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. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Apr 23;211:108027. doi: 10.1016/j.drugalcdep.2020.108027

The Role of Alcohol-Induced Blackouts in Symptoms of Depression among Young Adults

Mary Beth Miller a,c, Angelo M DiBello b,c, Jennifer E Merrill c, Clayton Neighbors d, Kate B Carey c
PMCID: PMC7263566  NIHMSID: NIHMS1587163  PMID: 32354579

Abstract

Objective:

Blackouts are associated with other alcohol-related consequences and depression among young adults, but the mechanisms underlying these associations are unclear. Using two separate samples, we tested the hypothesis that blackouts would be linked to symptoms of depression due in part to their association with other alcohol-related consequences.

Method:

Young adults who use alcohol completed assessments at baseline in Sample 1 (N1=381, 58% female) and baseline, 3 months, and 6 months in Sample 2 (N2=603, 53% female). Bootstrapped confidence intervals were used to examine the direct and indirect effects of blackouts on depressive symptoms, using cross-sectional mediation analysis in Sample 1 and a counterfactual approach with longitudinal data in Sample 2.

Results:

In both samples, alcohol-induced blackouts were associated with alcohol-related consequences, which in turn were associated with symptoms of depression. In Sample 1, blackouts had both direct and indirect (mediated) effects on depressive symptoms. In Sample 2, blackouts measured at baseline only had an indirect effect on depressive symptoms six months later through other alcohol-related consequences at three months.

Conclusions:

Among heavy-drinking college students, the majority of whom reported minimal symptoms of depression, blackouts were associated with increases in other alcohol-related consequences, which in turn were associated with increases in symptoms of depression. These findings suggest that prevention and intervention efforts targeting blackouts may help reduce other alcohol-related consequences among young adults.

Keywords: drinking, depression, alcohol-related consequences, college students, mental health

1. Introduction

Heavy alcohol use is a significant risk factor for death and disability across the globe (Rehm et al., 2017). It is especially prevalent among young adults, with approximately 27% of 18- to 30-year-olds in the United States reporting heavy episodic drinking (5+ drinks on one occasion) in the past two weeks and 11% reporting consumption of 10+ drinks in a row (Patrick et al., 2017). Depressive symptoms are also elevated among young adults, with one in five reporting at least mild symptoms of depression (Kenney et al., 2018) and one in 10 reporting symptoms consistent with major depressive disorder (Dawson et al., 2005; Miller et al., 2017). The combination of heavy drinking behavior and depressive symptoms creates a burden on the public health system, as the United States spends an estimated $224 billion to manage excessive alcohol use (Bouchery et al., 2011) and another $210 billion in costs associated with depression (Greenberg et al., 2015). Because heavy alcohol use is associated with symptoms of depression (Boden and Fergusson, 2011), research examining the mechanisms underlying these associations is needed to inform prevention and intervention efforts.

A number of studies have linked various drinking behaviors to symptoms of depression (Boden and Fergusson, 2011). Among women exposed to traumatic events, earlier initiation of alcohol use has been associated with more symptoms of depression (Berenz et al., 2019). Similarly, in a large survey of adolescents and young adults (14–20 years), individuals reporting greater alcohol use and more alcohol-related consequences (e.g., feeling guilt or remorse about drinking events) had higher odds of screening positive for depression (Patton et al., 2016). An epidemiological cohort study (N=1055) employing cross-lagged longitudinal models also suggests a strong bidirectional association between alcohol use and subsequent symptoms of depression among young adults (17–25 years) (Fergusson et al., 2009).

Despite evidence linking alcohol use to symptoms of depression, some data indicate that alcohol-related problems may be more relevant to the development of depression than drinking quantity alone. For example, Rosenthal and colleagues (2018) found that alcohol-related consequences (as opposed to drinking quantity) predict the onset of depressive symptoms among female college students. Similarly, in the longitudinal study described above (Fergusson et al., 2009), symptoms of alcohol use disorder (rather than alcohol use alone) were associated with symptoms of depression among young adults. These findings suggest something unique about alcohol-related consequences that contributes to depression onset and symptoms. Consistent with cognitive-behavioral theories of substance use (Rotgers, 2013), it is possible that the experience of negative alcohol-related consequences produces a pattern of thoughts and emotions/attitudes that contributes to or exacerbates symptoms of depression. In this case, prevention of high-risk drinking behaviors that lead to alcohol-related consequences (such as blackouts) may be important in the prevention and treatment of depression.

