Correlates of substance use and psychopathology with academic performance and life functioning among college students more

Correlates of substance use and psychopathology 1 1Running head: DRUG USE AND ADJUSTMENT Correlates of substance use and psychopathology with academic performance and life functioning among college students Jonathan M. Freedlander Towson University Correlates of substance use and psychopathology 2 Abstract Substance use, both licit and illicit, is strikingly common among college students. While a number of studies have investigated the effects that substance use has on functioning among college students, there is little consensus as to the nature of the correlations. The present study investigated the correlations among a detailed range of drug use behaviors, trait anxiety and depression, academic performance, and life functioning among college students. Trait anxiety and depression proved the most predictive of problems in life functioning and academic performance, followed by frequency of drug use variables, then age of first use variables. Life functioning measures were considerably more sensitive outcomes than measures of academic performance. The results of this study have important implications for designing behavioral intervention programs for college students. Correlates of substance use and psychopathology 3 Correlates of substance use and psychopathology with academic performance and life functioning among college students While the literature about substance use and functioning is limited relative to other areas of psychopathology, there is a solid literature base suggesting that substance use negatively correlates with life functioning. There is also a somewhat less solid literature base suggesting that substance use positively correlates with psychopathology. Substance use is most common in young adults and adolescents. Furthermore, the rate of substance use in this age range is great and substance use can often result in problems at school, at home, and in other areas. Epidemiology Alcohol Prevalence Alcohol use is strikingly common among young people. Studies have shown that roughly 90% of high school seniors drink alcohol (National Council on Alcoholism, 1986). Accordingly, most students start drinking before they reach college. In fact, drinking prevalence tends to be less in college than among high school seniors. In 1949, Straus and Bacon found that 74% of their sample of college students drank, 80% of the males and 61 % of the females. This prevalence has remained remarkably stable over time. A study conducted in the 1980s found that about 80% of college students drank, 86.3% of the males and 79.1% of the females (Engs & Hanson, 1985). This study suggests that drinking has become more acceptable among females over time. A 1989 survey of colleges receiving funding from the United States Department of Education's Fund for the Improvement of Postsecondary Education (FIPSE) found that 85% of college students used alcohol at least once in the past year and 66% used it at least once in the past month (Presley & Meilman, 1992). This study collected data from more than 56,000 college students at 78 2-year and 4-year institutions. Correlates of substance use and psychopathology 4 The FIPSE study also found that alcohol use was greater at 4-year institutions than it was at 2-year ones. Past year use was 77% and past month use was 53% at 2-year colleges, but past year use was 88% and past month use was 72% at 4-year institutions. Another national study, the National High School Senior (NHSS) survey, which conducts follow-up surveys of its high school samples, found similar levels of alcohol use among college students: 88% had used it at least once in the past year and 75% had used it in the past month (Johnston, O’Malley, & Bachman, 1993). Regional studies of alcohol use among college students show results consistent with national studies, though with greater variability. Past year use in these surveys ranges from 80% at a southern university (Wiggins & Wiggins, 1987) to 97% at a liberal arts college in New England (Meilman, Stone, Gaylor, & Turco, 1990). Alcohol use by students under age 21 is common. A 1990 survey of students under the legal drinking age in Massachusetts found that 91% of the men and 86% of the women had used alcohol at least once in the past year (Wechsler & Isaac, 1992). Furthermore, alcohol use is more prevalent among college students than among young adults of the same age who are not in college. In the previously mentioned NHSS survey, college students reported greater levels of alcohol use than did young adults not in college on all drinking measures, except daily use (Johnston, O’Malley, & Bachman, 1993). This was especially true for heavy drinking, reported by 43% of college students and 34% of those not attending college. The high levels of alcohol use and abuse among college students are surprising, as people with more education are more likely to adopt healthy behaviors readily and strongly. For example, college graduates, compared with people without a high school diploma, are les likely to smoke tobacco, less likely to be overweight, and more likely to wear seat belts Correlates of substance use and psychopathology 5 (Wechsler & Isaac, 1991). That college students have the greatest levels of alcohol use demonstrates the strength of social norms for drinking among college students. Alcohol Use Patterns While most alcohol use by college students does not meet the criteria for a substance dependency disorder, alcohol abuse and dependency is a significant problem among this population. In a series of studies spanning 20 years, the percentage of males who were heavy drinkers ranged from 20.1% to 32.2% and the percentage of females who were heavy drinkers ranged from 4.3% to 12.1% (Engs, 1977; Engs et al., 1985; Maddox et al., 1958; Wechsler & McFadden, 1979). Likewise, a survey of over 7,000 students at 22 New York State colleges found 22% of all respondents were heavy drinkers (use of an average of more than 1 ounce of absolute alcohol per day in the past 30 days). Men were nearly twice as likely to be heavy drinkers as women were (Barnes & Welte, 1983). At a small mid-Atlantic college, 65% of the students surveyed reported drinking two to five drinks per occasion and 46% reported drinking two to five drinks at least once a week (Cheny, 1987). A study conducted at Rutgers University found that 18% of the undergraduates abstained from alcohol, 25% were light drinkers, 20% were light-moderate drinkers, 17% were heavymoderate drinkers, and 19% were heavy drinkers (O’Hare, 1990). Among students who were under the legal drinking age at 14 colleges in Massachusetts, 20% of males and 90% of females reported drinking 10 or