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Truancy’s Effect on the Onset of Drug Use among Urban Adolescents Placed at Risk
Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
Original article
Truancy’s Effect on the Onset of Drug Use among Urban Adolescents Placed at Risk Kimberly L. Henry, Ph.D.a,* and David H. Huizinga, Ph.D.b a
Department of Psychology, Colorado State University, Fort Collins, Colorado b Institute of Behavioral Science, University of Colorado, Boulder, Colorado Manuscript received June 26, 2006; manuscript accepted November 14, 2006
Abstract
Purpose: To examine the relationship between truancy and the onset of drug use. Methods: Discrete time survival analysis was used to assess the effect of truancy on initiation of drug use after adjusting for several potential confounders from age 11 to 15 years, using data from the Denver Youth Survey, a longitudinal sample of youth who grew up in socially disorganized neighborhoods of Denver, CO. Results: In this population, truancy was a significant predictor of initiation of alcohol, tobacco, and marijuana use. The robust effect of truancy persisted after controlling for potential confounders, including school performance, school isolation, association with delinquent peers, personal delinquent values, parental monitoring, and family attachment. Conclusions: Although this study cannot point to a causal relationship, we argue that the effect may be at least in part due to the unsupervised, unmonitored time with peers that truancy affords a young person. Truancy prevention is a field of research that needs much more attention. Keeping youth in school every day is likely to have many beneficial effects, and effective truancy prevention efforts may also help to prevent or delay the onset of drug use among adolescents. © 2007 Society for Adolescent Medicine. All rights reserved.
Keywords:
Drug use; Adolescence; Truancy; Discrete-time survival analysis
A 2003 national survey of adolescents in the United States indicated that 11% of 8th-grade students, 16% of 10th-grade students, and 35% of 12th-grade students reported skipping 1 or more days of school during the previous 30 days [1]. These statistics are of concern due to the potentially deleterious effects imparted on an adolescent as a result of truancy. The most obvious negative consequence concerns the truant youth’s academic achievement [2]. However, additional negative consequences may result from the environment that truancy provides. That is, truancy affords a young person unmonitored and unstructured time (often with other youth), and this type of environment is known to propagate delinquency. Perhaps one of the most common sa*Address correspondence to: Kimberly Henry, Ph.D., Department of Psychology, Colorado State University, Fort Collins, CO 80523. E-mail address: [email protected]
lient problem behaviors that often takes place during unmonitored, unstructured time with peers is drug use. Despite the evidence that truancy is a common behavior and that it is reasonable to hypothesize that it is associated with drug use, very little research has examined truancy as a risk factor for drug use. In this paper, we attempt to fill this gap in the literature by assessing the effect of truancy on initiation of alcohol, tobacco, and marijuana using a longitudinal dataset representing youth who grew up in socially disorganized neighborhoods of Denver, CO. We focus on drug use as an outcome in this paper because adolescence is the time in life when most individuals begin experimenting with drugs [3,4], and this timeframe also represents the key period for involvement in truancy. Furthermore, research suggests that adolescents who initiate drug use during early to midadolescence are more likely to experience many negative outcomes in both the short and long term [4 –11].
1054-139X/07/$ – see front matter © 2007 Society for Adolescent Medicine. All rights reserved. doi:10.1016/j.jadohealth.2006.11.138
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K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
Theoretical framework for the relationship between truancy and the onset of drug use The role of the school in an adolescent’s life has been incorporated into most theories that describe adolescent problem behavior. For example, Catalano and Hawkins’ [12] social development model stresses the importance of school bonding as a critical component of pro-social development. Students who are not well bonded to school are more likely to follow an antisocial path through adolescence. As students become disengaged from school, one potential manifestation of this disengagement may be truancy, and the social development model would suggest that disengaged students are more likely to become involved with drug use. Cloward and Ohlin’s [13] strain theory provides a second potential explanation for the relationship between truancy and drug use. This framework hypothesizes that adolescents are more likely to engage in delinquency, including drug use, when they are experiencing a significant discrepancy between their personal aspirations and their perceived opportunities for achievement. Failure (or lack of success) in the school environment is often presumed as one of the major sources of strain. As students become disenfranchised with school, avoidance of school through actions such as truancy and self-medication through drug use may become more likely. Corroborating this line of reasoning, some research has suggested that drug use may be used as a coping mechanism to deal with stress associated with academic failure [14], and formal strain theories have incorporated similar notions of retreatist and emotional adaptations (to include drug use) as one of the major kinds of responses to strain. Thirdly, several seminal theories, including routine activities theory [15–17] and Stoolmiller’s concept of unsupervised wandering [18], propose that delinquent behavior is most likely to occur during periods of unstructured and unsupervised socializing with peers. These theories suggest that unstructured, unsupervised time with peers creates a setting that is more likely to propagate delinquency. Osgood et al [17] indicate that lack of adult authority figures limits the likelihood that adolescents will respond to social control pressures to behave in a pro-social manner, and exposure to delinquent peers in these types of settings may instigate delinquent behavior as well as make delinquent acts easier to carry out, and more rewarding. It is logical to believe that for some students and in some instances, truancy provides exactly this type of setting. When students are truant from school, they are much more likely to be unsupervised as well as unoccupied with pro-social activities. Data from the sample used in this study indicate that truant youth tend to skip school in pairs or groups (across waves, 70 –77% of truants said that they skipped school with other kids). Therefore, we hypothesize that truant students are more likely to initiate drug use at least in part because of the unstructured
