Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Alcohol Clin Exp Res (Hoboken). 2022 Dec 13;47(2):273–284. doi: 10.1111/acer.14985

Alcohol use contexts (social settings, drinking games/specials, and locations) as predictors of high-intensity drinking on a given day among U.S. young adults

Yvonne M Terry-McElrath 1,*, Brooke J Arterberry 1, Megan E Patrick 1
PMCID: PMC10084771  NIHMSID: NIHMS1871348  PMID: 36462939

Abstract

Background:

This study examined whether variability in young adult drinking social settings, drinking games/drink price specials, and locations differentiated daily high-intensity drinking (HID) likelihood; whether contexts varied by legal drinking age and college status (attending a 4-year college full-time); and whether legal drinking age and college status moderated drinking context/intensity associations.

Methods:

Participants (n=818 people, 46.3% female) were part of the Young Adult Daily Life Study in 2019–2022, originally selected because they were past 30-day drinkers from the 2018 U.S. national probability Monitoring the Future 12th grade sample and because they reported one or more days of alcohol use during 14-day data collection bursts across the following four years (n=5,080 drinking days). Weighted multi-level modeling was used to estimate drinking context/intensity associations. Drinking intensity was defined as moderate (females 1–3, males 1–4 drinks), binge (4–7, 5–9 drinks), or HID (8+, 10+ drinks). Models controlled for other within-person (weekend, historical time period) and between-person (sex, race/ethnicity) covariates.

Results:

Contexts differentiating HID and binge drinking days included drinking with large groups, strangers, pre-gaming, drinking games, and more drinking locations. Legal drinking age was associated with lower odds of free drinks but greater odds of drinking at bars/restaurants. College status was associated with lower odds of drinking alone or free drinks, but greater odds of drinking with friends, large groups, pre-gaming, drinking games, discounted price drinks, at bars/restaurants, at parties, and more drinking locations. Legal drinking age and college status moderated some context-intensity associations.

Conclusions:

Social settings, pre-gaming, drinking games, and drinking at more locations were associated with increased risk of HID on a given day. Legal drinking age and college status were associated with specific drinking contexts and moderated some context/intensity associations. Incorporating contexts associated with HID into interventions may be a promising approach to reducing HID and related consequences.

Keywords: alcohol, binge drinking, high-intensity drinking, young adult, drinking contexts

Introduction

High-intensity drinking is associated with negative individual and public health consequences including drinking-related injuries, alcohol poisoning, memory loss, and sexual risk (Patrick & Azar, 2017). High-intensity drinking has been defined as consuming 10+ drinks (i.e., drinking at twice the level of the general binge drinking threshold, or sex-specific levels of 8+ drinks for women and 10+ drinks for men; Patrick & Azar, 2017) and is most prevalent among young adults (Patrick & Terry-McElrath, 2019; Patrick et al., 2016b, 2022). Research indicates that high-intensity drinking is reported more frequently by men (vs. women) and those who identify as White (vs. those who identify as non-White) (Patrick et al., 2022; Terry-McElrath & Patrick, 2020). However, understanding what impacts the day-to-day likelihood that any one individual may engage in the behavior is key to developing intervention programming (Stanesby et al., 2019).

Although research among young adults has found that engaging in high-intensity drinking is associated more strongly with day-specific factors such as motives for drinking that day than between-person sociodemographic characteristics (Patrick & Terry-McElrath, 2021), it is particularly important to examine differences in the proximal contextual factors that may affect daily variation in alcohol consumption (Freisthler et al., 2014). The social-ecological model can help frame our understanding of alcohol consumption patterns that lead to negative consequences in relation to these daily proximal contextual factors, as it emphasizes that health-related behaviors—including high-intensity drinking—are affected by multiple levels of influence (Sallis et al., 2008). These levels can include situational contexts such as day of the week as well as more proximal contexts such as an individual’s interpersonal and physical environment. In regard to drinking behaviors, interpersonal factors may include social settings (e.g., drinking alone, with friends, large groups), while physical environmental factors may include the physical locations where drinking occurs (e.g., home, bars/restaurants) and situational characteristics (e.g., pre-gaming or discounted/free drinks).

Few studies have examined associations between drinking contexts and the specific behavior of high-intensity drinking. Between-person studies have indicated that larger social groups and having close relationships with other drinking individuals may relate to high-intensity drinking (Cox et al., 2019; Merrill et al., 2021). Research based on repeated measures across many days allows examination of what predicts day-to-day behavior, but only three such studies have examined contexts associated with high-intensity drinking, all using college student samples (Cox et al., 2022; Fairlie et al., 2015; Patrick et al., 2016a). Patrick et al. (2016a) utilized one dichotomous social context measure (whether the respondent was most often alone or with other people while drinking), and one three-category context measure (whether the respondent did most of their dinking [a] at home, [b] at a bar or party, or [c] some other place). Results indicated high-intensity drinking was associated with days when drinking occurred at bars/parties (vs. home) but was not associated with drinking days at “other locations” (vs. home) or alone (vs. with others). Fairlie et al. (2015) utilized measures related to pre-gaming (i.e., drinking before going out) and drinking games; high-intensity drinking days were associated with both contexts. Cox et al. (2022) used measures with multiple location (home, friend’s place, party, bar/restaurant, outside, study space, athletic facility, elsewhere) and social (alone, significant other, roommate, friends, family, stranger, acquaintance, someone else) contexts at both first and last drink; last drink contexts of party, friend’s house, and strangers differentiated high-intensity and binge drinking risk. Results from these studies are informative, but research on general alcohol use has identified other contexts that may be of relevance (e.g., large groups). Further, the studies by Cox et al. (2022), Fairlie et al. (2015) and Patrick et al. (2016a) were not able to examine whether pre-gaming, location, and social context associations pointed to context-specific risks or whether the odds of high-intensity drinking were primarily associated with multiple versus single drinking locations, regardless of whether or not pre-gaming occurred or whether drinking occurred within a specific location or social context. Drinking at multiple locations has been associated with increased consumption (Dietze et al., 2014; Labhart et al., 2017; White et al., 2020). Further research is needed to examine these issues, as well as to explore which contexts are particularly likely to result in high-intensity drinking on a given day among both those attending and not attending college.

Higher-risk alcohol use, such as binge drinking, has historically been more common among 4-year college students (Patrick & Terry-McElrath, 2017; Patrick et al., 2022), and a developmental increase in high-intensity drinking after high school has been found among those attending college while a similar pattern was not observed for those not attending college (Patrick et al., 2016b). College environments, particularly those associated with full-time 4-year campuses, provide high levels of social interaction with peers. Research has found individuals attending 4-year colleges have higher social drinking motives, and (among college students) such motives have been associated with greater odds of high-intensity drinking (Patrick & Terry-McElrath, 2021). The degree to which context and drinking intensity associations may be moderated by college status has not been addressed.