more times a month (Wechsler & Isaac, 1991). These students consumed large amounts of alcohol when they drank, 48% of males and 21% of females said they usually had five or more drinks on each occasion. Illicit substances prevalence Correlates of substance use and psychopathology 6 Several studies have examined the prevalence of illicit substance use on college campuses. While the prevalence of illicit substance use is not as great as is the prevalence of alcohol use, illicit substance use occurs to a significant degree among college students. In addition to reporting alcohol use trends, the FIPSE study also reported illicit substance use trends for 1989 (Presley & Weilman, 1992). This study found 21% of college students had used an illicit substance within the past month. Past year use for various drugs was 27% for cannabis, 5% for psychedelics, 5% for amphetamines, 2% for cocaine, 2% for benzodiazepines and barbiturates, and 0.7% for heroin. The FIPSE study also found that 6% of college students used cannabis on a weekly basis. Likewise, the previously mentioned NHSS survey also examined illicit substance use in college students (Johnston, O’Malley, & Bachman, 1993). In 1992, the NHSS survey found that 29% of college students had used an illicit substance within the past year. Past year use for various drugs was 27% for cannabis, 6% for psychedelics, 4% for amphetamines, 4% for cocaine, 2% for benzodiazepines and barbiturates, and 0.1% for heroin. This study also found that 2% of college students used cannabis daily. Studies of individual colleges show results consistent with national studies. At a large university in the Southwest, past year use was 38% for cannabis, 17% for cocaine, 7% for amphetamines, 6% for LSD, and 5% for benzodiazepines and barbiturates (Clifford, Edmonson, Koch, Dodd, 1987). Results from a southern university were similar. Past year use was 28% for cannabis, 5 % for LSD, 0.5% for amphetamines, 6% for cocaine, 6% for benzodiazepines, 4% for barbiturates, and 0.4% for heroin (Globetti, Globetti, Brown, & Stern, 1992). A study of Massachusetts college freshmen found that cannabis was the most commonly used drug after alcohol: 35% of the men and 28% of the women had used cannabis within the past year. The Correlates of substance use and psychopathology 7 next most frequently used illicit substances were psychedelics (6% of the men and 3% of the women) and cocaine (4% of men and 2% of women) (Wechsler & Isaac, 1991). A 1989 study of nearly 1,000 students at the University of Wisconsin at Madison found that use of an illicit substance at least once increased from first-year students to seniors: cannabis, from 56% to 70%, hallucinogens, from 6% to 20%, amphetamines, from 8% to 16%, and cocaine, from 8% to 29% (MacDonald, Barry, & Fleming, 1992). Additionally, this study found that students who met criteria for an alcohol abuse disorder were significantly more likely to have used an illicit drug, especially cannabis. Students with alcohol abuse disorders were more likely to have tried cannabis at an early age, more likely to have used cannabis in the previous 6 months, and had a higher frequency of cannabis use in the previous 6 months. A 30-year longitudinal study of a New England university found that generally, the number of students who had ever tried various illicit drugs tended to peak in 1978 and fell sharply over the next 20 years (Pope, Ionescu-Pioggia, & Pope, 2001). An exception was MDMA, use of which increased steadily from 1990 to 2000. As of 2000, 46% of students had used cannabis in their life and about 8% used cannabis weekly. Lifetime use for other illicit substances was similar to other studies: 10% for MDMA, 9% for LSD, 7% for cocaine, and 6% for amphetamines. Correlates of Substance Use Psychopathology Researchers estimate that between 25 and 50% of youth who seek treatment for substance abuse disorders has at least one comorbid diagnosis of a major depressive disorder (Bukstein, Glancy, & Kaminer, 1992; Deykan, Buka, & Seena, 1992; Stowell and Estroff, 1992). Substance users are also more likely to experience anxiety disorders, although evidence for this is not as Correlates of substance use and psychopathology 8 strong as for depressive disorders. A recent epidemiological study showed that depression in youth predicted substance use, but anxiety did not (Costella, Eckanli, Federman, & Angold, 1999). However, Christie et al (1988) found risk of a subsequent substance use disorder was double in young adults who previously had either a depressive or an anxiety disorder. Correlations between substance use and psychopathology may reflect attempts at selfmedication (Khantzian, 1985). A sample of inpatient adolescents at a psychiatric hospital commonly cited efforts to reduce tension or anxiety as reasons for substance use (Singer & White, 1991). When substance dependency and a depressive disorder co-occur among hospitalized youth, the onset of the depression usually precedes the onset of the substance abuse (Deykin, Levy, & Wells, 1987; Eisen, Youngman, Grob, & Dill, 1989). Research involving hospitalized adults found that those with adolescent-onset depression were more likely to have substance abuse problems than those with adult-onset depression (McGlasban, 1989). However, other research has failed to find strong links between substance use and psychopathology. (Huba, Newcomb, & Bentler, 1986; Kandel & Davies, 1996). For example, Hausell and White (1991) failed to find a correlation between psychological distress and subsequent substance use, although the time lag in this study (3 years) may have been too long to detect a relationship. A study of low-income African American students found that highly depressed sixth graders were no more likely to develop substance abuse problems that their “nonproblem” counterparts (Miller-Johnson et al., 1998). Socio-economic status Most of the evidence supporting the self-medication hypothesis comes from studies conducted with middle-class adolescents (Deykin et al., 1987; Singer & White, 1991). Among low-income youth, however, psychopathology is not a strong predictor of substance use (Luther Correlates of substance use and psychopathology 9 & Cushing, 1997; Miller-Johnson et al., 1998). Rather, for low-income youth, a variety of ecological determinants seems more important predictors than psychopathology. Risk factors for substance abuse among low-income youths include ready access to substances, opportunities for selling substances, and use of substances by adult role models (Centers & Weist, 1998; Luthar, 1999; Williams et al., 1998). Way and colleagues (1994) studied substance use and its correlation with self-reported depression measures among middle-class, suburban youth as compared to lower