and unsupervised opportunities with peers afforded by skipping school. Potential confounding variables of the relationship between truancy and initiation of drug use It is important to note that an observed relationship between truancy and onset of drug use could be spurious if some third variable(s) cause both truancy and onset of drug use. We identified four sets of potential confounding variables that may account for both truancy and initiation of drug use: these include school-related variables, peer-related variables, individual attitudes/beliefs, and family-related variables; each of these are detailed below. First, one might hypothesize that poor performance in school may lead to both truancy and drug use. As described earlier, ample evidence exists to suggest an association between school-related problems and drug use, both theoretically and from empirical studies. Second, the relationship between truancy and initiation of drug use may be confounded if association with delinquent peers causes a youth to both skip school and use substances. Several theories, including social learning theory [19], primary socialization theory [20], and peer cluster theory [21], emphasize the role of peers and social learning. Those with deviant peers may be more likely to obtain social rewards for involvement in deviance, including both truancy and drug use, and may learn and adopt attitudes favorable to delinquency. Third, truancy may not cause initiation of drug use, but rather students who have a propensity for delinquency may both skip school and use drugs [22]. This general propensity for deviance may be captured by a young person’s attitude toward delinquency. Finally, family-related variables may increase an adolescent’s involvement in both truancy and drug use. Indeed, a great deal of research has identified the importance of family on the pro-social development of youth in general [23,24] and with regard to substance use in particular [25]. While these family-related risk factors are known to increase the probability of drug use, it is quite likely that these variables may also increase the probability of truancy. We deal with the potential for a confounded effect between truancy and onset of drug use by adjusting for the potential confounding variables in each of the domains (e.g., school performance, school isolation, association with delinquent peers, personal delinquent values, parental monitoring, and family attachment). Of course, even if these identified potential confounders do, in fact, lead to both truancy and drug use, truancy could still have a legitimate and unique effect on initiation of drug use. To represent the broadest picture, we present two models for each initiation process. In the first, we look at the independent effect of truancy (as well as each potential confounder) on initiation. Second, we look at the effect of truancy in a full model that
K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
includes all potential confounders. While inclusion of the confounders does not allow the “causal effect” of truancy to be assessed (only a randomized study of the effect of truancy could make such claims), it does help us to better understand the unique contribution of truancy after adjusting for these other salient factors. Methods Sample The Denver Youth Survey (DYS) is based on a probability sample of households in high-risk neighborhoods of Denver, CO. These neighborhoods were selected on the basis of their social ecology in terms of population and housing characteristics. Only socially disorganized neighborhoods with high official crime rates (top one third) were included. The participants in the DYS include 1528 children and youth who lived in one of the randomly selected households and were 8, 10, 12, 14, or 16 years of age in 1988, and one of their guardians. The young people were interviewed annually from 1988 until 1992. The retention rate was over 90%. The analyses presented here focus on ages 12 to 15, to examine school experiences during the time adolescents are legally required to be in school. Only 1 of the 5 cohorts (the cohort born in 1976) was assessed each year from ages 12 to 15. Therefore, the analyses are restricted to this cohort (N ⫽ 304). In total, 54.6% of the individuals in the cohort are male. The subsample is also ethically diverse (9.5% are White, 33.2% are African American, and 47.7% are Hispanic). Measures The primary independent variable of interest, truancy, was measured by self-report. To thoroughly examine this variable, we constructed and utilized two forms of the variable. At each measurement occasion, students reported the number of times they had “skipped school without an excuse” during the previous year. The students also reported more detailed information about the least serious and most serious incidence of truancy (i.e., the length of time that they were out of school for the least serious occasion of truancy and for the most serious occasion of truancy). Using these items, a variable was constructed that approximated the number of days that each student had missed school during a period of 1 year. This specification of the variable represented the first format. Because the variable is skewed, a natural log transformation was applied. For the second format of the truancy variable, we used the available information to construct a categorical variable made up of six categories: non-truant, class skipper (skipped school but never missed more than one class at a time), minor truant (missed 3 or fewer days of school during the year), moderate truant (missed 4 –9 days of school during the year), chronic truant (missed 10 –35 days of school—the legal
