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Published in final edited form as: Addict Behav. 2013 Apr 2;38(8):10.1016/j.addbeh.2013.03.014. doi: 10.1016/j.addbeh.2013.03.014

The Impact of Pregaming on Subsequent Blood Alcohol Concentrations: An Event-Level Analysis

Adam E Barry a,*, Michael L Stellefson a, Anna K Piazza-Gardner a, Beth H Chaney a, Virginia Dodd b
PMCID: PMC3819100  NIHMSID: NIHMS515709  PMID: 23628431

Abstract

Pregaming has been highlighted as an especially deleterious college drinking ritual. The present study assessed (a) event-level associations between pregaming and biologic samples of blood alcohol concentration (BrAC) and (b) the impact of one’s alcohol-related behaviors (measured by AUDIT-C scores) on the likelihood that respondents would report pregaming prior to a night out drinking. The sample included adult (n = 1,029; collegiate and non-college-affiliated) bar patrons in a southeastern college community. Multiple and linear regressions were conducted to determine the association between pregaming and BrAC levels, and pregaming and the presence of an alcohol use disorder, respectively. After controlling for the influence of time of data collection, gender, age, college student status, and ethnicity, the linear regression model explained 15.5% (R2 = .155) of the variance in BrAC levels (F (10, 915) =16.838, p < 0.001), of which 10.8% was accounted for by self-reported pregaming alone. Furthermore, pregamers exhibited significantly higher BrACs compared to non-pregamers (β = .332, p < .001). Logistic regression analyses indicated that AUDIT-C scores were the only significant predictor of pregaming status (OR = 1.305, Wald = 64.843), such that respondents with higher AUDIT-C scores (B = 0.266) were more likely to pregame. This event-level study highlights the practice of pregaming as an insidious behavior associated with enhanced levels of drinking behavior and overall intoxication.

Keywords: Pregaming, Predrinking, Alcohol, Blood Alcohol Concentration, Event-Level

1. INTRODUCTION

Pregaming (i.e., preloading, prepartying, predrinking) has been highlighted as an especially deleterious college drinking ritual, associated with increased alcohol consumption, drinking game participation, greater alcohol-related problems, alcohol-induced blackouts, as well as higher blood alcohol concentrations (BAC) (LaBrie, Hummer, Kenney, Lac & Pedersen, 2011; Pedersen & LaBrie, 2007; Pedersen, LaBrie & Kilmer, 2009; Read et al., 2010). The term “pregaming” broadly encompasses the consumption of alcohol prior to going to a bar/club or attending a social gathering/function. College student pregamers have reported consuming an average of 3–5 drinks per pregaming session (DeJong, DeRicco & Schneider, 2010; Pedersen & LaBrie, 2007; Reed et al., 2010), with approximately one-third of students referred for alcohol violations reporting pregaming on the night of their incident (Borsari et al., 2007).

College students report pregaming for a variety of reasons, such as saving money, enhancing sociability (i.e., loosen up), reducing anxiety or because they are under the minimum legal drinking age. The most commonly cited reason, however, is to become intoxicated (DeJong, DeRicco & Schneider, 2010; Pedersen, LaBrie & Kilmer, 2009; Read et al., 2010). Thus, social motives predominantly influence pregaming behavior, as opposed to mood enhancement, coping or conformity motives (Pedersen & LaBrie, 2007).

1.1 Limitations of the Pregaming Literature

Since the literature base associated with pregaming is still in its infancy (term first appearing in scholarly articles in 2007), there are several noteworthy gaps. First, while previous examinations have linked pregaming to elevated BACs, it is important to note that these investigations have relied upon statistical estimates, rather than objective biological measures (Borsari et al., 2007; Pedersen & LaBrie, 2007; Pedersen, LaBrie & Kilmer, 2009; LaBrie & Pedersen, 2008; Read et al., 2010). Often, these estimated BAC levels are not accurate when compared to biologic samples (Carey & Hustad, 2002; Clapp et al, 2006; Clapp et al., 2009). We are aware of only one investigation including an objective measure (BrAC) of drinking (see Reed et al., 2011), in which intoxication levels resulting from pre-drinking were measured prior to patrons entering a drinking premise. Secondly, the vast majority of previous pregaming investigations have focused primarily on college students and do not include non-collegiate participants for comparison purposes (Borsari et al., 2007; DeJong, DeRicco & Schneider, 2010; Paves et al., 2012; Pedersen & LaBrie, 2007; Pedersen, LaBrie & Kilmer, 2009; Read et al., 2010). As a result, it is unclear whether pregaming is a phenomenon transpiring strictly among college students, or if others also engage in this behavior. Third, while previous investigations have examined associations between pregaming and various alcohol-related behaviors (e.g, daily consumption, binge drinking, alcohol-related consequences), no published studies, to date, have considered whether individuals self-reporting signs of an alcohol use disorder (AUD) are more likely to pregame. Lastly, the current literature lacks event-level analyses of pregaming behaviors (LaBrie & Pedersen, 2008; Paves et al., 2012).