class, inner-city youth. The study showed significant positive correlations between substance use and depression among middle-class youth, but not among their lower-class counterparts. Many of the suburban youth, but none of the inner-city youth, described their substance use as a way to “escape problems” or to relax. Inner-city youth more often described negative consequences of drug use they saw among their peers. Substance use and behavior Young people who use cigarettes, alcohol, and cannabis are more likely to exhibit behavioral non-conformity than are their non-using peers (Jessor & Jessor, 1977; Magnusson, 1988; Miller-Johnson et al., 1998; Moffitt, 1993). Teenage drug use tends to occur within a larger context of rebellious behavior (Allen, Leadbeater, & Aber, 1990). Such rebellious behavior includes low identification with conventional adult values and premature sexual intercourse (Capaldi, Crosby, & Stoolmiller, 1996; Turnban, Windle, & Windle, 1996). Furthermore, use of cigarettes, alcohol, and illicit substances correlates with greater risk for disruptive behavior problems (Kandel et al., 1997). Prospective studies have shown associations between novelty seeking and low harm avoidance, and early onset of cigarette, alcohol, and other substance use (Masse & Tremblay, 1997). Correlates of substance use and psychopathology 10 Substance use can often take up a great deal of time, which can interfere with fulfillment of everyday obligations (Johnson & Kaplan, 1990). Adolescents who use substances are prone to difficulty in areas that require mature coping, such as academic performance and interpersonal relationships (Newcomb, 1987; Newcomb & Bentler, 1988). Newcombe and colleagues suggest that adolescent substance use indicates premature involvement in adult roles, for which adolescents lack the necessary maturity. A nationwide study of adolescents from a wide range of socioeconomic and ethnic backgrounds found a single dimension encompassing substance use, academic performance, and church attendance (Donovan & Jessor, 1985). A study by Kandel and Davis (1996) found a significant correlation between substance use, poor academic performance, high thrill seeking, and low opinion of conventional institutions. Similarly, in a study of 1,363 adolescents, Hays and Ellickson (1996) found a high correlation among alcohol use and sociability, rebelliousness, and deviant behavior, including use of drugs other than alcohol. A 9-year longitudinal study of about 4,000 students at colleges nationwide found that, of 17 drinking problems, 10 showed significant increases and 4 showed significant decreases (Hanson and Engs, 1992). Three of the four decreases involved driving related problems. Studies (summarized in Globetti, Stem, Marasco, and Haworth-Hoeppner, 1988) have reported the following prevalence of alcohol-related problems among college students: drinking and driving, 33% to 41%; destruction of property, 6% to 7%; loss of friends, 7% to 8%; academic problems, 17% to 23%; problems with authorities, 3% to 15%; and student judiciary problems, 20% to 60%. Though alcohol-related problems tend to increase with level of use, even low levels of use can result in problems (Werch, Gorman, & Marty, 1987). Correlates of substance use and psychopathology 11 Several studies (Bachman, Johnston, O'Malley, 1991; Hughes & Dodder, 1983; Johnson, 1989; Samson, Maxwell, & Doyle, 1989) have found that age of first use of alcohol significantly predicts alcohol use and alcohol-related problems among college students. The earlier one begins using alcohol, the greater the quantity and frequency of use and the greater the number of alcohol-related problems. A study of undergraduates at a southern university found that 21% of students who began alcohol use at age 13 or younger were current heavy drinkers, compared to 6% of those who began drinking at age 19 or older (Haworth-Hoeppner, Globetti, Stem, & Morasco, 1989). . Predictors of substance-related problems A study of 825 undergraduates at a southern university found that as the number of reasons an individual has for using alcohol increases, so does the likelihood he or she will be dependent on alcohol and incur alcohol-related problems (Lo, 1991). The author suggests that drinking to decrease stress or to escape from problems may release inhibitions and increase trouble-making behavior. Similarly, studies have shown that those who use alcohol for personal reasons use more alcohol and have more alcohol-related problems than those who use alcohol for social reasons (McCarty and Kaye, 1984). Personal reasons include such motives as to escape, to forget, and to produce mood changes, while social reasons include such motives as to be sociable, to get along with others, to facilitate social interaction. Haden and Edmundson (1991) found that students tended to use illicit drugs out of personal motivations, whereas they tended to use alcohol due to social motivation. A number of studies support the importance of sensation seeking as a predictor of alcohol use and related problems. McCarty and Kaye (1984) found that, among college students, sensation seeking predicted alcohol-related problems more strongly than stress relief or the Correlates of substance use and psychopathology 12 desire to escape difficulties. Similarly, Ratliff and Burkhart (1984) found that sensation seeking strongly predicted heavy drinking in males and females. They found disinhibition to be particularly significant in discriminating heavy drinkers from light drinkers. A smaller study (n = 55) examined the personality traits of college females who drank and found that heavy-drinking females expressed greater fear of failure and sensation seeking than did light-moderate drinkers (Johnson, 1988). A survey of UCLA undergraduates found that extraversion and sensation seeking were the greatest predictors of alcohol use (Schall, Kemeny, & Maltzman, 1992). Brooks and colleagues (1981) found that students who exhibited high levels of trait anxiety or trait anger were at greater risk of experiencing educational, legal, physical, and other psychosocial problems related to alcohol use. Curiosity, however, did not significantly predict alcohol-related problems. Similarly, Clifford and colleagues (1991) found that self-reported life satisfaction predicted low-to-moderate substance use (licit and illicit) among college students. Academic performance Studies have not been able to define clearly the relationship between substance use and academic achievement. Wiggins and Wiggins (1987) failed to find a correlation between alcohol use and grade point average (GPA) among students at the University of North Carolina at Chapel Hill. Contrawise, studies by Maney (1990) and by Goodwin (1990) found a