One salient indicator of high-risk drinking – that is also associated with other alcohol-related problems (Hingson et al., 2016; Wilhite and Fromme, 2015) – is alcohol-induced blackout, or the inability to remember events that occurred while drinking. Alcohol-induced blackouts are periods of anterograde amnesia, during which individuals actively engage in behaviors while intoxicated that they later cannot remember, and they occur because the intoxicated brain is no longer able to create long-term memories (Wetherill and Fromme, 2016). Approximately 50% of young adults who drink report a lifetime history of blackout, with approximately 20% reporting a blackout in the past 30 days (Brett et al., 2016; LaBrie et al., 2011; Miller et al., 2018a; Miller et al., 2018b). Blackout experiences are associated with a range of other alcohol-related consequences, including missing class or work, doing something one later regrets, overdosing, and getting in trouble with the police (Hingson et al., 2016; Wilhite and Fromme, 2015). Because they are robust correlates of other alcohol-related consequences, which have been linked to symptoms of depression (Boden and Fergusson, 2011), blackouts may indicate a particularly salient pathway to symptoms of depression among those who drink. Indeed, blackouts have been associated with symptoms of depression in cross-sectional studies of young adults (Voloshyna et al., 2018) and individuals with alcohol use disorder (Neupane and Bramness, 2013). However, blackouts have not been examined as a longitudinal predictor of depressive symptoms, and the extent to which other alcohol-related consequences may contribute to or explain this association is unclear.

1.1. Objective and Hypotheses

This study aimed to advance understanding of the association between alcohol-induced blackouts and symptoms of depression. Consistent with literature linking problematic patterns of alcohol use to symptoms of depression (Boden and Fergusson, 2011), we hypothesized that the frequency of alcohol-induced blackouts would be associated with symptoms of depression. Moreover, we hypothesized that this association would be explained, at least in part, by the association between blackouts and other alcohol-related consequences. Specifically, because blackouts indicate intoxication to the point of experiencing alcohol-induced amnesia, we hypothesized that blackouts would be associated with other alcohol-related consequences (e.g., taking foolish risks, saying/doing embarrassing things), which in turn would be associated with symptoms of depression. Data were derived from two separate studies, one cross-sectional and one longitudinal, allowing us to test hypotheses both concurrently and over time. College students were chosen as a population of interest based on the high rates of heavy drinking, alcohol-induced blackouts, and symptoms of depression among young adults in college (Carter et al., 2010; Dawson et al., 2005; Wetherill and Fromme, 2016).

2. Materials and Methods

2.1. Participants and Procedure

Procedures in both studies were approved by the relevant Institutional Review Board. Sample 1 data were derived from a larger online assessment study examining alcohol-induced blackouts (Miller et al., 2018c). College students who reported an inability to remember events while drinking in the past 12 months were recruited via Qualtrics survey panels. Of the 521 individuals who completed the screening survey, 402 met screening criteria and completed the online survey. Of those, 18 were missing >70% data and 3 indicated random responding. Thus, the final sample included 381 participants (see Table 1 for sample characteristics).

Table 1.

Demographic characteristics of participants in Sample 1 (N=381) and Sample 2 (N=603).