Research focused on general alcohol use has identified a range of contexts associated with drinking risk. Alcohol consumption has been found to be greater on weekends versus weekdays (Demers et al., 2002; Finlay et al., 2012; Stanesby et al., 2019). Drinking at parties, bars, and events has been associated with greater alcohol consumption (Braitman et al., 2017; Demers et al., 2002; Finlay et al., 2012; Kairouz et al., 2002; Kuntsche et al., 2015; Mustonen et al., 2014; Paradis et al., 2011). Drinking at home and in restaurants is generally lower risk (Demers et al., 2002; Kairouz et al., 2002; Paradis et al., 2011; Pennay et al., 2015). Drinking with friends and in large groups (vs. with family, spouse/partner, or alone) has been associated with greater alcohol use (Ally et al., 2016; Braitman et al., 2017; Cullum et al., 2012; Demers et al., 2002; Kairouz et al., 2002; Kuntsche et al. 2015; Mustonen et al., 2014; Paradis et al., 2011; Smit et al., 2015; Thrul & Kuntsche 2015). Pre-gaming and drinking games are associated with higher levels of consumption (Barnett et al., 2013; Clapp et al., 2008; Kuntsche & Labhart, 2013; Kuntsche et al., 2015; Labhart et al., 2013, 2014; Merrill et al., 2013; Østergaard & Skov, 2014), and lower prices are associated with greater consumption (Sharma et al., 2017). One area in the literature that has received less attention is the extent to which contexts may differentiate high-intensity drinking days from binge drinking days (4–7 drinks for females/5–9 drinks for males) or moderate drinking days (1–3 drinks for females/1–4 drinks for males). Cox et al. (2022) examined this issue among college students, finding that parties and friend’s houses were contexts associated with increased risk of high-intensity (vs. binge) drinking, but did not control for number of drinking locations. To the degree that context-specific differentiation is evident after controlling for number of drinking locations, identifying particularly high-risk contexts may help strengthen intervention efforts to reduce high-intensity drinking.

Age and historical time are associated with young adult alcohol use prevalence and intensity (Patrick et al., 2017, 2022; Patrick & Terry-McElrath, 2019) and may also be associated with drinking contexts. Legal drinking age may particularly affect young adult drinking locations and, in turn, associations with drinking intensity. Among college students, being under the legal drinking age of 21 is associated with more drinks consumed at residence hall parties and fewer drinks consumed at bars/restaurants (Paschall & Saltz, 2007). Furthermore, high-intensity drinking prevalence has been shown to evidence meaningful change across historical time, particularly among early young adults (Patrick et al., 2017). Efforts to examine associations between legal drinking age status on a given drinking day and drinking context/intensity associations must account for the degree to which change in historical time may be affecting associations versus change in legal drinking age, particularly when the analyses utilize data collected over multiple years.

Using data collected during 2019–2022 from U.S. young adult drinkers one to four years after high school, we examined three research aims (RA):

  • RA1. The extent to which specific drinking contexts (social settings, drinking games/specials, and locations) differentiated drinking intensity that day, controlling for the total number of drinking locations reported that day;

  • RA2. The extent to which drinking contexts varied by (a) legal drinking age or (b) college status (controlling for historical time); and

  • RA3. Whether (a) legal drinking age or (b) college status moderated drinking context and drinking intensity associations.

Materials and Methods

Participants

The current study used data collected through the Young Adult Daily Life (YADL) Study in 2019, 2020, 2021, and 2022 (for details on study methods, see Patrick & Terry-McElrath, 2021). Participants were selected from 14,502 U.S. nationally representative 12th grade students participating in the Monitoring the Future (MTF) study in Spring 2018 (Miech et al., 2019). Eligibility requirements included reporting past 30-day alcohol use in the 2018 12th grade MTF survey. Of the 4,240 students who reported past 30-day drinking, 828 were excluded due to random selection into the MTF longitudinal study (Patrick et al., 2022), and another 1,208 were excluded due to missing contact information, leaving 2,204 individuals eligible for YADL. Data were collected from the end of May through July 2019 and from mid-February through May in 2020, 2021, and 2022. Each year, participants were asked to complete a 30-minute annual survey and 14 consecutive 5–7-minute daily surveys; respondents could receive incentives of up to $100 per year (in 2022, an additional $10 incentive was offered to those who delayed beginning their daily surveys to encourage daily survey completion). The study was approved by a University of Michigan Institutional Review Board.

Of the 2,204 eligible individuals, 1,114 (50.5%) participated in one or more years. Sensitivity analyses indicated that compared to the initial 2,204 eligible individuals, those participating in YADL differed at baseline (12th grade), such that they were more likely to be female, be non-binge drinkers, report higher grades, live in urban locations, and reside in the West or Northeast (vs. South). No significant differences were observed for racial/ethnic identity or parental education (see below for information on adjustments for nonresponse).

Most annual survey respondents (89.1%; 993 out of 1,114) completed at least one of 56 possible daily surveys (14 daily surveys x 4 years). A total of 35,647 daily surveys were completed. Mean per respondent among the 993 with any daily surveys was 35.9; thus, on average, respondents completed 64.1% (35.9/56) of all possible daily surveys. A total of 5,242 days (14.7% of 35,647) involved alcohol use. As the current research aims pertained only to alcohol use days, only participants reporting 1+ drinking days (83.8%, n=832) were retained; 14 respondents and a total of 162 drinking days (3.1% of the total 5,242 drinking days) were excluded because of missing context data. The final analytic sample included 818 participants with 5,080 drinking days (mean days per respondent was 6.2 [SD 5.61], range 1–41). As shown in Table 1, 53.7% were male; 64.3% identified as non-Hispanic White.

Table 1.

Descriptives

Drinking Days Respondents
n=5,080 n=818
%/Mean (SE) %/Mean (SE)
Drinking intensitya (%)
  Moderate 56.9 (1.99) 64.4 (1.59)
  Binge 30.3 (1.55) 24.9 (1.23)
  HID 12.8 (1.18) 10.8 (1.12)
Social settings
  Friends (%) 72.6 (1.73) 72.4 (1.39)
  A large group of people (%) 31.0 (1.40) 33.0 (1.57)
  Significant other/romantic partner (%) 27.1 (1.55) 27.9 (1.43)
  I was alone (%) 13.6 (1.65) 11.2 (1.05)
  People I don’t know well (%) 12.3 (0.86) 11.6 (1.03)
Drinking games/specials
  Any free drinks (%) 46.8 (1.72) 50.9 (1.61)
  Any pre-gaming (%) 20.5 (1.10) 19.5 (1.29)
  Any drinking games (%) 16.5 (1.13) 15.7 (1.11)
  Any discounted price drinks (%) 6.8 (0.56) 6.9 (0.84)
Locations
  At home (%) 48.8 (1.82) 43.5 (1.56)
  At someone else's apartment/house (%) 35.9 (1.73) 37.4 (1.56)
  At a bar/restaurant (%) 25.6 (1.33) 25.3 (1.37)
  At a party (%) 22.9 (1.15) 24.9 (1.48)
  At an event (%) 4.7 (0.56) 5.0 (0.84)
  In a car (%) 2.8 (0.32) 3.0 (0.42)
  At school or work (%) 2.5 (0.46) 1.9 (0.35)
  Number of locations (mean) 1.43 (0.02) 1.41 (0.03)
Within-person covariates
  Day of the week (%)
  Weekday (Sunday-Wednesday) 36.9 (1.00) 35.5 (1.43)
  Weekend (Thursday-Saturday) 63.1 (1.00) 64.5 (1.43)
Time periodb (%)
  2019 20.4 (1.04) 27.6 (1.55)
  2020 pre-COVID 17.0 (0.89) 17.1 (1.23)
  2020 early COVID 4.4 (0.62) 5.0 (0.80)
  2021 29.3 (1.05) 25.8 (1.30)
  2022 28.9 (1.09) 24.4 (1.17)
Age on drinking day (%)
  Less than 21 52.4 (1.58) 58.5 (1.60)
  21 or older 47.6 (1.58) 41.5 (1.60)
Between-person covariates
Sex (%)
  Female 46.3 (2.13)
  Male 53.7 (2.13)
Hispanic or Latino/ac (%)
  No 78.2 (1.96)
 Yes 21.8 (1.96)
Racec (%)
  American Indian or Alaska Native 1.3 (0.68)
  Arab, Middle Eastern or North African 0.6 (0.39)
  Asian or South Asian 3.3 (0.72)
  Black or African American 3.5 (0.78)
  Native Hawaiian or Other Pacific Islander 0.1 (0.08)
  White 77.0 (1.91)
  Other 6.2 (1.20)
  Multiracial 7.9 (1.16)
College status (%)
  Never full-time at 4-year college 34.8 (2.23)
  Ever full-time at 4-year college 65.2 (2.23)
  Age in years as of January 1, 2019 (mean) 18.9 (0.02)
Open in a new tab
a