significant negative correlation between alcohol use and GPA. The FIPSE survey (1992) also found a significant negative correlation between the number of alcoholic drinks consumed per week and GPA. In this study, D and F students drank three times as much alcohol per week as did A students. Yet even if there is a relationship between substance use and GPA, the direction of causality is not always clear. Poor academic achievement may lead to alcohol or other substance use to help the person deal with the negative emotions associated with academic problems. On Correlates of substance use and psychopathology 13 the other hand, heavy consumption of alcohol or other substances may cause impairments in cognitive or emotional functioning that lead to poor grades. Hypotheses The present study will investigate many aspects of the relationship between drug use and functioning in college students. As such, the following hypotheses are made: 1) frequency of substance use will negatively correlate with measures of functioning 2) psychopathology, as measured by the Trait Anxiety and Depression scales, will negatively correlate with functioning 3) substance use will predict psychopathology 4) substance use and psychopathology will show an interaction effect on functioning 5) age of first use will positively correlate with functioning (i.e., the younger a person was when they first used, the lower the functioning) 6) the number of reasons for using substances will negatively correlate with functioning 7) some substances, such as heroin and cocaine, will be associated with greater levels of impairment than others, such as cannabis and caffeine. Method Participants Two hundred and forty five college students participated in the online survey. Of these 245, 158 participants completed enough of the survey to be useful in analysis. Participant age was somewhat skewed towards older students (see Table 1), as was the class standing somewhat skewed towards upperclassmen (see Table 2). See Table 3 for the websites and mailing lists solicited for participants. Materials Correlates of substance use and psychopathology 14 The online survey (see http://tiger.towson.edu/~jfreed2/SurveySummary.asp.htm) consisted of questions about school status, substance use, academic performance and involvement, the Trait Anxiety and Depression scales, and the Life Functioning Questionnaire. The Trait Anxiety and Depression scales are well validated; the Trait Anxiety Scale has an alpha internal consistency-reliability coefficient of 0.91, while the Trait Depression Scales has an alpha reliability coefficient of 0.95 (Mehrabian, 1994). These scales each yield a single number representing the degree to which the individual displays anxiety or depression. The Life Functioning Questionnaire (LFQ) is also well validated and yields four subscales: degree of difficulty in friend functioning, family functioning, home functioning, and work functioning. The alpha internal consistency-reliability coefficients of these four subscales range from 0.84 to 0.88 (Altshuler, Mintz, & Leight, 2002). Procedures The online survey was accessible to participants for one month, from October 17 to November 17, 2005. Following the close of the survey, the data was downloaded from the Survey Monkey website and analyzed. Results Participants’ drug use characteristics in this study were similar to those reported in the literature, but were somewhat skewed towards greater levels of use. See Table 4 for the distribution of recent drug use (use within the past 3 months), Table 5 for the distribution of age of first use, and Table 6 for the distribution of frequency of use. Due to the complexity of this data set, multiple canonical correlations were calculated using different sets of predictor and outcome variables. Correlates of substance use and psychopathology 15 The first analysis tested all the drug use characteristics, age, class standing, trait anxiety, and trait depression as predictors and all the academic performance and life functioning measures as outcomes. A reduced model of this analysis found a significant canonical correlation, λ = 0.09, F(192, 937.36) = 1.85, p < 0.05, with two roots: root one, RC = 0.78, adjusted RC = 0.72, RC2 = 0.60, p < 0.05, and root 2, RC = 0.59, RC2 = 0.34, p < 0.05. The predictors used in this reduced model were age, class standing, recent use of [tobacco, alcohol, cannabis, dissociatives, inhalants], age of first use of [tobacco, alcohol, caffeine, psychedelics, prescription opiates, benzodiazepines/barbiturates, dissociatives, inhalants], frequency of use of [alcohol, caffeine, prescription amphetamines, heroin, dissociatives, inhalants], trait anxiety, trait depression, and number of reasons for use. The outcomes used in this model were GPA, classes dropped, extracurricular activities, absence, and problems in friend, family, home, and work functioning. See Table 7 for the overall significant predictors and outcomes in this model; see Tables 8 and 9 for the specific significant predictors and outcomes. The second analysis also used all of the measured variables, but used trait anxiety and depression scores as outcomes rather than predictors. A reduced model of this analysis also found a significant canonical correlation, λ = 0.13, F(220, 1129.8) = 1.28, p < 0.05, but with only one root, RC = 0.61, adjusted RC = 0.45, RC2 = 0.37, p < 0.05. The predictors in this reduced model were age, class standing, recent use of [tobacco, dissociatives], age of first use of [tobacco, alcohol, caffeine, psychedelics, prescription amphetamines, prescription opiates, heroin, benzodiazepines/barbiturates,dissociatives, inhalants], frequency of use of [alcohol, psychedelics, prescription amphetamines, illicit amphetamines, heroin, dissociatives, inhalants], and number of reasons for use. The outcomes in this model were GPA, classes dropped, extracurricular activities, absence, problems in friend, family, home, and work functioning, and Correlates of substance use and psychopathology 16 trait anxiety and depression. See Table 10 for the overall significant predictors and outcomes in this model; see Tables 11 and 12 for the specific significant predictors and outcomes. The third analysis tested the correlation between the frequency of use measures and trait anxiety and depression. In a reduced model, frequency of use of psychedelics and dissociatives predicted trait anxiety, λ = 0.95, F(2, 155) = 3.90, RC = 0.22, adjusted RC = 0.21, RC2 = 0.05, p < 0.05. Frequency of psychedelic use negative correlated with trait anxiety, t = -2.72, p < 0.05, while frequency of dissociative use positively correlated with trait anxiety, t = 2.55, p < 0.05. No other drug use frequency significantly correlated with trait anxiety, and none correlated