The Culture of Alcohol in the U.S. Military: Correlations With Problematic Drinking Behaviors and Negative Consequences of Alcohol Use

Abstract

Excessive alcohol use, especially binge and heavy drinking, represents a serious threat to force readiness across the Department of Defense. Though these behaviors are a matter of individual service member choice, they are influenced by perceptions of the culture of alcohol use in the military. This paper uses data from the 2018 Health Related Behaviors Survey of Active Duty service members to explore associations between perceived alcohol culture and excessive alcohol use, any serious drinking consequences, risky driving behaviors, productivity loss due to drinking, absenteeism, and presenteeism. Results from multivariate logistic regression reveal a strong, positive correlation between positive perceptions of drinking culture in the military and all outcomes. Targeting perceptions of the drinking culture is one way the military can reduce excessive and unhealthy use of alcohol and negative sequelae.

Keywords

military culture alcohol health behaviors readiness

Levels of hazardous alcohol use behaviors among Active Component service members are high, above that of civilians (Substance Abuse and Mental Health Services Administration [SAMHSA], 2019) and above benchmarks set by the Office of Disease Prevention and Health Promotion (Healthy People 2030; U.S. Department of Health and Human Services, 2021). Problematic alcohol use has been associated with a number of negative mental, physical, and social health outcomes (Greenfield et al., 2015; Rehm et al., 2017; Taylor et al., 2010). For the military, alcohol use among service members may have even more significant implications because any impairment could negatively impact the readiness of the force.

Several factors play a role in determining an individual’s propensity to use and misuse alcohol. These include individual-level demographic factors, such as age (Vasilenko et al., 2017) and gender (Wilsnack et al., 2018). However, factors outside the individual are also relevant to patterns of alcohol use (e.g., amount and setting). For example, some research has described a “culture of drinking” on college campuses (Task Force of the National Advisory Council on Alcohol Abuse and Alcoholism, 2002). Given that rates of alcohol use among service members and college students are similar (see Kreiger et al., 2018), it is possible that they may share a similar alcohol culture. Exactly how this larger culture is associated with any given individual’s own behavior, or consequences of those behaviors, has not been explored in the military context.

Problematic Alcohol Use in the Military

Data from the 2018 Health Related Behaviors Survey indicate that both binge drinking (i.e., at least five drinks on the same occasion at least once for men; at least four drinks on the same occasion at least once for women) and heavy drinking (i.e., binge drinking on at least 1 or 2 days a week in the past 30 days) are common. Among Active Component service members, 34.0% reported at least one instance of binge drinking in the past 30 days and 9.8% reported heavy drinking (Meadows et al., 2021). Binge drinking among U.S. adults age 18 and above from the 2018 NSDUH is 26.5% (SAMSHA, 2019). The rate of heavy drinking, though not directly comparable due to slight differences in the response options between the two data sources, is also lower among civilians, at 6.6% (SAMHSA, 2019).¹ However, at least one study found that when compared to a civilian sample matched on gender, age, race/ethnicity, marital status, educational attainment, parental status, and sexual orientation (when available), rates of both binge and heavy drinking were actually lower in the military (Wong et al., 2020). This suggests that the demographic characteristics of the military population (i.e., majority male and young) drive some of the observed differences in the two populations.

Further, the level of binge drinking in the military is above the goal set forward by the Office of Disease Prevention and Health Promotion in the Healthy People 2030 objective of 25.4% or fewer adults engaging in binge drinking (U.S. Department of Health and Human Services, 2021). Perhaps even more concerning, the percentage of service members who reported binge drinking significantly increased by 14% between the 2015 and 2018 HRB surveys, and the percentage of those reporting heavy drinking increased by 32% (Meadows et al., 2021).

Consequences of Alcohol Use in the Military

Like the civilian population, problematic alcohol use among service members can lead to serious negative consequences (Armed Forces Health Surveillance Branch, 2011) and substance use has been called a “public health crisis” for the military by the Institute of Medicine (IOM, 2013). Problematic alcohol use has been linked to other health and behavioral problems—comorbid mental health problems (e.g., depression and posttraumatic stress disorder; Debell et al., 2014; Grant et al., 2015), suicide attempts (Borges et al., 2017), sexual risk behaviors (Scott-Sheldon et al., 2016), sexually transmitted infections (Hood et al., 2020), aggression (Giancola et al., 2009), accidents, injuries, and hospitalizations (Cherpitel, 2007; Self et al., 2020), sexual assault (Lorenz & Ullman, 2016), interpersonal and domestic violence (Cafferky et al., 2018), decreased physical performance (Vella & Cameron-Smith, 2010), and impaired work performance (Thørrisen et al., 2019). Other negative consequences specific to problematic alcohol use among service members include loss of security clearance, dishonorable discharge, and inability to deploy.

Taken together, these serious consequences pose a potential readiness problem for the military if service members are unable to perform the duties required of them to meet their mission. One study estimated that alcohol misuse among active duty service members costs DoD roughly $900 million annually and a large portion (roughly 84%) of this cost is non-medical (Harwood et al., 2009). These non-medical costs include service members’ inability to deploy, early separation, absenteeism, and legal issues (e.g., DUIs) (Harwood et al., 2009; see also; Dall et al., 2007). Though this analysis is over a decade old, the fact that alcohol misuse behaviors have increased over time suggests that costs could be even greater today.

Though the extant literature largely focuses on the negative sequelae of problematic alcohol use, there are potentially positive aspects associated with alcohol use as well. Buvik (2020) found that coworkers perceive collective alcohol use as a means to build group cohesion (Buvik, 2020) and that studies of service members have found that alcohol use with other members of one’s unit after deployment is positively associated with cohesion and comradery (Breslau et al., 2016; Browne et al., 2008; DuPreez et al., 2012). Self-mediation theory suggests that alcohol is a means to address stress (e.g., trauma associated with deployment or sexual trauma) and related psychological health problems (e.g., PTSD and anxiety) (Hawn, Bountress et al., 2020a; Hawn, Cusack et al. 2020b; Schumm & Chard, 2012), though clearly use of alcohol under such circumstances can lead to misuse and abuse. Finally, some research has found that alcohol is used by individuals to cope with chronic pain (Zale et al., 2015), though, again, use in this case could easily become abuse, particularly when patients combine alcohol and opioid use (Witkiewitz & Vowles, 2018).

Defining Alcohol Culture

Despite individual-level factors that predict alcohol use and abuse, the extant literature also suggests that an individual’s environment can also impact alcohol use behaviors (Raninen & Livingston, 2020; Skog, 1985). This work, however, is not always clear in what is meant by the term “alcohol culture,” or how it should be measured (Savic et al., 2016). For example, prior research has focused on drinking culture that occurs at the national level and uses typologies (e.g., wet vs. dry) to assign culture to different countries (e.g., Ally et al., 2016). Other work calls for alcohol culture to be measured at a much less macro level (d’Abbs, 2015; Savic et al., 2016). Identifying alcohol culture among subcultures (e.g., college students [Martin & Zamboanga, 2018; Hennell et al., 2020], service members [Poehlman et al., 2011]) may in fact have a greater impact on public health campaigns that attempt to change behavior via a change in culture (e.g., Room et al., 2016). Using a socio-ecological approach, Sudhinaraset and colleagues (2017) argue that it is the interplay between individual (e.g., sociodemographic characteristics), microsystem (e.g., family members and peer groups), community (e.g., gender norms and neighborhood characteristics), and the macro/policy (e.g., media and advertising) levels that ultimately work in concert to influence both an individual’s beliefs and his/her behaviors with respect to alcohol. Nonetheless, there is broad consensus around the idea that alcohol culture encompasses attitudes, norms, customs, practices, habits, and behaviors surrounding use of alcohol.