Sample 1 (N=381) Sample 2 (N=603)
n (%) n (%)
Female sex 219 (58%) 318 (53%)
Race1 --- ---
 White/Caucasian 278 (73%) 238 (39%)
 Black/African American 45 (12%) 34 (6%)
 Asian 41 (11%) 165 (2’%)
 Native American or Native Alaskan 6 (2%) 4 (<1%)
 Native Hawaiian or Pacific Islander 1 (<1%) 3 (<1%)
 Other / Mixed / International 25 (7%) 54 (9%)
 Missing 0 (0%) 11 (2%)
 Hispanic/Latinx 67 (18%) 11 (20%)
Intervention condition N/A 401 (67%)
Blackout occurrence (past year in Sample 1; past month in Sample 2) 345 (91%) 336 (56%)
Positive depression screen 138 (36%) 252 (42%)
M (SD) M (SD)
Age 21.8 (2.8) 21.1 (2.1)
BL drinks per week 11.4 (10.4) 8.4 (7.2)
BL blackout frequency (past year in Sample 1; past month in Sample 2)2 1.5 (10) 0.7 (0.7)
BL other alcohol consequences (past month)3 8.7 (5.7) 5.2 (4.1)
M3 other alcohol consequences (past month) N/A 4.3 (4.4)
M6 other alcohol consequences (past month) N/A 4.1 (4.7)
BL depressive symptoms (past month) 7.7 (6.3) 19.5 (6.9)
M3 depressive symptoms (past month) N/A 17.7 (7.3)
M6 depressive symptoms (past month) N/A 18.1 (7.9)
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Note.

1

Categories were not mutually exclusive.

2

Response options (0) never, (1) less than monthly, (2) monthly, (3) weekly, and (4) daily or almost daily. BL = baseline. M3 = month three.

3

Blackouts item removed from total score. M6 = month six. N/A = not applicable.

Sample 2 data were derived from a larger alcohol intervention trial conducted at two public, four-year universities. Undergraduate students reporting 1+ heavy drinking episode (4/5+ drinks for women/men) in the past month were randomly assigned to one of three feedback groups. Two groups received personalized normative feedback (PNF), comparing their approval of drinking quantity (per occasion and per week) and frequency (per week) to the perceived and actual approval of drinking quantity/frequency among other same-sex college students on campus. One group received the PNF immediately following the baseline assessment (PNF only group), while the other reviewed the PNF with a research assistant who was trained to discuss the feedback in a motivational interviewing style (MI group). The third group received feedback comparing the number of hours per week they spent texting, using social media, and watching TV to the hours that other college students on campus engaged in each of those behaviors (control group). Assessments were completed in person at baseline and from remote locations at 3 and 6 months. Of the 603 participants who completed the baseline assessment (47% male, 39% White; see Table 1), 91% completed the 3-month assessment and 90% completed the 6-month assessment.

2.2. Measures

2.2.1. Demographics.

Participants reported their age and sex in the baseline survey.

2.2.2. Alcohol use.

The Daily Drinking Questionnaire (Collins et al., 1985) was used to characterize typical weekly drinking quantity in both samples. Participants were provided with standard drink definitions (e.g., 12oz beer) and asked to report how many drinks they consumed on each day of a typical week in the past month using a seven-day grid. Responses were summed to estimate the number of drinks consumed in a typical week.

2.2.3. Blackout frequency.

Frequency of alcohol-induced blackout was assessed in both samples using the blackout item from the 10-item Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993). The AUDIT has been validated as a measure of problematic alcohol consumption in multiple countries (Saunders et al., 1993) and among young adults in college (Kokotailo et al., 2004). Participants responded to the item, “How often during the last year/month have you been unable to remember what happened the night before because you had been drinking?” Response options were never (0), less than monthly (1), monthly (2), weekly (3), and daily or almost daily (4). The time frame used for Sample 1 was past year, and the time frame used at each assessment for Sample 2 was past month.

2.2.4. Other alcohol-related consequences.

Alcohol-related consequences were assessed in both samples using the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) (Kahler et al., 2005). This measure has demonstrated internal consistency and validity in predicting alcohol-related problems among young adults (Kahler et al., 2005). Participants indicated (yes/no) if they had experienced 24 consequences (e.g., felt very sick to my stomach; spent too much time drinking) as a result of alcohol use in the past 30 days. The BYAACQ includes an item assessing alcohol-induced blackout in the past 30 days (i.e., “I have not been able to remember large stretches of time while drinking heavily”). This item was removed from the BYAACQ at all time points to avoid confounds between predictor and outcome variables; therefore, possible scores ranged from 0 to 23. Without the blackout item, internal consistency was high at baseline (α1=.90; α2=.84), 1 month (α2=.88), and 3 months (α2=.90).

