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individual- and college-level correlates among South Korean international college ... Design: Cross-sectional online survey (student response rate = 41.6%).
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HEJ0010.1177/0017896914521599Health Education JournalSa et al.

Article

Binge drinking and drinking and driving among South Korean international college students in the USA

Health Education Journal 2015, Vol. 74(1) 84­–95 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0017896914521599 hej.sagepub.com

J Saa, D-C Seob, TF Nelsonc, DK Lohrmannd and NT Ellisd aCollege

of Education and Health Sciences, Touro University, CA, USA Womans University, Seoul, South Korea cUniversity of Minnesota, Minneapolis, USA dIndiana University School of Public Health, Bloomington, USA bEwha

Abstract Objective: To investigate two risky behaviours (i.e. binge drinking and drinking and driving) and their individual- and college-level correlates among South Korean international college students in the USA Design: Cross-sectional online survey (student response rate = 41.6%). Setting: South Korean college students (N = 1201) were recruited from 52 different four-year universities in the USA in 2009. Method: Self-reported binge drinking, drinking and driving, smoking, study-related stress, life dissatisfaction, region lived in, and type of university attended, were assessed using already-validated instruments. Results: A total of 92% of the sample reported at least one occasion of binge drinking in the previous 30 days. Among those who had driven a motor vehicle in the previous 30 days (n = 950; 79% of the overall sample), 67% had engaged in drinking and driving. Students’ binge drinking was positively associated with current cigarette use and higher levels of life dissatisfaction at college and study-related stress. Students’ drinking and driving was also positively associated with higher levels of life dissatisfaction at college and studyrelated stress. These relationships were stronger among students attending private institutions than among those attending public institutions. While both college-level correlates (university region and university type) were significantly associated with drinking and driving, none of the college-level correlates were significant in the binge drinking models. Conclusion: A very high proportion of South Korean international students attending colleges in the USA appear to engage in binge drinking and drinking and driving. Development of intervention programmes designed specifically for them is necessary.

Keywords Binge drinking, drinking and driving, international students, South Korea, USA Corresponding author: D-C Seo, College of Health Sciences, Ewha Womans University, 52, Ewhayeodae-gil, Soedaemoon-gu, Seoul, 120-750, South Korea. Email: [email protected]

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Introduction According to the Institute of International Education (2012a) college enrolment of international students in the United States of America (USA) increased to a record high of 723,277 students in 2010–2011. Asian students account for the largest proportion (64%) and those from South Korea account for the third largest proportion (10% or 73,351) of the total international college student enrolment (Institute of International Education, 2012b) To date, there is a paucity of data on binge drinking or drinking and driving among international college students on campuses in the USA. Binge drinking is defined as ‘a pattern of drinking alcohol that brings blood alcohol concentration to 0.08 gram-per cent or above’. For a typical adult, this pattern corresponds to consuming five or more drinks for men, or four or more drinks for women, in about two hours (National Institute on Alcohol Abuse and Alcoholism, 2007). Binge drinking constitutes a major public health problem among college students in the USA (Hingson et al., 2009) and South Korea (Chun et al., 2003, 2006). A recent national survey (Johnston et al., 2011) showed that about two in five (37%) college students had engaged in binge drinking during the previous two weeks. Using the Harvard School of Public Health College Alcohol Study (CAS) questionnaire Chun et al. (2003) found that 66% of 2348 students on 60 college campuses in South Korea had been involved in binge drinking in the previous two weeks. Drinking and driving is considered one of the most serious alcohol-related problems in the USA (Wechsler et al., 1998). According to national surveys, little change was observed between 2001 (35.5%) and 2008 (34.1%) in the prevalence of drinking and driving among college students who had driven a motor vehicle in the previous 30 days (American College Health Association, 2009; Wechsler et al., 2003). Previous studies have shown that male students and cigarette users are more likely to engage in binge drinking than their respective counterparts (Wechsler et al., 1995, 2000) and that higher levels of stress and life dissatisfaction are associated with binge drinking (Nelson et al., 2008; Okoro et al, 2004). The CAS found higher rates of binge drinking and lower rates of drinking and driving at universities in the Northeast USA than elsewhere (Wechsler and Nelson, 2008; Wechsler et al., 2003). University type (private versus public) is another potential college-level correlate of binge drinking. Given that private universities are less likely than public universities (1) to provide alcohol-free residences to their students, (2) to invest in institutional alcohol prevention efforts (e.g. counselling and treatment services for students with drinking problems), (3) to obtain public funds for their alcohol education and prevention programmes, and (4) to conduct a social norms marketing campaign to reduce problem drinking (Wechsler at al., 2004), university type may be a robust indicator of binge drinking, although a previous study failed to find a significant difference in binge drinking between private and public universities (Presley et al., 2002). Although many studies have investigated individual factors associated with binge drinking and drinking and driving in college students, there is a paucity of data on college-level correlates of the two risky behaviours (Presley et al., 2002; Seo and Li, 2009) and no such data exist for international students attending college in the USA. The present study was conducted (1) to examine the association of four student-level correlates (sex, life dissatisfaction at college, study-related stress, and cigarette use) and two college-level correlates (university region and university type) with binge drinking and drinking and driving among South Korean international students studying in the USA, and (2) to investigate whether these relationships differ by college characteristics (i.e. cross-level interactions between the student-level correlates and the collegelevel correlates).

