intervention.
      In the current study, one of the six ASSIST items assessing alcohol involvement presented a temporal parameter in the question and response options that differed from the original screening test. The correct original wording for the question and response options follows: “Has a friend or relative or anyone else ever expressed concern about your use of alcohol?” with response options “no, never (scored 0),” “yes, but not in the past three months (scored 3),” or “yes, in the past three months (scored 6).” The current study added a time specifier to the question: “In the past three months, has a friend or relative or anyone else ever expressed concern about your use of alcohol?” with response options “never,” “once or twice,” “monthly,” “weekly,” or “daily or almost daily.” Our version of this question allowed us to correctly identify which participants reported that others expressed concern about their alcohol use in the past three months (options “once or twice,” “monthly,” “weekly,” or “almost daily”); the study gave these participants a score of 6 for this item. Participants who reported “never” were given a score of 0 for this item. Because of how this item was asked, participants in this study who may have reported “yes, but not in the past three months” if presented with the original question and response options (with corresponding score of 3) would have answered “never” (and given a score of 0) in the current study. As a consequence, in the current study, we underestimated alcohol involvement scores by 3 points for some participants; however, this partial underestimation is the same across veterans and nonveterans and should not contribute to differential findings for these groups.

      2.2.2.3 Past-year and lifetime alcohol treatment

      The study asked participants: “Have you received treatment for your use of alcohol in the past year?” and “Have you ever received treatment for your use of alcohol?” with response options yes and no for both questions.

      2.3 Statistical analysis

      2.3.1 Data analytic plan

      For models with AUDIT-C as the outcome variable, the current study used the full sample of participants (N = 17,298). For this linear regression model, we first checked that the AUDIT-C met criteria for normal distribution (with skew between −2 and 2 and kurtosis between −7 and 7;
      • Hair J.F.
      • Black W.C.
      • Babin B.J.
      • Anderson R.E.
      Multivariate data analysis: A global perspective.
      ; for current investigation: skew = 1.14 [SE = 0.02], kurtosis = 1.18 [SE = 0.04]). For models with need for intensive alcohol treatment (from the ASSIST) as the outcome variable, we used a subgroup of participants who scored above the AUDIT-C cutoff (n = 12,887). For models with past-year or lifetime alcohol treatment as the outcome variable, the study used a subgroup of participants who were identified as needing either brief or intensive treatment based on the ASSIST (n = 2674).
      We corrected for family wise error (i.e., erroneously rejecting the null hypothesis) by assessing statistical significance at p < .001 for all planned analyses. The study team performed analyses using SPSS Version 28.
      For the first research question examining veteran vs. nonveteran differences in alcohol use and alcohol treatment, the study conducted four regression models—one linear regression (with the AUDIT-C as outcome variable) and three binary logistic regressions (with need for intensive treatment, past-year alcohol treatment, and lifetime alcohol treatment as outcome variables). These four models compared veteran and nonveteran alcohol use and alcohol treatment, controlling for demographic and social factors. The study used population and standardization weighting in these four models that weighted nonveterans to the population demographic distribution of veterans. Using Taylor series approximation (linearization) variance estimation, weighting accounted for the complex sampling design, noncoverage, and nonresponse (as described in a previous study using these data;
      • Blosnich J.R.
      • Garfin D.R.
      • Maguen S.
      • Vogt D.
      • Dichter M.E.
      • Hoffmire C.A.
      • Bernhard P.A.
      • Schneiderman A.
      Differences in childhood adversity, suicidal ideation, and suicide attempt among veterans and nonveterans.
      ). Our estimation of the coefficient on veteran status (veteran vs. nonveteran) is doubly robust because the study controlled for demographic differences both through weighting and by entering these variables as covariates in these four models (
      • Funk M.J.
      • Westreich D.
      • Wiesen C.
      • Til Stürmer M.
      • Brookhart A.
      • Davidian M.
      Doubly robust estimation of causal effects.
      ). However, because of this, the covariates in these four models (all variables except veteran vs. nonveteran status) are not interpretable but rather used solely for adjustment purposes. Our covariates in these four models included age, gender, sexual orientation, marital status, racial/ethnic identity, education, healthcare coverage (yes/no for any coverage), financial difficulty, social support, ACEs, and adult sexual trauma. The study z-scored financial difficulty and social support for ease of interpretation.
      For our second research question, we examined separately associations between our predictor and outcome variables in veterans and nonveterans. We employed a different weighting procedure to allow for appropriate interpretation of model estimates for veterans and nonveterans. For veterans, weights included a base sampling weight, a nonresponse adjustment, and a calibration to gender, pre−/post-9/11 activation, and deployment factors (branch, component, or geographic region;
      • Blosnich J.R.
      • Garfin D.R.
      • Maguen S.
      • Vogt D.
      • Dichter M.E.
      • Hoffmire C.A.
      • Bernhard P.A.
      • Schneiderman A.
      Differences in childhood adversity, suicidal ideation, and suicide attempt among veterans and nonveterans.
      ). For nonveterans, weights included the probability of selection into the KnowledgePanel and into the CHAI sample, matched to US Census benchmarks. Using this weighting strategy, the study examined separately four regression models (with AUDIT-C, need for intensive intervention, past-year treatment, and lifetime treatment as outcome variables) for veterans and for nonveterans. Predictor variables included age group, gender, racial/ethnic identity, sexual orientation, marital status, education, main source of healthcare coverage, financial difficulty, social support, ACEs, and adult sexual trauma. For the veteran models only, VA and Tricare health coverage and combat exposure were also included.