Drinking intensity use coded using biological sex-specific thresholds: moderate drinking = <4 drinks for women and <5 drinks for men; binge drinking = 4–7 drinks for women and 5–9 drinks for men; high-intensity drinking = 8+ drinks for women and 10+ drinks for men.

b

Pre-pandemic 2020 = February 17-March 10; Early pandemic 2020 = March 11-March 30.

c

Due to sample size limitations, racial/ethnic groups were combined as follows for analysis: Hispanic (21.8% of respondents); non-Hispanic White (64.3% of respondents), and non-Hispanic Other (14.0% of respondents).

Measures

Annual survey.

Biological sex was coded female or male. Separate items measured ethnicity (Hispanic or Latino/a) and race (Asian or South Asian; Native Hawaiian or other Pacific Islander; Black or African American; American Indian or Alaska Native; White; Arab, Middle Eastern or North African; Other). Due to limited sample sizes, race/ethnicity was recoded for analysis as a trichotomy indicating Hispanic, non-Hispanic White, or non-Hispanic Other including multiracial. College status indicated attending a 4-year college full-time at any point during data collection (vs. not). (As almost all variance for the college status measure occurred between-person [ICC 0.971, SE 0.006], the measure was included at the person-level. Unfortunately, there was not enough variability to separately investigate 2-year, community college, or vocational-technical school.) Age as of January 1, 2019 was used as the between-person age measure.

Daily surveys.

On days respondents reported any alcohol use, they were asked how many total drinks they had (response options of 1–25+ drinks). Drinking intensity was coded using sex-specific thresholds: moderate drinking (1–3 drinks for females/1–4 drinks for males), binge but not high-intensity drinking (4–7 for females/5–9 for males; hereafter referred to simply as binge drinking), or high-intensity drinking (8+ for females/10+ for males; Patrick, 2016; Patrick & Terry-McElrath, 2021). Social settings were asked as “In regards to who you were with while drinking, do any of the following apply?” (no/yes; not mutually exclusive): I was alone, my significant other/romantic partner, friends, a large group of people, people I don’t know well. Drinking game/special contexts included four measures. (1) Pre-gaming: “You said you had [# drinks] on [day]. How many of those were pre-gaming, that is, consumed before going out?” (coded as any vs. none). (2) Free drinks (i.e., obtaining a drink without paying): “Were any of the drinks you had on [day] free? (no/yes). (3) Discounted price drinks (i.e., obtaining a drink at a lowered price): “Were any of the drinks you had on [day] discounted in price (e.g., happy hour or another special?)” (no/yes). (4) Drinking games: “On [day], did you play any drinking games?” (no/yes). Drinking locations were asked as “Were you drinking at/in any of the following locations?” (no/yes; items not mutually exclusive): at a party, at home, at someone else’s apartment/house, at school or work, in a car, at a bar/restaurant, or at an event, such as a concert or sporting event. Number of locations was calculated as the sum of all reported drinking locations on a given day. Weekend indicated whether the day was a Thursday, Friday, or Saturday (vs. other days) (Del Boca et al 2004; Patrick et al., 2016c). Legal drinking age on drinking day was coded as a dichotomy indicating less than 21 versus 21 or older. Time period was coded using categorical indicators for 2019, pre-pandemic 2020 (February 17-March 10), early pandemic 2020 (March 11-March 30), 2021, and 2022. The decision to include separate indicators for both pre- and post-pandemic in the year 2020 was based on the recognition that the onset of the pandemic may have affected alcohol use contexts and drinking intensity via social distancing, masking, public gathering limitations, and occupancy limits for restaurants, bars, and entertainment venues (National Academy for State Health Policy, 2021). Available research indicates that during the early months of the COVID-19 pandemic, drinking alone, virtually with others, or with family increased (Clare et al., 2021; Jackson et al., 2021; McPhee et al 2020), while drinking with others in person decreased (Clare et al., 2021). Several studies found people perceived their drinking decreased due to reduced social and/or on-premise drinking opportunities (Bramness et al 2021; Clare et al., 2021; Jackson et al 2021).

Analyses

Descriptive analyses used SAS v.9.4 to obtain estimates at the drinking day level clustered within persons using survey commands. Multi-level regression analyses modeled drinking days at Level 1 nested within persons at Level 2 and were conducted using Mplus v.7.4 (Muthén and Muthén, 1998–2015). Analyses were specified as “type=twolevel random” using Montecarlo integration and MLR estimator to obtain robust standard errors. All analyses were weighted to adjust for sampling and nonresponse (based on extensive information available from MTF 12th grade measures including sex, race/ethnicity, region, number of parents in the household, average parental education, religious commitment, average high school grades, truancy, college plans, and substance use). Benjamini-Hochberg tests with a false discovery rate of 5% adjusted for multiple testing (Benjamini & Hochberg, 1995). Comparison of raw p-values with their Benjamini-Hochberg critical values (BHCV) indicated p=.011 was the largest value that remained smaller than its BHCV. Thus, discussion of results will be limited to those with p-values of .011 or lower (results are presented using raw p-values).

Supplement Table 1 provides details on the models specified for each research aim. In analyses for RA1 (examining the extent to which drinking contexts differentiated daily drinking intensity) and RA3 (examining whether legal drinking age or college status moderated daily drinking context and intensity associations), multi-level multinomial regression models used categorical drinking intensity as the dependent measure. These models estimated the relative risk of being in separate categories of the drinking intensity outcome variable versus a pre-set base category/class. To model predictors of increasing and adjacent drinking intensity levels (i.e., the risk of high-intensity and binge vs. moderate, and the risk of high-intensity vs. binge), two separate models were used. In Model 1, moderate drinking was set as the referent class to obtain estimates comparing the likelihood of binge versus moderate, and high-intensity versus moderate drinking. In Model 2, binge was set as the referent class to obtain estimates comparing the likelihood of high-intensity versus binge. Estimates for uncentered dichotomous context measures at the day level represented associations between having the specified context (vs. not) and daily drinking intensity, controlling for number of drinking locations that day, legal drinking age status, weekend, time period, and all between-person differences. Only one specific context was included per model. Estimates for number of locations at the day level represented associations between drinking at more locations than one’s personal mean and drinking intensity, controlling for the specific context included, legal drinking age status, weekend, time period, and all between-person differences. When examining moderation of drinking context/intensity associations by legal drinking age or college status (RA3), one set of models included interactions with one context measure and the age 21+ indicator; another set of models included interactions with one context measure and the college status indicator. Due to low overall prevalence (below 3%), moderation was not examined for the locations of either car or school/work.

For RA2, (examining the extent to which legal drinking age and college status were associated with daily drinking contexts controlling for time period), multi-level logistic regression was used for modeling each non-centered dichotomous context measure; multi-level Poisson regression was used to model the number of locations. Estimates from these models represented associations between either legal drinking age or college status and either the odds of reporting the specified drinking context or the expected rate of drinking locations after controlling for time period.

