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. Author manuscript; available in PMC: 2019 Mar 1.
Published in final edited form as: Addict Behav. 2017 Oct 26;78:22–29. doi: 10.1016/j.addbeh.2017.10.022

An Examination of Heavy Drinking, Depressed Mood, Drinking Related Constructs, and Consequences among High-Risk College Students using a Person-Centered Approach

Irene M Geisner 1, Kimberly Mallett 2, Lindsey Varvil-Weld 3, Sarah Ackerman 4, Bradley M Trager 5, Rob Turrisi 6
PMCID: PMC5783735  NIHMSID: NIHMS918531  PMID: 29121529

Abstract

INRODUCTION

Research has identified college students who experience depressed mood and consume alcohol are at an increased risk for experiencing alcohol problems. The present study identified profiles of differential alcohol use, depression, key psychosocial indicators of drinking (e.g., normative perceptions) and examined the relationship between these profiles and alcohol-related consequences.

METHOD

Students with a history of risky drinking and elevated depressed mood (n = 311; 62.4% female) completed a web-based survey assessing typical and peak drinking, depressive symptoms, descriptive norms, drinking to cope motives, protective behavioral strategies, and alcohol-related consequences.

RESULTS

Latent profile analysis was used to classify participants into distinct profiles focusing on alcohol use patterns and level of depressed mood and drinking related constructs. Profiles were then compared based on their association with reported rates of alcohol-related consequences. Four profiles emerged: 1) Mild Depression, Heavy Drinkers; 2) Mild Depression, Severe Drinkers; 3) Moderate Depression, Heavy Drinkers; and 4) Moderate Depression, Severe Drinkers. Findings revealed significant differences between the four profiles on both risky drinking and alcohol-related consequences.

CONCLUSION

These findings suggest the importance of assessing and addressing depressive symptoms among college students in order to reduce rates of risky drinking and alcohol-related consequences.

Keywords: comorbidity, depressed mood, drinking, profile analysis

Introduction

Much has been researched about risky college student drinking, the comorbidity with depressive symptoms, and the increased risk for alcohol-related problems for those struggling with both. National surveys show greater odds ratios of having alcohol use disorders for those struggling with depression (Kessler et al., 1997; Substance Abuse and Mental Health Services Administration [SAMHSA], 2008). Likewise, the odds are also greater for those with alcohol use disorders for experiencing depression (Boden & Fergusson, 2011; Kessler et al., 1997; Substance Abuse and Mental Health Services Administration [SAMHSA], 2008). Students who have struggled with both mood and alcohol problems are most likely to develop long-term issues, their depression is less likely to remit, and they may have less motivation to engage in treatment for either condition (Christiansen, Griffiths, & Jorm, 2004; Merrill, Reid, Carey, & Carey, 2014; Perkins, 1999). Finally, this comorbid population is at higher risk for suicide (Cherpitel, Borges, & Wilcox, 2004; Conner, Bagge, Goldston, & Ilgen, 2014; Nock, Hwang, Sampson, & Kessler, 2010). Thus it is important to study this population and understand how to reduce harm.

While previous research examined relationships between depressive symptoms, drinking, and consequences, the simultaneous influence of additional key constructs is not well understood. Specifically, coping motives, protective behavioral strategies and normative influences are significant correlates of risky drinking and related problems; however, the examination of the relationship of these constructs with depressed mood, alcohol consumption and consequences has been limited. Previous work in this area has been primarily variable centered (Paljarvi et al., 2009) and has focused on a limited number of constructs at a time (i.e., 1–2). Alternatively, person-centered approaches such as Latent Profile Analysis allows for the simultaneous examination of the relationship between multiple indicators in order to identify similar patterns and groups of individuals that may be at a particular risk (Mallett et al., 2015; Scaglione, et al., 2015; Varvil-Weld, Mallett, Turrisi, & Abar, 2012; Varvil-Weld, Mallett, Turrisi, Cleveland, & Abar, 2013). These profiles also offer insight into key variables that influence behaviors of interest (i.e., risky drinking, experiencing harmful consequences, etc.) and may identify high-risk groups and/or constructs as ideal targets for intervention efforts (e.g., Mallett, Marzell, Scaglione, Hultgren, & Turrisi, 2014; Reavy et al., 2016). Previous studies using a LPA framework have not accounted for depressed mood symptoms in their examination of risky drinking and alcohol-related problems, though some (Lau-Barraco, C., Linden-Carmichael, Braitman, & Stamates, 2016) did investigate differences between profile memberships on mental health symptoms finding that people who endorsed a lot of situations for drinking reported significantly more mental health symptoms. Considering that students who report increased depressive symptoms also experience more alcohol-related problems, and that mood disorders and risky drinking are prevalent in the college population, research understanding these patterns of behavior is warranted.