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definition of chronic truancy in Colorado is 10 or more days), and severe truant (missed 36 or more days of school—about once per week on average). To assess the unique effect of truancy, several control and potential confounding variables were assessed. First, both gender (coded as 1 for male, 0 for female) and race were included (as dummy variables comparing African American students, Hispanic students, and students of other ethnicities/races to White students) in all models. School performance was represented by a five-item scale, ranging from 1 to 5, including measures of academic performance and commitment to school work (coefficient alpha ⫽ .67). Social isolation at school represented the average of three items (e.g., “I often feel like nobody at school cares about me”). All items were measured on a five-point Likert scale ranging from “definitely disagree” to “definitely agree.” Coefficient alpha ⫽ .65. Personal attitude toward delinquency included 10 items (e.g., How wrong is it to purposely damage others’ property?), each measured on a four-point scale (very wrong, wrong, a little bit wrong, not wrong at all), coefficient alpha ⫽ .90. Friends’ attitudes toward delinquency included 16 items (e.g., During the past year, how many of your friends broke into a building to steal something?), each measured on a five-point scale (none of them, few of them, half of them, most of them, all of them). Coefficient alpha ⫽ .91. Parental/caregiver monitoring was made up of seven items indicating the extent to which the primary caregiver was aware of the students whereabouts, friendships, and activities (coefficient alpha ⫽ .55). All items ranged from 1 to 3, with 3 indicating better monitoring. Attachment to family was measured by an 11-item scale that assessed the extent to which the student and his/her family had a strong bond with one another (coefficient alpha ⫽ .77). All items ranged from 1 to 5, with 5 indicating better attachment. Means and standard deviations (SDs) for all independent variables are presented in Table 1. The dependent variable of interest was onset of drug use (alcohol, tobacco, and marijuana). Each year, the students reported the number of times in the past year that they had used each of these drugs. We limited alcohol use to consumption that was equal to or greater than one drink, to exclude sips or use that may be part of a family affair or religious service. At the first interview, students who had already initiated use of each respective drug indicated their age at first use. Using this information, an age of first use was constructed for each individual in the sample for each drug. Analysis Our research, question of interest concerned the association between truancy and initiation of alcohol, tobacco, and marijuana use. We utilized a type of survival model called a discrete-time proportional odds model [26] to analyze the
.48 .52 .55 .58 .39 .35 .38 .42 2.40 2.49 2.50 2.43 Age Age Age Age
12 13 14 15
81.3 67.7 51.9 41.6
6.5 10.2 10.8 12.9
6.4 12.6 17.1 20.7
2.6 3.9 7.5 9.1
1.0 3.9 8.7 8.7
2.3 1.7 4.0 7.0
3.99 3.93 3.75 3.60
.35 .58 .66 .70
1.53 1.62 1.79 1.85 .77 .70 .71 .71 2.33 2.14 2.17 2.13
.52 .53 .78 .75
1.32 1.44 1.51 1.63
.19 .48 .44 .54
3.73 3.74 3.67 3.57
Mean SD Mean Mean SD Mean
SD
Mean
SD
Mean
SD
Family attachment Parental monitoring Delinquent peers School isolation School performance Non-truant
Class skipper
Minor truant
Moderate truant
Chronic truant
Severe truant
Potential confounders Truancy (percentage in each group)
Table 1 Univariate statistics for independent variables
SD
K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
Delinquent values
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data. A separate series of models was tested for each of the three drugs. Truancy and each of the potential confounders were treated as contemporaneous time-dependent covariates (i.e., their values change within person over time), while gender and race were treated as time-independent covariates. Missing data To appropriately handle missing data, we employed multiple imputation (MI). The imputation was carried out using IVEware [27]. The amount of missing data was relatively small and was primarily related to the inability to locate respondents for a certain interview and/or refusal to participate. The range of missing observations across the variables considered in this paper ranged from a low of 0% to a high of 12.5%. In total, 10 imputed datasets were created. All analyses were performed on each of the imputed datasets, and the parameter estimates were then combined using the procedures outlined by Rubin [28]. Results We started by fitting an unconditional survival model for each drug, that is, a survival model that included only the time indicators (one time indicator for each age). The hazard function for first use of alcohol, tobacco, and marijuana use estimated from these models is presented in Figure 1. Each function describes the probability that a student would initiate use of a particular drug between the age j-1 and age j assessment, given that he or she had never used the drug coincident with or prior to the age j-1 assessment. A gentle increase in the hazard of initiation was observed over time for tobacco and marijuana, but a more rapid increase was observed for alcohol. The survival functions for each drug were also calculated. While the hazard function assesses the unique risk of initiation at each age, the survival function cumulates the risk of initiation at each age to assess the probability that a randomly selected adolescent will survive (i.e., not initiate use of a particular drug) through time period j. The probability that a randomly selected student in the sample would have not used marijuana through age 15 was .66. This same probability was .41 for initiation of alcohol use and .69 for initiation of tobacco use. After specifying unconditional discrete time survival models for each drug, we extended the models by including the time-independent control variables (gender and race), the time-dependent covariate of interest (truancy), and the time-dependent potential confounders. First, we tested the univariate effect of truancy (in the logged format described in the measurement section) and each potential confounder (i.e., besides the covariate of interest, the independent variables included just the time variables, gender, and race). The results of these models are presented under the heading Univariate Effects, Model 1 in Table 2 (for alcohol), Table 3 (for tobacco) and Table 4 (for marijuana). The univariate effect
K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
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Figure 1. Hazard function for onset of drug use.