Two aspects of the existing work on alcohol culture are especially pertinent to an examination of the military. First, some studies have examined how alcohol use is associated with the work environment. For example, Buvik (2020) found that workers view drinking together with coworkers as a way to bond and build group cohesion via a shared experience. He goes so far as to suggest that drinking together after work can have positive ramifications and is a way to “invest” in colleagues (Buvik, 2020, p. 86). Interestingly, some participants in the study noted that not partaking in after-work drinking rituals was viewed as rejecting comradery and not prioritizing colleagues. In the military, consuming alcohol has been “viewed a way to alleviate” boredom, release stress, celebrate achievements, and generally build cohesion with fellow service members (Ames & Cunradi, 2004; Ames et al., 2009).

Second, coaches’ views on alcohol consumption among college athletes are in some ways similar to the commander-service member relationship. Chow and colleagues (2019) examined how collegiate coaches view their roles as guiding influences for their student-athletes. The authors found three main coaching attitudes toward student drinking: enforcement (e.g., setting strict rules about drinking behaviors), conditional leniency (e.g., awareness that drinking will occur in some situations), and concerned communication (e.g., providing information about the consequences of alcohol use and creating an open culture for discussion). Earlier work on students’ attitudes had identified the same typology (Pitts et al., 2019). Pitts and colleagues (2019) reported that when athletes viewed their coaches’ approaches to alcohol use as higher in concerned communication and lower in conditional leniency, they reported less overall alcohol use and heavy episodic drinking (or binge drinking). Enforcement had no significant association with alcohol consumption. Taken together, these studies suggest that in a hierarchical subculture like the military, the culture of alcohol is likely to include aspects related to both leadership’s attitudes toward drinking as well as service members’ own beliefs about the potential positive associated with alcohol use, especially among other, fellow service members.

The Current Study

Linkages between alcohol use and risky behaviors and workplace performance may have implications for readiness. If service members’ beliefs about the drinking culture in the military are associated with these behaviors (e.g., binge and heavy drinking) and negative outcomes (e.g., drunk driving and absenteeism) it could point to one area where interventions, programs, and policies could be targeted in an attempt to prevent alcohol misuse. As such, the objective of this study is to identify how an individual’s beliefs about the culture of alcohol use in the military are associated with his/her own drinking behavior and negative consequences of alcohol use and abuse. Specifically, the analysis addresses three research questions:

Q1: How is service member perceived military culture around alcohol use associated with problem drinking (i.e., binge and heavy drinking)?

Q2: How is service member perceived military culture around alcohol use associated with drinking consequences, including driving while intoxicated or being a passenger in a car driven by an intoxicated person?

Q3: How is service member perceived military culture around alcohol use associated with work-related consequences, including absenteeism and presenteeism?

Methods

Data

Data come from the 2018 Department of Defense (DoD) Health Related Behaviors Survey (HRBS) (see Meadows et al., 2021). The HRBS is DoD’s flagship survey for understanding the health, health-related behaviors, and well-being of service members. The survey was confidential and web-based, using a stratified random sampling approach. Missing data were imputed. For this analysis, six percent of cases had at least one imputed variable among only the variable used in the analysis. This analysis also uses analytic weights designed to make the analytics sample representative of the eligible service member population. The final analytic sample consisted of 17,166 surveys.

Measures

Dependent Variables

Two measures assess alcohol misuse. Binge drinking is defined as consuming four/five or more drinks for a woman/man on one occasion during the past 30 days. This item was taken from the National Survey on Drug Use and Health (NSDUH; Substance Abuse and Mental Health Services Administration, 2019). Respondents who reported binge drinking at least 1 or 2 days a week during the past 30 days were classified as heavy drinkers. This definition is similar to the heavy drinking definition used by NSDUH (Substance Abuse and Mental Health Services Administration, 2019), which is binge drinking on five or more days in the past 30 days.

Any serious drinking consequences is a 10-item summary index of things that happen to the respondent while or after drinking, or because of using alcohol, over the past year (see Table 1). Serious drinking consequences are treated as a dichotomous variable (i.e., any serious consequence vs. no serious consequences) given how few respondents reported any negative consequence.

Table 1.

Drinking Consequences Items from the 2018 HRBS.

I found it harder to handle my problems because of drinking	I got into a fight where I hit someone other than a member of my family when I was drinking
I received military punishment (e.g., Court Martial, Article 15, Captain’s Mast, Office Hours, and Letter of Reprimand) because of my drinking	I did something sexually that I regretted
I was arrested for a drinking incident not related to driving	I was arrested for driving under the influence of alcohol
I got a lower score on my efficiency report or performance rating because of my drinking	I was hurt in an accident because of my drinking (e.g., vehicle, work, and other)
I hit my spouse/significant other after having too much to drink	My drinking caused an accident where someone else was hurt or property was damaged

The 2018 HRBS also includes two measures of risky driving behaviors resulting from use of alcohol. Risky driving behaviors is a dichotomous indicator of whether the service member has either driven when he or she had too much to drink or rode in a car or other vehicle driven by someone who had too much to drink in the past year.

Productivity loss due to drinking is a six-item summary index that assesses past year job-related issues that stemmed from drinking. The six issues include: I was hurt in an on-the-job accident because of my drinking; I was late for work or left work early because of drinking, a hangover, or an illness caused by drinking; I did not come to work at all because of a hangover, an illness, or a personal accident caused by drinking; I worked below my normal level of performance because of drinking, a hangover, or an illness caused by drinking; I was drunk while working; and I was called in during off-duty hours and reported to work feeling drunk. Productivity loss is treated as a dichotomous variable (i.e., any productivity loss vs. no productivity loss) given its highly skewed distribution.

Finally, two measures assess the extent to which service members have experienced any disruptions to work, school, productivity, or other responsibilities due to any mental or physical symptoms they had. Review of the distributions of absenteeism and presenteeism in the data suggested they were highly skewed and thus treated as dichotomous variables (i.e., one or more days in the past month vs. none). These measures were adapted from the Sheehan Disability Scale.

Culture of Alcohol

Service members’ perceptions of the military culture of alcohol use are measured by a four-item summary index: It is hard to “fit in” in my command if you do not drink; Drinking is part of being in my unit; at parties or social function at this installation, everyone is encouraged to drink; and Leadership is tolerant of off-duty alcohol intoxication or drunkenness. Each item was rated as agree or disagree and a sum score was created (range: 0–4). Higher scores indicate a greater belief that the local culture is tolerant of alcohol use.

Descriptive statistics, including percentages and 95% confidence intervals, for dependent measures and the culture of alcohol measure can be found in Table 2. Because the distributions of the dependent variable are heavily skewed, all outcomes are transformed into dichotomous variables (e.g., any serious drinking consequences received a 1 if any the respondent endorsed any of the 10 items in the survey measure).

Table 2.

Weighted Descriptive Statistics, by Gender.