Drinking Motives, Protective Behavioral Strategies, and Normative Perceptions

Motives

College students drink for different reasons, including social, enhancement, and coping (Carey & Correia, 1997; Cooper, Frone, Russell, & Mudar, 1995; Kuntsche, Knibbe, Gmel, & Engels, 2005). Those who are light or moderate social drinkers experience fewer drinking related consequences and report fewer mood issues (Geisner, Mallett, & Kilmer, 2012; Stewart, Loughlin, & Rhyno, 2001). However, those who drink for coping motives are more likely to experience depressive symptoms and alcohol-related problems (Hussong, Galloway, & Feagans, 2005; Hussong, Hicks, Levy, & Curran, 2001; Merrill & Read, 2010).

PBS

Protective behavioral strategies (PBS) consist of a variety of behaviors used to limit the amount of alcohol consumed and/or reduce harm related to drinking (i.e., use of taxi, designated driver, etc.). PBS are a central element of efficacious college student interventions (i.e., BASICS: Dimeff, Baer, Kivlahan, & Marlatt, 1999; Parent-Based Intervention: Turrisi, Jaccard, Taki, Dunnam, & Grimes, 2001) and have been studied extensively in relation to risky drinking and related problems (e.g., LaBrie, Lac, Kenney, & Mirza, 2011; Mallett et al., 2015; Martens, Ferrier, & Cimini, 2007; Varvil-Weld et al., 2013). The use of PBS has been found to decrease risky drinking among college students (Prince, Carey, & Maisto, 2013) and directly influence alcohol-related consequences (Benton et al., 2004; Mallett et al., 2015). Few studies to date have examined how use of protective behavioral strategies may be different for students with comorbid depressive symptoms and heavy drinking compared to heavy drinking alone (Martens, Neighbors, Lewis, Lee, Oster-Aaland, & Larimer, 2008a).

Norms

Descriptive drinking norms, or students’ perceptions of how much alcohol those around them (typical students, friends) are drinking, have been shown to relate to students’ own drinking behaviors (Borsari & Carey, 2001; Neighbors et al., 2007; Perkins, 2002). In addition, normative misperceptions are common for college students and relate to additional alcohol-related consequences (Borsari & Carey, 2003; Lewis & Neighbors, 2004). Understanding how normative perceptions relate to risk profiles will permit further refinement and targeting of intervention efforts.

While motives, protective behavioral strategies and normative perceptions are central to understanding college student drinking more generally, moving beyond simple relationships will enable for more nuanced and earlier prevention and intervention. Depressed students may be drinking for more coping motives to alleviate/self-medicate their distress. This may lead to increased “helplessness” which may inhibit use of strategies by those who may not use as many protective strategies or try to avoid problems (i.e., not care what happens to them when they drink). In addition, their depressed mood may lead them to misperceiving normative behaviors (Geisner, Kirk, Mittmann, Kilmer, & Larimer 2015). These (less use of PBS and higher normative perceptions) in turn may lead to more drinking and consequences. Thus, the potential overlap of these constructs needs to be better understood as in fact, they may have more of an impact on problematic drinking than when considered individually and may spiral and exacerbate each other, further increasing risk.

Current Study

To gain a better understanding of comorbidity between depressive symptoms and risky drinking, the present study has two aims. The first is to identify distinct comorbidity risk profiles utilizing a person-centered approach that incorporates depressive symptoms, drinking motives, protective behavioral strategies, normative perceptions, and alcohol use (e.g., typical and peak drinking). Research has demonstrated a positive association between coping motives and more alcohol-related problems among students (Merrill & Read, 2010; Merrill, Wardell, & Read, 2014). Therefore, we hypothesized a risky profile to emerge that included individuals who reported heavier drinking patterns, higher proportions of depressive symptoms, higher coping motives to drink, the use of fewer protective behavioral strategies, and higher normative drinking perceptions.