It is important to recognize that in the current study, historical and developmental time were confounded. We cannot fully disentangle whether observed changes over historical time (from 2019 to 2021) reflect developmental change versus historical trends including the COVID-19 pandemic, and thus we do not focus on these associations. By including time period indicators, models accounted (to some degree) for both developmental change and historical trends. The between-person age measure controlled for pre-existing age differences among respondents. The within-person age 21+ indicator accounted for differences based on being able to legally purchase alcohol for those who turned 21 during the study.

Results

Descriptive statistics are shown in Table 1; Supplement Table 2 provides ICCs. At the day level, the most prevalent drinking social setting was with friends (72.6%) followed by large group (31.0%) and significant other/romantic partner (27.1%). Drinking alone was reported on 13.6% of drinking days. For drinking games/specials, rank order was free drinks (46.8%), pre-gaming (20.5%), drinking games (16.5%) and discounted price drinks (6.8%). The most prevalent drinking location was at home (48.8%) followed by another’s apartment/house (35.9%) and bars/restaurants (25.6%). Less than 5% of drinking days occurred at events (4.7%), in cars (2.8%) or at school/work (2.5%). The mean number of drinking locations per drinking day was 1.43.

Drinking context and intensity associations (RA1)

Table 2 presents day-level (within-person) associations between contexts and drinking intensity, controlling for the number of drinking locations (full multivariable model results are shown in Supplement Tables 3–5). The only person-level context measure that differentiated high-intensity and binge drinking risk was number of drinking locations; respondents who drank at more locations than average had higher odds of high-intensity (vs. binge) drinking. At the day level, more drinking locations were associated with higher odds of high-intensity (vs. binge and moderate) and binge (vs. moderate) drinking in all models controlling for social settings, drinking games/specials, or specific locations.

Table 2.

Multivariable day-level (within-person) associations between drinking contexts and drinking intensity among U.S. young adults, 2019–2022

Binge (vs Moderate) HID (vs Moderate) HID (vs Binge)
AOR (95% CI) p AOR (95% CI) p AOR (95% CI) p
Social settings
  Friends 2.01 (1.45, 2.78) <.001 2.53 (1.34, 4.77) 0.004 1.26 (0.73, 2.17) 0.405
  A large group of people 2.15 (1.75, 2.66) <.001 4.20 (3.09, 5.71) <.001 1.95 (1.40, 2.71) <.001
  Significant other/romantic partner 0.87 (0.70, 1.09) 0.223 1.13 (0.85, 1.49) 0.403 1.29 (0.96, 1.74) 0.096
  I was alone 0.99 (0.68, 1.45) 0.959 0.68 (0.37, 1.25) 0.213 0.69 (0.41, 1.14) 0.149
  People I don’t know well 1.59 (1.18, 2.14) 0.002 2.89 (1.96, 4.25) <.001 1.82 (1.18, 2.81) 0.007
  Number of locations 2.16 (1.84, 2.53) <.001 3.76 (3.05, 4.64) <.001 1.74 (1.45, 2.09) <.001
Drinking games/specials
  Any free drinks 0.86 (0.70, 1.07) 0.170 1.10 (0.80, 1.52) 0.560 1.28 (0.93, 1.76) 0.137
  Any pre-gaming 2.63 (1.97, 3.50) <.001 4.08 (2.88, 5.77) <.001 1.55 (1.12, 2.16) 0.009
  Any drinking games 3.78 (2.73, 5.23) <.001 5.91 (4.04, 8.66) <.001 1.56 (1.11, 2.20) 0.010
  Any discounted price drinks 1.70 (1.14, 2.54) 0.010 3.08 (1.93, 4.91) <.001 1.81 (0.97, 3.38) 0.061
  Number of locations 2.39 (2.02, 2.83) <.001 4.10 (3.33, 5.06) <.001 1.72 (1.43, 2.06) <.001
Locations
  At home 0.65 (0.52, 0.80) <.001 0.60 (0.44, 0.81) 0.001 0.92 (0.67, 1.26) 0.608
  At someone else’s apartment/house 1.39 (1.12, 1.71) 0.003 1.06 (0.77, 1.45) 0.732 0.76 (0.52, 1.12) 0.169
  At a bar/restaurant 1.04 (0.81, 1.32) 0.782 1.30 (0.95, 1.77) 0.099 1.25 (0.91, 1.72) 0.159
  At a party 1.89 (1.45, 2.47) <.001 2.75 (1.93, 3.92) <.001 1.45 (1.05, 2.01) 0.025
  At an event 0.98 (0.65, 1.47) 0.906 0.85 (0.52, 1.39) 0.513 0.87 (0.50, 1.33) 0.522
  In a car 0.96 (0.56, 1.64) 0.882 0.70 (0.38, 1.30) 0.262 0.73 (0.39, 1.38) 0.332
  At school or work 0.18 (0.08, 0.42) <.001 0.17 (0.08, 0.40) <.001 0.94 (0.43, 2.07) 0.873
  Number of locations 2.39 (2.03, 2.82) <.001 4.24 (3.21, 5.25) <.001 1.77 (1.49, 2.11) <.001
Open in a new tab

Notes: n=5,080 drinking days from 818 individuals. HID=high-intensity drinking. Model 1 used moderate drinking as the referent class for the drinking intensity outcome; Model 2 used binge drinking as the referent. Each model included both number of total locations (person centered at the day level, and grand mean centered [person mean minus grand mean] at the person level), and one additional dichotomous drinking context measure (uncentered at the day level, and grand mean centered at the personal level). Estimates for “number of locations” shown here obtained from the model including the first listed context in the section (home, friends, free drinks). Additional controls included at the day level included weekend, time period, and age 21. Additional controls included at the person level included sex, race/ethnicity, college status, and age as of 1/1/2019. AOR = adjusted odds ratio from multivariable multinomial regression; CI=confidence interval. Bold font indicates comparisons significant at p≤.011.

Social settings.

Controlling for number of drinking locations, the odds of high-intensity (vs. binge) drinking were greater on days when respondents drank with large groups and strangers; the odds of both high-intensity and binge (vs. moderate) drinking were greater on days respondents drank with friends, large groups, and strangers.

Drinking games/specials.

Controlling for number of drinking locations, the odds of high-intensity (vs. binge) drinking were greater on days with pre-gaming or drinking games; the odds of both high-intensity and binge (vs. moderate) drinking were greater on days with pre-gaming, drinking games, or discounted price drinks.

Drinking locations.

After controlling for number of drinking locations, no specific location was associated with the odds of high-intensity (vs. binge) drinking. The odds of high-intensity and binge (vs. moderate) drinking were lower on days drinking occurred at home or at school/work, and greater on days drinking occurred at parties. The odds of binge (vs. moderate) drinking also were greater on days drinking occurred at another’s apartment/home.

Drinking day age and college status associations with drinking contexts (RA2)

As shown in Table 3, being age 21+ on a drinking day was associated with greater odds of drinking at bars/restaurants and lower odds of free drinks. As shown in Table 4, attending a 4-year college full-time was associated with greater odds of drinking with friends or large groups; drinking involving pre-gaming, drinking games, or discounted price drinks, and drinking at bars/restaurants or events. Attending a 4-year college full-time also was associated with a higher expected rate of number of drinking locations. In contrast, attending a 4-year college full-time was associated with lower odds of drinking alone or free drinks.

Table 3.