with trait depression. The fourth analysis tested the correlation of trait anxiety and depression with participation in extracurricular activities, frequency of absence, and the four measures of functioning from the LFQ. The overall canonical correlation was significant, λ = 0.46, F(12, 290) = 11.34, RC = 0.72, adjusted RC = 0.71, RC2 = 0.53, p < 0.05. See Table 13 for the overall significant predictors and outcomes in this model; see Tables 14 and 15 for the specific significant predictors and outcomes. The fifth analysis tested the correlation of age of first drug use with the four measures of functioning from the LFQ. In a reduced model, age of first use of tobacco, alcohol, psychedelics, and cocaine significantly predicted all four measures of functioning, λ = 0.82, F(16, 443.62) = 1.90, RC = 0.34, adjusted RC = 0.28, RC2 = 0.12, p < 0.05. Age of first cocaine use was the only predictor significantly correlated with an outcome; problems in family functioning, t = 1.99, p < 0.05. Together, the four measures of functioning predicted age of first tobacco use, F(4,148) = 3.92, R2 = 0.10, adjusted R2 = 0.07, p < 0.05. Individually, the measures of functioning predicted age of first use of several drugs (see Table 16). Correlates of substance use and psychopathology 17 Finally, in the sixth analysis, frequency of drug use predicted the four measures of functioning from the LFQ. In a reduced model, frequency of use of alcohol, illicit amphetamines, heroin, dissociatives, and inhalants significantly predicted the four measures of functioning problems, λ = 0.69, F(20, 478.54) = 2.81, p < 0.05. The canonical correlation had two significant roots: root one, RC = 0.41, adjusted RC = 0.32, RC2 = 0.17, p < 0.05, and root two, RC = 0.36, RC2 = 0.13, p < 0.05. See Table 17 for the overall significant predictors and outcomes in this model; see Tables 18 and 19 for the specific significant predictors and outcomes. Discussion Despite the complexity of the variable set, a number of overall themes clearly emerge. The strongest correlation among any of the analyses was that among trait depression and the various measures of functioning. In the first analysis, which incorporated all the variables, four of the five strongest predictor correlations involved trait depression. Trait depression significantly predicted problems in work, friend, and family functioning and frequency of absence. Likewise, trait depression proved to be the most predictive of all the predictors when correlated with the outcome variables as a whole. Furthermore, when predicting the predictor variables from the outcomes, the two strongest correlations were between problems in work functioning and trait depression and between participation in extracurricular activities and depression. In the fourth analysis, which correlated trait depression and anxiety with participation in extracurricular activities, frequency of absence, and the four LFQ measures, trait depression controlled a great degree of the variance, 52%. Trait depression was most predictive of the four LFQ measures, followed by absence and extracurricular activities. Taken together, these analyses Correlates of substance use and psychopathology 18 suggest that depression is a stronger marker of functional impairment among college students than any measure of drug use. However, trait depression did not correlate as strongly with measures of drug use as it did with measures of functioning. Contrary to much of the literature, trait anxiety correlated more strongly with drug use than trait depression did. In fact, in the second analysis, which correlated all measures of drug use with academic performance, LFQ measures, and trait anxiety and depression, trait anxiety was the most sensitive outcome measure, followed by problems in family functioning, trait depression, and classes dropped. Likewise, in the third analysis, which correlated frequency of drug use with trait anxiety and depression, only anxiety, and not depression, significantly correlated with frequency of drug use. As one might expect, the more frequently individuals used dissociatives (e.g. ketamine, PCP), the more they displayed evidence of trait anxiety. Interestingly however, the more frequently individuals used psychedelics, the less anxiety they displayed. It is possible that use of psychedelics is causally related to reduced levels of anxiety. For example, researchers are currently investigating MDMA as possible a treatment for post-traumatic stress disorder and psilocybin as a treatment for obsessive-compulsive disorder (Jerome, 2005). However, it is also possible that psychedelic use and reduced levels of anxiety are both merely correlates of some unmeasured third variable, or that people who are attracted to psychedelic use already have low levels of anxiety. When measured separately, there were no significant correlations between recent drug use and trait anxiety or depression. Nor were there any significant correlations between age of first use and trait anxiety or depression. Rather, significant correlations among drug use variables and trait anxiety and depression emerged more often in the analyses that included all variables. Correlates of substance use and psychopathology 19 This suggests that the relationship between drug use and psychopathology involves other mediating covariates, such as the degree to which the individual’s functioning is affected. The second overall theme that appears across analyses is that, second to trait depression and anxiety, frequency of use of certain drugs significantly predicted problems in functioning. Frequency of inhalant, heroin, and dissociative use were most predictive of functional impairment. These drug use frequencies primarily predicted problems in work and friend functioning. Frequency of prescription amphetamine, illicit amphetamine, and caffeine use also significantly predicted functional impairment, primarily problems in home functioning. However, these correlations were not as strong as those of the first three drug use frequencies. This is consistent with the literature; one would expect drugs such as heroin, inhalants, and dissociatives to be associated with the most problems. It is surprising, however, that frequency of cocaine use did not appear as a significant predictor of functional impairment. Some drug use frequencies negatively correlated with functional impairment, meaning that those who used certain drugs more frequently showed less impairment in certain areas of functioning. Frequency of alcohol use, for example, negatively correlated with problems in friend functioning. This finding is consistent with the literature and is probably due to the strong social norms for alcohol use among college students. One finding along these