The second aim of the study was to compare comorbidity risk profiles on the amount and type (i.e., physical, legal, academic, sexual) of alcohol-related consequences reported by the sample. First, we hypothesized the riskiest profiles (i.e., highest rates of depressive symptoms and alcohol use, high coping motives, low use of PBS, high normative perceptions) would report the highest rates of consequences consistent with the literature, reporting a positive relationship between comorbidity and increased rates of alcohol-related problems (Christiansen et al., 2004; Perkins, 1999). Further, we examined if certain profiles that emerged were more prone to experience specific types of consequences (physical, legal, academic, sexual) in order to identify specific types of risks.

Methods

Sample Characteristics

The final sample consisted of 311 students (62.4% female; mean age 20.13 [SD=1.34]) from a large, public university in the Pacific Northwest of the United States. With respect to race, 59.7% of the sample identified as white or Caucasian, 19.4% identified as Asian or Pacific Islander, 1.2% identified as black or African American, 8.4% identified as multiracial, less than 1% identified as Native American, and the remaining proportion of the sample were of some other racial background. Distinct from race, 7.8% of the sample reported being of Hispanic/Latino ethnicity.

Procedures

Details on the procedures on the study are detailed elsewhere (Geisner, Varvil-Weld, Mittmann, Mallett, & Turrisi, 2015b). Briefly, participants were recruited between fall 2011 and winter of 2013 through email invitations to log on to the study website, view an online consent form and complete a brief 15-minute screening survey about their mood and alcohol use, perceptions of depression among other students, and demographic information. Students (N = 2675) participated in the screening. 356 participants (13.3%) meeting criteria for high-risk drinking (consuming 4+/5+ drinks (women/men) and score of 8 or greater on the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, & de la Fuente, 1993)), and elevated depressed mood (score of 14 or greater on the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996)) were directed to the longer baseline survey which assessed further constructs (motives, protective behavioral strategies, etc.). According to Beck et al. (1996), scores 0–13 indicate minimal depression; 14–19 mild depression; 20–28 moderate depression; 29–66 indicate severe depression. The baseline survey was completed by 311 students (89% of those eligible) within 1–2 weeks of the screening.

Baseline Measures

Coping motives

Five items were used to assess students coping drinking motives (Cooper, 1994). Sample items from this measure are “Do you drink to cheer you up when you are in a bad mood” and “Do you drink to forget your worries.” Response options ranged from 0 ‘Never/Almost never’ to 4 ‘Almost always/Always’. Items were summed to produce a composite variable (α = .85).

Protective behaviors

The 17-item version of the Protective Behavioral Strategies Scale was used to assess strategies participants used to drink more safely and responsibly (Lewis, Rees, Logan, Kaysen, & Kilmer, 2010; Martens et al., 2005). Items included statements such as, “When drinking alcohol, you knew where your drink had been at all times” and “You stopped drinking at a pre-determined time”. Response options ranged from 0 ‘Never’ to 4 ‘Always’. Items were summed to create a composite score (α = 79), which is consistent with others who have used the measure in this way (Murphy, Dennhardt, Skidmore, Borsari, Barnett, Colby, & Martens, 2012; Neilson, Gilmore, Pinsky, Shepard, Lewis, & George, 2015; Neighbors, Lee, Lewis, Fossos, & Walter, 2009).

Depression symptoms

The Patient Health Questionnaire-9 (PHQ-9; Spitzer, Kroenke, Williams, & the Patient Health Questionnaire Primary Care Study Group, 1999) is a nine-item measure used for depression screening, and recent efforts to utilize the measure in a college setting for screening, severity monitoring, and detecting change have been documented (Klein & Chung, 2008). Cut off for the PHQ-9 scores can be interpreted as follows: minimal depression 0–4, mild depression 5–9, moderate depression 10–14, moderately severe depression 15–19, severe depression 20–27. Cronbach’s α was .83. The PHQ was administered during the baseline assessment so was thus included in the LPA instead of the BDI used at screening.