Drinking day associations between legal drinking age and drinking contexts: U.S. young adults, 2019–2022

Legal drinking age status
Less than 21 21 or older
Mean/% (SE) Mean/% (SE) AOR (95% CI) p
Social settings (%)
  Friends 74.5 (1.83) 70.5 (2.65) 1.36 (0.85, 2.18) 0.194
  A large group of people 35.1 (1.54) 26.4 (1.93) 1.07 (0.67, 1.72) 0.784
  Romantic partner 24.6 (1.83) 29.9 (2.21) 0.82 (0.43, 1.57) 0.550
  I was alone 10.4 (1.10) 17.2 (2.94) 0.56 (0.30, 1.06) 0.077
  People I don’t know well 13.4 (0.95) 11.1 (1.19) 1.03 (0.59, 1.80) 0.907
Drinking games/specials (%)
  Free drinks 58.4 (2.11) 34.0 (2.10) 0.54 (0.34, 0.86) 0.009
  Pre-gaming 21.6 (1.46) 19.3 (1.42) 0.93 (0.58, 1.48) 0.755
  Drinking games 20.0 (1.46) 12.5 (1.24) 1.01 (0.63, 1.65) 0.955
  Discounted price drinks 5.9 (0.74) 7.9 (0.78) 1.15 (0.59, 2.24) 0.674
Locations (%)
  At home 44.7 (2.05) 53.4 (2.44) 0.93 (0.56, 1.53) 0.765
  At another’s apartment/house 43.6 (2.12) 27.4 (1.88) 0.73 (0.45, 1.19) 0.206
  At a bar/restaurant 15.5 (1.28) 36.7 (2.10) 2.93 (1.84, 4.69) <.001
  At a party 28.3 (1.42) 16.8 (1.44) 0.93 (0.57, 1.54) 0.786
  At an event 4.5 (0.74) 4.9 (0.73) 1.01 (0.38, 2.70) 0.979
  In a car 2.8 (0.39) 2.8 (0.51) 1.72 (0.72, 4.13) 0.221
  At school or work 2.3 (0.68) 2.7 (0.61) 0.71 (0.14, 3.71) 0.687
AIRR (95% CI) p
Number of locations (mean) 1.42 (0.03) 1.45 (0.03) 1.48 (0.21, 10.59) 0.695
Open in a new tab

Notes: n=5,080 drinking days from 818 individuals. SE = standard errors accounting for clustering within respondents. AOR=adjusted odds ratio; AIRR=adjusted incidence rate ratio; CI=confidence interval. One context per model; time period (referent=2019) included as a covariate. Bold font indicates comparisons significant at p≤.011.

Table 4.

Drinking day associations between college status and drinking contexts: U.S. young adults, 2019–2022

Full-time at 4-year college during 2019–2022
Never Ever
Mean/% (SE) Mean/% (SE) AOR (95% CI) p
Social settings (%)
  Friends 57.9 (3.91) 77.4 (1.54) 2.31 (1.59, 3.36) <.001
  A large group of people 20.9 (2.75) 34.3 (1.52) 2.19 (1.43, 3.35) <.001
  Romantic partner 30.0 (3.21) 26.2 (1.77) 0.70 (0.44, 1.12) 0.138
  I was alone 25.9 (4.81) 9.6 (1.18) 0.30 (0.17, 0.55) <.001
  People I donť know well 9.6 (1.78) 13.2 (0.97) 1.47 (0.89, 2.40) 0.131
Drinking games/specials (%)
  Free drinks 52.0 (4.27) 45.1 (1.79) 0.59 (0.41, 0.87) 0.007
  Pre-gaming 8.4 (1.37) 24.5 (1.27) 4.37 (2.78, 6.85) <.001
  Drinking games 8.0 (1.36) 19.2 (1.30) 3.18 (2.01, 5.03) <.001
  Discounted price drinks 3.1 (0.63) 8.1 (0.69) 2.90 (1.69, 4.97) <.001
Locations (%)
  At home 51.1 (4.25) 48.1 (1.95) 1.00 (0.67, 1.47) 0.980
  At another’s apartment/house 30.7 (3.09) 37.6 (2.01) 1.28 (0.91, 1.81) 0.159
  At a bar/restaurant 17.4 (2.05) 28.3 (1.62) 2.42 (1.57, 3.71) <.001
  At a party 16.5 (2.43) 25.0 (1.24) 1.84 (1.20, 2.83) 0.005
  At an event 3.8 (1.11) 5.1 (0.65) 1.46 (0.72, 2.97) 0.293
  In a car 3.8 (0.84) 2.5 (0.33) 0.64 (0.36, 1.14) 0.131
  At school or work 3.2 (1.13) 2.3 (0.47) 0.77 (0.24, 2.51) 0.666
AIRR (95% CI) p
Number of locations (mean) 1.26 (0.04) 1.49 (0.03) 2.12 (1.45, 3.08) <.001
Open in a new tab

Notes: n=5,080 drinking days from 818 individuals. SE = standard errors accounting for clustering within respondents. AOR=adjusted odds ratio; AIRR=adjusted incidence rate ratio; CI=confidence interval. One context per model; time period (referent=2019) included as a covariate. Bold font indicates comparisons significant at p≤.011.

Moderation by drinking day age or college status (RA3)

Drinking day age.

Results across models showed no direct within-person associations between being 21+ on a drinking day and daily drinking intensity in a multivariable context (Supplement Tables 3–5). Three of 16 age interaction models were significant at the p<.011 level (results not tabled): age 21× friends interaction for high-intensity versus both binge (p=0.005) and moderate (p=0.002) drinking; age 21× alone interaction for high-intensity versus moderate drinking (p=0.007), and age 21+ × party interaction for high-intensity versus binge drinking (p=0.005). Group-specific models (results not tabled) showed that when not 21+ on a drinking day, drinking with friends was associated with greater odds of high-intensity versus binge (AOR 3.58 [1.61, 7.95], p=0.002) and moderate (AOR 8.52 [4.06, 17.92], p<.001) drinking, while no associations were observed when 21+ (p-values 0.401 and 0.331, respectively). When not 21+ on a drinking day, drinking alone was associated with lower odds of high-intensity versus moderate drinking (AOR 0.23 [0.08, 0.63], p=0.004), but no associations were observed when 21+ (p=0.991). Finally, when not 21+ on a drinking day, drinking at a party was not associated with the odds of high-intensity versus binge drinking (p=0.950), but when 21+, parties were associated with greater odds of high-intensity versus binge drinking (AOR 2.12 [1.35, 3.33], p=0.001).

College status.

Results across models showed no direct between-person associations between attending a 4-year college full-time and daily drinking intensity in the multivariable context (Supplement Tables 3–5). Two of 16 college interaction models were significant at the p<.011 level (results not tabled): college × friends interaction for high-intensity versus moderate (p=0.001) and binge versus moderate (p=0.001) drinking; and college × strangers interaction for high-intensity versus moderate (p=0.008) and binge versus moderate (p<.001) drinking. Group-specific models (not tabled) showed that among college respondents, drinking with friends was associated with greater odds of high-intensity versus moderate (AOR 4.88 [1.77, 13.45], p=0.002) and binge versus moderate (AOR 2.77 [1.80, 4.27], p<.001) drinking; no significant associations were observed among non-college respondents (p-values 0.836 and 0.328, respectively). Similarly, among college respondents, drinking with strangers was associated with greater odds of high-intensity versus moderate (AOR 3.69 [2.30, 5.91], p<.001) and binge versus moderate (AOR 1.72 [1.21, 2.45], p=0.003) drinking; associations among non-college respondents were not significant (p-values 0.317 and 0.101, respectively).