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Methods Data Directors or Deans of the Office of International Student Services (OISS) of the top 100 host universities, identified by the Institute of International Education (Institute of International Education, 2009) were contacted via email to ask for permission to recruit students for participation in an online survey. Of the 100 universities, 52 (13 private and 39 public) four-year universities agreed to participate in the survey. A survey invitation email with a link to complete the online survey was sent to the OISS database manager of the 52 universities that had a list of students whose country of origin was South Korea. A total of 3561 South Korean students were contacted by the invitation email sent by the OISS database manager. A total of 1480 South Korean international college students participated in this study in 2009 (student response rate: 41.6%). Of the 1480 students, 251 showed more than 50% of no responses to the survey questions or systematic response patterns (e.g. answering all questions 1) and 28 selected the option, ‘I did not fill out this survey seriously. Do not use my answers in your research’. These 279 students were dropped from analysis, reducing the sample size to 1201.

Measures In order to increase reliability and validity, instruments which had been validated and used in other studies were used to develop a 13-item questionnaire for this study. The questionnaire was developed through a combination of questions derived from the National College Health Risk (NCHR) Survey (n = 2 items) (Centers for Disease Control and Prevention, 1997), the CAS questionnaire (n = 3 items) (Wechsler et al., 2002) and the Behavioral Risk Factor Surveillance System (BRFSS) questionnaire (n = 5 items) (Centers for Disease Control and Prevention, 2009). The NCHR Survey is the college student version of the Youth Risk Behavior Survey, which was rated as having high reliability (κ = 61–100%) (Brener et al., 1995) and high validity (Centers for Disease Control and Prevention, 2004). The CAS questionnaire is commonly employed in investigating alcohol-related issues and has demonstrated an adequate level of reliability and validity (Dowdall and Wechsler, 2002). A comprehensive BRFSS validity and reliability study reported that questions on the BRFSS questionnaire were at least moderately reliable and valid and that many questions were determined to be of high reliability and validity (Nelson et al., 2001). The survey instrument consisted in the following six sections: (1) demographics (n = 6 items), (2) stress (n = 2 items), (3) binge drinking (n = 1 item), (4) drinking and driving (n = 2 items), (5) smoking (n = 2 items). Demographic variables such as gender and age were assessed by questions adapted from the NCHR Survey (Centers for Disease Control and Prevention, 1997) (n = 2) and the BRFSS questionnaire (Centers for Disease Control and Prevention, 2009) (n = 2). Two other questions (one for the length of stay as a student in the USA and one for university type and location) were developed specifically for this study. For stress, respondents were asked to answer two questions (one adapted from the CAS questionnaire (Wechsler et al, 2002) and one developed for this study; Cronbach’s α = .79): (1) ‘In general, how satisfied are you with your life at college in the USA?’ (1 = very satisfied, 2 = somewhat satisfied, 3 = somewhat dissatisfied, 4 = very dissatisfied, 5 = don’t know) and (2) ‘How stressful are your academic studies?’ (1 = not at all, 2 = a bit, 3 = fairly, 4 = very, 5 = don’t know).