      2.3.2 Missing data

      We excluded 13 % of participants from the original study due to missing data on any of the key variables, which was the result of skipped questions on various predictor variables and the AUDIT-C.

      3. Results

      3.1 Participant characteristics

      The majority of veterans and nonveterans in our sample were married, White, heterosexual, and had some form of health care coverage. Among veterans needing intensive treatment, 16 % (vs. 9 % in nonveterans) received past year alcohol treatment and 36 % (vs. 14 % in nonveterans) received lifetime alcohol treatment. Among veterans needing brief treatment, 3 % (also 3 % in nonveterans) received past year alcohol treatment and 13 % (vs. 7 % in nonveterans) received lifetime alcohol treatment. See Table 1 for population weighted characteristics for Veterans and nonveterans.
      Table 1Full sample characteristics and measures by veteran status, with population weights.
      VariableVariable subcategoryVeteran weighted mean (SD) or %

      n = 13,451
      Nonveteran weighted mean (SD) or %

      n = 3847
      Age (in years)38.42 (10.41)
      Significant group differences at p < .05.
      44.13 (15.89)
      Significant group differences at p < .05.
      Age groups
      18–243.7 %
      Significant group differences at p < .05.
      9.7 %
      Significant group differences at p < .05.
      25–3438.9 %
      Significant group differences at p < .05.
      24.1 %
      Significant group differences at p < .05.
      35–4431.6 %
      Significant group differences at p < .05.
      19.3 %
      Significant group differences at p < .05.
      45–5416.3 %17.9 %
      55–647.9 %
      Significant group differences at p < .05.
      16.4 %
      Significant group differences at p < .05.
      65+1.6 %
      Significant group differences at p < .05.
      12.6 %
      Significant group differences at p < .05.
      Men82.7 %
      Significant group differences at p < .05.
      47.6 %
      Significant group differences at p < .05.
      Racial/ethnic identity
      White

      (non-Hispanic)
      66.4 %
      Significant group differences at p < .05.
      62.6 %
      Significant group differences at p < .05.
      Black

      (non-Hispanic)
      12.7 %12.0 %
      Hispanic11.4 %
      Significant group differences at p < .05.
      16.3 %
      Significant group differences at p < .05.
      Multiracial

      (non-Hispanic)
      5.7 %
      Significant group differences at p < .05.
      1.8 %
      Significant group differences at p < .05.
      Other

      (non-Hispanic)
      3.8 %
      Significant group differences at p < .05.
      7.3 %
      Significant group differences at p < .05.
      Sexual orientation
      Heterosexual95.5 %
      Significant group differences at p < .05.
      92.1 %
      Significant group differences at p < .05.
      Gay/lesbian1.8 %
      Significant group differences at p < .05.
      3.8 %
      Significant group differences at p < .05.
      Bisexual1.9 %
      Significant group differences at p < .05.
      2.9 %
      Significant group differences at p < .05.
      Other0.8 %1.2 %
      Marital status
      Never married19.9 %
      Significant group differences at p < .05.
      31.3 %
      Significant group differences at p < .05.
      Married/ in a domestic relationship63.4 %
      Significant group differences at p < .05.
      55.3 %
      Significant group differences at p < .05.
      Divorced/ separated/widowed16.7 %
      Significant group differences at p < .05.
      13.4 %
      Significant group differences at p < .05.
      Education
      No college12.4 %
      Significant group differences at p < .05.
      38.9 %
      Significant group differences at p < .05.
      Some college29.1 %
      Significant group differences at p < .05.
      18.8 %
      Significant group differences at p < .05.
      Associates17.3 %
      Significant group differences at p < .05.
      11.0 %
      Significant group differences at p < .05.
      Bachelors24.4 %
      Significant group differences at p < .05.
      18.1 %
      Significant group differences at p < .05.
      Masters or PhD16.8 %
      Significant group differences at p < .05.
      13.2 %
      Significant group differences at p < .05.
      Health coverage
      VA32.1 %NA
      Tricare29.7 %NA
      Employer coverage46.8 %
      Significant group differences at p < .05.
      59.8 %
      Significant group differences at p < .05.
      Other government coverage9.1 %
      Significant group differences at p < .05.
      32.4 %
      Significant group differences at p < .05.
      No coverage5.6 %
      Significant group differences at p < .05.
      7.8 %
      Significant group differences at p < .05.
      Other private coverage1.5 %
      Significant group differences at p < .05.
      3.4 %
      Significant group differences at p < .05.
      Financial difficulty