Discussion

Using a social-ecological framework (Sallis et al., 2008; Sorensen et al., 2003), the current study examined the extent to which drinking contexts differentiated daily drinking intensity (RA1), whether legal drinking age and college status were associated with drinking contexts (RA2), and whether legal drinking age and college status moderated associations between drinking contexts and daily drinking intensity (RA3). Results showed engaging in high-intensity drinking on a specific day was associated with interpersonal (social situations) and physical environmental (drinking games/specials and locations) factors. Contexts differentiating high-intensity drinking days from both binge or moderate drinking days included social settings of drinking with large groups and people the individual did not know well, and physical environmental factors of pre-gaming, drinking games, and a higher number of drinking locations. Additional contexts that differentiated high-intensity from moderate drinking included the social settings of drinking with friends and physical environmental factors of discounted price drinks as well as locations of home, parties, and school/work.

The findings from the current study both support and expand prior research. Previous studies using repeated measures across days with college students indicated drinking at bars/parties (Cox et al., 2022; Patrick et al., 2016a) as well as pre-gaming and drinking games (Fairlie et al., 2015) were associated with high-intensity drinking; the current study was able to observe these associations—as well as others—among a general young adult sample including both college and non-college participants. Further, the current study joined Cox et al. (2022) in identifying contexts that differentiated high-intensity drinking days from binge or moderate drinking days. Previous studies found greater alcohol consumption was associated with drinking at multiple locations (Dietze et al., 2014; Labhart et al., 2017; White et al., 2020, as well as specific locations of parties, bars, events (Braitman et al., 2017; Demers et al., 2002; Finlay et al., 2012; Kairouz et al., 2002; Kuntsche et al., 2015; Mustonen et al., 2014; Paradis et al., 2011), drinking with friends and large groups (Ally et al., 2016; Braitman et al., 2017; Cullum et al., 2012; Demers et al., 2002; Kairouz et al., 2002; Kuntsche et al. 2015; Mustonen et al., 2014; Paradis et al., 2011; Smit et al., 2015; Thrul & Kuntsche 2015), and participating in pre-gaming and drinking games (Barnett et al., 2013; Clapp et al., 2008; Kuntsche & Labhart, 2013; Kuntsche et al., 2015; Labhart et al., 2013, 2014; Merrill et al., 2013; Østergaard & Skov, 2014). Cox et al. (2022) found that the locations of party and friend’s house at last drink on a particular drinking day were associated with increased risk of high-intensity (vs. binge) drinking. The current study found that after controlling for number of drinking locations, no specific locations differentiated high-intensity and binge drinking risk; contexts that differentiated high-intensity days from binge or moderate drinking days included social settings of drinking with large groups and people the individual did not know well, and physical environmental factors of pre-gaming, drinking games.

It is important to recognize that alcohol consumption on any particular day may involve complex overlays of locations, social settings, and possibly drinking games/specials. In this study, none of the daily drinking contexts were mutually exclusive. Therefore, one drinking day may have involved drinking alone at home. Another drinking day may have involved different types of drinking at different locations, such as pre-gaming with friends at another’s house, then drinking with friends, a significant other, and strangers at a party with free drinks and drinking games. The current study focused on what contexts differentiated drinking intensity, and the findings provide opportunities for prevention and intervention. Once high-risk contexts are identified, they can be incorporated into interventions designed to reduce drinking risk such as policy-level efforts to set minimum drink prices (Stockwell et al., 2012) or by including protective behavioral strategies that are specific to high-risk drinking contexts using personalized feedback (Arterberry et al., 2014; Reid and Carey, 2015) or tailored messaging provided through ecological momentary interventions (EMIs). Incorporating contexts in EMI and just-in-time interventions could be feasible by sending text messages to the individual during high-risk times such as the weekend to remind them about contexts that may be associated with high-intensity drinking and provide personalized protective behavioral strategy options to reduce negative consequences. One potential avenue for targeting drinking contexts in a brief intervention could be to discuss specific protective behavioral strategies (e.g., avoiding shots) to reduce high-intensity drinking risk in specific contexts (e.g., at parties). Personalized feedback and just-in-time interventions may provide enhanced flexibility for targeting drinking context risks.

In the current study, after controlling for within- and between-person drinking contexts, drinking intensity did not significantly vary based on legal drinking status. This raises questions as to which contexts support similar drinking intensity on days people are under versus over legal drinking age. Being 21+ was associated with greater odds of drinking at bars/restaurants and stronger association between parties and high-intensity drinking, but being under 21 was associated with higher odds of free drinks and stronger associations between drinking with friends and high-intensity drinking. While the age-related increase in drinking at bars/restaurants is not surprising, the corresponding decrease in free drinks is notable for its implications regarding supply mechanisms for underage drinking. The decrease in free drinks on drinking days when the respondent was at or over age 21 is likely related to the ability to legally order and purchase alcohol (underage drinkers may be more likely to select contexts with free drinks). However, the decrease may also reflect societal-level support for encouraging underage alcohol consumption. There are many reasons free drinks may be provided, and from a wide range of sources including friends, family members, fraternity and/or sorority houses, bartenders, etc. Regardless of source, it may be worthwhile to consider the importance of underage drinking (regardless of proximal source) to the beverage industry. Research indicates that total underage drinking is associated with almost 10% of overall U.S. consumer alcohol spending; in 2016, this equated to approximately $17.5 billion (Eck et al., 2021).

The current study found that college status was associated with at least one specific context in all categories examined: social situations, drinking games/specials, and locations. Results indicated greater emphasis on social drinking for college young adults. Non-college young adults were more likely to drink alone, while college respondents were more likely to drink with friends and large groups. Further, positive associations between social settings of friends and strangers and drinking intensity were stronger among college than non-college respondents. These findings are in line with prior research indicating that those attending college report higher social drinking motives, and such motives are associated with greater odds of high-intensity drinking (Patrick & Terry-McElrath, 2021). In the current study, college respondents also had greater odds than non-college respondents of reporting other contexts associated with increased high-intensity drinking risk: more drinking locations per day, pre-gaming, and drinking games. When controlling for drinking context associations, no direct associations were observed between college status and drinking intensity. Given that past research has found that college students have historically higher levels of alcohol consumption than their non-college peers, these results suggest that differences in drinking contexts, and the degree to which contexts are associated with drinking intensity, may play a meaningful role in differential alcohol use among college/non-college young adults.

Limitations and future directions

Results are subject to limitations. The sample was based on individuals reporting past 30-day alcohol use as 12th grade students. Individuals who dropped out of school prior to 12th grade were not included; thus, the sample was predominately (65.2%) college-attending. The requirement to have recently used alcohol while in 12th grade resulted in a majority non-Hispanic White sample. Data about previous day drinking contexts and behaviors might be subject to bias or recall error, and the measure of drinking intensity did not include duration of time spent drinking. The study was also subject to confounding between developmental and historical time (although as shown in Supplemental Tables 3–5, time period had very few significant associations). Such limitations notwithstanding, analyses examined within-person drinking context/intensity associations across three years using a national young adult sample. While analyses were able to examine differences associated with full-time attendance at a 4-year college (vs. not), future research is needed that can examine more detailed definitions of college attendance (e.g., part-time, 2-year institutions) and drinking context/intensity associations. Additionally, future studies should examine how drinking contexts and the nature of associations between contexts and drinking intensity change across the life course.

Drinking in social situations, drinking games/specials, and number of drinking locations were associated with day-to-day differences in the likelihood of young adult high-intensity versus binge or moderate drinking and are appropriate for consideration as intervention targets. By including drinking contexts in personalized feedback interventions, clinicians would have the opportunity to identify and intervene for drinking contexts that lead to high-risk drinking behaviors such as high-intensity drinking, which in turn leads to more severe consequences. As clinicians and researchers, we need to continue to identify the most salient risk factors such as drinking contexts associated with young adult high-intensity versus binge or moderate drinking to reduce the incidence of severe alcohol-related negative consequences.