lines that was not consistent with the literature was the negatively correlation between frequency of heroin use and problems in family functioning. A possible explanation of this might be that individuals who use heroin frequently are so detached from their families that the concept of family functioning defined by the LFQ is no longer an appropriate measure. Following frequency of use measures, age of first use measures were the next most predictive variables. Of the age of first use measures, age of first tobacco use was the most Correlates of substance use and psychopathology 20 predictive. Age of first tobacco use positively correlated with problems in friend functioning and negatively correlated with problems in home and family functioning. That is, the younger people were when they started using tobacco, the less they exhibited problems in friend functioning, but the more they exhibited problems in home and family functioning. These results are consistent with the literature. Teenagers who begin using tobacco at younger ages are more likely to be outgoing and rely more on peers for emotional support than on adults. Young smokers are also more likely to engage in a context of rebellious behavior, which may cause or be the result of problems with family or in home functioning. That tobacco use was the most sensitive of the age of first use variables is also consistent with the literature, as tobacco is typically the one of the first drugs that teenagers use, the other being alcohol. Other age of first use variables were also significant predictors, though weaker than age of first tobacco use. In the first analysis, age of first psychedelic use positively correlated with problems in work functioning, i.e., the younger people were when they started using psychedelics, the less problems they had in work functioning. It is unclear what would account for this relationship. In the same analysis, number of classes dropped significantly predicted age of first benzodiazepine/barbitruate use, first prescription opiate use, first dissociative use, and first psychedelic use in that order. That classes dropped correlated with so many age of first use variables is surprising, in that such a finding has not previously been reported. However, it makes sense that people who start using a wide array of drugs at an early age would tend to show more difficulty in meeting responsibilities. Additionally, problems in family functioning negatively correlated with age of first alcohol use and of first tobacco use. Problems in friend functioning positively correlated with age of first alcohol use. These results are consistent with those mentioned previously. GPA Correlates of substance use and psychopathology 21 positively correlated with age of first psychedelic use, first benzodiazepine/barbitruate use, and first dissociative use. It is interesting to note that, across all the analyses, most correlations with GPA were with age of first use measures. Problems in work functioning positively correlated with age of first prescription amphetamine use. This presents another anomaly, as one would expect people who begin using amphetamines at a younger age to display more problems in work functioning. Recent drug use variables were weaker predictors than frequency of use or age of first use; nonetheless, they were involved in some significant correlations. In the second analysis, which correlated all drug use variables with all academic and functioning variables and trait anxiety and depression, the reduced model showed that all outcomes together significantly predicted recent tobacco use. This finding supports the notion that smokers have more difficulty in various areas of functioning than non-smokers. Likewise in the first analysis, recent tobacco use significantly negatively correlated with participation in extracurricular activities; i.e., those who had used tobacco in the past three months were less likely to be involved in extracurricular activities. As tobacco use is often part of a larger context of rebellious behavior, it makes sense that those who use tobacco would be less likely to participate in school sponsored activities. Recent cannabis use showed a significant positive correlation with frequency of absence. This was the only significant correlation seen for any cannabis-related variable, which supports the notion that cannabis use is not generally associated with major deficiencies in functioning. Recent dissociative use significantly predicted a greater number of classes dropped and a lower GPA. Recent alcohol use also predicted a lower GPA, but predicted less problems in friend functioning. While GPA was one of the less sensitive outcome variables, it makes sense that Correlates of substance use and psychopathology 22 recent alcohol or dissociative use would affect it adversely, as these drugs interfere with normal cognitive function to a greater degree than many other drugs. Of the outcome variables, trait depression and anxiety and the four LFQ measures were the most sensitive across all the analyses. Frequency of absence and participation in extracurricular activities were also sensitive outcome measures in across the analyses, though considerably less sensitive than trait depression and anxiety and the LFQ measures. GPA was a sensitive outcome measure in many analyses; however, it was the least sensitive of the outcome measures and showed the strongest correlation with class standing. These results suggest that drug use, depression, and anxiety are more likely to be associated with problems in general life functioning than with academic problems. Conclusion The results of this experiment depict a complex interaction among drug use, depression and anxiety, academic performance, and life functioning. With the exception of a few anomalies, most of the results concur with the available literature. The present study examined drug use variables in considerably greater detail than most other studies of college drug use and functioning to date. Future studies should inquire about similar variable sets, including the distinctions between categories of drugs, frequency of use, and age of first use, but with larger sample sizes. The fact that the age and class standing of the participants in the present study was skewed towards older students may have accounted for some of the anomalies seen in the results and would hopefully be corrected in future research. It is also interesting to note that rates of drug use seen in this study were greater than those described in the literature. There are a number of possible explanations for this: it could be due to the greater general age of the sample, it could be that regular internet users have higher rates of drug use than non-users, or it could