      (scale 1–5)
      2.40 (1.15)
      Significant group differences at p < .05.
      2.25 (1.09)
      Significant group differences at p < .05.
      Social support (scale 12–84)64.51 (15.78)
      Significant group differences at p < .05.
      62.73 (15.44)
      Significant group differences at p < .05.
      ACEs
      023.9 %
      Significant group differences at p < .05.
      26.9 %
      Significant group differences at p < .05.
      1–233.2 %
      Significant group differences at p < .05.
      36.1 %
      Significant group differences at p < .05.
      3–418.5 %19.1 %
      5–611.1 %
      Significant group differences at p < .05.
      9.0 %
      Significant group differences at p < .05.
      7+13.3 %
      Significant group differences at p < .05.
      8.9 %
      Significant group differences at p < .05.
      Adult sexual trauma history13.1 %14.6 %
      Combat exposure45.9 %NA
      AUDIT-C (scale 0–12)3.26 (2.61)
      Significant group differences at p < .05.
      2.56 (2.33)
      Significant group differences at p < .05.
      Need for intensive treatment (from ASSIST)4.6 %
      Significant group differences at p < .05.
      2.5 %
      Significant group differences at p < .05.
      Past-year alcohol treatment1.5 %1.3 %
      Lifetime alcohol treatment7.3 %
      Significant group differences at p < .05.
      2.6 %
      Significant group differences at p < .05.
      ACE = Adverse Childhood Experiences, AUDIT-C = Alcohol Use Disorders Identification Test – Consumption, ASSIST = Alcohol, Smoking, and Substance Involvement Screening Test. Need for intensive treatment prevalence may be underestimated due to survey administration error; see measures section for detail.
      low asterisk Significant group differences at p < .05.

      3.2 Combined veterans and nonveterans models

      Veterans demonstrated higher alcohol consumption (B = 0.30, CI = 0.18–0.42) and higher lifetime alcohol treatment utilization (OR = 2.80, 95 % CI = 1.67–4.70) than nonveterans. Based on our more conservative significance cutoff of p < .001, veterans did not demonstrate higher need for intensive treatment (OR = 1.63, 95 % CI = 1.18–2.23, p = .003). Veterans and nonveterans did not differ significantly on past-year alcohol treatment (p = .23).

      3.3 Separate veteran and nonveteran models

      3.3.1 Alcohol consumption (AUDIT-C)

      In veterans, the following predictor variables were associated with higher alcohol consumption: a) identifying as a man (vs. woman), B = 0.96, CI = 0.84–1.07, b) identifying as White (vs. Black), B = 0.39, CI = 0.23–0.56, c) being aged 25–34 (vs. 45–54 and vs. 55–64), B = 0.45/0.64, CI = 0.29–0.62/0.46–0.82, d) lower social support, B = −0.18, CI = −0.25 to −0.12, and e) combat exposure, B = 0.43, CI = 0.32–0.55. Having other government health coverage (i.e., not TriCare or VA health coverage, but another form of government health coverage, such as Medicare), B = −0.47, CI = −0.70 to −0.24, or Tricare, B = −0.44, CI = −0.60 to –0.29, was associated with lower alcohol consumption (Table 2).
      Table 2AUDIT-C veteran and nonveteran models, with population weights, N = 17,298.
      Predictor variablesVariable subcategoryVeteran model

      n = 13,451
      Nonveteran model

      n = 3847
      B95 % CIB95 % CI
      Age

      (ref group: 25–34)
      18–24−0.12−0.40-0.160.04−0.45-0.54
      35–44−0.15−0.29- -0.010.12−0.15-0.39
      45–54−0.45
      p < .001.
      −0.62- -0.29−0.11−0.46-0.24
      55–64−0.64
      p < .001.
      −0.82- -0.46−0.07−0.46-0.32
      65+−0.12−0.48-0.23−0.16−0.64-0.32
      Gender: men

      (ref group: women)
      0.96
      p < .001.
      0.84–1.070.82
      p < .001.
      0.61–1.04
      Racial/ethnic identity

      (ref group: White [non-Hispanic])
      Black (non-Hispanic)−0.39
      p < .001.
      −0.56- -0.23−0.09−0.51-0.33
      Hispanic−0.02−0.20-0.17−0.10−0.47-0.27
      Multiracial (non-Hispanic)−0.18−0.41-0.06−0.21−0.68 − 0.27
      Other (non-Hispanic)-0.27−0.57-0.03−0.73
      p < .001.
      −1.05- -0.40
      Sexual orientation

      (ref group: heterosexual)
      Gay/lesbian0.410.04–0.78−0.35−0.73-0.03
      Bisexual0.19−0.17- 0.560.37−0.53-1.26
      Other−0.42−1.14-0.290.96−0.40-2.32
      Marital status

      (ref group:

      never married)
      Married/ in a domestic relationship−0.24
      p < .01.
      −0.40- -0.08−0.05−0.36-0.26
      Divorced/ separated/widowed0.11−0.09-0.31−0.08−0.49-0.34
      Education

      (ref group: no college)
      Some college−0.11−0.33-0.10−0.07−0.39-0.26
      Associates−0.31
      p < .01.
      −0.54- -0.09−0.10−0.45-0.26
      Bachelors−0.17−0.39-0.040.20−0.11-0.50
      Masters or PhD−0.18−0.40-0.05−0.06−0.38-0.26
      Health coverage

      (ref group:

      no coverage)
      VA−0.13−0.26-0.01NANA
      Tricare−0.44
      p < .001.
      −0.60 - -0.29NANA
      Employer coverage−0.11−0.26-0.04−0.10−0.49-0.28
      Other government coverage−0.47
      p < .001.
      −0.70 - -0.24−0.65
      p < .01.
      −1.07- -0.22
      Other private coverage−0.03−0.54-0.49−0.61−1.17- -0.05
      Financial difficulty

      (z-scored)
      0.04−0.03-0.10−0.03−0.15-0.09
      Social support

      (z-scored)
      −0.18
      p < .001.
      −0.25- − 0.120.00-0.12-0.11
      ACEs