Supplementary Material

Supplementary material

Acknowledgments

Data collection and manuscript preparation were supported research grants from the National Institute on Alcohol Abuse and Alcoholism (R01AA023504) and the National Institute on Drug Abuse (R01DA001411 and R01DA016575). The study sponsors had no role in the study design, collection, analysis or interpretation of the data, writing of the manuscript, or the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the study sponsor.

Footnotes

BJ Arterberry serves as Vice President of the Source Research Foundation. YM Terry-McElrath and ME Patrick have no conflicts of interest to declare.

References

  1. Ally A, Lovatt M, Meier PS, Brennan A, Holmes J (2016) Developing a social practice-based typology of British drinking culture in 2009–2011: implications for alcohol policy analysis. Addict 111:1568–1579. doi: 10.1111/add.13397 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arterberry BJ, Smith AE, Martens MP, Cadigan JM, Murhpy JG (2014) Protective behavioral strategies, social norms, and alcohol-related outcomes. Addict Res Theory 22(4):279–285. doi: 10.3109/16066359.2013.838226 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barnett NP, Orchowski LM, Read JP, Kahler CW (2013) Predictors and consequences of pregaming using day- and week-level measurements. Psychol Addict Behav 27(4):921–933. doi: 10.1037/a0031402 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300. [Google Scholar]
  5. Braitman AL, Linden-Carmichael AN, Henson JM (2017) Protective behavioral strategies as a context- specific mediator: a multilevel examination of within- and between- person associations of daily drinking. Exp Clin Psychopharmacol 25:141–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bramness JG, Bye EK, Moan IS, Rossow I (2021) Alcohol use during the COVID-19 pandemic: self-reported changes and motives for change. Eur Addict Res 27:257–262. doi: 10.1159/000515102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Clapp JD, Min JW, Shillington AM, Reed MB, Cross JK (2008) Person and environment predictors of blood alcohol concentrations: a multi-level study of college parties. Alcohol Clin Exp Res 32(1):100–107. doi: 10.1111/j.1530-0277.2007.00547.x [DOI] [PubMed] [Google Scholar]
  8. Clare PJ, Aiken A, Yuen WS, Upton E, Kypri K, Degenhardt L, Bruno R, McCambridge J, McBride N, Hutchinson D, Slade T, Mattick R, Peacock A (2021) Alcohol use among young Australian adults in May-June 2020 during the COVID-19 pandemic: a prospective cohort study. Addict 116(12):3398–3407. doi: 10.1111/add.15599 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cox MJ, Egan KL, Suerken CK, Reboussin BA, Song EY, Wagoner KG, Wolfson M (2019) Social and situational party characteristics associate with high-intensity alcohol use among youth and young adults. Alcohol Clin Exp Res 43(9):1957–1966. doi: 10.1111/acer.14143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cox MJ, Stevens AK, Janssen T, Jackson KM (2022) Event-level contextual predictors of high-intensity drinking events among young adults. Drug Alc Depend 239:109590. doi: 10.1016/j.drugalcdep.2022.109590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cullum J, O’Grady M, Armeli S, Tennen H (2012) The role of context-specific norms and group size in alcohol consumption and compliance drinking during natural drinking events. Basic Appl Soc Psych 34(4):304–312. doi: 10.1080/01973533.2012.693341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Del Boca FK, Darkes J, Greenbaum PE, Goldman MS (2004) Up close and personal: temporal variability in the drinking of individual college students during their first year. J Consult Clin Psychol 72:155–164. [DOI] [PubMed] [Google Scholar]
  13. Demers A, Kairouz S, Adlaf EM, Gliksman L, Newton-Taylor B, Marchand A (2002) Multilevel analysis of situational drinking among Canadian undergraduates. Soc Sci Med 55:415–424. [DOI] [PubMed] [Google Scholar]
  14. Dietze PM, Livingston M, Callinan S, Room R (2014). The big night out: what happens on the most recent heavy drinking occasion among young Victorian risky drinkers? Drug Alcohol Rev 33:346–353. doi: 10.1111/dar.12117 [DOI] [PubMed] [Google Scholar]
  15. Eck RH, Trangenstein PJ, Siegel M, Jernigan DH (2021). Company-specific revenues from underage drinking. J Stud Alcohol Drugs 82(3):368–376. 10.15288/jsad.2021.82.368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fairlie A, Maggs JL, Lanza ST (2015) Prepartying, drinking games, and extreme drinking among college students: a daily-level investigation. Addict Behav 42:91–95. doi: 10.1016/j.addbeh.2014.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Finlay AK, Ram N, Maggs JL, Caldwell LL (2012) Leisure activities, the social weekend, and alcohol use: evidence from a daily study of first-year college students. J Stud Alcohol Drugs 73:250–259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Freisthler B, Lipperman-Kreda S, Bersamin M, Gruenewald PJ (2014. Tracking the when, where, and with whom of alcohol use: integrating ecological momentary assessment and geospatial data to examine risk for alcohol-related problems. Alcohol Res 36(1):29–38. [PMC free article] [PubMed] [Google Scholar]
  19. Jackson KM, Merrill JE, Stevens AK, Hayes KL, White HR (2021) Changes in alcohol use and drinking context due to the COVID-19 pandemic: a multimethod study of college student drinkers. Alcohol Clin Exp Res 45(4):752–764. doi: 10.1111/acer.14574 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kairouz S, Gliksman L, Demers A, Adlaf EM (2002) For all these reasons, I do….drink: a multilevel analysis of contextual reasons for drinking among Canadian undergraduates. J Stud Alcohol 63:600–608. [DOI] [PubMed] [Google Scholar]
  21. Kuntsche E, Labhart F (2013) Drinking motives moderate the impact of pre-drinking on heavy drinking on a given evening and related adverse consequences—an event-level study. Addict 108:1747–1755. doi: 10.1111/add.12253 [DOI] [PubMed] [Google Scholar]
  22. Kuntsche E, Otten R, Labhart F (2015) Identifying risky drinking patterns over the course of Saturday evenings: an event-level study. Psychol Addict Behav 29(3):744–752. doi: 10.1037/adb0000057 [DOI] [PubMed] [Google Scholar]
  23. Labhart F, Anderson KG, Kuntsche E (2017). The spirit is willing, but the flesh is weak: why young people drink more than intended on weekend nights—an event-level study. Alcohol Clin Exp Res 41(11):1961–1969. doi: 10.1111/acer.13490 [DOI] [PubMed] [Google Scholar]
  24. Labhart F, Graham K, Wells S, Kuntsche E (2013). Drinking before going to licensed premises: an event-level analysis of predrinking, alcohol consumption, and adverse outcomes. Alcohol Clin Exp Res 37(2):284–291. doi: 10.1111/j.1530-0277.2012.01872.x [DOI] [PubMed] [Google Scholar]
  25. Labhart F, Wells S, Graham K, Kuntsche E (2014) Do individual and situational factors explain the link between predrinking and heavier alcohol consumption? An event-level study of types of beverage consumed and social context. Alcohol Alcohol 49(3):327–335. doi: 10.1093/alcalc/agu001 [DOI] [PubMed] [Google Scholar]