	Males (83.3%)	Females (16.7%)
	% (95% CI)	% (95% CI)
Dependent variables
Binge drinking (0–1)	35.2 (33.8, 36.6)	28.2 (69.9, 73.6)
Heavy drinking (0–1)	10.6 (9.7, 11.5)	5.9 (4.8, 6.9)
Any serious drinking consequences (0–1)	6.0 (5.3, 6.7)	7.7 (6.4, 8.9)
Risky driving behaviors (0–1)	4.8 (4.1, 5.5)	5.2 (4.3, 6.1)
Any productivity loss due to drinking (0–1)	5.9 (5.3, 6.7)	4.2 (3.4, 5.0)
Any presenteeism (0–1)	28.9 (27.1, 30.7)	19.2 (18.0, 20.3)
Any absenteeism (0–1)	7.7 (6.9, 8.5)	12.7 (11.3, 14.0)
Perceived alcohol culture (0–4)
0 items endorsed	71.2 (69.9, 72.5)	74.7 (72.9, 76.5)
1 item endorsed	17.1 (16.0, 18.3)	14.2 (12.8, 15.7)
2 items endorsed	5.6 (4.9, 6.3)	5.8 (4.8, 6.8)
3 items endorsed	2.9 (2.4, 3.3)	2.6 (2.0, 3.2)
4 items endorsed	3.2 (2.6, 3.8)	2.7 (2.0, 3.4)

Notes: N = 17,166. Analyses are weighted. (0–1) indicates the variable is dichotomous; numbers in the table represent percentages.

Control Variables

A set of control variables is included in each multivariate analysis and includes: service branch (Army, Air Force, Marine Corps, Navy, and Coast Guard), pay grade (E1-E4, E5-E6, E7-E9, W1-W5, O1-O3, and O4-O6), age group (in years, 17–24, 25–34, 35–44, and 45 and over), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic Asian, and other), education (high school or less, some college, bachelor’s degree, or more), and marital status (married, cohabiting, never married, and separated/widowed).

Analysis

Because the dependent variables are treated as dichotomous, multivariate logistic regression is used to answer the research questions. All analyses use the analytic weights designed for the HRB data, which are the products of design and nonresponse weights (see Meadows et al., 2021 for details). Analyses for men and women are run separately given the higher prevalence rates of alcohol use and abuse among men. Odds ratios (ORs) and 95% confidence intervals (CIs) are presented in results tables. ORs below one indicate a negative association between two variables (i.e., lower odds of occurrence), whereas ORs above one indicate a positive association between two variables (i.e., greater odds of occurrence). An OR of one indicates no association.

Results

Perceived Military Alcohol Culture and Problem Drinking

The first research question explores the association between perceived alcohol culture and problem drinking, operationalized as binge and heavy drinking. Table 3 presents the results from the two logistic regression models for binge and heavy drinking. Panel A shows that for males, higher scores on the perceived alcohol culture measure are associated with greater odds of reporting both recent binge and heavy drinking (OR = 1.27 [95% CI = 1.18, 1.36] and OR = 1.52 [95% CI = 1.40, 1.66, respectively). Specifically, for every one-unit increase on the perceived alcohol culture measure, we expect to see about 27% increase in the odds of binge drinking and 52% increase in the odds of heavy drinking among male service members.²

Table 3.

Logistic Regression: Perceived Alcohol Culture and Problem Drinking.

	Panel A: Males (83.3%)		Panel B: Females (16.7%)
	Binge Drinking	Heavy Drinking	Binge Drinking	Heavy Drinking
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Perceived alcohol culture	1.27*** (1.18, 1.36)	1.52*** (1.40, 1.66)	1.18** (1.07, 1.31)	1.40*** (1.19, 1.63)
Control variables
Service branch
Army (ref.)	—	—	—	—
Air Force	0.60*** (0.55, 0.65)	0.53*** (0.45, 0.62)	0.62*** (0.55, 0.71)	0.54*** (0.41, 0.70)
Marine Corps	1.39*** (1.23, 1.56)	1.53*** (1.29, 1.82)	1.16 (0.93, 1.45)	1.10 (0.72, 1.68)
Navy	1.45*** (1.29, 1.63)	1.56*** (1.30, 1.86)	1.28** (1.08, 1.52)	1.46* (1.07, 2.00)
Coast Guard	0.97 (0.85, 1.09)	0.88*** (0.71, 1.09)	1.21* (1.00, 1.46)	1.11 (0.77, 1.59)
Pay grade
E1-E4 (ref.)	—	—	—	—
E5-E6	0.86** (0.76, 0.97)	1.06 (0.88, 1.28)	0.94 (0.75, 1.18)	1.23 (0.75, 2.04)
E7-E9	1.14* (1.00, 1.30)	1.36** (1.09, 1.68)	1.40** (1.09, 1.79)	1.81** (1.14, 2.86)
W1-W5	1.14 (0.87, 1.50)	1.41 (0.91, 2.17)	1.14 (0.63, 2.06)	0.50 (0.14, 1.82)
O1-O3	1.22** (1.04, 1.43)	0.74* (0.57, 0.97)	0.86 (0.68, 1.10)	0.73 (0.42, 1.27)
O4-O6	1.03 (0.88, 1.21)	0.83 (0.64, 1.09)	0.95 (0.73, 1.24)	0.97 (0.54, 1.74)
Age group (years)
17–24 (ref.)	—	—	—	—
25–34	1.27*** (1.15, 1.39)	1.17* (1.00, 1.36)	1.40*** (1.19, 1.65)	1.22 (0.90, 1.64)
35–44	0.93 (0.84, 1.04)	0.91 (0.76, 1.08)	0.91 (0.76, 1.09)	0.99 (0.70, 1.39)
45 and over	0.59*** (0.51, 0.69)	0.65*** (0.50, 0.82)	0.50*** (0.38, 0.68)	0.58 (0.32, 1.06)
Race/ethnicity
Non-Hispanic White (ref.)	—	—	—	—
Non-Hispanic Black	0.78** (0.66, 0.93)	0.76 (0.56, 1.01)	0.76** (0.61, 0.94)	1.07 (0.68, 1.69)
Hispanic	0.99 (0.85, 1.15)	1.01 (0.80, 1.27)	1.05 (0.84, 1.32)	1.20 (0.80, 1.79)
Non-Hispanic Asian	0.95 (0.77, 1.16)	1.11 (0.83, 1.49)	0.73* (0.55, 0.97)	0.54 (0.29 1.02)
Other	1.25 (0.99, 1.57)	1.15 (0.80, 1.64)	1.30 (0.94, 1.80)	1.25 (0.66, 2.36)
Education
High school or less	1.16*** (1.05, 1.28)	1.10 (0.94, 1.28)	1.02 (0.87, 1.19)	1.02 (0.72, 1.45)
Some college	0.98 (0.88, 1.09)	0.97 (0.81, 1.17)	0.97 (0.71, 1.18)	1.02 (0.73, 1.44)
Bachelor’s degree + (ref)	—	—	—	—
Marital status
Married (ref.)
Cohabiting	1.13 (0.93, 1.37)	0.89 (0.68, 1.16)	0.92 (0.71, 1.18)	0.93 (0.61, 1.44)
Never married	0.90 (0.79, 1.04)	0.99 (0.81, 1.21)	1.10 (0.93, 1.31)	0.95 (0.68, 1.32)
Separated/divorced/widowed	1.22 (0.99, 1.49)	1.28 (0.98, 1.66)	1.23* (1.00, 1.50)	1.33 (0.91, 1.96)
Intercept	0.49*** (0.43, 0.56)	0.08*** (0.06, 0.10)	0.31*** (0.25, 0.37)	0.03*** (0.02, 0.05)

Notes: Odds ratios above/below one indicate higher/lower odds of the dependent (or outcome) variable compared to the reference category for that set of predictors, holding all other variables in the model at the sample mean. For example, for service branch, odds ratios represent the increase/decrease in odds of the outcome for each service branch relative to the Army (i.e., the reference group). For perceived alcohol culture, the odds ratio represents the increase (above one) or decrease (below one) for every one-unit increase in the culture measure, which ranges from 0 to 4.

N = 17,166.

OR = odds ratio. CI = confidence interval. (ref.) indicates the reference category in regression models.