      (ref group: no ACEs)
      1–20.08−0.06-0.220.250.01–0.50
      3–40.23
      p < .01.
      0.07–0.390.45
      p < .01.
      0.14–0.77
      5–60.200.001–0.400.09−0.31-0.50
      7+0.11−0.09-0.310.13−0.29-0.56
      Adult sexual trauma history0.08−0.09-0.240.460.11–0.82
      Combat exposure0.43
      p < .001.
      0.32–0.55NANA
      AUDIT-C = Alcohol Use Disorders Identification Test – Consumption, ACE = Adverse Childhood Experiences. Veteran model estimated with linear regression model with population weights including a base sampling weight, a nonresponse adjustment, and a calibration to sex, pre/post-9/11 activation, and deployment factors (branch, component, and geographic stratum). Nonveteran model estimated with linear regression model with population weights including the probability of selection into the KnowledgePanel and into the CHAI sample, matched to U.S. Census benchmarks (18+ non-incarcerated) on age, sex, race/ethnicity, census region, education, and household income.
      low asterisk p < .01.
      low asterisklow asterisk p < .001.
      In nonveterans, the following predictor variables were also associated with higher alcohol consumption: a) identifying as a man (vs. woman), B = 0.82, CI = 0.61–1.04, and b) identifying as White (vs. “other” on the racial/ethnic categories) B = 0.73, CI = 0.40–1.05 (Table 2).

      3.3.2 Need for intensive treatment (ASSIST)

      In Veterans, the following predictor variables were associated with being identified as needing intensive treatment for alcohol use: 1) identifying as a man (vs. woman), OR = 2.40, CI = 1.75–3.28; 2) greater financial difficulty, OR = 1.32, CI = 1.16–1.51; and 3) lower social support, OR = 0.62, CI = 0.55–0.69 (Table 3). In nonveterans, only greater ACEs, OR = 6.33, CI = 1.94–20.65, were associated with being identified as needing intensive treatment for alcohol use (Table 3).
      Table 3ASSIST need for intensive treatment veteran and nonveteran models, with population weights, n = 12,887.
      Predictor variablesVariable subcategoryVeteran model

      n = 10,135
      Nonveteran model

      n = 2752
      OR95 % CIOR95 % CI
      Age

      (ref group: 25–34)
      18–240.250.06–1.140.780.24–2.53
      35–441.110.83–1.500.880.46–1.71
      45–540.620.40–0.942.200.88–5.52
      55–640.870.48–1.580.230.06–0.90
      65+0.270.06–1.210.450.06–3.49
      Gender: men

      (ref group: women)
      2.40
      p < .001.
      1.75–3.282.301.13–4.68
      Racial/ethnic identity

      (ref group: White [non-Hispanic])
      Black (non-Hispanic)1.410.96–2.071.550.60–3.97
      Hispanic1.170.80–1.701.580.70–3.53
      Multiracial (non-Hispanic)0.830.47–1.46
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Other (non-Hispanic)1.520.80–2.891.770.46–6.82
      Sexual orientation

      (ref group:

      heterosexual)
      Gay/lesbian0.820.39–1.751.160.32–4.26
      Bisexual0.670.28–1.630.800.21–3.03
      Other1.570.50–4.960.840.07–9.57
      Marital status

      (ref group:

      never married)
      Married/ in a domestic relationship1.310.91–1.890.850.38–1.88
      Divorced/ separated/widowed1.260.85–1.870.13
      p < .01.
      0.03–0.47
      Education

      (ref group: no college)
      Some college0.890.61–1.300.810.28–2.32
      Associates0.50
      p < .01.
      0.31–0.820.580.21–1.59
      Bachelors0.930.61–1.400.630.29–1.36
      Masters or PhD0.940.56–1.550.640.23–1.79
      Health coverage

      (ref group:

      no coverage)
      VA0.970.72–1.29NANA
      Tricare0.640.42–0.98NANA
      Employer coverage0.750.54–1.030.760.30–1.90
      Other government coverage1.310.83–2.070.910.35–2.39
      Other private coverage0.740.21–2.62
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Financial difficulty

      (z-scored)
      1.32
      p < .001.
      1.16–1.511.250.91–1.71
      Social support

      (z-scored)
      0.62
      p < .001.
      0.55–0.690.880.66–1.16
      ACEs

      (ref group: no ACEs)
      1–21.150.78–1.693.191.33–7.69
      3–41.240.82–1.895.42
      p < .001.
      2.19–13.46
      5–61.440.92–2.275.57
      p < .01.
      1.83–16.92
      7+1.370.88–2.136.33
      p < .01.
      1.94–20.65
      Adult sexual trauma history1.340.93–1.931.560.61–4.01
      Combat exposure1.411.08–1.85NANA
      ACE = Adverse Childhood Experiences. Veteran model estimated with binary logistic regression model with population weights including a base sampling weight, a nonresponse adjustment, and a calibration to sex, pre/post-9/11 activation, and deployment factors (branch, component, and geographic stratum). Nonveteran model estimated with binary logistic regression model with population weights including the probability of selection into the KnowledgePanel and into the CHAI sample, matched to U.S. Census benchmarks (18+ non-incarcerated) on age, sex, race/ethnicity, census region, education, and household income.
      low asterisk p < .01.
      low asterisklow asterisk p < .001.
      a Numbers too low to analyze in model.