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Binge drinking was measured by a question adapted from the BRFSS questionnaire (Centers for Disease Control and Prevention, 2009): ‘Binge drinking is defined as consuming five or more drinks (for men) or four or more drinks (for women) in about two hours. Considering all types of alcoholic beverages, how many times were you involved in binge drinking during the past 30 days?’ Drinking and driving was assessed with two questions (Cronbach’s α = .82) taken verbatim from the CAS questionnaire (Wechsler et al., 2002): (1) ‘In the past 30 days, how often did you drive a car, truck, or motorcycle?’ and (2) ‘In the past 30 days, how many times did you drive after drinking alcohol?’ The responses were the number of times students engaged in the two behaviours during the previous 30 days. Smoking was measured using two questions (Cronbach’s α = .83) taken verbatim from the BRFSS questionnaire (Centers for Disease Control and Prevention, 2009): (1) ‘Have you smoked at least 100 cigarettes in your entire life?’ (1 = yes, 2 = no, 3 = don’t know/not sure) and (2) ‘Do you now smoke cigarettes every day, some days, or not at all?’ (1 = every day, 2 = some days, 3 = not at all, 4 = don’t know/not sure).

Data analysis and model specification This study used HLM version 6.08 to perform hierarchical linear modelling to investigate the individual- and college-level correlates of binge drinking and drinking and driving and cross-level interactions, taking the hierarchical structure into account (Raudenbush and Bryk, 2001). The level-1 model (i.e. student-level model) estimated the relation between student-level correlates (sex, life dissatisfaction at college, study-related stress, and current cigarette use) and the outcome variable. The level-2 model (i.e. college-level model) examined the effect of college-level correlates (university region and university type) on the outcome variable.

Results Of the 1201 participants, 52% were men, 64% were between 18 and 28 years of age, 48% were undergraduate students and 76% were single. Their mean age was 26 years (SD = 5.7). Respondents to this study were from 24 different states in the USA (25% from the Northeast, 15% from the Midwest, 27% from the South and 33% from the West Region). A total of 92% of the sample reported at least one occasion of binge drinking (51% 1–2 times and the other 41% more than twice) in the previous 30 days. Among those who had driven a motor vehicle in the previous 30 days (n = 950; 79% of the overall sample), 67% had engaged in drinking and driving. The overall mean frequency of binge drinking in the previous 30 days among the participants (Step 1) was 3.077 occasions (Table 1). The intraclass correlation (ICC), the percentage of the total variance attributed to between-college variation, was (0.253/(0.253 + 1.446)) = 0.15. This indicates that 15% of the total variance in students’ binge drinking can be explained by differences between colleges. As indicated in the χ2 model difference test, the model was gradually improved by adding student-level (Step 2) and college-level correlates (Step 3) and by allowing cross-level interactions (Step 4). In terms of the fixed effects, life dissatisfaction at college (γ = 0.696), study-related stress (γ = 0.254) and current cigarette use (γ = 1.190) were positively associated with students’ binge drinking. Sex was significant in Step 2 and Step 3 but no longer significant at the final model when its significant interaction with university type was entered. None of the college-level correlates (university region and university type) was significant. However, significant cross-level interactions were observed. Life-dissatisfaction differentiating effects on binge drinking differed between public and private universities (γ = −0.537) and between different university regions (γ = 0.096). Private universities and universities located elsewhere,

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Table 1.  Hierarchical linear regression models for South Korean international college students’ binge drinking (N = 1201). Null model (Step 1) Fixed effects Student-level  Intercept   Male (vs. female) Life dissatisfaction at college   Study-related stress  Current cigarette use (vs. non-use) College-level   University region   Public (vs. private) Cross-level interaction  University region × sex  University region × life dissatisfaction  University region × study-related stress  University region × cigarette use   University type × sex  University type × Life dissatisfaction  University type × study-related stress  University type × cigarette use Random effects  Student-level residuals  College-level residuals Goodness of fit  Deviance  Estimated parameters, n  Hierarchical model testinga, χ2 (df)

Level-1 model (Step 2)

Intercept-asoutcomes model (Step 3)

Intercept-andslope-as-outcomes model (Step 4)