1.3 Current Investigation

To address the aforementioned gaps in the pregaming literature, the present study assessed event-level associations between pregaming and actual biologic samples of BrAC among a sample of adult (both college and non-college-affiliated) bar patrons in a southeastern college community. Specifically, two research questions guided this investigation: (1) Does pregaming account for a significant proportion of the variance in respondents’ BrACs, above and beyond demographic covariates such as college-status? (2) Does the presence of signs for an AUD predict ones pregaming status?

2. METHODS

During the Fall of 2011, we conducted a series of anonymous field studies on six distinct Friday evenings (from 10:00pm – 2:00am) in a southeastern college community restaurant and bar district (outlet density of seven late-night drinking establishments). The field studies were modeled after methods reported by Thombs et al. (2009) and O’Mara et al. (2009). Intercept interviews were used to recruit participants exiting bars or walking on public sidewalks. A trained recruiter would obtain verbal informed consent from interested and willing participants before proceeding with a face-to-face interview using a structured 3–5 minute survey assessment. This assessment collected data on basic demographics, self-reported alcohol-related behaviors, and whether the respondent engaged in pregaming prior to coming to the district. Upon completion of this brief assessment, the participant’s breath alcohol concentration (BrAC) was measured using a hand-held breath alcohol testing device (Intoximeter, Alco-Sensor IV). All procedures were approved by the university’s Institutional Review Board.

2.1 Measures

Pregaming Status was assessed using the item “Did you drink alcohol prior to going out tonight (i.e., “pregame”)?” Respondents provided dichotomous yes or no responses.

To determine whether respondents exhibited behaviors indicative of an AUD, the three-item AUDIT-C (Bush et al., 1998) was administered to assess frequency of drinking (“How often did you have a drink containing alcohol in the past year?”), typical drinking quantity (“How many drinks did you have on a typical day when you were drinking in the past year?”), and binge drinking (“How often did you have 6 or more drinks on one occasion in the past year?”). Each item has five possible response options, scored from 0 to 4 points. Higher scores indicate more problematic alcohol use. The overall AUDIT-C score is an aggregate of all item scores, with total possible scores ranging from 0 to 12. Typically, cut-off scores suggesting an AUD are 4 for females and 5 for males (Dawson et al., 2012; Johnson et al., 2012; Reinhert and Allen, 2007).

BrAC Samples were collected using Alco-Sensor IV’s, a hand-held breath alcohol testing device approved for evidential use in the United States (National Highway Traffic Safety Administration), Canada (Department of Justice), England and Wales (Secretary of State). To prevent the collection of artificially inflated BrAC measurements due to residual alcohol in the mouth, participants who indicated alcohol consumption within the previous eight minute period rinsed their mouth with water prior to providing a BrAC sample (Wright and Cameron, 1998).

2.2 Data Analysis

In addition to descriptive statistics, we ran two separate, distinct regression analyses to answer our research questions. First, we conducted a simultaneous multiple regression to determine whether pregaming status would significantly impact one’s BrAC level, above and beyond several demographic covariates and the influence of time of data collection. Then, we tested a logistic regression model to determine if behaviors indicative of an AUD significantly predicted individual pregaming status, above and beyond the demographic controls.