2.2.5. Symptoms of depression.

In Sample 1, the Patient Health Questionnaire-8 (PHQ-8; Kroenke et al., 2009) was used to measure depressed mood and related impairments in the past two weeks. PHQ-8 construct and criterion validity in predicting symptoms of depression have been established in outpatient and community samples (Kroenke and Spitzer, 2002; Kroenke et al., 2009). Participants indicated on how many days they experienced eight symptoms of depression (e.g., “trouble falling or staying asleep, or sleeping too much”). Response options were 0 (not at all), 1 (several days), 2 (more than half the days), and 3 (nearly every day). Responses were summed to create a total ‘symptoms of depression’ score for use in primary analyses. Internal consistency was high (α1 = .92). For descriptive purposes, a cut-off score ≥10 (Kroenke et al., 2009) was used to indicate a positive screen for depression.

In Sample 2, symptoms of depression were assessed using the 20-item Center for Epidemiologic Studies Depression Scale (Radloff, 1977). Participants reported how many times in the past week they had experienced symptoms such as feeling lonely on a scale from 0 (rarely or none of the time; less than 1 day) to 3 (most or all of the time; 5–7 days). Internal consistency was high at baseline (α2=.89), 3 months (α2=.90), and 6 months (α2=.90). A cut-off score ≥20 was used to characterize participants as screening positive for depression. This cut-point has demonstrated good sensitivity (83%) and specificity (78%) in general population samples (Vilagut et al., 2016).

2.3. Analysis Plan

Sample 1 analyses were conducted in IBM SPSS Statistics 25. Mediation was tested using bootstrapped confidence intervals for indirect effects in the PROCESS 3.4.1 macro (Hayes, 2013; MacKinnon et al., 2004). Within these models, the indirect effect represents the strength of the association between the independent and dependent variables that is attributable to the mediator (Hayes, 2013). Sample 1 analyses examined cross-sectional associations between blackout frequency and number of other alcohol-related consequences (a-path); other alcohol-related consequences and symptoms of depression, controlling for blackouts (b-path); blackouts and depressive symptoms (c-path); and blackouts and depressive symptoms, controlling for other alcohol-related consequences (c’-path; see Figure 1). Sex and baseline drinks per week were included as covariates. Coefficients are presented in unstandardized and standardized form, using standardized coefficients as indices of effect size.

For Sample 2, data were analyzed using a counterfactual approach to mediation (Imai et al., 2010; Pearl, 2014; VanderWeele, 2015). The counterfactual or potential outcomes framework considers what would happen to each individual at varying levels of “exposure” to the independent variable (X) and mediator (M). Relative to traditional mediation approaches, the counterfactual approach includes more rigorous attention to temporal order and potential confounds, including possible interactions between independent variables and mediators.

Formulas for calculating direct and indirect effects are simplified when there is no interaction between the independent variable and the mediator, as was the case in these data.1 In the absence of an XM interaction, the direct effect represents the effect of X on Y when M is held constant, represented by the formula: direct effect = (θ1)(x0-xa). In this formula, θ1 is the coefficient for X in the equation Y’= θ0 + θ1X + θ2M +θ3C1 + … θjCj, where C1-Cj are covariates, x0 is the value of X in the absence of exposure (i.e., 0 blackouts), and xa is the value of X in the presence of exposure (defined here as the average among those who experienced blackouts [1.30]). The indirect effect represents the difference in Y when holding X constant and changing the mediator from the value that would have been observed in the absence of exposure (Mx0) to the value that would have been observed in the presence of exposure (Mxa). In the absence of an XM interaction, the formula for the indirect effect is: indirect effect = (θ2β1)(x0-xa). In this formula, θ2 is the coefficient for M in the equation Y’= θ0 + θ1X + θ2M +θ3C1 + … θjCj; β1 is the coefficient for X in the equation M’ = β0 + β1X + β 2C1 + … β jCj; and x0 and xa are the same as defined above.