be Correlates of substance use and psychopathology 23 characteristic of the sorts of people who frequent the websites that were solicited for participants. It is also possible that rates of drug use are increasing. Nonetheless, the results of the present study have important implications for psychosocial interventions in college populations. Most importantly, the finding that depression and anxiety are more predictive of functional impairment than drug use supports the idea that prevention programs should focus more on identifying and reaching out to those students who suffer from mood problems than on those who use drugs. Furthermore, users of certain drugs, such as heroin, inhalants, and dissociatives, should be considered more important targets for intervention than users of other drugs, such as cannabis and psychedelics. Finally, because both mood problems and drug use were more predictive of impairment in general life functioning than in academic performance, intervention programs designed for both students with mood problems and students who use drugs should focus more on problems in general functioning than on academic problems. Correlates of substance use and psychopathology 24 Table 1 Distribution of age: Age 18 19 20 21 22 23 or older n 12 20 21 20 19 66 % 7.6% 12.7% 13.3% 12.7% 12% 41.8% Correlates of substance use and psychopathology 25 Table 2 Distribution of class standing Class standing Freshman Sophomore Junior Senior Graduate student n 17 25 37 40 39 % 10.8% 15.8% 23.4% 25.3% 24.7% Correlates of substance use and psychopathology 26 Table 3 Solicitations for participants posted Websites www.craigslist.com www.tribe.net www.myspace.com Mailing lists American Psychological Association – Division 28 MindVox (www.mindvox.com) Multidisciplinary Association for Psychedelic Studies (MAPS) Forum (www.maps.org) University of Maryland Baltimore County Linux Users Group (UMBC-LUG) Correlates of substance use and psychopathology 27 Table 4 Recent substance use (within the past 3 months) Substance Alcohol Caffeine Cannabis Tobacco Psychedelics Prescription amphetamines Prescription opiates Benzodiazepines or barbiturates Cocaine/crack Dissociatives Illicit amphetamines Heroin Inhalants % 82 72 49 46 25 15 15 13 11 8 4 1 1 Table 5 Age of first use - % Substance Tobacco Alcohol <= 12 15.8 13.3 13 5.7 7.6 14 12.7 17.7 15 6.3 13.9 16 8.2 16.5 17 5.1 7.6 18 9.5 8.9 19 2.5 6.3 >= 20 Never used 6.3 27.8 4.4 3.8 Correlates of substance use and psychopathology 28 Caffeine Cannabis Psychedelics Presc amph Illicit amph Cocaine Heroin Presc opiates Benzo/barb Dissociatives Inhalants 59.5 5.7 0.0 1.9 0.0 0.0 0.0 1.3 0.0 0.6 1.9 4.4 7.0 0.6 1.3 0.0 0.6 0.0 1.9 1.3 0.0 1.9 3.8 7.0 5.7 1.3 1.3 0.6 0.6 3.8 1.9 1.9 2.5 6.3 15.2 6.3 4.4 1.3 1.3 0.0 1.9 2.5 2.5 2.5 7.6 11.4 10.1 4.4 1.9 3.2 1.3 6.3 5.7 4.4 3.2 3.8 8.9 7.6 2.5 3.2 3.2 0.0 6.3 3.2 7.6 1.9 2.5 15.8 8.9 7.0 1.9 3.8 1.3 5.7 7.0 5.7 1.3 1.3 1.9 5.1 7.6 1.9 7.0 0.6 7.0 5.7 5.7 0.0 1.3 4.4 8.2 8.9 7.0 14.6 3.8 8.2 9.5 7.0 0.0 9.5 22.8 47.5 60.8 81.6 65.8 92.4 57.6 63.3 64.6 84.8 Table 6 Frequency of use - % Substance never rarely once a a few once a a few daily or more month times a week times a almost than month week every once a day day 44.9 13.9 3.8 3.8 1.3 6.3 7.0 19.0 8.2 12.0 37.3 57.0 72.8 8.9 7.6 21.5 30.4 15.8 10.1 0.6 7.0 8.9 2.5 24.7 7.0 10.8 2.5 2.5 13.3 0.6 3.8 0.0 0.0 25.3 20.3 8.2 0.0 1.9 8.9 24.1 5.1 0.0 2.5 0.6 27.8 6.3 1.3 1.9 Tobacco Alcohol Caffeine Cannabis Psychedelics Presc amph Correlates of substance use and psychopathology 29 Illicit amph Cocaine Heroin Presc opiates Benzo/barb Dissociatives Inhalants 86.1 77.2 95.6 72.2 70.3 79.1 94.3 10.8 17.1 2.5 18.4 19.0 18.4 3.8 1.3 1.9 0.6 3.2 3.8 0.6 0.6 0.0 1.3 0.0 2.5 1.3 0.6 0.0 0.0 0.6 0.0 0.6 1.9 0.0 0.0 0.0 0.0 0.0 1.3 1.3 0.0 0.0 0.6 0.6 0.0 0.0 1.3 0.0 0.0 1.3 1.3 1.3 1.9 1.3 1.3 1.3 Table 7 Signifcant outcomes and predictors in first analysis, p < 0.05 Predicting Work functioning Predicting outcomes from predictors Friend functioning Family functioning Home functioning Extracurricular activities Classes dropped Predicting predictors from Predicting Trait depression Trait anxiety F (24, 128) 4.74 4.37 2.62 2.55 2.11 1.86 F (8, 114) 20.16 12.4 R2 0.47 0.45 0.33 0.32 0.28 0.26 R2 0.53 0.41 Adjusted R2 0.37 0.35 0.20 0.20 0.15 0.12 Adjusted R2 0.50 0.37 Correlates of substance use and psychopathology 30 outcomes Frequency of inhalant use Frequency of heroin use Frequency of dissociative use Recent tobacco use Age Class standing Age of first tobacco use 3.91 3.35 3.02 2.76 2.59 2.46 2.42 0.18 0.16 0.14 0.13 0.13 0.12 0.12 0.13 0.11 0.10 0.08 0.08 0.07 0.07 Table 8 Predicting specific outcomes from predictors in first analysis, p < 0.05 Var Trait depression Trait depression Age Trait depression Trait depression Age of first tobacco use Age of first psychedelic use Trait depression Recent tobacco use Frequency of alcohol use Frequency of dissociative use Trait depression Recent cannabis use Predicting Work functioning Friend functioning Classes dropped Family functioning Absence Friend functioning Work functioning Home functioning Extracurricular activities Friend functioning Home functioning Extracurricular activities Absence t 4.97 3.75 2.81 2.78 2.49 2.32 2.23 2.21 -2.21 -2.17 2.14 -2.00 1.99 Correlates of substance use and psychopathology 31 Table 9 Predicting specific predictors from outcomes in first analysis, p < 0.05 Variable Work functioning Extracurricular activities Classes dropped Work functioning Friend functioning Absence Extracurricular activities Friend functioning Work functioning Friend functioning Classes dropped Work functioning Classes dropped Family functioning GPA Absence Classes dropped Classes dropped Family functioning Home functioning Friend functioning Friend functioning GPA Classes dropped Friend functioning Predicting Trait depression Trait depression Age Frequency of inhalant use Trait depression Frequency of inhalant use Trait anxiety Age of first tobacco use Trait anxiety Trait anxiety Age of first benzo use Frequency of heroin use Age of first opiate use Frequency of heroin use Class standing Recent tobacco use Age of first dissociative use Recent inhalant use Frequency of dissociative use Frequency of caffeine use Frequency of dissociative use Frequency of alcohol use Recent dissociative use Num of reasons for using Frequency of heroin use t 4.55 -4.49 4.01 3.39 3.23 -3.19 -3.17 3.11 3.11 3.07 -2.89 2.87 -2.74 -2.74 -2.71 2.70 -2.66 2.61 -2.57 2.55 2.51 -2.48 -2.47 2.44 2.41 Correlates of substance use and psychopathology 32 Classes dropped Absence Work functioning Classes dropped Classes dropped Absence Family functioning GPA Friend functioning Classes dropped GPA Family functioning Work functioning GPA Friend functioning Classes