3. RESULTS

3.1 Participants Characteristics

The final sample (n = 1,029) represented current drinkers 18 years of age or older. The sample was primarily composed of White (73%) males (62.2%); however, Hispanics (10.1%), Asians (5.7%), Blacks (3.6%) and several other ethnicities (5.7%) were also represented. Respondents were an average of 28 years of age (SD = 8.84), with 61.8% (n=583) currently enrolled in college (33.5% undergraduate, 28.3% graduate or professional). 40% (n=407) of respondents indicated they had pregamed before going out on the evening they were surveyed. Approximately 62.5% of respondents (n=633) exhibited signs of an AUD per the gender-based criteria, with an average AUDIT-C score of 5.04 (SD = 2.42) across the entire sample. The average BrAC reading was under the legal limit at 0.039%, yet there was substantial variability in this measure (SD = .045).

3.2 Does Pregaming Predict BrAC?

Before testing the multiple regression model to determine whether pregaming status predicted BrAC levels, preliminary analyses were conducted on the dataset to ensure no violations to underlying statistical assumptions. Specifically, the variance inflation factors (range = 1.017–3.599) were far less than Myers (1990) suggested rule of thumb value of 10, and the tolerance statistics were all greater than the 0.2 cut-off recommended by Menard (1995). In total, the regression model explained 15.5% (R2 = .155) of the variance, F (10, 915) = 16.838, p < 0.001. It is noteworthy that only two variables reached statistical significance. Pregamers (β = .332) exhibited significantly (p < .001) higher BrACs than those that did not pregame. Time (β = .092) was also significant (p < .003), such that BrACs were higher later in the evening (See Table 1). Moreover, the part correlation between pregaming and BrAC level (semipartial correlation coefficient = .329) indicated this single variable accounted for 10.8% of the total variance in BrAC.

Table 1.

Multiple Regression Analyses Predicting BrAC Levels

Variable B SE B β t p
Constant −0.047 0.023 −2.038 0.042
Pregame Status 0.031 0.003 0.332** 10.822 0.001
Time of Survey 0.005 0.002 0.092* 2.960 0.003
Male 0.005 0.003 0.050 1.628 0.104
Undergrad Student −0.006 0.004 −0.062 −1.536 0.125
Graduate Student −0.002 0.004 −0.016 −0.435 0.664
White 0.010 0.006 0.098 1.698 0.090
Hispanic −0.003 0.007 −0.017 −0.360 0.719
Black 0.002 0.009 0.006 0.164 0.869
Asian −0.006 0.008 −0.033 −0.783 0.434
Age 0.000 0.000 0.054 1.453 0.147
R2 .155
F 16.838**
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*

p < .010

**

p < .001.

3.3 Do Signs of an AUD Predict Pregaming Status?

Logistic regression was performed to assess the impact of one’s alcohol-related behaviors and several demographic characteristics on the likelihood that respondents would report pregaming. Overall, −2 × log-likelihood and associated chi-square statistics (χ2 = 84.513, df = 9, p < .001) were statistically significant, indicating the model was a good fit for the data. The non-significant Hosmer-Lemeshow goodness of fit test (χ2 with 8 df = 13.082, p= .109) provided further support for the model’s fit and general reliability. Overall, AUDIT-C score was the only significant predictor of pregaming status (OR = 1.305, Wald = 64.843), such that respondents with higher AUDIT-C scores (B = 0.266) were more likely to pregame (See Table 2). In other words, respondents were 1.3 times more likely to pregame with each one-unit increase in the twelve-point AUDIT-C score.

Table 2.

Logistic Regression Model Predicting Pregaming Status

95% C.I. for Odds Ratios
Odds Ratio Lower Upper Wald Sig.
AUDIT-C 1.305 1.223 1.393 64.843* 0.001
Male 0.916 0.673 1.248 0.306 0.580
Age 1.012 0.992 1.032 1.321 0.250
White 1.215 0.648 2.276 0.368 0.544
Hispanic 0.943 0.441 2.019 0.023 0.881
Black 1.469 0.550 3.921 0.590 0.442
Asian 1.309 0.571 2.996 0.405 0.525
Undergrad Student 0.853 0.574 1.268 0.619 0.431
Graduate student 1.057 0.733 1.525 0.088 0.767
  Constant 0.114 18.859 0.001
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*

p < .001.