Analyses were conducted using structured equation modeling with full information maximum likelihood in Stata 16.0. The exposure variable was the number of blackouts reported at baseline (T1). The mediator was the number of other alcohol-related negative consequences at 3 months (T2). The outcome was the number of depression symptoms at 6 months (T3). Covariates included T1 other alcohol-related negative consequences, T1 symptoms of depression, T1 drinks per week, sex, intervention conditions, and study site. Distributions of endogenous residuals did not show substantial departure from normality for other alcohol-related negative consequences at T2 (skewness=−0.83, kurtosis=5.20) or depression symptoms at T3 (skewness=−0.92, kurtosis=3.52). Direct, indirect, and total effects were calculated from formulas provided above and described in VanderWeele (2015). Standard errors were based on bias-corrected bootstrapping with 1000 samples. Bivariate correlations among variables are depicted in Table 2, and model results are presented in Tables 3 and 4.

Table 2.

Bivariate correlations among variables in Sample 1 (N=381) and Sample 2 (N=603).

Sample 1 (N=381) 1. 2. 3. 4. 5.
1. Female sex ---
2. Drinks per week -0.22 ---
3. Blackouts -0.28 0.30 ---
4. Other consequences -0.19 0.41 0.46 ---
5. Depressive symptoms −0.04 0.12 0.23 0.30 ---
Sample 2 (N=603) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
1. Male sex ---
2. Intervention condition 0.02 ---
3. Drinks per week 0.19 0.01 ---
4. BL blackouts 0.06 −0.03 0.46 ---
5. M3 blackouts 0.09 −0.07 0.43 0.58 ---
6. M6 blackouts 0.06 −0.06 0.44 0.59 0.58 ---
7. BL other consequences1 −0.002 −0.02 0.48 0.47 0.43 0.41 ---
8. M3 other consequences1 0.05 -0.10 0.42 0.44 0.54 0.44 0.57 ---
9. M6 other consequences1 0.02 −0.08 0.37 0.43 0.39 0.50 0.54 0.59 ---
10. BL depressive symptoms -0.14 −0.01 0.11 0.08 0.10 0.03 0.28 0.25 0.11 ---
11. M3 depressive symptoms -0.14 0.01 0.09 0.10 0.09 0.04 0.25 0.28 0.18 0.52 ---
12. M6 depressive symptoms −0.07 −0.04 0.02 0.08 0.06 0.07 0.25 0.23 0.21 0.44 0.50
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Note.

1

Blackouts item removed from total score. Bold values indicate p<.05. BL = baseline. M3 = month three. M6 = month six.

Table 3.

Parameter estimates from Study 2 mediation model (N=603).

Criterion Predictor B SE (B) Z p β β L95% β H95%
T2 Problems (M) T1 Blackouts (X) 1.029 0.228 4.51 <0.001 0.181 0.102 0.259
T1 Problems 0.383 0.046 8.38 <0.001 0.356 0.273 0.439
T1 Depression 0.065 0.017 3.94 <0.001 0.139 0.070 0.207
T1 Drinks per week 0.092 0.026 3.54 <0.001 0.151 0.067 0.235
Sex 0.114 0.303 0.38 0.707 0.013 −0.054 0.080
Intervention (MI) −1.388 0.358 −3.87 <0.001 −0.148 −0.223 −0.073
Intervention (PNF) −0.415 0.361 −1.15 0.250 −0.045 −0.120 0.031
Site −0.024 0.303 −0.08 0.938 −0.003 −0.070 0.065
Intercept 0.431 0.424 1.02 0.309 0.000 −0.065 0.065
T3 Depression (Y) T2 Problems (M) 0.241 0.103 2.35 0.019 0.106 0.018 0.194
T1 Blackouts (X) −0.686 0.554 −1.24 0.216 −0.053 −0.137 0.031
T1 Problems 0.304 0.113 2.68 0.007 0.124 0.033 0.215
T1 Depression 0.551 0.040 13.82 <0.001 0.513 0.440 0.585
T1 Drinks per week −0.029 0.060 −0.48 0.634 −0.021 −0.105 0.064
Sex −0.122 0.713 −0.17 0.864 −0.006 −0.076 0.063
MI intervention −0.192 0.858 −0.22 0.823 −0.009 −0.088 0.070
PNF intervention 1.231 0.853 1.44 0.149 0.058 −0.021 0.137
Site −1.162 0.712 −1.63 0.103 −0.058 −0.127 0.012
Intercept 5.350 1.010 5.30 <0.001 0.000 −0.067 0.067
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Note. T1 = baseline. T2 = 3 months. T3 = 6 months. M = mediator. MI = Motivational Interviewing. PNF = personalized normative feedback. X = independent variable.

