Less consistent contributors to those regression analyses included age , female sex , EA ethnicity, cannabis use , and DTC with stress . When considered in the context of other predictors in these regression analyses, usual drinking quantities, baseline alcohol problems, alcohol expectancies, and peer drinking did not add significantly. The proportions of the variance explained across regression equations for each key time point ranged from 0.20 to 0.31. While not significantly related to drinking patterns in Table 1, assignment to an experimental condition was added to each regression analysis in Table 3 but did not contribute significantly to any of the results. When these regression analyses were repeated using the nontransformed usual and maximum drinks that were considered as counts, the 6 Poisson regressions had Pearson v2 /df values from 1.54 to 2.36. As a result, the negative binomial regressions were run yielding ratios ranging from 0.80 to 1.02. For both Poisson regressions and negative binomial analyses, the results were essentially the same as those shown in Table 3.This paper describes patterns of drinking quantities in 462 freshmen university students across 3 periods of their initial stages of college. As predicted by Hypothesis 1, and consistent with the literature regarding celebrations, high drinking quantities characterized the Sun God Festival Period. The average of 6 maximum drinks exceeds the National Institute on Alcohol Abuse and Alcoholism guidelines for “binge” or heavy-episodic drinking . Such high alcohol intake carries risks for many alcohol consequences, as discussed above . The rise in usual and maximum drinks was observed even though the entire month was considered, not just the single day of the Sun God celebration. That finding may indicate that the heavy drinking associated with a campus-wide festival or, perhaps,cannabis grow supplier those associated with championship football games or other college celebrations, may have an impact beyond the special day itself. This extended period of heavier drinking might reflect both anticipation of the upcoming heavy drinking celebration as well as feelings of a continuing festival atmosphere following the event.
These speculations aside, universities should recognize such extended high drinking activities on their campuses and take steps to mitigate such heavy alcohol consumption, perhaps by implementing some of the suggestions offered below. Consistent with Hypothesis 2, but as demonstrated in only one other recent paper , during the summer both usual and maximum drinks per occasion decreased by about 30% from the Sun God Period. In the summer, students are likely to leave the relatively heavy drinking campus environment, live with their parents, and associate with high school friends, situations similar to their precollege environments. During that time, parents might mitigate heavy drinking by monitoring their offspring’s behaviors . This raises the possibility that, looking toward a likely rebound in drinking quantities as they return to school, campus heavy drinking prevention programs might consider incorporating both students and their parents into drinking-related education seminars during the period between the freshman and sophomore college years. Using as models the results of several recent intervention studies related to specific heavy drinking vulnerabilities, different types of prevention approaches, including those offered through the Internet, might be useful in diminishing heavy drinking during the subsequent college year . The prediction of relatively high drinking when returning to school was also supported by the current data, as maximum drinking quantities increased 31% compared with summer levels. The latter phenomenon underscores the importance of also considering the beginning of the sophomore year as a period of vulnerability toward heavy drinking that might rival the increased quantities seen in the transition from high school to college. The beginning of the sophomore year might also be an important time to implement prevention protocols to minimize a predisposition toward heavy drinking similar to those that can be offered to entering students . Perhaps similar increases might be seen in later college years, although the current study did not evaluate those potential phenomena. Hypothesis 4 was supported, as the regression analysis in Table 3 indicated significant contributions from baseline items representing demography, substance use, as well as environment/attitude domains. The variability of drinking practices documented in this study across the 55 weeks is also important to note.
These findings underscore the importance of longitudinal research regarding college drinking practices, as data from 1 period or 1 type of predictor alone did not adequately describe campus drinking patterns over the year. While not the major emphasis of these analyses, the relationship of the low LR to alcohol to drinking patterns over the year is worth comment. For many years, our group has been interested in how the low LR to alcohol predicts higher alcohol quantities in the near and distant future in adolescents and young adults . In the current study of college freshmen, on a univariate level in Table 1 the low LR correlated significantly with higher usual and maximum alcohol quantities at all 3 drinking periods. In the regression analyses, for both Sun God and summer periods the low LR remained a robust predictor of heavy drinking even when considered in the context of baseline drinking quantities and problems. These data indicate that universities interested in predicting which students might be more likely to drink heavily during celebrations and who might, therefore, benefit from additional education about the risks of alcohol might consider screening for the low LR as a marker of a vulnerability toward excessive alcohol use. It is also interesting to note how different types of baseline norms related to outcomes in the current analyses. Higher descriptive norms were among the most consistent correlates of higher alcohol quantities during all 3 periods in both univariate and regression analyses. Injunctive norms, however, did not contribute to regressions predicting drinking quantities at any time point. This finding is consistent with 1 study that suggested that descriptive norms might be more likely to be related to drinking parameters in the short run , and with a study indicating that the 2 norms do not represent a unitary concept . However, longer term follow-ups and some experimental results suggest that in some contexts and after considering other characteristics of subjects, injunctive norms may be as closely related to drinking outcomes . Thus, more work is needed regarding the relative usefulness of these measures as indicators of risks for campus heavy drinking. One of the more unexpected findings in Table 1 related to the negative relationship between baseline scores on the BDI and alcohol drinking quantities in the summer and return to school periods. However,cannabis drainage system the BDI did not add to the prediction of drinking quantities when entered into the regression analyses in Table 3, and, thus, depression scores are not considered a major predicator of drinking quantities at any of the 3 key time points.