Bold coefficients are statistically significant. *** p < 0.001 ** p < 0.01 * p < 0.05.

Results for females, shown in panel B, are similar. Higher scores on the perceived alcohol culture measure are associated with greater odds of reporting both recent binge and heavy drinking (OR = 1.18 [95% CI = 1.07, 1.31] and OR = 1.40 [95% CI = 1.1.19, 1.63], respectively). For every one-unit increase on the perceived alcohol culture measure, we expect to see about 18% increase in the odds of binge drinking and 40% increase in the odds of heavy drinking among female service members.

Perceived Military Alcohol Culture and Drinking Consequences

The second research question explores the association between perceived alcohol culture and drinking consequences, operationalized as any serious drinking consequences, risky driving behaviors (both drunk driving and riding as a passenger in a car driven by an intoxicated person), and productivity loss due to drinking. Table 4 presents the results from the three logistic regression models for these outcomes.

Table 4.

Logistic Regression: Perceived Alcohol Culture and Drinking Consequences.

	Panel A: Males (83.3%)			Panel B: Females (16.7%)
	Any Serious Consequences	Risky Driving Behaviors	Any Productivity Loss	Any Serious Consequences	Risky Driving Behaviors	Any Productivity Loss
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Perceived alcohol culture	1.75*** (1.58, 1.94)	1.44*** (1.29, 1.61)	1.69*** (1.53, 1.87)	1.48*** (1.28, 1.79)	1.57*** (1.36, 1.81)	1.58** (1.36, 1.83)
Control variables
Service branch
Army (ref.)	—	—	—	—	—	—
Air Force	0.59*** (0.49, 0.72)	0.60*** (0.48, 0.74)	0.51*** (0.42, 0.62)	0.56*** (0.44, 0.72)	0.66** (0.49, 0.89)	0.62*** (0.45, 0.84)
Marine Corps	1.57*** (1.25, 1.98)	0.85 (0.64, 1.14)	1.83*** (1.47, 2.29)	1.60** (1.14, 2.24)	0.70 (0.37, 1.34)	1.39 (0.90, 2.16)
Navy	1.24 (0.96, 1.59)	1.33* (1.02, 1.73)	1.48*** (1.18, 1.87)	1.14 (0.79, 1.65)	1.07 (0.75, 1.52)	1.15 (0.80, 1.65)
Coast Guard	0.95 (0.70, 1.29)	1.28 (0.98, 1.68)	0.69** (0.52, 0.92)	0.70 (0.48, 1.01)	1.27 (0.79, 2.04)	0.77 (0.51, 1.17)
Pay grade
E1-E4 (ref.)	—	—	—	—	—	—
E5-E6	1.09 (0.79, 1.50)	1.09 (0.83, 1.43)	0.94 (0.72, 1.23)	0.81 (0.3, 1.23)	0.73 (0.48, 1.13)	0.64* (0.42, 0.99)
E7-E9	1.02 (0.74, 1.41)	1.23 (0.90, 1.68)	1.22 (0.92, 1.63)	1.32 (0.80, 2.19)	1.11 (0.67, 1.82)	1.12 (0.68, 1.85)
W1-W5	1.21 (0.51, 2.87)	0.55 (0.28, 1.06)	0.95 (0.52, 1.72)	1.16 (0.38, 3.52)	1.81 (0.62, 5.27)	1.93 (0.74, 5.05)
O1-O3	0.67* (0.45, 1.00)	0.83 (0.58, 1.18)	0.79 (0.56, 1.11)	0.84 (0.54, 1.32)	0.74 (0.46, 1.22)	0.82 (0.49, 1.38)
O4-O6	1.09 (0.73, 1.63)	1.45* (1.01, 2.08)	1.42 (0.99, 2.03)	0.97 (0.53, 1.77)	1.04 (0.63, 1.72)	1.27 (0.74, 2.16)
Age group (years)
17–24 (ref.)	—	—	—	—	—	—
25–34	1.39*** (1.11, 1.73)	1.32** (1.07, 1.62)	1.29* (1.01, 1.65)	1.43* (1.01, 2.01)	1.34 (0.96, 1.86)	1.31 (0.94, 1.83)
35–44	1.03 (0.81, 1.32)	0.95 (0.75, 1.20)	0.95 (0.76, 1.19)	0.93 (0.64, 1.35)	1.20 (0.83, 1.72)	1.04 (0.74, 1.48)
45 and over	0.41*** (0.27, 0.61)	0.54*** (0.38, 0.76)	0.59* (0.36, 0.97)	0.35** (0.16, 0.77)	0.55* (0.31, 0.98)	0.73 (0.42, 1.28)
Race/ethnicity
Non-Hispanic White (ref.)	—	—	—	—	—	—
Non-Hispanic Black	0.93 (0.63, 1.39)	1.23 (0.81, 1.87)	0.81 (0.52, 1.26)	1.25 (0.78, 2.01)	0.99 (0.62, 1.59)	0.84 (0.52, 1.35)
Hispanic	1.17 (0.84, 1.63)	0.92 (0.64, 1.34)	0.87 (0.61, 1.24)	1.53 (0.96, 2.42)	1.13 (0.71, 1.80)	0.78 (0.42, 1.29)
Non-Hispanic Asian	0.67 (0.40, 1.10)	0.54* (0.32, 0.92)	0.70 (0.41, 1.18)	0.18*** (0.09, 0.37)	0.27*** (0.12, 0.64)	0.46 (0.19, 1.10)
Other	1.09 (0.58, 2.04)	1.15 (0.67, 1.97)	1.59 (0.95, 2.66)	1.56 (0.86, 2.83)	1.96* (0.99, 3.86)	1.85 (0.98, 3.49)
Education
High school or less	0.89 (0.71, 1.12)	1.08 (0.84, 1.38)	0.96 (0.79, 1.18)	1.07 (0.80, 1.44)	1.07 (0.80, 1.43)	1.17 (0.85, 1.60)
Some college	1.16 (0.91, 1.48)	1.00 (0.75, 1.33)	1.04 (0.81, 1.32)	1.16 (0.85, 1.59)	1.06 (0.78, 1.44)	1.11 (0.77, 1.60)
Bachelor’s degree + (ref)	—	—	—	—	—	—
Marital status
Married (ref.)	—	—	—	—	—	—
Cohabiting	1.07 (0.78, 1.47)	0.73 (0.50, 1.06)	1.31 (0.95, 1.82)	0.91 (0.54, 1.51)	1.01 (0.67, 1.52)	1.12 (0.71, 1.77)
Never married	0.86 (0.67, 1.10)	0.89 (0.66, 1.21)	0.81 (0.61, 1.07)	1.53*** (1.14, 2.04)	1.15 (0.83, 1.59)	1.23 (0.86, 1.76)
Separated/divorced/widowed	2.06*** (1.49, 2.83)	1.69** (1.15, 2.49)	1.51* (1.05, 2.18)	1.63** (1.13, 2.34)	1.46 (0.97, 2.18)	1.16 (0.76, 1.78)
Intercept	0.04*** (0.03, 0.05)	0.02*** (0.02, 0.03)	0.04*** (0.03, 0.05)	0.03*** (0.02, 0.04)	0.03*** (0.02, 0.04)	0.03*** (0.02, 0.04)

N = 17,166.

OR = odds ratio. CI = confidence interval. (ref.) indicates the reference category in regression models.

Bold coefficients are statistically significant. *** p < 0.001 ** p < 0.01 * p < 0.05.