Table 4.

Direct and indirect effects from Study 2 mediation model (N=603).

B SE (B) β β BC L95% β BC H95%
Direct Effect −0.895 0.682 −0.089 −0.225 0.057
Indirect Effect 0.324 0.170 0.032 0.004 0.074
Total Effect −0.571 0.691 −0.057 −0.191 0.090
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3. Results

3.1. Sample 1 Concurrent Model

In Sample 1 (N=381), there was a significant total effect of blackout frequency on concurrent symptoms of depression (c = 1.45, SE = 0.36; 95% CI = 0.75, 2.15; standardized c = 0.22). Blackout frequency was positively associated with number of other alcohol-related consequences (a = 2.18, SE = 0.28; 95% CI = 1.63, 2.73; standardized a = 0.36); and other alcohol-related consequences, in turn, were positively associated with symptoms of depression (b = 0.28, SE = 0.06; 95% CI = 0.15, 0.40; standardized b = 0.25). There was a direct effect of blackouts on depressive symptoms, such that more frequent blackouts in the past year were associated with more concurrent symptoms of depression (c’ = 0.85, SE = 0.38; 95% CI = 0.11, 1.59). Blackouts were also associated with depressive symptoms indirectly through their association with other alcohol-related consequences (a*b = 0.60, SE = 0.18; 95% CI = 0.29, 0.99; standardized a*b = 0.09). Neither sex (B = 0.36, SE = 0.67; 95% CI = −0.96, 1.69; β=0.02) nor drinks per week (B = 0.04, SE = 0.03; 95% CI = −0.03, 0.10; β=0.06) were significant covariates in the total effect model.

3.2. Sample 2 Longitudinal Model

Table 3 presents the parameter estimates from the Sample 2 longitudinal model. The top of Table 3 presents path coefficents for the mediator (T2 other alcohol-related negative consequences) as a function of the exposure (T1 blackouts) and covariates. Results revealed a significant effect of T1 blackouts on T2 other alcohol-related negative consequences, controlling for T1 other alcohol-related negative consequences and all other covariates. In addition to T1 blackouts, unique effects on T2 other alcohol-related negative consequences were observed for T1 depression symptoms, T1 drinks per week, and the MI intervention condition.

The bottom of Table 3 presents path coefficients for the outcome (T3 depression symptoms) as a function of the exposure (T1 blackouts), the mediator (T2 other alcohol-related negative consequences), and covariates. Results indicated a significant positive association between T2 other alcohol-related negative consequences and T3 depression symptoms, controlling for T1 blackouts, T1 other alcohol-related negative consequences, and T1 depression symptoms, as well as the other covariates. In addition to T2 other alcohol-related negative consequences, T1 other alcohol-related negative consequences and T1 depression symptoms were uniquely associated with T3 depression symptoms. T1 blackouts were not uniquely associated with T3 depression, nor were any of the other covariates.

Table 4 presents tests of direct and indirect effects. Results demonstrated a small but significant indirect effect of T1 blackouts on T3 depression symptoms through T2 other alcohol-related consequences. Neither the direct effect nor the total effect was significant.

4. Discussion

This study confirms previous research documenting associations between alcohol-induced blackouts and other alcohol-related consequences (Hingson et al., 2016; Wilhite and Fromme, 2015) and between problematic alcohol use and symptoms of depression (Boden and Fergusson, 2011; Neupane and Bramness, 2013; Voloshyna et al., 2018). This study extends these findings by demonstrating that alcohol-induced blackouts are associated with increased symptoms of depression due in part to their association with other alcohol-related consequences. This association was found in two separate samples of heavy drinkers, both cross-sectionally and over time. However, the magnitude of the indirect effects was relatively small, especially for the longitudinal sample. The standardized indirect effect in the longitudinal study was .032, which, given its composition as a product of two standardized coefficients, would be approximately comparable to a correlation effect size of .18.