Another notable result is the general lack of relationship between alcohol expectancies and drinking quantities in these analyses. In Table 1, the AEQ score did not correlate significantly with drinking quantities at any time point. This result may have reflected the fact that, due to limited testing time available with these students, a 12-item short version of the adolescent AEQ was used. While this format related to heavier drinking in a large study of 17-year-olds from a British general population sample , the short form may not have been powerful enough to perform in a more robust manner in the current study of college students. In viewing the current results, it is important to remember that the data were extracted from a larger study that evaluated a prevention protocol aimed at reducing heavy drinking on campus . However, as shown at the bottom of Table 1, the experimental condition did not relate to the pattern of either usual or maximum drinks across time periods. This probably occurred because the major impact of the prevention protocol was only on subjects with low LR who were in one of the video groups. reflecting this non-significant relationship with alcohol quantities in any of the 3 time frames highlighted in the current analyses, the experimental condition was not entered as a separate item in the regression analyses in Table 3. When the group assignment in the larger study was forced into the regressions, the results reported in Table 3 did not change. It will be important to determine whether similar findings are observed in universities where no prevention protocol was present. As is true with all studies, additional caveats should be considered in viewing the current results. First, the data were extracted from a campus prevention study that was not initially structured to address the questions raised in the current analyses. Thus, the data available were limited to those recorded for the prevention protocol, and some additional items that may have been of interest here were not gathered such as summertime activities, participation in other campus events, grades, and academic majors. Similarly, the analyses were limited to 8 time points built around the timing of the prevention protocol, and did not, for example, gather drinking data at multiple time points within any 1 period of interest.
Second, the study was carried out at a single university in Southern California that has large Asian and Hispanic populations but few African Americans, and it is important to see whether similar results are observed at other universities. Third, only baseline predictors of outcomes were used in these analyses, and future work is needed to evaluate time-varying predictors. Fourth, all drinking information was gathered through self-reports. Fifth, due to unavoidable delays in funding, our planned start date of early October 2013 was delayed until January 2014, and results might be a bit different if baseline had occurred earlier in the freshman year. That change impacted on the specific time frames that were compared, where, for example, the baseline period became the time frame after returning to school from winter break. Sixth, while the predictors used in these analyses were selected based on their relationships to heavy drinking in prior reports, and the emphasis was placed on those that added significantly to the regression analyses, Type 1 errors could still occur. Finally, reflecting the interest in the literature on “binge drinking” and our historical focus of LR as a predictor of higher drinking quantities, the emphasis was on usual and maximum drinks, and future work is needed on additional outcomes such as alcohol problems and drinking frequencies.Approximately 1% of the population experiences homelessness in a given year, with an estimated 600,000 Americans homeless nightly. Homelessness is defined by the Federal Homeless Emergency Assistance and Rapid Transition to Housing Act of 2009, which defines as homeless people who lack a fixed, regular residence , and those who are at imminent risk for losing their housing in the next 14 days.The median age of single adults experiencing homelessness is rising. Among homeless adults not living in families, approximately 50% are aged 50 and above. This trend is projected to continue: adults born in the second half of the “baby boom”have an elevated risk of homelessness throughout their lives.In the homeless population, adults 50 and older have prevalence rates of geriatric conditions higher than those of housed adults 15-20 years older.As a result, experts consider homeless adults to be “older adults” at age 50.4 Homeless adults have a higher prevalence of substance use than housed individuals.In 1996, a nationally representative study of homeless adults, the National Study of Homeless Assistance Providers and Clients , found a higher prevalence of substance use disorders than those in the general population, but a lower prevalence of SUDs in older, compared to younger, homeless adults.Prevalence of SUDs are thought to decline with age, but this may be changing as those born during the baby boom age.In the general population, the National Survey of Drug Use and Health has shown that the prevalence of illicit substance use in adults 50 and older has doubled since 2002.With the aging of the homeless population and the changes in substance use in older adults, little is known about the prevalence of substance use disorders in older homeless adults.There are few community-based samples of substance use in homeless adults since NSHAPC, and none specifically examining older homeless adults.Older adults who use substances face additional health risks compared with younger adults.