      3.3.3 Past-year alcohol treatment

      For both veterans and nonveterans, no predictor variables were significantly associated with past-year alcohol treatment (Table 4).
      Table 4Past-year alcohol treatment veteran and nonveteran models, with population weights, n = 2674.
      Predictor variablesVariable subcategoryVeteran model

      n = 2100
      Nonveteran model

      n = 574
      OR95 % CIOR95 % CI
      Age

      (ref group: 25–34)
      18–240.360.04–3.20
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      35–441.210.72–2.050.270.07–1.01
      45–540.960.39–2.351.320.41–4.28
      55–641.690.52–5.53
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      65+0.450.05–3.75
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Gender: men

      (ref group: women)
      1.530.82–2.880.680.19–2.39
      Racial/ethnic identity

      (ref group: White [non-Hispanic])
      Black (non-Hispanic)1.040.49–2.222.610.69–9.96
      Hispanic1.330.65–2.722.460.52–11.61
      Multiracial (non-Hispanic)1.180.53–2.66
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Other (non-Hispanic)3.66
      p < .01.
      1.54–8.717.961.61–39.35
      Sexual orientation

      (ref group: heterosexual)
      Gay/lesbian1.070.31–3.620.840.08–8.60
      Bisexual0.910.19–4.43
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Other1.390.22–8.78
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Marital status

      (ref group:

      never married)
      Married/ in a domestic relationship0.480.26–0.904.981.02–24.33
      Divorced/ separated/widowed1.300.68–2.511.470.17–13.00
      Education

      (ref group:

      no college)
      Some college0.650.32–1.310.360.08–1.70
      Associates0.570.24–1.342.000.52–7.62
      Bachelors0.820.39–1.720.230.02–2.37
      Masters or PhD0.910.39–2.151.310.20–8.35
      Health coverage

      (ref group:

      no coverage)
      VA1.94
      p < .01.
      1.24–3.06NANA
      Tricare1.310.63–2.74NANA
      Employer coverage0.840.50–1.432.850.63–12.98
      Other government coverage2.69
      p < .01.
      1.34–5.403.600.56–23.30
      Other private coverage0.160.02–1.37
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Financial difficulty

      (z-scored)
      1.270.98–1.641.200.65–2.23
      Social support

      (z-scored)
      1.110.88–1.390.630.36–1.09
      ACEs

      (ref group: no ACEs)
      1–20.670.30–1.49
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      3–41.600.76–3.34
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      5–61.660.72–3.80
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      7+1.600.75–3.43
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Adult sexual trauma history1.140.65–2.001.490.45–4.96
      Combat exposure1.030.64–1.66NANA
      ACE = Adverse Childhood Experiences. Veteran model estimated with binary logistic regression model with population weights including a base sampling weight, a nonresponse adjustment, and a calibration to sex, pre/post-9/11 activation, and deployment factors (branch, component, and geographic stratum). Nonveteran model estimated with binary logistic regression model with population weights including the probability of selection into the KnowledgePanel and into the CHAI sample, matched to U.S. Census benchmarks (18+ non-incarcerated) on age, sex, race/ethnicity, census region, education, and household income.
      low asterisk p < .01.
      a Numbers too low to analyze in model.

      3.3.4 Lifetime alcohol treatment

      In veterans, only lower education was associated with lifetime alcohol treatment (not having gone to college vs. having a bachelors, OR = 2.45, CI = 1.53–3.94; not having gone to college vs. having a masters or PhD, OR = 2.37, CI = 1.42–3.94; Table 5). In nonveterans, no predictor variables were significantly associated with lifetime alcohol treatment (Table 5).
      Table 5Lifetime alcohol treatment veteran and nonveteran models, with population weights, n = 2674.
      Predictor variablesVariable subcategoryVeteran model

      n = 2100
      Nonveteran model

      n = 574
      OR95 % CIOR95 % CI
      Age

      (ref group: 25–34)
      18–240.470.14–1.610.390.02–6.86
      35–441.170.85–1.602.781.07–7.25
      45–541.180.74–1.871.600.53–4.86
      55–640.830.43–1.613.250.68–15.44
      65+0.330.08–1.3712.59
      p < .01.
      2.45–64.81
      Gender: men

      (ref group: women)
      1.86
      p < .01.
      1.24–2.813.261.25–8.52
      Racial/ethnic identity

      (ref group: White [non-Hispanic])
      Black (non-Hispanic)1.330.87–2.041.120.28–4.41
      Hispanic1.380.93–2.050.410.12–1.36
      Multiracial (non-Hispanic)0.980.58–1.662.320.21–25.04
      Other (non-Hispanic)1.080.51–2.282.410.38–15.38
      Sexual orientation

      (ref group:

      heterosexual)
      Gay/lesbian1.850.85–4.040.310.05–2.06
      Bisexual2.031.02–4.080.090.00–2.31
      Other1.140.25–5.235.700.49–66.11
      Marital status

      (ref group:

      never married)
      Married/ in a domestic relationship0.640.45–0.920.780.32–1.92
      Divorced/ separated/widowed1.070.72–1.610.700.15–3.25
      Education

      (ref group:

      no college)
      Some college0.660.43–0.990.550.20–1.50
      Associates0.48
      p < .01.
      0.29–0.770.250.05–1.30
      Bachelors0.41
      p < .001.
      0.25–0.660.200.06–0.69
      Masters or PhD0.42
      p < .001.
      0.25–0.700.190.05–0.77
      Health coverage