of truancy is a significant predictor of onset of all three drugs. The odds of initiation of alcohol use were 1.99 times higher, the odds of initiation of tobacco use were 1.65 times higher, and the odds of initiation of marijuana use were 2.33 times higher for each one unit increase in the log number of days truant. To increase our understanding of the effect size and more thoroughly examine the effect, we also considered
a second form of the univariate effect of truancy, in which truancy was specified as a categorical variable. Because of the low frequency of severe truants, we combined the chronic and severe categories. The categorical format of truancy was examined by including four dummy coded indicators to compare class skippers, minor truants, moderate truants, and chronic truants to non-truants, respectively.
Table 2 Effect of truancy on first use of alcohol Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.69 ⫺.46 ⫺.21 1.22 .35 .15 ⫺.36
.09 .17 .14 .16 .20 .27 .21
7.36 ⫺2.73 ⫺1.44 7.68 1.77 .57 ⫺1.75
1.99 .63 .81 3.37 1.43 1.17 .70
1.65 .45 .61 2.47 .96 .68 .47
Est.
1.04 1.58 2.12 2.26
t
OR
⫹
2.38 .88 1.08 4.60 2.11 1.99 1.04
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.15 ⫺2.33 ⫺1.69 ⫺1.57 .13 ⫺.80 ⫺.33 ⫺.14 .53 ⫺.04 ⫺.48 1.12 ⫺.23 .59 ⫺.28
⫹
.29 .23 .20 .22 .21 .37 .34 .44 .10 .21 .17 .18 .26 .32 .26
⫺7.34 ⫺10.13 ⫺8.45 ⫺7.24 .59 ⫺2.16 ⫺.98 ⫺.32 5.23 ⫺.20 ⫺2.82 6.14 ⫺.87 1.83 ⫺1.09
.12 .10 .18 .21 1.13 .45 .72 .87 1.69 .96 .62 3.07 .80 1.81 .75
.07 .06 .12 .14 .75 .22 .37 .37 1.39 .63 .44 2.15 .48 .96 .45
.21 .15 .27 .32 1.73 .93 1.39 2.05 2.06 1.46 .86 4.39 1.32 3.41 1.25
.37 .29 .44 .38
2.53 4.86 3.75 4.38
2.57 4.01 5.13 5.35
1.24 2.29 2.18 2.53
5.33 7.03 12.07 11.33
Model 4 .35 .28 .40 .33
2.97 5.71 5.31 6.78
2.83 4.85 8.33 9.56
1.43 2.82 3.81 4.98
5.64 8.34 18.19 18.35
.94 1.39 1.64 1.68
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
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Table 3 Effect of truancy on first use of tobacco Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.50 ⫺.46 ⫺.01 .96 .23 ⫺.35 ⫺.45
.09 .19 .20 .18 .24 .34 .22
5.52 ⫺2.45 ⫺.07 5.42 .93 ⫺1.02 ⫺2.04
1.65 .63 .99 2.61 1.25 .71 .64
1.38 .44 .67 1.85 .78 .36 .42
Est.
