Drinking patterns, however, fluctuate over time. Changes in college drinking can reflect periods of transition, such as moving from home to the freshman year of college . Other transitions have less often been studied, such as when students return to their home environment over the summer , or the transition from summer back to university. Drinking is also likely to escalate during celebrations, including 21st birthdays, spring-break parties, and campus-centered events such as football games . An annual celebration at our university is the 1-day Sun God Festival, which on May 16, 2014 occurred during our study of the impact of a program aimed at decreasing campus heavy drinking . That year the festival attracted more than 16,000 participants, among which 155 attendees received student-conduct violations primarily for alcohol, 70 needed help to sober up on site, 21 required more intense medically monitored treatment for intoxication on site, 21 were transported to a healthcare facility, and 11 participants were arrested . Our 2014 campus heavy drinking prevention study gathered drinking-related data at 8 time points over 55 weeks. Ninety-two percent of students who entered the study completed at least 7 assessments, which allowed us to compare drinking patterns across a range of relatively rarely studied transitions, and to evaluate predictors of drinking patterns at each time point. In light of concerns expressed about heavy episodic, or “binge,” drinking, the analyses focus on drinking quantities. These longitudinal data were used to test the following 4 hypotheses. Despite the single-day duration of the Sun God event, Hypothesis 1 predicted that drinking quantities will increase during the month of the Sun God Festival compared with earlier alcohol-related practices. reflecting recent results with students mandated for counseling for drinking infractions and potential dampening effects parents might have on drinking practices, Hypothesis 2 stated that students will decrease drinking quantities over the summer. reflecting drinking practices related to specific environments , Hypothesis 3 anticipated students will resume higher quantities upon return to school.
Hypothesis 4 stated that predictors of drinking patterns over time will encompass a wide range of characteristics that include demography,pots for cannabis plants substance use, and environment as well as attitudes toward drinking.Following approval by our university’s Human Protections Committee, in November, 2013, questionnaires were emailed to 4,000 freshmen using questions derived from the Semi-Structured Assessment for the Genetics of Alcoholism interview . Data were gathered as part of an experimental protocol in which subjects with low and high LRs were assigned either to watch 1 of 2 sets of videos aimed at decreasing heavy drinking or to a control group and followed over time . With about a 70% response rate to the mailings, we identified recent drinkers who did not meet lifetime criteria for alcohol or illicit drug dependence, bipolar disorder, antisocial personality disorder, or schizophrenia . Asian individuals who became physically ill after 1 or 2 drinks, and who were probable homozygotes for aldehyde dehydrogenase mutations, were also excluded . The drinking related questions included recent 30-day histories of the days on which alcohol was consumed, numbers of standard drinks on usual and maximum drinking days, and alcohol problems. Subjects’ LRs to alcohol early in their drinking careers, and before tolerance was likely to develop, were evaluated using the Self-Rating of the effects of Alcohol questionnaire as the average number of standard drinks required for up to 4 effects the first 5 times of drinking . These included drinks to first feeling any effect, slurring speech, unsteady gait, and unwanted falling asleep, with higher scores indicating needing more drinks for effects, or a lower LR per drink . The Cronbach alpha for the SRE in the current sample was 0.88, with repeat reliabilities in the literature >0.66 . Based on the time frame described at the bottom of Fig. 1, 90% of subjects who were invited to participate agreed to enter the experimental protocol where they were paid $20 for each of 8 Internet-based assessments. As part of the prevention study , students were randomly assigned to either a control condition with no intervention or watched 5 alcohol-related educational videos over the first 3 months. Subjects were then followed and evaluated with Internet-based assessments similar to the baseline SSAGA based questionnaire . Among the 500 students enrolled, 462completed at least 7 of the 8 assessments and were included in these analyses, with any missing data handled using SPSS multiple imputation . Potential baseline predictors representing the 3 domains had to be limited to those included in the campus heavy drinking prevention study.