Regression coefficient (standard error) 3.077 (0.107)*

2.458 (0.151)* 0.379 (0.071)* 0.309 (0.051)*

2.323 (0.260)* 0.381 (0.063)* 0.309 (0.062)*

  1.781 (0.379)* 0.036 (0.163) 0.696 (0.048)*

0.099 (0.041)** 0.469 (0.115)*

0.099 (0.036)*** 0.474 (0.142)***

0.254 (0.093)*** 1.190 (0.465)**

0.260 (0.103)** −0.313 (0.250)

  0.059 (0.123) 0.505 (0.408)   −0.036 (0.057) 0.096 (0.035)*** 0.061 (0.030)** 0.241 (0.133) 0.413 (0.172)** −0.537 (0.081)* −0.243 (0.099)** −1.056 (0.472)**

Variance (standard error) 1.341 (0.057)* 1.446 (0.062)*

1.339 (0.057)*

1.319 (0.056)*

0.253 (0.086)*

0.242 (0.082)*

0.186 (0.066)*

0.181 (0.064)*

3667.816490 3

3583.829939 7

3576.082850 9

  3559.223420 17

84.0 (4)*

7.8 (2)**

16.9 (8)**

The intraclass correlation (ρ) was 0.15 (0.253/(0.253 + 1.446)). This analysis was performed using HLM 6.08 with full maximum likelihood estimation. aEach model was compared with the immediate previous model. *p < .001 **p < .05 ***p < .01

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Frequency of binge drinking in the past 30 days

4.84

Private universities Public universities

4.21

3.58

2.95

2.32 -1.10

-0.35 0.40 1.15 Life dissatisfaction at university

1.90

Figure 1.  Cross-level interaction between university type and life dissatisfaction at college on binge drinking.

Frequency of binge drinking in the past 30 days

3.84

Private universities Public universities

3.54

3.23

2.93

2.63 -1.79

-0.91 -0.04 0.84 Study-related stress

1.72

Figure 2.  Cross-level interaction between university type and study-related stress on binge drinking.

respectively, had a larger life-dissatisfaction and study-related stress differentiating effect on binge drinking than public universities and universities located in the Northeast. As shown in Figures 1 and 2, students engaged in binge drinking more in private universities than in public universities as their levels of dissatisfaction with college and stress were higher. Likewise, students engaged in binge drinking more in universities located elsewhere than universities in Northeast as their levels of dissatisfaction with college and stress were higher. Public universities had a larger sex differentiating effect on binge drinking than private universities. In other words, the sex difference in the binge drinking rate (i.e. men engage in binge drinking more often than women) was larger in public universities than in private universities. The overall mean frequency of drinking and driving in the previous 30 days among the participants (Step 1) was 2.925 (Table 2). The ICC was (0.411/(0.411 + 1.886)) = 0.18, indicating that

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Table 2.  Hierarchical linear regression models for drinking and driving among South Korean international college students with vehicles (N = 950). Null model (Step 1) Fixed effects Student-level  Intercept  Life dissatisfaction at college   Study-related stress College-level   University region   Public (vs. private) Cross-level interaction  University region × life dissatisfaction  University region × study-related stress  University type × life dissatisfaction  University type × study-related stress Random effects  Student-level residuals  College-level residuals Goodness of fit  Deviance  Estimated parameters, n  Hierarchical model testinga, χ2 (df)

Level-1 model (Step 2)

Interceptas-outcomes model (Step 3)

Intercept-and-slopeas-outcomes model (Step 4)

Regression coefficient (standard error)   2.925 (0.131)*

2.854 (0.125)* 0.463 (0.071)*

3.232 (0.237)* 0.472 (0.076)*

3.262 (0.229)* 1.087 (0.177)*

0.031 (0.069)

0.038 (0.069)

0.346 (0.091)**   0.242 (0.211)*** −0.483 (0.223)***   0.127 (0.041)***

0.268 (0.125)*** −0.459 (0.231)

0.132(0.043)*** −0.578 (0.174)** −0.281 (0.272)*** Variance (standard error) 1.767 (0.083)* 1.886 (0.093)*