.97 1.42 1.72 2.05
t
OR
⫹
1.98 .91 1.46 3.70 2.01 1.38 .98
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.82 ⫺3.00 ⫺2.64 ⫺2.67 ⫺.11 ⫺1.10 ⫺.52 ⫺.33 .35 .04 ⫺.18 .79 ⫺.30 .01 ⫺.25
⫹
.38 .36 .29 .26 .33 .48 .38 .51 .11 .23 .23 .21 .29 .40 .27
⫺7.46 ⫺8.41 ⫺9.14 ⫺10.18 ⫺.35 ⫺2.28 ⫺1.37 ⫺.64 3.29 .16 ⫺.80 3.78 ⫺1.05 .01 ⫺.93
.06 .05 .07 .07 .89 .33 .59 .72 1.42 1.04 .83 2.20 .74 1.01 .78
.03 .02 .04 .04 .47 .13 .28 .26 1.15 .66 .53 1.46 .42 .46 .46
.12 .10 .13 .12 1.69 .86 1.25 1.96 1.76 1.63 1.30 3.32 1.30 2.20 1.32
.46 .39 .55 .53
1.99 3.26 2.38 2.93
2.51 3.56 3.69 4.78
1.01 1.66 1.26 1.68
6.23 7.63 10.84 13.61
Model 4 .46 .38 .51 .48
2.10 3.79 3.37 4.25
2.65 4.14 5.59 7.76
1.07 1.99 2.06 3.02
6.58 8.65 15.20 19.97
.92 1.27 1.31 1.56
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
The results of these analyses are presented under Univariate Effects, Model 2 in Tables 2– 4. The estimates indicate that all types of truants have a higher odds of initiation as compared to non-truants. Next, we tested multivariate models that included all potential confounders. The results of these models are reported under the heading Full Model, Model 3 in Tables 2– 4. After adjusting for all potential confounders, the effect of truancy was diminished, yet remained robust. Holding constant gender, race, and all potential confounders, the odds of initiation of alcohol use was 1.69 times higher, the odds of initiation of tobacco use was 1.42 times higher, and the odds of initiation of marijuana use was 1.93 times higher for each one unit increase in the log number of days truant. Model 4 presents the results of the multivariate models that considered the categorical measure of truancy in the same way as described above for the univariate model. Although not shown in the tables, these models also adjusted for all covariates and potential confounders. The estimates indicate that, even after adjusting for potential confounders, all types of truants have a higher odds of initiation as compared to non-truants. To ensure that the proportionality assumption was not violated, time by covariate interactions were included for all time-independent and time-dependent covariates. The only
variable that resulted in a significantly better fit when allowed to vary across time was involvement with delinquent peers. The parameter estimates indicated that the effect of delinquent peer association on the initiation of tobacco and marijuana use was more robust at the earlier ages (i.e., ages 12 and 13). No time by delinquent peer association effects were noted for alcohol initiation. Allowing the effect of involvement with delinquent peers to vary across time did not significantly change the effect of truancy on initiation of tobacco use (odds ratio [OR] ⫽ 1.43, 95% confidence interval [CI]: 1.16, 1.76) or marijuana use (OR ⫽ 1.89, 95% CI: 1.49, 2.39). In addition, we assessed the extent to which the effect of truancy differed by gender and race by including a series of interaction variables. No gender or race differences were found for initiation of alcohol or marijuana use. However, one difference was identified for initiation of tobacco. Specifically, the effect of truancy on initiation of tobacco use was more robust for boys as compared to girls (interaction effect: b ⫽ .53, standard error [SE] ⫽ .24, p ⬍ .05). It is important to note that the small sample size limits are able to detect small differences over time and/or small differences by race and gender. Therefore, it is possible that more differences exist but are not detected in this study. Although many of the potential confounders do not have
K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17
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Table 4 Effect of truancy on first use of marijuana Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.85 ⫺.71 ⫺.11 1.28 .42 ⫺.64 ⫺.51
.11 .20 .18 .17 .23 .30 .21
7.65 ⫺3.58 ⫺.58 7.48 1.85 ⫺2.14 ⫺2.40
2.33 .49 .90 3.58 1.52 .53 .60
1.88 .33 .63 2.56 .97 .29 .40
Est.
