SAMPLE

The initial sample of participants (698 males, 682 females) was predominantly white (89%) compared to 83% of the NJ population (U.S. Bureau of Census, 1981). Half of the participants were Catholic, 30% were Protestant, 9% are Jewish, and the remaining 11% had another or no religion, analogous to the religious composition of NJ. The median family income of participants’ parents at T1, (between $20,000- $29,000), was also comparable to that of the entire state at that time (U.S. Bureau of Census, 1981). Most of the participants lived with both biological parents (79%), 10% lived with a single parent, and 11% lived in another household arrangement, which is consistent with Census data for that time (U.S. Bureau of Census, 1981). Overall, participants were comparable to those who refused to participate on demographic characteristics and selected behaviors that were assessed during the initial telephone interview, except that participants displayed slightly higher levels of parental income and education. Yet, both variables exhibited adequate variation in the sample. The sample is most representative of white, working- and middle-class youth living in a metropolitan area of the Eastern United States. (For more detail on sample and design, see Pandina, Labouvie, and White, 1984.)

The participants were tested initially between 1979 and 1981 (Time 1, T1) at the ages of 12 (youngest cohort), 15 (middle cohort), and 18 (oldest cohort) (N=1380). These participants returned three years later in 1982-1984 (Time 2, T2), again in 1985-1987 (Time 3, T3), and finally in 1992-94 (Time 4, T4). Ninety-one percent of the original participants returned at T4. Those who dropped out were more likely to be male and to be older. We tested for attrition bias on all of the variables used in the analyses and none of the differences was statistically significant. Therefore, sample attrition did not affect the results presented.

DESIGN

Year of TestAge at Each Test Time

(Birth Year) T1 T2 T3 T4 (‘79-’81) (‘82-’84) (‘85-’87) (‘92-’94)__________________________________________________________________

I. Youngest (1967 - 69) 12---------------15---------------18--------------25 II. Middle (1964 - 66) 15---------------18---------------21--------------28 III. Oldest (1961 - 63) 18---------------21---------------24--------------31

Eligible adolescents were recruited through a random sampling of telephone numbers. Between 1979-1981 successive rounds of telephone calls were carried out to fill specified quotas of 200-225 males and females aged 12, 15, or 18. An initial anonymous telephone interview served to identify households with eligible adolescents and to obtain demographic information. Following the telephone survey, field interviewers visited prospective participants in their homes to gather additional demographic data, interview parents, and enroll adolescents in the study. Subsequently, participants came to the test site for a full day of data collection.

MEASURES

Subject’s marijuana consumption: Marijuana use was measured as the product of last year frequency times typical quantity. These data were collected at all four points in time and were log transformed.Subject’s marijuana related problems: A cumulative measure of problems associated with the use of marijuana, spanning the domains of legal, social, interpersonal, interpersonal, physical and mental health. These data were collected at all four points in time and were log transformed.Subject’s alcohol consumption: Alcohol use was measured as the product of last year frequency times typical quantity. These data were collected at all four points in time and were log transformed.Subject’s alcohol related problems: A cumulative measure of problems associated with the use of alcohol, spanning the domains of legal, social, interpersonal, interpersonal, physical and mental health. These data were collected at all four points in time and were log transformed.Negative affect: A sum of items reflecting depression and hostility from the SCL-90 (Derogatis, 1977), and stress (Dohrenwend & Dohrenwend, 1981; Moos, 1986).Arousal needs: A sum of items reflecting sensation seeking and risk taking attitudes and behaviors (Zuckerman, 1979).Educational attainment: Highest grade completed at T4. Subjects were classified as either completing some/all of high school or as attending and/or completing college.Occupational attainment: Full-time employment at T4. Based upon the distribution, subjects were classified as either administrative/professional or another occupation.Marital status: Marital status at T4. Subjects reported that they either were never married, married, living together as married, divorced, separated, or widowed.Depression: Inventory to Diagnose Depression at T4.Parents’ alcohol use: Participants reported on their parents’ frequency of alcohol use. In addition, parents completed questionnaires (at T1) regarding their own frequency of beer, wine, and hard liquor. We took the maximum value for beer, wine, and hard liquor to create the measure for frequency of alcohol use.Parental hostility: Subscale derived from factor analysis of the Streit (1978) Family Perception Inventory to measure parental hostility/control. For each parent, the participant responded to how often parents engaged in certain behaviors. The parental hostility scale includes 17 items measured at baseline.