1.751 (0.081)*

1.728 (0.081)*

0.411 (0.133)*

0.334 (0.119)*

0.269 (0.094)*

0.241 (0.094)*

3149.199794 3

3085.092742 5

3081.467472 7

  3066.362561 11

61.4 (2)*

6.4 (2)***

12.1 (4)***

The intraclass correlation (ρ) was 0.18 (0.412/(0.412 + 1.884 )). This analysis was performed using HLM 6.08 with full maximum likelihood estimation. aEach model was compared with the immediate previous model. *p < .001 **p < .01 ***p < .05

18% of the total variance in drinking and driving was attributable to differences between colleges. In terms of fixed effects, life dissatisfaction at college (γ = 1.087) and study-related stress (γ = 0.346) were positively associated with students’ drinking and driving whereas sex was not in any step of the hierarchical models. Unlike the binge drinking models, both college-level correlates (university region and university type) were significantly (p < .05) associated with drinking and driving in the full model. Participants attending colleges located in the Northeast and public universities reported less drinking and driving than those in universities located elsewhere and in private universities,

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respectively. As both college-level correlates showed interactions with life dissatisfaction and study-related stress, these interactions were probed. Participants in private universities engaged in more frequent drinking and driving than those attending public universities as their levels of dissatisfaction with college and stress were higher. Likewise, students in universities located elsewhere engaged in more frequent drinking and driving than those attending universities in Northeast as their levels of dissatisfaction with college and stress were higher.

Discussion This study examined binge drinking and drinking and driving in a sample of South Korean international students studying in four-year colleges and universities in the USA. The finding that 92% of the participants had been involved in binge drinking in the previous 30 days is alarming and sharply contrasts with the 45% of binge drinking rate in the previous 30 days among college students in the USA (Hingson et al., 2009) and the considerably lower proportion of students who identify as Asian or Pacific Islander in other surveys (Wechsler et al, 2002). The high rate of binge drinking among the participants might be not only due to study-related stress (Johnson et al., 2005) and Korean drinking culture, but also due to loneliness (Bonin et al., 2000) and acculturative stress which may occur in the process of adjusting to a new environment (Poyrazli et al., 2004; Ye, 2006). A recent study (Lee, 2012) affirms this, where a positive relationship was found between acculturative stress and drinking among Korean international college students in the USA. Compared with other international college students, it may be more difficult for Asian students to adjust to US culture due to the larger cultural distance (Ye, 2006), leading many Asian students to experience acculturative stress (Poyrazli et al., 2004). An interesting finding of this study is that sex effect on binge drinking was no longer significant at the final model when its significant interaction with university type was entered. This means that sex difference in binge drinking (i.e. men engage in binge drinking more often than women) is mainly accounted for by the larger sex difference in public universities than in private universities. This further implies that male Korean international students in public universities may have more difficulty coping with study-related stress and acculturative stress than those in private universities. Another reason for the high rate of binge drinking may be the relatively low cost of alcohol in the USA (Kim, 2009; Kuo et al., 2003; O’Mara et al., 2009). Prior research has shown a negative relationship between alcohol price and alcohol consumption. In addition, two studies (Naimi et al., 2007; Wechsler et al., 2000) found that beer is the alcoholic beverage most consumed by binge drinkers. Given that (1) college students are sensitive to alcohol prices (O’Mara et al., 2009; Wechsler et al., 2000), (2) the price of South Korean domestic beer is higher than that of other types of alcoholic beverage in South Korea (Chung, 2004), and (3) the average price of beer brands such as Miller and Budweiser is about 1.7 times lower in the USA than in South Korea (Korea Consumer Agency, 2008), the high rate (92%) of binge drinking among South Korean international students may have been due to the comparatively low cost of beer in the USA. This rate (92%) is higher than that (66%) of their counterparts in South Korea (Chun et al., 2003), although the two studies assessed binge drinking within different timeframes (previous 30 days versus previous two weeks). In addition to the substantial difference in the prevalence of binge drinking between South Korean international students and US college students, differences in the prevalence of drinking and driving also deserve mention. The drinking and driving rate of this sample (67%) is almost twice that (36%) of the 2001 CAS participants (Wechsler et al., 2003). Of the study sample, 52% (mean age = 26 years) were graduate students. The proportion of graduate students in our sample is consistent with the international college student enrolment data, which show that the number of graduate international students has been slightly higher than that of undergraduate international