1.37 2.15 2.68 2.76
t
OR
⫹
2.90 .72 1.29 5.01 2.37 .95 .91
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.43 ⫺2.96 ⫺3.20 ⫺2.93 .05 ⫺.42 ⫺.25 ⫺.90 .66 ⫺.20 ⫺.39 1.00 ⫺.38 ⫺.35 ⫺.09
⫹
.37 .33 .30 .28 .28 .44 .42 .64 .12 .24 .23 .21 .30 .35 .28
⫺6.63 ⫺9.02 ⫺10.69 ⫺10.59 .17 ⫺.94 ⫺.60 ⫺1.42 5.52 ⫺.84 ⫺1.72 4.68 ⫺1.26 ⫺.99 ⫺.31
.09 .05 .04 .05 1.05 .66 .78 .40 1.93 .82 .68 2.72 .69 .71 .92
.04 .03 .02 .03 .60 .28 .34 .12 1.53 .51 .44 1.79 .38 .35 .53
.18 .10 .07 .09 1.83 1.57 1.77 1.41 2.44 1.31 1.06 4.13 1.23 1.41 1.59
.52 .41 .52 .50
2.32 4.77 4.06 4.21
3.35 6.98 8.15 8.21
1.21 3.14 2.96 3.08
9.28 15.52 22.44 21.86
Model 4 .51 .39 .46 .44
2.66 5.49 5.83 6.25
3.93 8.58 14.61 15.80
1.44 3.99 5.93 6.65
10.75 18.47 36.00 37.57
1.21 1.94 2.10 2.10
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for Age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
a significant direct effect on initiation after adjusting for all other variables in the model, it is important to note that some of the identified potential confounders may in fact act as more distal predictors of initiation. For example, poor family attachment and poor monitoring are both significant predictors of initiation of marijuana use when assessed independently; however, once truancy and the other potential confounders are included in the model, the direct effect of these parent variables no longer exists. It is reasonable to hypothesize that poor family attachment and poor monitoring may lead to truancy, which then leads to initiation of drug use. That is, these family variables may have important indirect effects. There is support in the literature for this type of mediated effect [29 –31]. A similar argument could be made for the indirect effect of school performance on initiation via truancy. It also should be noted that some of the identified potential confounders could actually be influenced by truancy. For example, it is possible that involvement in truancy leads to increased association with delinquent peers, increased endorsement of delinquent values, or a decline in academic performance. To the extent that this is true, then controlling for these variables may attenuate the true effect of truancy. Future studies designed to unpack causal ordering of these
types of relationships is necessary to more fully understand how truancy affects initiation of drug use.
Discussion In this paper, we have demonstrated the effect of truancy on initiation of drug use among urban adolescents who grew up in socially disorganized neighborhoods of Denver, CO. Consistent with our hypothesis, truancy significantly increased the odds of initiation of drug use after adjusting for gender, race, and potential confounders. Our own theoretical orientation for the best explanation of the observed relationship follows the thinking of Osgood and colleagues [16,17] and Stoolmiller [18]. These researchers have shown that delinquent behavior (including drug use) is especially likely to occur in situations of unsupervised, unstructured time with peers. The results in this paper corroborate these findings. For many adolescents and in many situations, truancy provides a context for initiation because of the unstructured and unsupervised time that it provides. Given this orientation, we would hypothesize that truancy demonstrated by adolescents who skip school for nondelinquent reasons (e.g., to help a family member) would not put them at increased risk for initiation of drug use. Unfortunately,
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we do not have adequate data to know for sure if this is the correct mechanism to explain the relationship. A future study that more closely tracks both truancy and drug use that takes place during truant days is necessary. Given the results presented in this paper, along with previous studies that have demonstrated the harmful consequences of truancy, more research into the etiology and prevention of truancy is necessary. Although more work is needed, some research suggests that programs aimed at reducing truancy may have a deterrent effect on delinquency, including drug use. In one study, Garry [32] reported that shoplifting arrests declined by 60% and purse snatching dropped by nearly 50% when intensive truancy sweeps were conducted by the police. Thinking proactively about adolescent truancy, Office of Juvenile Justice and Delinquency Prevention (OJJDP), in conjunction with the Department of Education, started the Truancy Reduction Demonstration Program. Funding several programs across the country, these initiatives have allowed for the identification of a set of critical components that are predictive of positive outcomes for children and families [33]. These critical components include collaboration between key agencies (e.g., schools, communities, juvenile authorities); creation of a context of support; family involvement in efforts; comprehensive approaches that seek to remedy the root causes of truancy; and use of incentives and sanctions. Besides these efforts, several other recent studies have reported on the effectiveness of school and community based truancy prevention programs [34,35]. These programs have demonstrated encouraging results. However, much more work needs to be done to understand why students skip school and how best to prevent it. Given that truancy may play a salient role in adolescent drug use, truancy prevention is an extremely important topic for future research. A commitment to developing and testing truancy prevention efforts is imperative. Limitations The sample for this study includes urban youth who grew up in socially disorganized neighborhoods of Denver, CO. Future research is necessary to determine if these results can be generalized to other types of youth. In this study, data were collected in one-year intervals. Our research question of interest may have been better addressed by assessing the lagged effect of truancy on initiation of drug use. This type of analysis would have ensured that truancy occurred before the onset of drug use. Because our hypothesis posits that the effect of truancy is largely due to the environment that is afforded by skipping school (unmonitored, unstructured time with peers), it does not make sense to think that the effect of unmonitored, unstructured time would lead to the onset of drug use one year later. That is, it is likely that the effect occurs much more proximally.