Although rates of blackouts were inflated in Sample 1 due to study design (i.e., eligibility criteria included a history of blackout), 56% of the heavy-drinking young adults in Sample 2 reported a blackout in the past month. In addition, more than 1 in 3 heavy drinkers in both samples screened positive for depression. Given the prevalence and societal costs of these symptoms, efforts to prevent and/or reduce the frequency of these behaviors have strong public health implications.

Based on previous studies linking problematic alcohol use to depression (Acuff et al., 2019a; Boden and Fergusson, 2011), we were surprised that blackouts had a direct effect on symptoms of depression only in the cross-sectional (not prospective) model. Indeed, in the longitudinal model, blackouts were associated with six-month symptoms of depression only indirectly through their association with other alcohol-related consequences at three months. Genome-wide association studies suggest strong genetic overlap between alcohol use and depressive disorders, perhaps due to individual differences in stress reactivity and behavioral control (Ellingson et al., 2016; Polimanti et al., 2019). In this case, individuals who are genetically predisposed to experience alcohol-induced blackouts may be the same individuals who experience alcohol-related consequences and, subsequently, symptoms of depression.

Consistent with hypotheses, we documented concurrent and prospective associations between alcohol-induced blackout and other alcohol-related consequences. The inability to retain long-term memories may limit individuals’ awareness of their current environment, placing them at increased risk for negative outcomes. Similarly, the experience of a blackout indicates a level of alcohol consumption sufficient to produce cognitive impairment, at which point other alcohol-related consequences are also likely to occur. While previous research has identified blackouts as a strong concurrent predictor of other alcohol-related consequences (Hingson et al., 2016), relatively limited research has examined this association prospectively (Wilhite and Fromme, 2015). Thus, this study contributes to the literature by documenting (a) that the association between blackouts and other alcohol-related consequences persists over time and (b) that blackouts predict increases in, not just current levels of, other alcohol consequences.

Although alcohol-related consequences helped explain the association between blackouts and symptoms of depression in this sample, the mechanism(s) linking alcohol-related consequences to symptoms of depression are unclear. Research with college students suggests that alcohol-related consequences are a stronger predictor of depression than drinking quantity (Rosenthal et al., 2018). For young adults attending college, where high levels of drinking can seem normative, it is possible that social, economic, or legal problems related to drinking are necessary to impact symptoms of depression. This would be consistent with literature linking stressful life events to depressive episodes (Kessler, 1997). However, multiple studies have found that the association between problematic alcohol use and symptoms of depression is maintained when controlling for social or environmental variables (e.g., socioeconomic status, social support, negative life events) (Boden and Fergusson, 2011). Given the maintenance of this association, previous researchers have speculated that alcohol use negatively influences neurocognitive or metabolic functioning in such a way that increases risk for psychiatric disorders such as depression (Berenz et al., 2019; Boden and Fergusson, 2011; Sjoholm et al., 2010). In this case, the additional experience of alcohol-related consequences may then exacerbate these underlying vulnerabilities to depression.

4.1. Limitations

This study examined a novel pathway from high-risk drinking behavior (i.e., alcohol-induced blackout) to symptoms of depression in two separate samples of young adults. However, the data were limited in several ways. First, all participants in Sample 1 reported a history of alcohol-induced blackout at baseline, and participants in Sample 2 were heavy drinkers recruited for an alcohol intervention trial; therefore, findings may not generalize to young adults who do not drink heavily. Second, we used screening measures of depression that, while validated, reflect symptoms of depression, rather than clinical depression. Third, we used cross-sectional data to examine mediated effects in Sample 1. Cross-sectional mediation analyses are often biased and inconsistent with longitudinal findings (Maxwell et al., 2011); however, we believe the replication of findings (even if one instance is cross-sectional) is important in demonstrating the reliability of indirect effects.