Typical weekly drinking

The Daily Drinking Questionnaire was used to assess the number of standard drinks participants consumed in a typical week (DDQ; for reliability and validity see Collins, Parks, & Marlatt, 1985; Dimeff et al., 1999). Participants were asked to provide responses for each of the 7 days. A drink was defined at the beginning of the questionnaire as four ounces of wine, 10 ounces of wine cooler, 12 ounces of beer (eight ounces of Canadian, Malt Liquor, or Ice beer, or 10 ounces of microbrew), or one cocktail with one ounce of 100 proof liquor or one and a quarter ounces of 80 proof liquor.

Drinking norms

Parallel items to the DDQ ask students their perceptions of the drinking habits of a typical student on their campus (Drinking Norms Rating Form; for reliability and validity see Baer, Stacy, & Larimer, 1991). Students were asked to “Consider a typical week during the last month. How much alcohol, on average (measured in number of drinks), do you think a typical student drinks on each day of a typical week?” Again, the definition of a standard drink was provided prior to the questions.

Peak drinking

Participants were asked to report the number of drinks consumed on a peak occasion in the past month (Dimeff et al., 1999). Information about standard drink equivalents was provided.

Alcohol-related consequences

The Rutgers Alcohol Problem Inventory (RAPI; White & Labouvie, 1989) was used to assess alcohol-related consequences. Participants indicated how often they experienced a range of alcohol-related problems in the past year, on a scale from 0 ‘None’ to 3 ‘More than 5 times.’ A total of 22 items from the RAPI were broken into five subscales. Items and alphas for each subscale are listed in Table 1.

Table 1.

RAPI Consequence Subscales

Subscale Items Alpha
Academic α=.79
Not able to do your homework or study for a test?
Went to work orschool high or drunk?
Neglected your responsibilities?
Driving α=.87
Missed a day (or part of a day) of school or work?
Drove shortly after having more than two drinks?
Drove shortly after having more than four drinks?
Loss of Control α=.82
Felt that you needed more alcohol than you used to use in order to get the same effect?
Noticed a change in your personality?
Felt that you had a problem with alcohol?
Tried to cut down or quit drinking?
Kept drinking when you promised yourself not to?
Withdrawal α=.74
Had withdrawal symptoms, that is, felt sick because you stopped or cut down on drinking?
Passed out or fainted suddenly?
Felt physically or psychologically dependent?
Social α=.85
Got into fights, acted bad, or did mean things?
Caused shame or embarrassment to someone?
Relative avoided you?
Had a fight, argument or bad feelings with a friend?
Had a fight, argument or bad feelings with a family member?
Was told by a friend or neighbor to stop or cut down drinking?
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Data Cleaning

Missing data (less than 5% for all variables) were missing at random (MAR; Rubin, 1976) and were imputed using EM methods (Schafer & Graham, 2002). Outliers (i.e., values more than 3.29 standard deviations from the mean) were recoded to 3.29 standard deviations of the mean (Tabachnick & Fidell, 2012). All but one variable met the aforementioned criteria for outlier adjustment (protective behavioral strategies), and outliers accounted for less than 5% of responses on all variables, Both EM and outlier adjustments were completed prior to any subsequent analyses.

Analysis Strategy

Latent profile analysis (LPA) was used to achieve the first aim of the study. LPA is a data-driven approach, and the goal is to identify subgroups of individuals who are similar to each other but distinct from other subgroups. LPA analyses were conducted in MPlus (version 8; Muthén & Muthén, 1998–2017). Based on the recommendations of Lanza, Collins, Lemmon, and Schafer (2007), a one-profile solution was first fit to the data, as if all individuals belonged to the same profile. Additional profiles were added to the model one at a time, and fit indices were examined after each addition to determine which number of profiles fit the data best. Indices included the Akaike Information Criteria (AIC; Akaike, 1974), the Bayesian Information Criteria (BIC; Schwartz, 1978), and log-likelihood values. Smaller numbers indicate better fit for each index. The parametric bootstrapped likelihood ratio test (bLRT) was used to compare solutions with k and k−1 profiles. A significant bLRT indicates the k-profile solution offered an improvement in fit. Finally, an entropy value of >.80 suggests good classification quality. After the best-fitting solution was identified, individuals were assigned to their most likely profile based on posterior probabilities. This approach has been used successfully in previous research with LPA, as long as classification quality is acceptable (e.g., Agrawal, Lynskey, Madden, Bucholz, & Heath, 2007; Varvil-Weld et al., 2013). Additional covariate analyses were conducted to examine whether race and gender were associated with profile membership.