For males, perceived alcohol culture was positively and significantly associated with any serious consequences of drinking (OR = 1.75 [95% CI = 1.62, 1.95]), risky driving behaviors (OR = 1.44 [95% CI = 1.29, 1.61]), and productivity loss due to drinking (OR = 1.69 [95% CI = 1.53, 1.87]). For every one-unit increase on the perceived alcohol culture measure, we expect to see about 75% increase in the odds of experiencing any serious drinking-related consequence, 44% increase in the odds of risky driving behaviors, and 69% increase in the odds of a loss of productivity loss due to alcohol use among male service members.

Similar to the results for males, for females, perceived alcohol culture was positively and significantly associated with any serious consequences of drinking (OR = 1.48 [95% CI = 1.28, 1.72]), risky driving behaviors (OR = 1.57 [95% CI = 1.36, 1.81]), and productivity loss due to drinking (OR = 1.58 [95% CI = 1.36, 1.83]). For every one-unit increase on the perceived alcohol culture measure, we expect to see about 48% increase in the odds of experiencing any serious drinking-related consequence, 57% increase in the odds of risky driving behaviors, and 58% increase in the odds of a loss of productivity loss due to alcohol use among female service members.

Perceived Military Alcohol Culture and Absenteeism and Presenteeism

The third research question explores the association between perceived alcohol culture and work-related outcomes, operationalized as absenteeism and presenteeism. Absenteeism measures whether the respondent has missed at least 1 day of work or school in the past month due to do any mental or physical symptoms. Presenteeism measures whether the respondent has been less productive than usual due to any mental or physical symptoms on at least 1 day in the past month. Unlike the productivity loss outcome, neither absenteeism nor presenteeism is directly asked about in relation to alcohol use in the survey. Table 5 presents the results from the two logistic regression models for these outcomes.

Table 5.

Logistic Regression: Perceived Alcohol Culture and Absenteeism and Presenteeism.

	Panel A: Males (83.3%)		Panel B: Females (16.7%)
	Absenteeism	Presenteeism	Absenteeism	Presenteeism
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Perceived alcohol culture	1.29*** (1.17, 1.44)	1.46*** (1.36, 1.57)	1.35*** (1.19, 1.53)	1.41*** (1.28, 1.55)
Control variables
Service branch
Army (ref.)	—	—	—	—
Air Force	0.74*** (0.62, 0.87)	0.70*** (0.63, 0.78)	0.83* (0.70, 0.99)	0.68*** (0.60, 0.77)
Marine Corps	1.09 (0.86, 1.37)	1.21** (1.05, 1.41)	0.99 (0.73, 1.34)	1.26* (1.01, 1.56)
Navy	1.10 (0.89, 1.36)	1.30*** (1.13, 1.49)	1.16 (0.92, 1.46)	1.39** (1.17 1.66)
Coast Guard	0.88 (0.67, 1.15)	0.82** (0.69, 0.97)	0.91 (0.69, 1.19)	0.89 (0.73, 1.08)
Pay grade
1-E4 (ref.)	—	—	—	—
E5-E6	0.94 (0.75, 1.17)	0.99 (0.86, 1.15)	1.06 (0.80, 1.41)	1.01 (0.82, 1.25)
E7-E9	1.18 (0.95, 1.46)	1.01 (0.87, 1.17)	1.34* (1.00, 1.78)	1.34 (1.07, 1.67)
W1-W5	0.81 (0.50, 1.30)	0.90 (0.66, 1.22)	0.82 (0.42, 1.61)	0.89 (0.52, 1.51)
O1-O3	0.97 (0.71, 1.31)	1.04 (0.85, 1.27)	0.79 (0.58, 1.09)	0.85 (0.67, 1.08)
O4-O6	0.87 (0.64, 1.17)	0.90 (0.74, 1.09)	0.83 (0.61, 1.13)	0.99 (0.77, 1.26)
Age group (years)
17–24 (ref.)	—	—	—	—
25–34	0.96 (0.82, 1.14)	0.82*** (0.73, 0.92)	0.97 (0.79, 1.20)	0.96 (0.81, 1.13)
35–44	1.14 (0.96, 1.37)	1.21*** (1.06, 136)	1.13 (0.91, 1.40)	1.13 (0.96, 1.33)
45 and over	1.28* (1.02, 1.62)	1.26** (1.06, 1.49)	1.13 (0.80, 1.62)	0.84 (0.61, 1.13)
Race/ethnicity
Non-Hispanic White (ref.)	—	—	—	—
Non-Hispanic Black	1.35* (1.04, 1.75)	1.01 (0.82, 1.23)	1.11 (0.87, 1.42)	1.17 (0.95, 1.45)
Hispanic	0.79 (0.61, 1.03)	0.95 (0.79, 1.14)	0.77 (0.57, 1.05)	0.92 (0.74, 1.14)
Non-Hispanic Asian	0.94 (0.67, 1.32)	0.88 (0.69, 1.13)	1.08 (0.74, 1.58)	1.01 (0.76, 1.35)
Other	1.08 (0.73, 1.59)	1.04 (0.77, 1.40)	1.07 (0.72, 1.57)	0.69* (0.51, 0.94)
Education
High school or less	1.10 (0.93, 1.31)	1.17** (1.04, 1.32)	0.93 (0.76, 1.15)	0.92 (0.79, 1.07)
Some college	1.23* (1.02,1.48)	1.10 (0.97, 1.26)	1.04 (0.85, 1.26)	1.07 (0.92, 1.25)
Bachelor’s degree + (ref)	—	—	—	—
Marital status
Married (ref.)	—	—	—	—
Cohabiting	0.91 (0.62, 1.34)	1.13 (0.89, 1.43)	1.09 (0.80, 1.49)	1.11 (0.88, 1.40)
Never married	0.75* (0.57, 0.99)	0.78*** (0.65, 0.92)	0.72*** (0.57, 0.90)	0.78*** (0.66, 0.93)
Separated/divorced/widowed	1.46* (1.07, 1.98)	1.20 (0.96, 1.49)	1.21 (0.94, 1.57)	1.27* (1.04, 1.55)
Intercept	0.07*** (0.05, 0.09)	0.18*** (0.15, 0.21)	0.12*** (0.09, 0.16)	0.33*** (0.27, 0.39)

N = 17,166.

OR = odds ratio. CI = confidence interval. (ref.) indicates the reference category in regression models.

Bold coefficients are statistically significant. *** p < 0.001 ** p < 0.01 * p < 0.05.

For males, perceived alcohol culture was positively and significantly associated with reporting at least 1 day of absenteeism in the past 30 days (OR = 1.29 [95% CI = 1.13, 1.39]) and at least 1 day of presenteeism in the past 30 days (OR = 1.46 [95% CI = 1.36, 1.57]). For every one-unit increase on the perceived alcohol culture measure, we expect to see about 29% increase in the odds of absenteeism and a 46% increase in the odds of presenteeism among male service members.

For females, perceived alcohol culture was positively and significantly associated with reporting at least 1 day of absenteeism in the past 30 days (OR = 1.35 [95% CI = 1.19 1.53]) and at least 1 day of presenteeism in the past 30 days (OR = 1.41 [95% CI = 1.28, 1.55]). For every one-unit increase on the perceived alcohol culture measure, we expect to see about 35% increase in the odds of absenteeism and a 41% increase in the odds of presenteeism among female service members.

Discussion

This study has examined how service members’ beliefs about the culture of alcohol use in the military are associated with both their behavior surrounding alcohol use and the negative consequences of unhealthy use. The analysis found that perceptions of the military drinking culture, specifically the belief that drinking is expected and supported by leadership, are positively associated with not only engaging in more hazardous types of use (i.e., binge and heavy drinking) but also negative outcomes related to alcohol use. Ultimately, such perceptions present a threat to force readiness because of their associations with these behaviors and outcomes. Further, perceptions of alcohol use and drinking behaviors established during service may have continued negative consequences for some service members after leaving the military (Norman et al., 2014), which suggests that alcohol abuse problems in this population may be borne by the Department of Veterans Affairs and the U.S. health care system.