Despite these limitations, this study provides important evidence that truancy may play an important role in the onset of drug use. While this study cannot point to causal effects, the robust effect persists even after adjusting for salient potential confounding variables. This important study highlights the need for more focus on a behavior (i.e., truancy) that has been given relatively little attention. Acknowledgment This research was supported by grant K01 DA01781001A1 (PI: Kimberly L. Henry) from the National Institute on Drug Abuse and grant 96-MU-FX-0017 (PI: David Huizinga) from the Office of Juvenile Justice and Delinquency Prevention, US Department of Justice. Points of view or opinions in this article are those of the authors and do not necessarily represent official positions or policies of the National Institute of Drug Abuse or the Office of Juvenile Justice and Delinquency Prevention. References [1] Johnston LD, Bachman, JG, O’Malley PM, et al. Monitoring the Future: A Continuing Study of American Youth. 2003 [computer file]. Ann Arbor, MI: Inter-University Consortiom for Political and Social Research, 2004. [2] Wehlage GG, Rutter RA. Dropping out: How much do schools contribute to the problem? Teach Coll Rec 1986;87:374 –92. [3] Kandel DB, Logan JA. Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. Am J Public Health 1984;74:660 – 6. [4] Office of Applied Studies. Summary of findings from the 2000 National Household Survey on drug abuse. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2001. [5] Ellickson PL, Hays RD, Bell RM. Stepping through the drug use sequence: Longitudinal scalogram analysis of initiation and regular use. J Abnorm Psychol 1992;101:441–51. [6] Anthony JC, Petronis KR. Early-onset drug use and risk of later drug problems. Drug Alcohol Depen 1995;40:9 –15. [7] Hawkins JD, Graham JW, Maguin E, et al. Exploring the effects of age alcohol use initiation and psychosocial risk factors on subsequent alcohol misuse. J Stud Alcohol 1997;8:280 –90. [8] Kandel DB, Yamaguchi K, Chen K. Stages of progression in drug involvement from adolescence to adulthood: Further evidence for the gateway theory. J Stud Alcohol 1992;53:447–57. [9] Grant BF, Dawson DA. Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. J Subst Abuse 1997;9:103–10. [10] Brook JS, Balka EB, Whiteman M. The risks for late adolescence of early adolescent marijuana use. Am J Public Health 1999;89: 1549 –54. [11] National Institute on Drug Abuse Research Report Series. Marijuana Abuse. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2002. [12] Catalano RF, Hawkins JD. The social development model: A theory of antisocial behavior. In: Hawkins JD (ed). Delinquency and Crime: Current Theories. New York, NY: Cambridge University Press, 1996: 149 –97. [13] Cloward RA, Ohlin LE. Delinquency and Opportunity: A Theory of Delinquent Gangs. New York, NY: Free Press, 1960.
K.L. Henry and D.H. Huizinga / Journal of Adolescent Health 40 (2007) 358.e9 –358.e17 [14] Brunswick AF, Messeri PA. Origins of cigarette smoking in academic achievement, stress and social expectations: Does gender make a difference? J Early Adolesc 1984;4:353–70. [15] Cohen LE, Felson M. Social change and crime rate trends: A routine activity approach. Am Sociol Rev 1979;44:588 – 608. [16] Osgood DW, Anderson AL. Unstructured socializing and rates of delinquency. Criminology 2004;42:519 –50. [17] Osgood DW, Wilson JK, O’Malley PM, et al. Routine activities and individual deviant behavior. Am Sociol Rev 1996;61:635–55. [18] Stoolmiller M. Antisocial behavior, delinquent peer association, and unsupervised wandering for boys: Growth and change from childhood to early adolescence. Multivar Behav Res 1994;29:263– 88. [19] Bandura A. Social Learning Theory. Oxford, England: Prentice-Hall, 1977. [20] Oetting ER, Donnermeyer JF. Primary socialization theory: The etiology of drug use and deviance. I. Subst Use Misuse 1998;33:995– 1026. [21] Oetting ER, Beauvais F. Peer cluster theory, socialization characteristics, and adolescent drug use: A path analysis. J Couns Psychol 1987;34:205–13. [22] Jessor R, Jessor SL. Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. New York, NY: Academic Press, 1977. [23] Bronfenbrenner U. Ecology of the family as a context for human development: Research perspectives. Develop Psychol 1986;22: 723– 42. [24] Repetti RL, Taylor SE, Seeman TE. Risky families: Family social environments and the mental and physical health of offspring. Psychol Bull 2002;128:330 – 66. [25] Velleman RDB, Templeton LJ, Copello AG. The role of the family in preventing and intervening with substance use and misuse: A com-