The second aim of the study was to assess whether the profiles identified in the first aim were associated with different levels of alcohol-related consequences. The automatic BCH approach in Mplus was used to examine the mean differences in consequences across the profiles (Asparouhov & Muthén, 2014; Bakk & Vermunt, 2014). The BCH approach is preferred over alternative classify and analyze approaches (e.g., ANOVA) because it calculates the profiles and conducts the mean differences in the same analytic model, uses weights, robust standard errors, and a maximum likelihood approach (see Bakk & Vermunt, 2014). Class specific means are then estimated, and the differences between the means are evaluated using Wald tests (Bakk & Vermunt, 2016).

Results

Aim 1: LPA

Model fit indices are shown in Table 2. The decreases in AIC, BIC, and log-likelihood values leveled off at the addition of the fifth profile to the model. Further, the five-profile solution included profiles of less than 10 individuals, making interpretation and additional analyses difficult. Thus the four-profile solution was retained. These four profiles are described in more detail below, in order from most common and lowest risk to least common and highest risk. Figure 1 and Table 3 show the means for each of the 6 indicators by student profile.

Table 2.

LPA model fit indices

Number of Profiles AIC BIC bLRT Entropy Value Entropy Value
1 12407.87 12452.75 – −6191.94 –
2 12195.55 12266.60 226.33* −6078.77 0.93
3 12097.05 12194.28 112.50* −6022.52 0.82
4 12040.01 12163.43 71.03* −5987.01 0.79
5 12025.04 12174.64 28.97* −5972.52 0.84
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Note: AIC = Akaike’s Information Criterion; BIC = Bayesian Information Criterion; bLRT = bootstrapped likelihood ratio test

Note 2:

*

p <.05

Figure 1.

Figure 1

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Total for coping motives, protective behavioral strategies, depressive symptoms, weekly drinking, descriptive drinking norms and peak drinking for each student profile

Table 3.

Means (SE) for depression and alcohol use indicators for the 4-group solution

Latent Depression and Drinking Profiles
Indicators MiDHD
(n = 169)		 MiDSD
(n = 65)		 MoDHD
(n = 61)		 MoDSD
(n = 16)
Depression
 Coping Motives 6.75 (0.45) 8.31 (0.79) 14.80 (0.81) 13.28 (1.30)
 Depression 9.98 (0.40) 9.77 (0.59) 15.76 (1.48) 18.20 (1.66)
Alcohol Use
 Weekly Drinking 11.49 (0.58) 23.92 (1.62) 18.20 (1.54) 43.21 (2.97)
 Peak Drinking 7.58 (0.24) 12.98 (0.77) 9.10 (0.49) 19.58 (1.01)
 Protective Behaviors 30.54 (0.86) 25.42 (1.20) 23.74 (2.24) 18.23 (1.29)
 Norms 16.91 (0.82) 22.56 (1.48) 16.20 (1.42) 23.28 (2.95)
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Note. SE = Standard error. MiDHD = Mild Depression, Heavy Drinkers; MiDSD = Mild Depression, Severe Drinkers; MoDHD = Moderate Depression, Heavy Drinkers; MoDSD = Moderate Depression, Severe Drinkers.

Profile 1 – Mild Depression, Heavy Drinkers

The most common profile in the sample (n=169; 54%) reported moderate levels of drinking and relatively low levels of depressive symptoms. Students in this profile reported drinking 7.6 drinks on average during their peak drinking occasion in the past month, and 11.5 drinks during a typical week. They endorsed the lowest levels of coping motives and highest levels of protective behavioral strategies of any profile in the sample. Their drinking norms were also relatively low.

Profile 2 – Mild Depression, Severe Drinkers

The second profile consisted of 21% of the sample (n=65), and depressive symptoms in this profile were similar to the Mild Depression, Heavy Drinkers. However they had higher levels of peak and weekly drinking (13.0 and 23.9 drinks, respectively). They were higher risk in terms of other drinking behaviors, and had higher coping motives, engaged in fewer protective behavioral strategies, and had higher drinking norms.