ANALYSIS TO DEVELOP TRAJECTORIES

We analyzed the data using a growth mixture model, which is a semi-parametric latent-class based modeling technique (see Muthen & Shedden, 1999; Roeder, Lynch, & Nagin, 1999). This mixture model method allows for cross-group differences in the shape of developmental trajectories and is, therefore, especially suited for identifying heterogeneity in types of developmental trajectories (Nagin & Tremblay, 1999). This technique is based on the assumption that the population is composed of a mixture of distinct groups defined by their developmental trajectories. The approach allows for identification of population heterogeneity in the level of a behavior at a given time, as well as in the development of the behavior over time. Further, the approach allows one to fit censored normal distributions to the longitudinal data, which often reflect the distribution of substance use in adolescent samples. Finally, the approach makes full use of the data to determine parameter estimates and does not lose data through listwise deletion of cases (Hill,, White, Chung, Hawkins & Catalano, 2000). The analyses were restricted to users only. Since subjects were followed from mid-adolescence (age 15) into adulthood (age 31), we could model both the development of and the maturation out (cessation) of marijuana related problems. We conducted these analyses using both consumption and problems and found parallel results.

AIM OF STUDY

The purpose of this study was to develop trajectories of marijuana use among adolescents and young adults over a 16 year span. Differences in predictors and outcomes within these trajectory groups were examined.

Marijuana Problems Trajectories

0

0.5

1

1.5

2

2.5

3

Age 15 Age 18 Age 21 Age 25 Age 28 Age 31

Lo

g o

f P

rob

lem

s

Traj. 1 Traj. 2 Traj. 3 Traj. 4Traj. 5 Traj. 6 Traj. 7

05

1015202530

%

Percentage of Marijuana Users in Each Trajectory Group

40

45

50

55

60

65

Alco

hol U

se

Subject's Lifetime Alcohol Use

2030405060708090

Alco

hol

Prob

lem

s

Subject's Lifetime Alcohol Problems

100105110115120125130

Nega

tive A

ffect

Subject's Lifetime Negative Affect

REFERENCES

Adreason, N.C., Endicott, J., Spitzer, R., & Winoder, G. (1977). The family history method using diagnostic criteria. Archives of General Psychiatry, 34, 1229-1235.

Cloninger, C.R. (1987). Tridimensional Personality Questionnaire (Version 4). Unpublished manuscript.

Derogatis, L.R. (1977). SCL-90-R (questionnaire form). Administration, Scoring and Procedures Manual, Vol. 1, Baltimore: Johns Hopkins University, School of Medicine.

Dohrenwend, B.S., & Dohrenwend, B.P. (1981). Stressful Life Events and Their Contents. Reseda, CA: Watson.

Hill, K., White, H.R., Chung, I-J., Hawkins, J.D., & Catalano, R.F. (2000). Early adult outcomes of adolescent alcohol use: Person- and variable-centered analyses of binge drinking trajectories. Alcoholism: Clinical and Experimental Research, 24, 892-901.

Moos, R.H. (1986). Life Stressors and Social Resources Inventory. Palo Alto, CA: Social Ecology Laboratory, Stanford University.

Muthen, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.

Nagin, D.S., & Tremblay, R.E. (1999). Trajectories of boy’s physical aggression, opposition, and hyperactivity on the path to physically violent and non violent juvenile delinquency. Child Development, 70, 1181-1196.