4. DISCUSSION

This event-level study revealed pregaming to be a significant predictor of a biologic measure of BAC, such that respondents who pregamed exhibited significantly higher BrAC levels even after accounting for time of data collection, age, sex, race and college-status. These findings corroborate results from previous investigations that document pregamers as exhibiting greater alcohol consumption (Pedersen & LaBrie, 2007) and higher estimated BAC than non-pregamers (Borsari, et al., 2007). Moreover, our results echo DeJong, DeRicco & Schneider’s (2010) contention that pregaming activities are intended to “set the stage for becoming intoxicated later” (p314). We would go one step further and assert that pregaming sets the stage for participants to become more intoxicated than their peers who choose not to pregame.

Our results also suggest that those exhibiting signs of an AUD are more likely to engage in the pregaming before beginning a night of drinking. While previous literature links pregaming behaviors with greater drinking and increased alcohol-related problems, it is plausible to speculate these linkages have emerged because pregamers are more likely to be problematic drinkers, and thus, more likely to experience alcohol-related consequences and drink in excess. Future investigations should endeavor to more fully conceptualize and operationalize the alcohol-related behaviors of pregamers, paying special attention to problematic drinking behaviors and alcohol use disorders.

While commonly associated with college students, college-status (undergraduate or graduate) did not significantly impact pre-gaming status in the current investigation. Our results highlight pregaming as a behavior that drinkers choose to engage in, regardless of whether they are enrolled as a college student. Also, none of the other included demographic variables (age, sex, or race) significantly impacted pregaming status. The findings related to sex parallel previous investigations which assert pregaming behaviors do not differ by gender (DeJong, DeRicco & Schneider, 2010; LaBrie & Pedersen, 2008; Pedersen & LaBrie, 2007; Reed et al., 2011). While investigations examining ethnic differences in pregaming behaviors are few and far between, those that are published have documented group differences (Paves et al., 2012). Consequently, future pregaming research should further investigate the effects of age and race/ethnicity among more diverse samples.

4.1 Limitations

While the present study accounts for numerous gaps in our current understanding of pregaming, there are several limitations which should be noted. The homogeneity of the sample and its relative lack of racial and ethnic diversity are an important caveat. Moreover, the setting in which data were collected (restaurant and bar district) most likely led to an underrepresentation of underage drinkers. Our reliance on self-report data should also be considered because recall bias may have been experienced during the intercept interview by participants who were impaired/intoxicated. Lastly, we are unable to place the pregaming event in context of the overall evening of data collection. We cannot say when the pregaming event took place, how long the pregaming event lasted, or the time between pregaming and the other drinking events occurring that evening.

5. Conclusion

As the findings reported herein highlight, even after accounting for demographic factors, pregaming leads to significantly higher objective BrACs. When considered in unison with the fact that respondents exhibiting signs of an AUD are more likely to pregame, this research suggests pregaming to be an insidious behavior worthy of additional inquiry. More definitive research is warranted to further identify specific factors (e.g. social, emotional, cognitive, environmental, and attitudinal) that may explain or predict pregaming behaviors. Findings from such investigations can inform alcohol-based programs and interventions, as well as influence marketing/social campaigns designed to educate individuals about the risks of pregaming behavior.

Highlights.

  • We investigated the impact of pregaming on objective measures of BrAC levels, as well as the links between pregaming and alcohol use disorders.

  • Pregamers exhibited significantly higher BrAC levels than non-pregamers, even after controlling for several demographic covariates.

  • AUDIT-C scores significantly predicted pregaming status.

  • Respondents were 1.3 times more likely to pregame with each one-unit increase in AUDIT-C score

  • Neither college-status, age, sex or ethnicity significantly impacted BrAC levels or pregaming status

Acknowledgments

Role of Funding Sources

No funding was used to support this research and/or the preparation of the manuscript.

Footnotes

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Contributors

Barry conceptualized the manuscript and prepared the first draft of the manuscript. Stellefson and Chaney consulted on the statistical analysis. Piazza-Gardner contributed to the conceptual development of the discussion and conclusion section. Both Stellefson and Piazza-Gardner reviewed the study design, statistical approaches, and each successive draft of the manuscript. While Barry and Chaney were primarily responsible for overseeing data collection, all authors were involved in the planning of the study and contributed to the data collection process. Barry takes responsibility for the paper as a whole.

Conflict of Interest

The authors have no conflicts of interest to report.

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