Profile 3 – Moderate Depression, Heavy Drinkers

The third profile was 20% of the sample (n=61). This profile reported more moderate drinking (9.1 peak drinks and 18.2 weekly drinks), however students in this profile reported much higher levels of depressive symptoms than students in the first two profiles. Profile 3 students reported moderate levels of protective behavioral strategies and drinking norms. Interestingly, this profile endorsed the highest level of coping motives of any profile in the sample.

Profile 4 – Moderate Depression, Severe Drinkers

The fourth and final profile was the smallest (n=16; 5%) and highest risk. Students in this profile reported the highest levels of peak and weekly drinking (19.6 and 43.2 drinks, respectively). They also reported the highest levels of depressive symptoms of any profile in the sample. They endorsed high levels of coping motives, engaged in the fewest protective behavioral strategies, and had high drinking norms.

Follow-up covariate analyses indicated there were significant associations between gender and profile membership. Males were at increased odds of belonging to the Moderate Depression, Severe Drinkers profile and to the Mild Depression, Severe Drinkers profile relative to the Mild Depression, Heavy Drinkers profile (B=1.50, p<.05). Conversely, males were at decreased odds of belonging to the Moderate Depression, Heavy Drinkers profile relative to the Mild Depression, Severe Drinkers profile (B=−1.49, p<.05). There were no significant associations between race and profile membership.

Aim 2: Profile Differences in Alcohol-Related Consequences

Table 4 and Figure 2 show the means for each of the consequence subscales by profile. There were significant differences by profile for total consequences and all five of the consequence subscales. For the overall RAPI score, loss of control, withdrawal, and social consequences, all profiles were significantly different from each other. For these outcomes, the Moderate Depression, Severe Drinkers profile experienced the highest level of total consequences, followed by the Moderate Depression, Heavy Drinkers, then Mild Depression, Severe Drinkers, and finally Mild Depression, Heavy Drinkers. This pattern of results varied for academic and driving consequences. In both cases, the Mild Depression, Heavy Drinkers group had the lowest consequences relative to the other groups.

Table 4.

Means (standard error) for each of the consequence subscales by profile

Latent Depression & Drinking Profiles
MiDHD
(n=169)
54.3%		 MiDSD
(n=65)
20.9%		 MoDHD
(n=61)
19.6%		 MoDSD
(n=16)
5.1%		 χ2
Alcohol-Related Consequences 
 RAPI Total 11.55b,c,d
(.73)		 21,45a,c,d
(1.48)		 32.78a,b,d
(2.65)		 49.55a,b,c
(4.68)		 137.30**
 Academic 2.82b,c,d
(.20)		 5.35a,d
(.45)		 6 49a,d
(61)		 9 93a,b,c
(103)		 88.43**
 Driving .34b,c,d
(.07)		 99a
(.20)		 .90a
(.23)		 1.80a
(51)		 20.19**
 Loss of Control 2.62b,c,d
(21)		 5.68a,c,d
(50)		 8.62a,b,d
(.78)		 11.81a,b,c
(1.32)		 116.90**
 Withdrawal .53b,c,d
(10)		 1.26a,c,d
(.22)		 2.76a,b,c
(39)		 5.02a,b,c
(80)		 62.11**
 Social 2.32b,c,d
(.21)		 3.40a,c,d
(.40)		 6 49a,b,d
(.74)		 10.39a,b,c
(1.46)		 56.91**
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Note 1. MiDHD = Mild Depression, Heavy Drinkers; MiDSD = Mild Depression, Severe Drinkers; MoDHD = Moderate Depression, Heavy Drinkers; MoDSD = Moderate Depression, Severe Drinkers.

Note 2. The χ2 represent the overall test results (Wald tests), which were produced using the BCH approach;

**

p < .001.

Note 3.

a

= Mean difference as compared to the MiDHD profile;

b

= Mean difference as compared to the MiDSD profile;

c

= Mean difference as compared to the MoDHD profile;

d

= Mean difference as compared to the MoDSD profile p < 0.05.

Figure 2.