Measuring the culture of alcohol use in any community is not straightforward. How one views their own community’s drinking culture may or may not reflect their actual behavior, or even be a view that is widely shared. Indeed, studies of college students have found that they frequently overestimate the prevalence of excessive alcohol use among their peers, leading to misperceptions and false expectations about normative drinking behavior (Dumas et al., 2019; Utpala-Kumar & Deane, 2012). Nonetheless, existing research suggests that one’s own perception of cultural norms is an important predictor of one’s own behavior. For example, Cox et al. (2019) found that when first-year college students overestimated their peer’s heavy alcohol use (i.e., binge drinking), they were more likely to drink heavily themselves. Thus, the measure of the alcohol culture used in this study focused on service members’ subjective views on alcohol in terms of social connectedness and “fitting in,” expectations about use, and leadership’s implicit or explicit support of excessive use rather than a more objective measures that account for the actual behavior of fellow community members.

Nonetheless, we cannot rule out that service members who use alcohol in an unhealthy manner frame their perceptions of the alcohol culture in such a way that it makes their own behaviors more acceptable. With cross-sectional data, it is not possible to rule out reverse causality. Similarly, it is also possible that service members self-select into peer groups whose views and behaviors related to alcohol use more closely resemble their own. However, it is important to keep in mind that, unlike college students, who are free to selectively interact with peers and join groups that share similar attitudes and behaviors toward alcohol use, service members are assigned to installations, units, and coworkers based on organizational need. Presumably, this assignment is not associated with a service member’s own drinking attitudes and behaviors. If this is the case, our measure of alcohol culture may allow us to remove some, though certainly not all, of the selection bias that could account for the observed association between perceived alcohol culture and health behaviors.

Threats to readiness pose serious risk to the ability of military members to do their jobs. Excessive alcohol use is clearly one of those risks, in part because it is associated with other negative outcomes such as disciplinary action, legal infractions, accidents, or other health conditions that may prevent service members from deploying. Perhaps less obvious, though, are workplace related consequences, such as those examined in this study (e.g., productivity loss). Because service member perceptions about the military culture surrounding alcohol use are also predictors of negative workplace consequences, they, too, are potential threat to readiness.

Ultimately, it is important to keep in mind that only 11-12% of the sample endorsed 2 or more of the pro-alcohol culture items. Clearly, more than culture, at least as defined here, is responsible for these behaviors. It is also important to keep in mind that there can be positive aspects to collective alcohol consumption, namely, in the form of group bonding and social cohesion. The key emphasis, however, is responsible alcohol use. This may take reshaping culture norms in the military.

How might the findings reported here be used to change cultural perceptions surrounding alcohol use? First, there is a place for leadership to actively intervene and combat the perception that they approve of or endorse excessive alcohol use. A qualitative study by Poehlman and colleagues (2011) found that some sailors and Marines perceive inconsistent messages and application of rules and regulations about alcohol by leadership, especially unit commanders and non-commissioned officers (NCOs). Second, assess how service members perceive the drinking behavior of their peers and target misperceptions about peers’ use of alcohol, especially binge and heavy drinking behaviors. Third, actively create a culture that explains, supports, and encourages responsible use of alcohol. NCOs in the aforementioned qualitative study by Poehlman and colleagues noted that because they are not allowed to fraternize with their subordinates, they are not in a position to model acceptable drinking behavior nor monitor alcohol use to prevent problematic use. Without a direct mechanism, DoD and the Services must consider other methods to get the message out (e.g. information campaigns like “Own Your Limits”). Finally, creating an environment where responsible drinking is the norm may require changes to the areas both inside and outside the gate. Off-installation, bars and restaurants target service members with special pricing on alcohol (Beese et al., 2018). Inside the installation, alcohol is easy to access and offered at reduced prices (via its tax-free status) at exchanges, and, at the time of writing, some commissaries (U.S. Department of Defense, 2005). Reducing the availability and incentives to purchase large quantities of inexpensive alcohol may also send a signal that excessive use is not an acceptable behavior.

Limitations

Due to the cross-sectional nature of the data, we are not able to establish causality. We do not know if a service member’s view of the culture of alcohol in the military caused him or her to engage in binge or heavy drinking or to experience any negative consequences related to use. Second, like many military surveys, response rates to the 2018 HRBS were low (9.6%). Though this does not necessarily indicate that the survey findings are flawed, it increases the likelihood of response bias. Third, as with any survey that asks about sensitive health behaviors, respondents may have responded in a socially desirable way. That is, they may have underestimated the amount of alcohol they consume and/or any negative consequences associated with their drinking behaviors out of shame or guilt, or to fall more into line with what they believe is socially acceptable behavior. Conversely, some respondents may have inflated their ratings of cultural acceptance of alcohol use to reflect their own behaviors.

Finally, several limitations are associated with the measure of alcohol culture used here. The measure of alcohol culture in the HRBS has not been psychometrically validated. For this reason, the culture items are treated in this analysis as a summary index, rather than a scale. Although the measure of “alcohol culture” used in this analysis is grounded in anthropological, sociological, and public health aspects of culture, in other disciplines it may more closely resemble a “climate” or an “environment.” Future research should further explore alternate measures of alcohol culture in the military (see Savic et al., 2016), how culture may vary by subgroup (e.g., gender, age, occupation, and deployment status), and how these different measures may be associated with service member health behaviors. Ultimately, more qualitative work in this area is needed to fully understand the many nuanced reasons for why service members engage in unhealthy alcohol use, and how to best develop policies and programs that address them.

Conclusion

Alcohol use is very common among service members, and unfortunately, in some cases use turns into abuse and other negative consequences of unhealthy use. Why such deleterious behaviors continue despite general awareness of the problem remains unclear. One possible culprit is the persistence of a military-specific culture that subtly encourages or even expects excessive alcohol use. Given the strong positive association between the culture of alcohol and use, abuse, and negative behaviors, changing that culture, and the way service members view alcohol use, may be one avenue for the military to reduce alcohol use to responsible levels.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Office of the Secretary of Defense.

ORCID iD

Sarah O. Meadows

Notes

Author Biographies

Dr. Sarah O. Meadows is Senior Sociologist. Her research focuses on the health and well-being of civilian and military families with a focus on readiness and resilience. Dr. Meadows has expertise in both quantitative and qualitative method, with a particular focus on survey methods.

Robin Beckman, M.PH., is a research programmer with substantive expertise in health and health care research.

Dr. Charles C. Engel is a psychiatrist, clinical epidemiologist, and health services researcher. His research has focused on health system strategies for mitigating the chronic mental and physical health effects of war, terrorism, and disasters.

Dr. Diana D. Jeffery was the Project Officer for the 2011, 2015, and 2018 Health Related Behavior Surveys. A health psychologist, she retired from federal service in 2020 after 12 years at the National Cancer Institute, and 11 years at the for the Department of Defense, Defense Health Agency.

References

Ally

A. K.

Lovatt

Meier

P. S

Meier

A. S.

Brennan

Holmes

(2016). Developing a social practice-based typology of British drinking culture in 2009-2011: Implications for alcohol policy analysis. Addiction, 111(9), 1568–1579. https://doi.org/10.1111/add.13397

Ames

G. M.

Cunradi

(2004). Alcohol use and preventing alcohol-related problems among young adults in the military. Alcohol Research & Health, 28(4), 252–257.