      (ref group:

      no coverage)
      VA1.451.06–1.99NANA
      Tricare1.070.70–1.64NANA
      Employer coverage0.750.53–1.050.680.24–1.99
      Other government coverage1.140.72–1.830.810.26–2.49
      Other private coverage
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Numbers too low to analyze in model.
      Financial difficulty

      (z-scored)
      0.980.84–1.131.010.66–1.53
      Social support

      (z-scored)
      0.930.81–1.071.761.13–2.75
      ACEs

      (ref group: no ACEs)
      1–21.380.89–2.153.010.52–17.40
      3–41.821.14–2.882.720.41–18.13
      5–62.00
      p < .01.
      1.23–3.265.420.78–37.59
      7+1.751.06–2.898.271.20–57.11
      Adult sexual trauma history1.310.88–1.953.141.01–9.73
      Combat exposure0.870.65–1.17NANA
      ACE = Adverse Childhood Experiences. Veteran model estimated with binary logistic regression model with population weights including a base sampling weight, a nonresponse adjustment, and a calibration to sex, pre/post-9/11 activation, and deployment factors (branch, component, and geographic stratum). Nonveteran model estimated with binary logistic regression model with population weights including the probability of selection into the KnowledgePanel and into the CHAI sample, matched to U.S. Census benchmarks (18+ non-incarcerated) on age, sex, race/ethnicity, census region, education, and household income.
      low asterisk p < .01.
      low asterisklow asterisk p < .001.
      a Numbers too low to analyze in model.