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students since the 2001–2002 academic year (Institute of International Education, 2012c). This supports the representativeness of the sample in this study, making our findings an unlikely artefact of selection bias, although caution should be used in comparing the data presented in the current study with other study results due to the large proportion of graduate students. Positive relationships were found between the two risky behaviours (binge drinking and drinking and driving) and higher levels of life dissatisfaction at college and study-related stress. These findings indicate that students may have engaged more in the two risky behaviours as their levels of dissatisfaction with college and study-related stress became higher. This might be due to the fact that higher levels of stress and life dissatisfaction may lead to binge drinking (Nelson et al., 2008; Okoro et al., 2004) and that many alcohol-related problems, including drinking and driving, occur after binge drinking (Wechsler and Nelson, 2008; Wechsler et al., 2003). Another notable finding was that drinking and driving occurred less often at public universities and at universities in the Northeast USA, affirming the previous finding (Wechsler et al., 2003). This may be because (1) students attending universities in the region where relatively stronger enforcement activities against drinking and driving are performed than in other regions (e.g., zero tolerance laws and many sobriety checkpoints) are less likely to drink and drive, and (2) public universities provide more alcohol education and prevention programmes and offer stronger social norms to reduce drinking and associated problems than private universities (Wechsler et al., 2004). Most studies of college drinking have thus far focused on individual factors on single campuses, and they have neglected college-level factors. In addition to personal characteristics, college-level factors may also be significant and meaningful indicators of binge drinking as well as drinking and driving because students at the same institution tend to share common characteristics relating to these risky behaviours (Dowdall and Wechsler, 2002; Nelson et al., 2005; Presley et al., 2002; Seo and Li, 2009). The rates of the risky behaviours vary considerably by college (Dowdall and Wechsler, 2002; Wechsler et al., 2003), and these differences may be because of variations in religion, socioeconomic status, alcohol control policies or drinking laws (University of Minnesota, 2011; Wechsler et al., 2003, 2004). Because the personal characteristics of college students are not always the best indicators of their alcohol use and its consequences (Presley et al., 2002), intervention programmes that are only oriented to students might result in limited success (Larimer and Cronce, 2007). Given the complexities of the multilevel factors influencing student alcohol use behaviours (Presley et al., 2002), more emphasis needs to be placed on multidimensional approaches that suit the needs of each college and university and the college-level factors that influence student alcohol use (Dowdall and Wechsler, 2002). Also, it is important to consider a cogent model of college student drinking prevention that addresses both individual factors and college factors to draw a more complete picture of college drinking and its associated problems (Presley et al., 2002). This study has its limitations. First, this study used a cross-sectional survey design. As such, causal relationships cannot be inferred from the present findings. Second, the participating colleges were located in 24 states, which may limit the ability to generalise the current findings to the students studying in other states. Related to this, it is noted that selection bias may have confounded the findings of this study. Third, three-quarters of the participant universities were public universities. Thus, the small number of the private universities may have confounded the results of the current study. Fourth, this study included only two college-level variables (university region and university type). Future research is warranted that identifies and assesses more college-level correlates (e.g. university alcohol policies) that influence students’ binge drinking and drinking and driving above and beyond student-level variables. Fifth, the response rate of this study was not high enough to assume representativeness of the sample. Future research is

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recommended to make additional efforts to ensure representativeness of the sample or account for possible non-response bias.

Conclusions Despite these limitations, this study contributes to the literature by (1) revealing the current status of binge drinking and drinking and driving among South Korean international college students in the USA and (2) examining the effect of both student-level and college-level correlates of the two risky behaviours in the understudied group. As part of a comprehensive strategy to prevent the two risky behaviours, health educators at colleges in the USA where a substantial number of international students are found may need to develop programmes to assess international students’ alcohol consumption and implement high-risk-drinking prevention programmes. These programmes should focus on stressors and problems the students encounter while adjusting to a new environment, recognising the positive relationships between the two risky behaviours (binge drinking and drinking and driving) and higher levels of life dissatisfaction at college and study-related stress. Funding This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012S1A3A2033416).

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