Figure 2

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Mean for each of the RAPI subscales for each student profile

Discussion

The current study identified four unique college student profiles of co-occurring depression and alcohol use, drinking related constructs, and their association with experiencing alcohol-related consequences. Consistent with our initial hypothesis, groups with varying risk emerged. Individuals in the Mild Depression, Heavy Drinkers profile exhibited the least amount of risk behavior by endorsing the lowest coping motives, normative perceptions, drinking rates, and the highest use of protective behavioral strategies. On the other hand, the Moderate Depression, Severe Drinkers exhibited the highest risk reporting high coping motives and normative drinking perceptions, and low use of protective behavioral strategies. Compared to the Mild Depression, Heavy Drinkers, students in this highest-risk profile consumed 2.5 times as many drinks on peak occasions (19.6 vs. 7.6) and nearly 4 times as many drinks during a typical week (43.2 vs. 11.5). Further, findings suggested that normative perceptions were consistently higher in the heavier drinking profiles, but drinking motives and to a lesser degree protective behaviors were higher among moderately depressed profiles. Taken together, these findings highlight the risk associated increased drinking and depression and the need for screening to identify these individuals for early intervention/treatment as well as variables that may be useful to target.

Further, significant relationships between student profiles and alcohol-related consequences were observed. As expected, individuals in the Moderate Depression, Severe Drinkers profile consistently reported the highest rates of consequences across all the subscales. However, one particularly interesting finding that emerged was that individuals in the Moderate Depression, Heavy Drinkers profile endorsed significantly more alcohol problems than Mild Depression, Severe Drinkers across total consequences and 3 of the 5 subscales (loss of control, withdrawal, and social). Individuals in the Mild Depression, Severe Drinkers group drank noticeably more than those in the Moderate Depression, Heavy Drinkers profile (13.0 vs. 9.1 drinks during peak occasions and 23.9 vs. 18.2 drinks per week, respectively); however, alcohol-related problems did not coincide with the higher rates of alcohol consumption. These findings indicate the significant association between depressive symptoms and higher reports of alcohol-related problems, and the need to address both issues when intervening with these individuals. Simply providing a brief alcohol intervention alone may not successfully reduce related problems among individuals experiencing depressive symptoms. Additional screening may allow individuals in need of additional treatment to be identified and appropriately referred for further care.

Notable findings were also observed among the patterns of specific consequence subscales. First, Mild Depression profiles reported fewer consequences than Moderate Depression profiles with the exception of driving related consequences. This suggests driving after drinking is more strongly associated with drinking behaviors alone. In terms of social consequences, Mild Depression profiles were not significantly different from each other and both were associated with the lowest rates of interpersonal problems. Conversely, individuals in the Moderate Depression profiles reported higher rates of social problems suggesting that increased depressive symptoms combined with alcohol may increase the risk of these specific consequences.

Implications

Clinical implications for prevention efforts may be informed by these findings. Past research has shown that a previous history of alcohol problems increases risk for future depression and vice versa (Hasin & Grant, 2002; Sihvola et al., 2008). Further, depression plays a role in experiencing alcohol-related consequences, above and beyond drinking (Martens et al., 2008b). Regardless of the primary problem, drinking, mood symptoms, and related problems clearly form a vicious cycle (deGraaf, Bijl, Spijker, Beekman, & Vollebergh, 2003). In the present study, students in the highest-risk profile (Moderate Depression, Severe Drinkers) had the most depressive symptoms and lowest use of protective behavioral strategies, and those who were moderately depressed scored twice as high on coping motives for drinking despite level of drinking. This finding is quite alarming and associated with both acute and chronic risks. Specifically, these finding suggests that individuals are engaging in self-medication coping strategies and are not utilizing as many protective behaviors, and they may experience immediate harmful consequences as well as problematic and chronic drinking patterns over time. Identifying these high-risk students since they are susceptible to serious and potentially lethal consequences should be done as early as possible. Screening and Brief Intervention with Referral to Treatment (SBIRT) programs would benefit from using profiles such as developed here to identify and help those who are at highest risk.