For demography, for reasons stated in the Introduction, we selected age, sex, and self-reports of an EA ethnicity, with the latter representing the largest ethnic background that related to heavy drinking in a past university-based study . Regarding substance use patterns, reflecting our long-term interest in LR, we included SRE-based LR scores along with SSAGA based alcohol problems and usual and maximum quantities in the prior month. Recent cannabis use from the SSAGA was included because of the high prevalence of experience with this drug on campus as well as the relationship between alcohol and cannabis use patterns . Finally, several environment and attitude items that have related to higher drinking quantities in our prior studies were selected as baseline predictors, including a short version of the Alcohol Expectancy Questionnaire that has a Cronbach alpha of 0.88 in this population and similar retest reliabilities. As described elsewhere , this AEQ version included 3 items with the highest factor loadings from each of 4 AEQ sub-scales . The second relevant measure was the Beck Depression Inventory with Cronbach alpha of 0.91 and retest reliability of 0.93 . Using alcohol to cope with stress was measured by the 6-item Drinking to Cope scale that used a 4-point scale to measure frequencies of using alcohol to cope with specific stressors . Injunctive norms were evaluated using a modification of the scale of Lewis and colleagues as the sum of the subject’s estimate of approval on a 7-point scale regarding 14 drinking behaviors by the typical same-sex person, with higher scores indicating greater approval, and descriptive norms related to the usual number of drinks per occasion estimated for typical students . Finally, the perceptions of drinking in 4 close peers were based on the Important People and Activities Scale that included an estimate of whether each peer drank alcohol in the prior month, and, if so,cannabis flood table the frequency and maximum number of drinks per day . Statistical analyses included product–moment correlations between baseline characteristics and drinking usual and maximum quantities during the 30 days prior to each assessment. Baseline items that related significantly to a relevant outcome were simultaneously entered into multiple linear regression analyses to determine which predictors were most robust when considered in the context of other significant predictors, as well as the proportion of the variance explained . As alcohol outcomes are sometimes evaluated as count variables, the regression analyses were also run using Poisson and negative binomial approaches. Differences in drinking quantities between assessments were evaluated with repeated measure analyses of variance. reflecting non normal distributions of drinking quantities, square-root transformations were used for these variables in correlations, multiple linear regressions, and repeated measure analyses.From among the original 500 students enrolled in the campus prevention protocol, the subjects in the current analyses were 462 individuals who completed at least 7 of the 8 assessments over the 55 weeks, of which all but 12 participated in all 8 periods. As shown in the first data column of Table 1, regarding demography, at the time of the baseline assessment used in these analyses , subjects were an average of 18 years old, 63% were female, and about a third were EA.
While not shown in the table, 36% were Asian, 16% Hispanic, and 14% listed other ethnic backgrounds . Substance use patterns in the month prior to baseline included an average of 4 standard drinks on a usual occasion and a maximum of 6 drinks at any occasion in the past month, with more than 40% having used cannabis in that same time period. Early in their drinking careers, these students required an average of 4 drinks to produce up to 4 potential alcohol effects as measured by the SRE questionnaire. While at baseline no subject was alcohol dependent, 45% reported 1 or more of 19 possible alcohol problems in the prior month, including about 20% each for ARBs and/or drinking more or for longer periods than intended, with about 15% each reporting needing more drinks to get effects and/ or consuming 4 or more drinks per occasion and/or drinking heavily for at least 2 consecutive days . Table 1 also lists scores for several environment and attitude characteristics shown in our prior work to relate to heavier drinking, including alcohol expectancies , depressive symptoms, using alcohol to cope with stress , descriptive and injunctive drinking norms, and drinking among peers. The final item in Table 1 reports that in the prevention protocol, 86% viewed educational videos, while 14% were controls, and, as discussed below, the assignment to active intervention versus control groups did not relate to patterns of increases and decreases in drinking over time. Table 2 and Fig. 1 present maximum and usual drinks per occasion across Periods 5, 6, and 7 that include the spring– summer–fall periods of the study. To place these dates into perspective, the legend in Fig. 1 presents dates for the academic quarters. While the emphasis in these analyses is on the Sun God–summer–return to school time frames set off by vertical bars in Fig. 1, the previously described school year drinking patterns for Times 1 to 4, and 8 are also shown. The current results demonstrate changes from prior to subsequent periods, including 18% increases in maximum drinking quantities during the Sun God Festival Period , 29% reductions in alcohol quantities over the summer , and 31% increases when students returned to school in the fall . Note that if the time frame prior to the Sun God Festival is used as a base, the decrease from Time 4 to summer was almost 17%. During summer months, 60.0% of these students lived with their parents, 22.1% were away from campus but not with parents, and 18.1% remained in campus dorms. While not shown in the figure, the patterns of drinking across the key time periods were similar for students in the control group and those in the active educational groups during the campus prevention protocol. Returning to Table 1, the remaining 6 data columns give product–moment correlations between baseline characteristics and drinking quantities the 30 days prior to Sun God, summer, and school return assessments. Regarding demography, on a univariate level an EA ethnicity was associated with higher drinking quantities in all follow-up periods, older age related to lower drinking during Sun God and school return periods, but female sex was only related to lower maximum drinks over the summer. All baseline substance-related variables correlated with higher quantities over the year, including higher SRE scores that indicated a lower LR per drink, higher baseline alcohol quantities, and cannabis use. Among environment/attitude baseline measures, higher depression scores correlated with lower drinking during the summer and return to school periods, and higher injunctive norms only related to heavier drinking during the Sun God and school return periods. Other than for AEQ, higher scores for all remaining variables in this group of potential predictors were more consistently related to higher alcohol intake across time frames. The experimental condition in which a person was placed did not relate to whether drinking at any time period across the 55 weeks was higher or lower. Therefore, to better identify baseline characteristics that are more likely to stand alone as predictors, all significant predictors of drinking for each follow-up period were entered into a simultaneous entry multiple linear regression analysis to evaluate which items performed most robustly when evaluated in the context of others. As shown in Table 3, the most consistent predictors of higher drinking quantities across multiple periods included higher SRE scores , higher baseline maximum quantities, and descriptive norms .