Ames

G. M.

Duke

M. R.

Moore

R. S.

Cunradi

C. B

(2009). The impact of occupational culture on drinking behavior of young adults in the U.S. Navy. Journal of Mixed Methods Research, 3(2), 129–150. https://doi.org/10.1177/1558689808328534

Armed Forces Health Surveillance Branch . (2011). Alcohol-related Diagnoses, Active Component, U.S. Armed Forces, 2001-2010. Military Surveillance Monthly Report, 18(10), 9–13.

Beese

Toomey

T. L.

Hunt

Lenk

K. M.

Widome

Nelson

T. F

(2018). How soldiers perceive the drinking environment in communities near military installations. Journal of Alcohol and Drug Education, 62(1), 71–90.

Borges

Bagge

C. L.

Cherpitel

C. J.

Conner

K. R.

Orozco

Rossow

(2017). A meta-analysis of acute use of alcohol and the risk of suicide attempt. Psychological Medicine, 47(5), 947–949. https://doi.org/10.1017/S0033291716002841

Breslau

Setodji

C. M.

Vaughan

C. A.

(2016). Is cohesion within military units associated with post-deployment behavioral and mental health outcomes? Journal of Affective Disorders, 198(July), 102–107. https://doi.org/10.1016/j.jad.2016.03.053

Browne

Iversen

Hull

Workman

Barker

Horn

Jones

Murphy

Greenberg

Rona

Hotopf

Wessely

Fear

N. T.

(2008). How do experiences in Iraq affect alcohol use among male UK armed forces personnel? Journal of Occupational and Environmental Medicine, 65(9), 628–633. https://doi.org/10.1136/oem.2007.036830

Buvik

(2020). It’s time for a drink! Alcohol as an investment in the work environment. Drugs: Education, Prevention, and Policy, 27(1), 86–91. https://doi.org/10.1080/09687637.2019.1570082

10.

Cafferky

B. M.

Mendez

Anderson

J. R.

Stith

S. M

(2018). Substance use and intimate partner violence: A meta-analytic review. Psychology of Violence, 8(1), 110–131. https://doi.org/10.1037/vio0000074

11.

Cherpitel

C. J.

(2007). Alcohol and injuries: A review of international emergency room studies since 1995. Drug and Alcohol Review, 26(2), 201–214. https://doi.org/10.1080/09595230601146686

12.

Chow

G. M.

Bird

M. D.

Soendergaard

Yang

(2019). How coaches can prevent and address alcohol consumption among student-athletes. Journal of Clinical Sport Psychology, 13(4), 524–542. https://doi.org/10.1123/jcsp.2018-0083

13.

Cox

M. J.

DiBello

A. M.

Miesel

M. K.

Ott

M. Q.

Kenney

S. R.

Clark

M. A.

Barnette

N. P.

(2019). Do misperceptions of peer drinking influence personal drinking behavior? Results from a complete social network of first-year college students. Psychology of Addictive Behaviors, 33(3), 297–303. https://doi.org/10.1037/adb0000455

14.

d’Abbs

(2015). Reform and resistance: Exploring the interplay of alcohol policies with drinking cultures and drinking practices. Contemporary Drug Problems, 42(2), 118–129. https://doi.org/10.1177/0091450915587535

15.

Dall

T. M.

Zhang

Chen

Y. J.

Askarinam Wagner

R. C.

Hogan

P. F.

Fagan

N. K.

Olaia

S. T

Tornberg

D.N

(2007). Cost associated with being overweight and with obesity, high alcohol consumption, and tobacco use within the Military Health System’s TRICARE Prime–Enrolled population. American Journal of Health Promotion, 22(2), 120–143. https://doi.org/10.4278/0890-1171-22.2.120

16.

Debell

Fear

N. T.

Head

Batt-Rawden

Greenberg

Wessley

Goodwin

(2014). A systematic review of the comorbidity between PTSD and alcohol misuse. Social Psychiatry and Psychiatric Epidemiology, 49(9), 1401–1425. https://doi.org/10.1007/s00127-014-0855-7

17.

Dumas

T. M.

Davis

J. P.

Neighbors

(2019). How much does your peer group really drink? Examining the relative impact of overestimation, actual group drinking and perceived campus norms on university students’ heavy alcohol use. Addictive Behaviors, 90(March), 409–414. https://doi.org/10.1016/j.addbeh.2018.11.041

18.

DuPreez

Sundin

Wessely

Fear

N. T.

(2012). Unit cohesion and mental health in the UK armed forces. Occupational Medicine, 62(1), 47–53. https://doi.org/10.1093/occmed/kqr151

19.

Giancola

P. R.

Levinson

C. A.

Corman

M. D.

Godlaski

A. J.

Morris

D. H.

Phillips

J. P

Holt

J. C. D.

(2009). Men and women, alcohol, and aggression. Experimental and Clinical Psychopharmacology, 17(3), 154–164. https://doi.org/10.1037/a0016385

20.

Grant

B. F.

Goldstein

R. B.

Saha

T. D.

Chou

Jung

Zhang

Pickering

R. P.

Ruan

Smith

S. M.

Huang

Hasin

D. S.

(2015). Epidemiology of DSM-5 Alcohol Use Disorder: Results from the National Epidemiologic Survey and Related Conditions III. JAMA Psychiatry, 72(8), 757–766. https://doi.org/10.1001/jamapsychiatry.2015.0584

21.

Greenfield

T. K.

Karriker-Jaffe

K. J.

Kaplan

L. M.

Kerr

W. C.

Wilsnack

S. C.

(2015). Trends in alcohol arms to others (AHTO) and co-occurrence of family-related AHTO: The four US National Alcohol Surveys, 2000-2015. Substance Abuse Research and Treatment, 9(S2), 23–31. https://doi.org/10.4137/SART.S23505

22.

Harwood

H. J.

Zhang

Dall

T. M.

Olaiya

S. T.

Fagan

N. K

(2009). Economic implications of reduced binge drinking among the Military Health System’s TRICARE Prime Plan beneficiaries. Military Medicine, 174(7), 728–736. https://doi.org/10.7205/milmed-d-03-9008

23.

Hawn

S. E.

Bountress

K. E.

Sheerin

C. M.

Dick

D. M.

Amstadter

A. B.

(2020a). Trauma-related drinking to cope: A novel approach to the self-medication model. Psychology of Addictive Behaviors, 34(3), 465–476. https://doi.org/10.1037/adb0000552

24.

Hawn

S. E.

Cusack

S. E.

Amstadter

A. B.

(2020b). A systematic review of the self-medication hypothesis in the context of posttraumatic stress disorder and comorbid problematic alcohol use. Journal of Traumatic Stress, 33(5), 699–708. https://doi.org/10.1002/jts.22521

25.

Hennell

Piacentini

Limmer

(2020). Exploring health beahviours: Understanding drinking practice using the lens of practice theory. Sociology of Health & Illness, 42(3), 627–642. https://doi.org/10.1111/1467-9566.13051

26.

Hood

K. B.

Pollack

L. M.

Jackson

D. D.

Boyer

C. B.

(2020). Associations among behavioral risk, sociodemographic identifiers, and sexually transmitted infections in male and female Army enlisted personnel. Military Medicine. https://doi.org/10.1093/milmed/usaa247

27.

Institute of Medicine (2013). Substance use disorders in the U.S. Armed Forces. Washington, DC: The National Academies Press. https://doi.org/10.17226/13441

28.

Kreiger

Young

C. M.

Anthenien

A. M.

Neighbors

(2018). The epidemiology of binge drinking among college-age individuals in the United States. Alcohol Research: Current Reviews, 39(1), 23–30.

29.