Pandina, R.J., Labouvie, E.W., & White, H.R. (1984). Potential contributions of the life span developmental approach to the study of adolescent alcohol and drug use: the Rutgers Health and Human Development Project, a working model. Journal of Drug Issues, 14, 253-268.

Roeder, K., Lynch, K.G., & Nagin, D.S. (1999). Modeling uncertainty in latent class membership: A case study in criminology. Journal of the American Statistical Association, 94, 766-776.

Streit, F. (1978). Technical Manual: Youth Perception Inventory. Highland Park, NJ: Essence Publications.

U.S. Bureau of the Census (1981). Current Population Survey: Money, Income and Poverty of Families and Persons in the United States: 1980. Current Population Reports, No. 127.

Zimmerman, M., & Coryell, W. (1987). The inventory to diagnose depression (IDD): A self-report scale to diagnose major depressive disorder. Journal of Consulting and Clinical Psychology, 55, 55-59.

Zuckerman, M. (1979). Sensation Seeking: Beyond the Optimal Level of Arousal. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

RESULTS

1. More than 1/4 of the marijuana users in our sample followed trajectory 3, where adolescent limited (ages 15-24) use-related problems was evident. Approximately 16% of the users in our sample exhibited heightened levels of use-related problems over longer periods of time (groups 6 & 7; ages 18-31).

2. Lifetime alcohol use-related problems: The number of alcohol related problems is directly related to the number of marijuana use-related problems.

3. Lifetime alcohol use: Levels of alcohol consumption were highest in trajectory group 7, but could not be differentiated in groups 4, 5 or 6. Trajectory group 7 exhibited a continuous heightened level of consumption as well as problems related to use.

4. Lifetime levels of negative affect: Subjects in groups 1 and 3 exhibited lowest levels of negative affect than all of the other groups. It appears that low levels of negative affect act as a buffer against the development of marijuana use behaviors.

5. Lifetime arousal needs: Subjects in groups 1, 2 or 3 could not be differentiated in terms of arousal needs. Subjects in group 6, while attaining similarly high levels of arousal needs as group 7, dropped significantly in the level of marijuana consumption and problems by age 31, while subjects in group 7 remained high. It appears that heightened levels of arousal needs serve as a risk for heightened levels of marijuana use behaviors, even into adulthood.

6. Parental hostility atT1: Subjects in groups 5, 6 and 7 exhibited the highest levels of parental hostility. High levels of perceived parental hostility are a risk for the development of higher levels of marijuana use behaviors, even into adulthood.

7. Parental alcohol use: Subjects in groups 1, 2 and 3 exhibited lowest levels of parental alcohol use. Low levels of parental alcohol use are associated with lower levels of subject’s marijuana use.

8. Divorced at T4: Among subjects aged 31, group 7 exhibited the highest percentage of divorces.

9. Educational attainment at T4: Groups 1 and 7 exhibited the lowest percentage of college attendees. The fact that trajectory 1 subjects had relatively few who attended college is interesting and other mediating or moderating factors (school performance, intelligence) for this finding should be explored.

10. Depression at T4: Trajectory groups 1 and 3 exhibited the lowest percentage of depressed subjects at T4. The finding that trajectory group 5 (moderate continuous users) exhibited the highest percentage of depressed subjects should be explored more fully. Other mediating or moderating factors influencing the relationship of marijuana use to depression may include reasons for use and the propensity to self report instances or symptoms of depression.

ACKNOWLEDGEMENT

Supported by NIDA 03395 & NIAAA 05823,11699,11594.

9095

100105110115120

Arou

sal N

eeds

Subject's Lifetime Arousal Needs

0

5

10

15

20

%

Percentage of Divorced Subjects (Age 31 at T4)

3032343638404244

Alco

hol U

se

Parental Alcohol Use

4042444648505254

Host

ility

at T

1

Parental Hostility at T1

10

15

20

25

30

%

Percentage of Subjects Depressed at T4

60

65

70

75

80

85

%

Percentage of Subjects Attending College