  26. McPhee MD, Keough MT, Rundle S, Heath LM, Wardell JD, Hendershot CS (2020) Depression, environmental reward, coping motives and alcohol consumption during the COVID-19 pandemic. Front Psychiatry 11:574676. doi: 10.3389/fpsyt.2020.574676 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Merrill JE, Boyle HK, López G, Miller MB, Barnett NP, Jackson KM, Carey KB (2021) Contextual factors associated with high-intensity drinking events among young adults: a qualitative inquiry. Alcohol Clin Exp Res 45:1317–1330. doi: 10.1111/acer.14623 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Merrill JE, Vermont LN, Bachrach RL, Read JP (2013) Is the pregame to blame? Event-level associations between pregaming and alcohol-related consequences. J Stud Alcohol Drugs 74:757–764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Miech RA, Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME (2019) Monitoring the Future national survey results on drug use, 1975–2018: volume I, secondary school students. Institute for Social Research, The University of Michigan, Ann Arbor, MI. http://monitoringthefuture.org/pubs/monographs/mtf-vol1_2018.pdf [Google Scholar]
  30. Mustonen H, Mäkelä P, Linton T (2014) Toward a typology of drinking occasions: latent classes of an autumn week’s drinking occasions. Addict Res Theory 22(6):524–534. doi: 10.3109/16066359.2014.911845 [DOI] [Google Scholar]
  31. National Academy for State Health Policy (2021) 2020 COVID-19 state restrictions, re-openings, and mask requirements. https://www.nashp.org/2020-state-reopening-chart/ Accessed 8/26/21.
  32. Østergaard J, Skov PR (2014) Do pre-drinkers consume more alcohol than non-pre-drinkers on an event-specific night out? A cross-national panel mobile survey of young people’s drinking in England and Denmark. Drug Alcohol Rev 33:376–384. doi: 10.1111/dar.12161 [DOI] [PubMed] [Google Scholar]
  33. Paradis C, Demers A, Nadeau L, Picard E (2011) Parenthood, alcohol intake, and drinking contexts: occasio furem facit. J Stud Alcohol Drugs 72:259–269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Paschall MJ, Saltz RF (2007) Relationships between college settings and student alcohol use before, during and after events: a multi-level study. Drug Alcohol Rev 26:635–644. [DOI] [PubMed] [Google Scholar]
  35. Patrick ME (2016) A call for research on high-intensity alcohol use. Alcohol Clin Exp Res 40:256–259. doi: 10.1111/acer.12945 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Patrick ME, Azar B (2017) High-intensity drinking. Alcohol Clin Exp Res 39:e1–e7. [PMC free article] [PubMed] [Google Scholar]
  37. Patrick ME, Cronce JM, Fairlie AM, Atkins DC, Lee CM (2016a) Day-to-day variations in high-intensity drinking, expectancies, and positive and negative alcohol-related consequences. Addict Behav 58:110–116. doi: 10.1016/j.addbeh.2016.02025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Patrick ME, Schulenberg JE, Miech RA, Johnston LD, O’Malley PM, Bachman JG (2022) Monitoring the Future Panel Study annual report: national data on substance use among adults ages 19 to 60, 1976–2021. Institute for Social Research, The University of Michigan, Ann Arbor, MI. http://monitoringthefuture.org/panelreport2022 [Google Scholar]
  39. Patrick ME, Terry-McElrath YM (2017) High-intensity drinking by underage young adults in the United States. Addiction 112(1):82–93. doi: 10.1111/add.13556 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Patrick ME, Terry-McElrath YM (2019) Prevalence of high-intensity drinking from adolescence through young adulthood: national data from 2016–2017. Subst Abuse 13:1–5. 10.1177/1178221818822976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Patrick ME, Terry-McElrath YM (2021) Drinking motives and drinking consequences across Days: differences and similarities between moderate, binge, and high-intensity drinking. Alcohol Clin Exp Res 45:1078–1090. doi: 10.1111/acer.14591 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Patrick ME, Terry-McElrath YM, Kloska DD, Schulenberg JE (2016b) High-intensity drinking among young adults in the United States: prevalence, frequency, and developmental change. Alcohol Clin Exp Res 40(9):1905–1912. DOI: 10.1111/acer.13164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Patrick ME, Terry-McElrath YM, Miech RA, Schulenberg JE, O’Malley PM, Johnston LD (2017) Age-specific prevalence of binge and high-intensity drinking among US young adults: Changes from 2005–2015. Alcohol Clin Exp Res 41(7):1319–1328. DOI: 10.1111/acer.13413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Patrick ME, Yeomans-Maldonado G, Griffin J (2016c) Daily reports of positive and negative affect and alcohol and marijuana use among college student and nonstudent young adults. Subst Use Misuse 51(1):54–61. doi: 10.3109/10826084.2015.1074694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pennay A, Miller P, Busija L, Jenkinson R, Droste N, Quinn B, Jones SC, Lubman DI (2015) ‘Wide-awake drukenness’? Investigating the association between alcohol intoxication and stimulant use in the night-time economy. Addict 110:356–365. doi: 10.1111/add.12742 [DOI] [PubMed] [Google Scholar]
  46. Reid AE, Carey KB (2015) Interventions to reduce college student drinking: state of the evidence for mechanisms of behavior change. Clin Psychol Rev 40:213–224. doi: 10.1016/j.cpr.2015.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sallis JF, Owen N, Fisher EB (2008) Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K (editors), Health behavior and health education (4th ed., pp. 465–485. San Francisco: John Wiley Sons. [Google Scholar]
  48. Sharma A, Sinha K, Vandenberg B (2017) Pricing as a means of controlling alcohol consumption. Br Med Bull 123(1):149–158. 10.1093/bmb/ldx020 [DOI] [PubMed] [Google Scholar]
  49. Smit K, Groefsema M, Luijten M, Engels R, Kuntsche E (2015) Drinking motives moderate the effect of the social environment on alcohol use: an event-level study among young adults. J Stud Alcohol Drugs 76:971–980. [DOI] [PubMed] [Google Scholar]
  50. Sorensen G, Emmons K, Hunt MK, Barbeau E, Goldman R, Peterson K, Kuntz K, Stoddard A, Berkman L (2003) A model for incorporating the social context in health behavior interventions: applications for cancer prevention for working-class, multiethnic populations. Prev Med 37:188–197. doi: 10.1016/s0091-7435(03)00111-7 [DOI] [PubMed] [Google Scholar]
  51. Stanesby O, Labhart F, Dietze P, Wright CJC, Kuntsche E (2019). The contexts of heavy drinking: a systematic review of the combinations of context-related factors associated with heavy drinking occasions. PLoS ONE 14(7): e0218465. doi: 10.1371/journal.pone.0218465 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Stockwell T, Auld MC, Zhao J, Martin G (2012) Does minimum pricing reduce alcohol consumption? The experience of a Canadian province. Addict 107(5):912–920. 10.1111/j.1360-0443.2011.03763.x [DOI] [PubMed] [Google Scholar]
  53. Terry-McElrath YM, Patrick ME (2020). US adolescent alcohol use by race/ethnicity: consumption and perceived need to reduce/stop use. J Ethn Subst Abuse 19(1):3–27. doi: 10.1080/15332640.2018.1433094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Thrul J, Kuntsche E (2015) The impact of friends on young adults’ drinking over the course of the evening—and event-level analysis. Addict 110:619–626. doi: 10.1111/add.12862 [DOI] [PubMed] [Google Scholar]
  55. White J, Bell S, Batty GD (2020). Does the social context of early alcohol use affect alcohol-related harms in adulthood? Findings from a national birth cohort. Prev Med 130:105947. doi: 10.1016/j.ypmed.2019.105947 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material
NIHMS1871348-supplement-Supplementary_material.docx (55.6KB, docx)

RESOURCES