Finally, these analyses aggregate between- and within-person effects. This precludes us from determining if associations occur within persons (i.e., as a result of blackout events, individuals experience consequences that then lead to depressive symptoms), between persons (i.e., the individuals who experience blackouts are also those who are more likely to experience alcohol-related consequences and symptoms of depression), or both. Similarly, while data from Sample 2 reflect longitudinal associations, these data cannot be used to establish causal effects. As an example, it is possible that the onset of depressive symptoms occurred prior to baseline and influenced participants’ experience of alcohol-induced blackouts at baseline. The inability of these data to establish causal influences is particularly notable in light of data indicating that the association between alcohol use and symptoms of depression may be bidirectional. Multiple studies have identified depressive symptoms as a predictor of heavy alcohol use and related problems, both cross-sectionally (Bravo et al., 2018; Kenney et al., 2018) and over time (Acuff et al., 2018; Geisner et al., 2018). Indeed, after accounting for covariates and all other variables in the model, depressive symptoms were also a prospective predictor of alcohol-related consequences in Sample 2. The association between depressive symptoms and alcohol-related problems is consistent with studies indicating that symptoms of depression are associated with ruminative thinking and use of alcohol to cope with negative affect, which in turn are associated with drinking and alcohol-related consequences (Bravo et al., 2018; Kenney et al., 2018). Deficits in self-regulation may also play a role in the association between alcohol use and symptoms of depression (Acuff et al., 2019a).

4.2. Clinical Implications

Because they are markers of high levels of intoxication and robust correlates of alcohol-related harm (Hingson et al., 2016; Wilhite and Fromme, 2015), blackouts may represent an opportune target for prevention and intervention efforts. In two separate samples, participants who had experienced a blackout in the past 30 days were more likely than those who had not experienced a blackout to report decreases in drinking quantity and alcohol-related consequences after receiving personalized normative feedback on their alcohol use (Miller et al., 2018a; Miller et al., 2018b). However, the experience of blackouts alone has not been associated with naturalistic changes in actual drinking behavior (Acuff et al., 2019b; Marino and Fromme, 2018). As such, healthcare providers are encouraged to screen for alcohol-induced blackouts in clinical settings and provide an evidence-based alcohol intervention, if appropriate. Among high-risk drinkers, such as those who experience blackouts, reductions in alcohol use have also been associated with decreased likelihood of depression three years later (Knox et al., 2019). Thus, data from this and other studies suggest that prevention of heavy drinking (in particular, drinking that leads to blackout) may mitigate symptoms of depression among young adults.

4.3. Conclusion

This study provides evidence of a longitudinal association between alcohol-induced blackouts and symptoms of depression. Drinkers experiencing blackouts are at elevated risk for other alcohol-related consequences, a known risk factor for depression. Thus, blackouts may serve as an early risk factor for depression among drinkers and a potential cue for early intervention. Also, given its prospective association with both alcohol-related harm and mental health symptoms, blackout drinking may represent an important treatment target among heavy drinkers, particularly those reporting symptoms of depression.

Highlights.

  • Blackouts are associated with other types of alcohol problems.

  • In turn, alcohol problems are associated with symptoms of depression.

  • Alcohol problems mediate the association between blackouts and depressive symptoms.

  • Blackouts may be an important treatment target for heavy drinking.

Role of Funding Sources

This work was supported by a Research Excellence Award (PI Miller) from the Center for Alcohol and Addiction Studies at Brown University and research grants from the National Institute on Alcohol Abuse and Alcoholism (K23AA026895, PI Miller; R21AA025676, PI DiBello; K01AA022938, PI Merrill; R01AA014576, PI Neighbors; R01AA012518, PI Carey). NIH had no role in study design; data collection, analysis, or interpretation; manuscript preparation; or the decision to submit the paper for publication. The authors have no conflicts of interest to report.

Footnotes

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1

The interaction effect between T1 blackouts and T2 alcohol-related problems in predicting T3 depression, including all covariates, was essentially zero, Z=0.01, p=0.99.

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