      4. Discussion

      The current study is the first to use nationally representative samples of veterans and nonveterans to not only compare prevalence of alcohol use and alcohol treatment, but also to compare predictors of alcohol use and alcohol treatment. We found that veterans reported higher alcohol consumption and higher lifetime alcohol treatment utilization than nonveterans. However, differences in alcohol consumption between veterans and nonveterans were modest (with a mean of 3.26 among veterans vs. a mean [age−/sex−/race-ethic-adjusted] of 2.92 among nonveterans on a 0–12 scale). Differences in lifetime alcohol treatment were more prominent, with veterans being 2.8 times more likely to receive treatment compared with nonveterans. This represents a particularly novel finding, as previous studies investigating differences in treatment utilization among veterans and nonveterans have focused on SUD treatment broadly and only assessed past-year treatment utilization, with conflicting findings (
      • Boden M.T.
      • Hoggatt K.J.
      Substance use disorders among veterans in a nationally representative sample: Prevalence and associated functioning and treatment utilization.
      ;
      • Golub A.
      • Vazan P.
      • Bennett A.S.
      • Liberty H.J.
      Unmet need for treatment of substance use disorders and serious psychological distress among veterans: A nationwide analysis using the NSDUH.
      ).
      One possible reason that veterans receive alcohol treatment more than nonveterans is that the VA provides more alcohol screenings and alcohol treatment than traditional health care facilities. However, we did not find that current VA health care coverage was associated with alcohol treatment utilization. Another reason may be that because veterans report higher alcohol consumption, they have a greater need for alcohol treatment utilization. Additionally, the transition out of active military service can be difficult, and this may lead to higher prevalence of alcohol use and AUD, which may then lead individuals to seek treatment (e.g.,
      • Hoopsick R.A.
      • Fillo J.
      • Vest B.M.
      • Homish D.L.
      • Homish G.G.
      Substance use and dependence among current reserve and former military members: Cross-sectional findings from the National Survey on drug use and health, 2010–2014.
      ;
      • Wright K.M.
      • Foran H.M.
      • Wood M.D.
      • Eckford R.D.
      • McGurk D.
      Alcohol problems, aggression, and other externalizing behaviors after return from deployment: Understanding the role of combat exposure, internalizing symptoms, and social environment.
      ). The fact that veteran participants in the current study had separated an average of nine years ago (and had nearly a decade to seek treatment) may partially explain why this study found differences in lifetime treatment between veterans and nonveterans but no differences in past-year treatment.
      Gender was a prominent predictor in several contexts. For veterans, men reported higher alcohol consumption and were 2.4 times more likely to be identified as needing intensive alcohol treatment than women (based on self-reported alcohol-related problems). For nonveterans, men also reported higher alcohol consumption than women, but they were not more likely to be identified as needing intensive alcohol treatment than women. This finding suggests that veteran men demonstrate especially high need for alcohol treatment services. This finding aligns with one previous research finding that veterans reported higher past-year AUD and heavy episodic drinking than nonveterans only in men aged 18–25 (
      • Hoggatt K.J.
      • Lehavot K.
      • Krenek M.
      • Schweizer C.A.
      • Simpson T.
      Prevalence of substance misuse among US veterans in the general population.
      ). However, our finding conflicts with one finding that nonveteran men reported higher heavy episodic drinking than veterans (
      • Bachrach R.L.
      • Blosnich J.R.
      • Williams E.C.
      Alcohol screening and brief intervention in a representative sample of veterans receiving primary care services.
      ) and another finding that veterans reported higher lifetime AUD than nonveterans only in women (
      • Evans E.A.
      • Upchurch D.M.
      • Simpson T.
      • Hamilton A.B.
      • Hoggatt K.J.
      Differences by veteran/civilian status and gender in associations between childhood adversity and alcohol and drug use disorders.
      ). Differences in findings may be attributable to differences in samples (e.g., younger age) or measurement of alcohol outcomes.
      Many prominent differences existed between our veteran and nonveteran models. For veterans, higher financial difficulty and lower social support were associated with need for intensive treatment. In contrast, for nonveterans, only ACEs predicted need for intensive treatment. In previous studies, financial difficulty, social support, and ACEs have all been found to be related to alcohol problems among both veterans and nonveterans (
      • Aronson K.R.
      • Perkins D.F.
      • Morgan N.R.
      • Bleser J.A.
      • Vogt D.
      • Copeland L.A.
      • Finley E.P.
      • Gilman C.L.
      The impact of adverse childhood experiences (ACEs) and combat exposure on mental health conditions among new post-9/11 veterans.
      ;
      • Bravo A.J.
      • Kelley M.L.
      • Hollis B.F.
      Social support, depressive symptoms, and hazardous alcohol use among navy members: An examination of social support as a protective factor across deployment.
      ;
      • Brick L.
      • Nugent N.R.
      • Kahana S.Y.
      • Bruce D.
      • Tanney M.R.
      • Fernandex M.I.
      • Bauermeister J.A.
      Interaction effects of neighborhood disadvantage and individual social support on frequency of alcohol use in youth living with HIV.
      ;
      • Cohen E.
      • Feinn R.
      • Arias A.
      • Kranzler H.R.
      Alcohol treatment utilization: Findings from the National Epidemiologic Survey on alcohol and related conditions.
      ;
      • Groh D.R.
      • Jason L.A.
      • Davis M.I.
      • Olson B.D.
      • Ferrari J.R.
      Friends, family, and alcohol abuse: An examination of general and alcohol-specific social support.
      ;
      • Halvorson M.A.
      • Ghaus S.
      • Cucciare M.A.
      Care utilization and patient characteristics of veterans who misuse alcohol.
      ;
      • Hughes K.
      • Bellis M.A.
      • Hardcastle K.A.
      • Sethi D.
      • Butchart A.
      • Mikton C.
      • Jones L.
      • Dunne M.P.
      The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis.
      ;
      • Lee R.D.
      • Chen J.
      Adverse childhood experiences, mental health, and excessive alcohol use: Examination of race/ethnicity and sex differences.
      ;
      • McCabe C.T.
      • Mohr C.D.
      • Hammer L.B.
      • Carlson K.F.
      PTSD symptomology and motivated alcohol use among military service members: Testing a conditional indirect effect model of social support.
      ), but these studies did not account for the shared variance of all three factors. Mental health interventions for veterans that incorporate modules on social support, such as VetChange, significantly reduced alcohol consumption (e.g.,
      • Livingston N.A.
      • Mahoney C.T.
      • Ameral V.
      • Brief D.
      • Rubin A.
      • Enggasser J.
      • Litwack S.
      • Helmuth E.
      • Roy M.
      • Solhan M.
      • Rosenbloom D.
      • Keane T.
      Changes in alcohol use, PTSD hyperarousal symptoms, and intervention dropout following veterans' use of VetChange.
      ). Financial assistance programs may also be a helpful adjunct for many veterans with alcohol use problems, given that they have been shown to improve health outcomes (e.g.,
      • Nelson R.E.
      • Montgomery A.E.
      • Suo Y.
      • Cook J.
      • Pettey W.
      • Gundlapalli A.
      • Greene T.
      • Evans W.
      • Gelberg L.
      • Kertesz S.G.
      • Tsai J.
      • Byrne T.H.
      Temporary financial assistance decreased health care costs for veterans experiencing housing instability.
      ). Conversely, for nonveterans, focus on ACEs may be particularly helpful. For example, a trauma-informed motivational intervention for adults with a history of ACEs led to reductions in alcohol use (
      • Goldstein E.
      • Topitzes J.
      • Birstler J.
      • Brown R.L.
      Addressing adverse childhood experiences and health risk behaviors among low-income, black primary care patients: Testing feasibility of a motivation-based intervention.
      ).
      Several factors were related to overall alcohol consumption but not being identified as needing treatment. For veterans, these factors included identifying as White (vs. Black), being aged 25–34 (vs. 45–54 and vs. 55–64), and combat exposure. For nonveterans, identifying as White (vs. “other”) was associated with alcohol consumption but not needing treatment. Thus, across veterans and nonveterans, White individuals may consume higher amounts of alcohol, but do not report greater negative consequences. This finding may be due to White privilege, with White individuals' greater access to various resources potentially buffering against the negative effects of higher alcohol use. Conversely, experiences of discrimination for non-White individuals may especially heighten the negative consequences of alcohol use. For instance, several studies have found that associations between race and alcohol problems are accounted for by experiences of racial discrimination, as well as other environmental factors (
      • McKone K.
      • Kennedy T.M.
      • Piasecki T.M.
      • Molina B.
      • Pedersen S.L.
      In-the-moment drinking characteristics: An examination across attention-deficit/hyperactivity disorder history and race.
      ;
      • Zapolski T.C.
      • Pedersen S.L.
      • McCarthy D.M.
      • Smith G.T.
      Less drinking, yet more problems: Understanding african american drinking and related problems.
      ;
      • Zemore S.E.
      • Ye Y.
      • Mulia N.
      • Martinez P.
      • Jones-Webb R.
      • Karriker-Jaffe K.
      Poor, persecuted, young, and alone: Toward explaining the elevated risk of alcohol problems among Black and Latino men who drink.
      ). For veterans, the 25–34 age group reported higher alcohol consumption (vs. 45–54 and vs. 55–64), but they did not report higher need for treatment than any other group. One previous study found that veterans aged 18–25 demonstrated greater risky alcohol consumption (
      • Hoggatt K.J.
      • Lehavot K.
      • Krenek M.
      • Schweizer C.A.
      • Simpson T.
      Prevalence of substance misuse among US veterans in the general population.
      ). Younger adult veterans may demonstrate higher alcohol consumption due to a variety of factors, including using alcohol to cope with the transition to adulthood, proximity to separation from active duty, and the age of veterans at the start of the Iraq War. More research is needed to better understand these age/cohort effects in veterans, and which factors may protect this group from greater alcohol problems. Alternatively, higher alcohol consumption in younger veterans may not cause immediate problems (or they may not be aware of problems), but may lead to greater problems in the future.
      The current study has several limitations. First, we used a cross-sectional design, which did not allow us to investigate temporal relationships among variables. Future research comparing veterans and nonveterans should also include longitudinal designs, as some of our predictor variables, such as financial difficulty and adult sexual assault, likely demonstrate bidirectional relationships with our alcohol outcomes (alcohol problems and alcohol treatment utilization). Second, due to an error on the ASSIST, we partially underestimated alcohol-related problems; thus, future studies should seek to validate these results using the ASSIST measure with no errors. Third, we used a cutoff for the AUDIT-C in our models of those at higher risk for alcohol problems that has been recommended in prior literature (
      • Aalto M.
      • Tuunanen M.
      • Sillanaukee P.
      • Seppä K.
      Effectiveness of structured questionnaires for screening heavy drinking in middle-aged women.
      ;
      • Aalto M.
      • Alho H.
      • Halme J.T.
      • Seppä K.
      AUDIT and its abbreviated versions in detecting heavy and binge drinking in a general population survey.
      ); however, research has also recommended lower cutoffs (e.g.,
      • Bradley K.A.
      • Bush K.R.
      • Epler A.J.
      • Dobie D.J.
      • Davis T.M.
      • Sporleder J.L.
      • Maynard C.
      • Burman M.L.
      • Kivlahan D.R.
      Two brief alcohol-screening tests from the alcohol use disorders identification test (AUDIT): Validation in a female veterans affairs patient population.
      ;
      • Bush K.
      • Kivlahan D.R.
      • McDonell M.B.
      • Fihn S.D.
      • Bradley K.A.
      The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Ambulatory care quality improvement project (ACQUIP).
      ); thus, future research may use lower cutoffs to reduce the likelihood of missing participants who do not endorse very heavy drinking but still may experience alcohol problems and may benefit from treatment. Fourth, we had lower power to detect effects in the treatment analyses subsample due to the smaller subsample used (those who needed intensive treatment). Future research should recruit a larger number of participants with lifetime and/or current AUD to increase power in investigating treatment utilization. Fifth, we used self-report measures, which are less objective than clinical interviews, especially in reporting alcohol problems, as these problems may be minimized by some. Sixth, we did not assess current and lifetime AUD; comparing these actual diagnoses across veterans and nonveterans would be helpful in future research. Finally, we used panel recruitment for nonveterans. The study selected veterans from the US Veterans Eligibility Trends and Statistics (USVETS) and sent them a letter for recruitment, whereas we selected nonveterans from members of KnowledgePanel and sent them an email for recruitment. Unmeasured differences may exist between the veteran and nonveteran sample due to nonveterans' KnowledgePanel membership (e.g., more free time, higher organizational capacity). Future research should use the same recruitment approach for veterans and nonveterans.