dropped Work functioning Home functioning GPA Friend functioning Extracurricular activities Absence Family functioning Work functioning GPA Age of first psychedelic use Frequency of heroin use Frequency of dissociative use Age of first caffeine use Class standing Frequency of dissociative use Age of first alcohol use Age Recent alcohol use Recent dissociative use Recent alcohol use Age of first tobacco use Age of first psychedelic use Age of first psychedelic use Age of first alcohol use Age of first inhalant use Frequency of amphetamine use Frequency of amphetamine use Age of first benzo use Frequency of inhalant use Recent tobacco use Recent cannabis use Frequency of inhalant use Age of first amph use Age of first dissociative use -2.39 -2.39 2.36 -2.34 2.33 -2.30 -2.30 -2.26 -2.23 2.22 -2.15 -2.15 2.15 2.14 2.14 -2.11 2.10 2.06 2.05 2.05 -2.03 2.02 -2.00 2.00 1.99 Correlates of substance use and psychopathology 33 Table 10 Signifcant outcomes and predictors in second analysis, p < 0.05 Predicting F (22, 130) R2 Adjusted R2 Trait anxiety Predicting outcomes from predictors Trait depression Classes dropped Predicting Predicting predictors from outcomes use Frequency of dissociative use Recent tobacco use Age Frequency of inhalant use Frequency of heroin Family functioning 1.89 1.76 1.66 1.64 F (10, 142) 3.28 2.74 2.57 2.42 2.34 0.24 0.23 0.22 0.22 R2 0.19 0.16 0.15 0.16 0.14 0.11 0.10 0.09 0.09 Adjusted R2 0.13 0.10 0.09 0.09 0.08 Correlates of substance use and psychopathology 34 Class standing 2.12 0.13 0.07 Table 11 Predicting specific outcomes from predictors in second analysis, p < 0.05 Var Frequency of psychedelic use Frequency of illicit amph use Age Frequency of alcohol use Frequency of heroin use Age of first heroin use Frequency of psychedelic use Age of first heroin use Frequency of presc amph use Frequency of dissociative use Frequency of presc amph use Frequency of presc amph use Number of reasons for use Family functioning Friend functioning Family functioning Work functioning GPA Classes dropped Classes dropped Classes dropped Friend functioning Family functioning Predicting Trait anxiety Family functioning Classes dropped Friend functioning Family functioning Trait anxiety Trait depression Trait depression Trait anxiety Home functioning Trait depression Home functioning Classes dropped Frequency of dissociative use Frequency of inhalant use Frequency of amphetamine use Age of first psychedelic use Age Age of first caffeine use Class standing Age of first inhalant use Age of first alcohol use Age of first tobacco use t -3.52 3.21 2.85 -2.62 -2.55 -2.38 -2.20 -2.14 2.14 2.14 2.01 2.00 1.99 -2.36 2.30 -2.30 2.28 -2.27 -2.22 2.19 -2.18 2.18 -2.18 Correlates of substance use and psychopathology 35 GPA Classes dropped Classes dropped Home functioning Extracurricular activities GPA Work functioning Age of first psychedelic use Recent dissociative use Age of first amphetamine use Frequency of amphetamine use Recent tobacco use age of first benzo use Age of first amph use 2.15 2.09 -2.06 2.06 -2.04 2.00 2.00 Correlates of substance use and psychopathology 36 Table 12 Predicting specific predictors from outcomes in second analysis, p < 0.05 Var Classes dropped Work functioning Friend functioning Absence Work functioning Classes dropped Classes dropped Friend functioning GPA Classes dropped Classes dropped Family functioning Friend functioning Home functioning Work functioning Absence Classes dropped Extracurricular activities GPA Absence Friend functioning Absence predicting Age Frequency of inhalant use Age of first tobacco use Frequency of inhalant use Frequency of heroin use Age of first benzo use Age of first opiate use Frequency of dissociative use Class standing Age of first dissociative use Num of reasons for using Frequency of heroin use Frequency of heroin use Frequency of caffeine use Frequency of dissociative use Recent tobacco use Age of first psychedelic use Frequency of amphetamine use Recent dissociative use Frequency of heroin use Frequency of alcohol use Frequency of dissociative use t 3.89 3.53 3.47 -3.25 2.93 -2.83 -2.8 2.74 -2.69 -2.58 2.57 -2.57 2.55 2.55 2.55 2.53 -2.45 2.44 -2.42 -2.42 -2.37 -2.36 Table 13 Signifcant outcomes and predictors in fourth analysis, p < 0.05 Predicting Work functioning Friend functioning Family functioning F (2, 150) 45.08 34.24 21.76 R2 0.38 0.31 0.22 Adjusted R2 0.37 0.30 0.21 Predicting outcomes from predictors Correlates of substance use and psychopathology 37 Home functioning Extracurricular activities Absence Predicting Predicting predictors from outcomes Trait depression Trait anxiety 19.1 10.6 0.20 0.12 0.19 0.11 6.14 F (6, 146) 26.75 16.70 0.08 R2 0.52 0.41 0.06 Adjusted R2 0.50 0.38 Table 14 Predicting specific outcomes from predictors in fourth analysis, p < 0.05 Var Trait depression Trait depression Trait depression Trait depression Predicting Work functioning Friend functioning Home functioning Family functioning t 5.12 3.12 3.04 2.94 Correlates of substance use and psychopathology 38 Trait depression Trait depression absence Extracurricular activities 2.79 -2.59 Table 15 Predicting specific predictors from outcomes in fourth analysis, p < 0.05 Var Work functioning Extracurricular activities Friend functioning Extracurricular activities Predicting Trait depression Trait depression Trait anxiety Trait anxiety t 4.49 -4.35 3.22 -3.2 Correlates of substance use and psychopathology 39 Friend functioning Work functioning Trait depression Trait anxiety 3.17 3.12 Table 16 Predicting specific predictors from outcomes in fifth analysis, p < 0.05 Var Friend functioning Family functioning Work functioning Home functioning Family functioning Predicting Age of first tobacco use Age of first alcohol use Age of first psychedelic use Age of first tobacco use Age of first tobacco use t 3.03 -2.39 2.14 -2.08 -2.07 Correlates of substance use and psychopathology 40 Table 17 Signifcant outcomes and predictors in sixth analysis, p < 0.05 Predicting F (5, 147) R2 Adjusted R2 Predicting outcomes from predictors Family functioning Friend functioning Predicting 5.00 4.43 F (4, 148) 4.22 4.09 0.15 0.13 R2 0.10 0.10 0.12 0.10 Adjusted R2 0.08 0.08 Predicting predictors from outcomes Frequency of dissociative use Frequency of heroin use Correlates of substance use and psychopathology 41 Frequency of inhalant use 3.58 0.09 0.06 Table 18 Predicting specific outcomes from predictors in sixth analysis, p < 0.05 Var Frequency of illicit amph use Frequency of alcohol use Frequency of heroin use Frequency of alcohol use Predicting Family functioning Friend functioning Family functioning Family functioning t 3.85 -3.5 -3.47 -2.53 Correlates of substance use and psychopathology 42 . 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