Lorenz

Ullman

S. E.

(2016). Alcohol and sexual assault victimization: Research findings and future directions. Aggression and Violent Behavior, 16(November-December), 82–84. https://doi.org/10.1016/j.avb.2016.08.001

30.

Martin

J. L.

Zamboanga

B. L.

(2018). Putting multisite college alcohol research into context: A call to assess the drinking culture on college campuses. Addictive Behaviors, 77(February), 291–292. https://doi.org/10.1016/j.addbeh.2017.03.019

31.

Meadows

S. O.

Engel

C. C.

Collins

R. L.

Beckman

R. L.

Breslau

Bloom

E. L.

Dunbar

M. S.

Gilbert

Grant

D. M.

Hawes-Dawson

Holliday

S. B.

MacCarthy

Pedersen

E. R.

Robbins

M. W.

Rose

A. J.

Ryan

Schell

T. L.

Simmons

(2021). 2018 Department of Defense Health Related Behaviors Survey (HRBS): Results for the Active Component. Santa Monica, CA: RAND Corporation. Retrieved from https://www.rand.org/pubs/research_reports/RR4222.html

32.

Norman

S. B.

Schmied

Larson

G. E.

(2014). Predictors of continued problem drinking and substance use following military discharge. Journal of Studies on Alcohol and Drugs, 75(4), 557–566. https://doi.org/10.15288/jsad.2014.75.557

33.

Pitts

Chow

G. M.

Yang

(2019). Athletes’ perceptions of their head coach’s alcohol management strategies and athlete alcohol use. Addiction Research & Theory, 26(3), 174–182. https://doi.org/10.1080/16066359.2017.1341976

34.

Poehlman

J. A.

Schwerin

M. J.

Pemberton

M. R.

Isenberg

Lane

M. E.

Aspinwall

(2011). Socio- cultural factors that foster use and abuse of alcohol among a sample of enlisted personnel at four Navy and Marine Corps installations. Military Medicine, 176(4), 397–401. https://doi.org/10.7205/milmed-d-10-00240

35.

Raninen

J. R.

Livingston

(2020). The theory of collectivity of drinking cultures: How alcohol became everyone’s problem. Addiction Classics, 115(9), 1773–1776. https://doi.org/10.1111/add.15057

36.

Rehm

Gmel

G. E.

Gmel

Hasan

O. S. M.

Imtiaz

Popova

Probst

Roerecke

Room

Samokhvalov

A. V.

Shield

K. D.

Shuper

P. A.

(2017). The relationship between different dimensions of alcohol use and burden of disease – an update. Addiction, 112(6), 968–1001. https://doi.org/10.1111/add.13757

37.

Room

Callinan

Dietze

(2016). Influences on the drinking of heavier drinkers: Interactional realities in seeking to ‘change drinking cultures. Drug and Alcohol Review, 35(1), 13–21. https://doi.org/10.1111/dar.12283

38.

Savic

Room

Mugavin

Pennay

Livingston

(2016). Defining ‘drinking culture’: A critical review of its meaning and connotation in social research on alcohol problems. Drugs: Education, Prevention and Policy, 23(4), 270–282. https://doi.org/10.3109/09687637.2016.1153602

39.

Schumm

J. A.

Chard

K. M

(2012). Alcohol and stress in the military. Alcohol Research, 24(4), 401–407.

40.

Scott-Sheldon

L. A.

Carey

K. B.

Cunningham

Johnson

B. T.

Carey

M. P

The MASH Research Team . (2016). Alcohol use predicts sexual decision-making: A systematic review and meta-analysis of the experimental literature. AIDS and Behavior, 10(0–1), 19–39. https://doi.org/10.1007/s10461-015-1108-9

41.

Self

A. R.

Oetting

A. A.

Clausen

S. S.

Stahlman

(2020). Alcohol-related emergency department visits, hospitalizations, and co-occurring injuries, active component, U.S. Armed Forces, 2009-2018. Military Surveillance Monthly Report, 27(7), 7–14.

42.

Skog

O. J.

(1985). The collectivity of drinking culture: A theory of the distribution of alcohol culture. British Journal of Addiction, 80(1), 83–99. https://doi.org/10.1111/j.1360-0443.1985.tb05294.x

43.

Substance Abuse and Mental Health Services Administration . (2019). Key substance use and mental health indicators in the United States: Results from the 2018 National survey on Drug use and health. https://nsduhweb.rti.org/respweb/homepage.cfm

44.

Sudhinaraset

Wiglewsorth

Takeuchi

(2017). Social and cultural contexts on alcohol use: Influences in a social-ecological framework. Alcohol Research: Current Reviews, 38(1), 35–45.

45.

Task Force of the National Advisory Council on Alcohol Abuse and Alcoholism . (2002). A call to action: Changing the culture of drinking at U.S. Colleges. National Institute on Alcohol and Alcoholism. https://www.collegedrinkingprevention.gov/media/taskforcereport.pdf

46.

Taylor

Irving

H. M.

Kanteres

Room

Borges

Cherpitel

C. J.

Bond

Greenfield

Rehm

(2010). The more you drink the harder you fall: A systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. Drug and Alcohol Dependence, 110(1–2), 108-116. https://doi.org/10.1016/j.drugalcdep.2010.02.011

47.

Thørrisen

M. M

Bonsaken

Hashemi

Kjeken

van Mechelen

Wågø Aas

(2019). Association between alcohol consumption and impaired work performance (presenteeism): A systematic review. BMJ Open, 9, 1–15. https://doi.org/10.1136/bmjopen-2019-029184

48.

U.S. Department of Health and Human Services . (2021). Healthy People 2030. https://health.gov/healthypeople

49.

U.SDepartment of Defense . (2005). Armed services exchange regulations. Department of Defense Instruction (DoDI) 1330.21.

50.

Utpala-Kumar

Deane

F. P.

(2012). Heavy episodic drinking among university students: Drinking status and perceived normative comparisons. Substance Use & Misuse, 47(3), 278–285. https://dx-doi-org.web.bisu.edu.cn/10.3109/10826084.2011.636134

51.

Vasilenko

S. A.

Evans-Polce

R. J.

Lanza

S. T.

(2017). Age trends in rates of substance use disorders across ages 18-90: Difference by gender and race/ethnicity. Drug and Alcohol Dependence, 180(November), 260–264. https://doi.org/10.1016/j.drugalcdep.2017.08.027

52.

Vella

L.D.

Cameron-Smith

(2010). Alcohol, athletic performance, and recovery. Nutrients, 2(8), 781–789. https://doi.org/10.3390/nu2080781

53.

Wilsnack

R. W.

Wilsnack

S. C.

Gmel

Kantor

L. W.

(2018). Gender differences in binge drinking: Prevalence, predictors, and consequences. Alcohol Research: Current Reviews, 39(1), 57–76.

54.

Witkiewitz

Vowles

K. E.

(2018). Alcohol and opioid use, co-use, and chronic pain in the context of the opioid epidemic: A critical review. Alcoholism: Clinical and Experimental Research, 42(3), 478–488.

55.

Wong

Meadows

S. O.

Schell

T. L.

Chan

W. Y.

Jaycox

L. H.

Osilla

K. C.

Schuler

M. S.

Roth

(2021). The Behavioral Health of Minority Active Duty Service Members. Santa Monica, CA: RAND Corporation. Retrieved from https://www.rand.org/pubs/research_reports/RR4247.html

56.

Zale

E.L.

Maisto

S.A.

Ditre

J. W.

(2015). Interrelations between pain and alcohol: An integrative review. Clinical Psychology Review, 37(April), 57–71. https://doi.org/10.1016/j.cpr.2015.02.005