      5. Conclusion

      This study represents the first investigation to compare prevalence and predictors of alcohol use and treatment utilization among veterans and nonveterans in a national sample. Strengths of our study include accounting for the shared variance of a variety of relevant predictors and using population weights to increase generalizability of our findings. We found that veterans reported higher alcohol use consumption and higher lifetime alcohol treatment utilization than nonveterans. We also found several differences between veteran and nonveteran models in associations between predictors and our alcohol outcomes (alcohol problems and alcohol treatment utilization). These findings underscore the importance of understanding how veteran and nonveteran treatment needs may differ, which can help to shape policy and clinical interventions that will improve treatment for both veterans and nonveterans. Our findings suggest that veterans may benefit from interventions targeting financial problems and social support, whereas nonveterans may benefit from a focus on ACEs.

      CRediT authorship contribution statement

      Funding acquisition: Aaron I. Schneiderman.
      Conceptualization: Rachel M. Ranney, Dawne Vogt, John R. Blosnich, Claire A. Hoffmire, Yasmin Cypel, Aaron I. Schneiderman, and Shira Maguen.
      Methodology: Paul A. Bernhard and Shira Maguen.
      Data curation: Paul A. Bernhard.
      Formal analysis: Rachel M. Ranney and Paul A. Bernhard.
      Roles/Writing – original draft: Rachel M. Ranney.
      Writing – review and editing: Rachel M. Ranney, Paul A. Bernhard, Dawne Vogt, John R. Blosnich, Claire A. Hoffmire, Yasmin Cypel, Aaron I. Schneiderman, and Shira Maguen.

      Declaration of competing interest

      None.

      Acknowledgements

      This work was supported by the Department of Veterans Affairs, Office of Patient Care Services, Health Outcomes of Military Exposures, Epidemiology Program. This research is also supported by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment, the Medical Research Service of the Veterans Affairs San Francisco Health Care System, and the Department of Veterans Affairs Sierra Pacific (VISN 21) Mental Illness Research, Education, and Clinical Center (MIRECC).

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