Thus, the profiles offer targets for intervention efforts. For individuals who were experiencing depression and engaging in risky drinking, targeting coping motives (e.g., correcting perceptions that alcohol improves mood when it actually exacerbates depression), trying to enhance the use of protective behavioral strategies, and correcting normative misperceptions could reduce risky behaviors and drinking consequences. While the entire sample used in this study were at least mildly depressed and heavy drinkers, those who were in the most severe profile, utilized nearly two times less PBS than those in the least severe profile. It is possible that students’ motivation for engaging with strategies, their hopelessness, their decreased care about their own well-being and safety (i.e. aspects of depression) may be inhibiting their use of PBS. Focusing more on components of protective behavioral strategies may be necessary for this group of student, especially for strategies which may be less salient the highest risk group due to their depression (e.g., obtaining social support from others in non-drinking activities). Finally, correcting normative misperceptions not only around drinking, but also about depressed mood (see Geisner, Kirk, Mittman, Kilmer, & Larimer, 2015a) may help high-risk students reduce their problematic behaviors. It is important to note that normative perceptions appeared to be higher for those who endorsed heavier drinking patterns and may be less associated with depressive symptoms. While many programs already exist, developing a stepped-care model utilizing multi variable selection criteria and tailoring the content according to profile variables could help reduce consequences and suffering as well as prevent escalation to more severe disorders.

Limitations & Future Directions

The findings should be considered in light of some limitations when drawing conclusions. The data were collected on one campus and with a moderate sample size which limited generalizability. It should be noted the sample included higher risk students that reported a recent history of depressive symptoms and risky drinking. While this may limit generalizability, we specifically focused our research questions in the hopes of targeting and understanding those with comorbidities so as to minimize harms which are often more severe and potentially fatal. While this was the population of interest, these findings may not generalize to general college populations. Next, the data are cross-sectional and not longitudinal. Future studies that examine transitions into and out of profiles would be helpful to identify students who naturally move into lower risk profiles compared to those who worsen or remain in high-risk profiles across time. This may have implications for the development of chronic patterns of both alcohol consumption and depression. In addition, future studies can utilize the revised version of the PBSS (the PBSS-20) which has increased content validity for the serious harm reduction subscale (Treloar, Martens, McCarthy, 2015) as well as using of the same measure (i.e., PHQ for both rather than BDI for screening and PHQ for baseline) for screening and baseline to avoid measurement issues. Finally, studies are needed that include a large number of schools as well as examining non-college students to replicate and generalize findings.

Conclusion

The current study examined relationships between drinking, depression, and consequences in a sample of college students using a person-centered approach. Four profiles of students were identified with varying levels of depressive symptoms and alcohol consumption, drinking motives, normative perceptions, and use of protective behavioral strategies. Findings revealed significant differences between profiles and rates of consequences indicating individuals in the Moderate Depression profiles reported experiencing more consequences than Mild Depression profiles, despite having similar drinking patterns. Further, the study identified differences between the four profiles on key variables that can be targeted by interventions to reduce risky drinking related behaviors (i.e., drinking motives, normative perceptions, and protective behavioral strategies). These findings suggest the importance of assessing and addressing depressive symptoms among college students in order to reduce rates of risky drinking and alcohol-related consequences.

Highlights.

  • Latent profile analysis was used to classify participants into distinct profiles

  • Four distinct patterns of alcohol use and level of depressed mood emerge

  • Significant differences between profiles and rates of consequences were found

Acknowledgments

Role of Funding Sources

Funding for this study was provided by NIAAA Grant R21AA019993. NIAAA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

This research was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (NIAAA; R21AA019993) awarded to Irene Markman Geisner. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.

Footnotes

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Contributors

Example: Dr. Geisner designed the study and wrote the protocol and edited the manuscript. Dr. Geisner and Dr. Mallett conducted literature searches and provided summaries of previous research studies, outlined and wrote sections of the introduction and discussion. Drs. Varvil-Weld and Trager conducted the statistical analysis and wrote the results section along with consultations from Dr. Turrisi. All authors contributed to and have approved the final manuscript.

Conflict of Interest

All authors declare that they have no conflicts of interest.

Contributor Information

Irene M. Geisner, Center for the Study of Health and Risk Behaviors, Department of Psychiatry and Behavioral Sciences, University of Washington

Kimberly Mallett, Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University.

Lindsey Varvil-Weld, Department of Biobehavioral Health & Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University.

Sarah Ackerman, Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University.

Bradley M. Trager, Department of Biobehavioral Health & Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University

Rob Turrisi, Department of Biobehavioral Health & Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University.

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