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prehensive review of family interventions, with a focus on young people. Drug Alcohol Rev 2005;24:93–109. Singer JD, Willett JB. It’s about time: Using discrete-time survival analysis to study duration and the timing of events. J Educ Stat 1993;18:155–95. Raghunathan TE, Lepkowski J, VanHoewyk J, et al. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Method 2001;27:85–95. Rubin DL. Multiple imputation for nonresponse in surveys. New York, NY: Wiley & Sons, 1987. Forgatch MS, DeGarmo DS. Two faces of Janus: Cohesion and conflict. In: Cox MJ, Brooks-Gunn J (eds). Conflict and Cohesion in Families: Causes and Consequences. Mahwah, NJ: Lawrence Erlbaum Associates Publishers, 1999:167– 84. Kerr M, Stattin H. What parents know, how they know it, and several forms of adolescent adjustment: Further support for a reinterpretation of monitoring. Develop Psychol 2000;36:366 – 80. Stattin H, Kerr M. Parental monitoring: A reinterpretation. Child Develop 2000;71:1072– 85. Garry EM. First Step to a Lifetime of Problems. Washington DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention, 1996. National Center for School Engagement. Truancy Prevention in Actions: Best Practices and Model Truancy Programs. Available at: http://www.schoolengagement.org/TruancypreventionRegistry/Admin/ Resources/. Accessed May 15, 2006. McCluskey CP, Bynum TS, Patchin JW. Reducing chronic absenteeism: An assessment of an early truancy initiative. Crime Delinquency 2004;50:214 –34. Fantuzzo J, Grim S, Hazan H. Project START: An evaluation of a community-wide school-based intervention to reduce truancy. Psychol School 2005;42:657– 67.
Original article
Truancy’s Effect on the Onset of Drug Use among Urban Adolescents Placed at Risk Kimberly L. Henry, Ph.D.a,* and David H. Huizinga, Ph.D.b a
Department of Psychology, Colorado State University, Fort Collins, Colorado b Institute of Behavioral Science, University of Colorado, Boulder, Colorado Manuscript received June 26, 2006; manuscript accepted November 14, 2006
Abstract
Purpose: To examine the relationship between truancy and the onset of drug use. Methods: Discrete time survival analysis was used to assess the effect of truancy on initiation of drug use after adjusting for several potential confounders from age 11 to 15 years, using data from the Denver Youth Survey, a longitudinal sample of youth who grew up in socially disorganized neighborhoods of Denver, CO. Results: In this population, truancy was a significant predictor of initiation of alcohol, tobacco, and marijuana use. The robust effect of truancy persisted after controlling for potential confounders, including school performance, school isolation, association with delinquent peers, personal delinquent values, parental monitoring, and family attachment. Conclusions: Although this study cannot point to a causal relationship, we argue that the effect may be at least in part due to the unsupervised, unmonitored time with peers that truancy affords a young person. Truancy prevention is a field of research that needs much more attention. Keeping youth in school every day is likely to have many beneficial effects, and effective truancy prevention efforts may also help to prevent or delay the onset of drug use among adolescents. © 2007 Society for Adolescent Medicine. All rights reserved.
Keywords:
Drug use; Adolescence; Truancy; Discrete-time survival analysis
A 2003 national survey of adolescents in the United States indicated that 11% of 8th-grade students, 16% of 10th-grade students, and 35% of 12th-grade students reported skipping 1 or more days of school during the previous 30 days [1]. These statistics are of concern due to the potentially deleterious effects imparted on an adolescent as a result of truancy. The most obvious negative consequence concerns the truant youth’s academic achievement [2]. However, additional negative consequences may result from the environment that truancy provides. That is, truancy affords a young person unmonitored and unstructured time (often with other youth), and this type of environment is known to propagate delinquency. Perhaps one of the most common sa*Address correspondence to: Kimberly Henry, Ph.D., Department of Psychology, Colorado State University, Fort Collins, CO 80523. E-mail address: [email protected]
lient problem behaviors that often takes place during unmonitored, unstructured time with peers is drug use. Despite the evidence that truancy is a common behavior and that it is reasonable to hypothesize that it is associated with drug use, very little research has examined truancy as a risk factor for drug use. In this paper, we attempt to fill this gap in the literature by assessing the effect of truancy on initiation of alcohol, tobacco, and marijuana using a longitudinal dataset representing youth who grew up in socially disorganized neighborhoods of Denver, CO. We focus on drug use as an outcome in this paper because adolescence is the time in life when most individuals begin experimenting with drugs [3,4], and this timeframe also represents the key period for involvement in truancy. Furthermore, research suggests that adolescents who initiate drug use during early to midadolescence are more likely to experience many negative outcomes in both the short and long term [4 –11].
1054-139X/07/$ – see front matter © 2007 Society for Adolescent Medicine. All rights reserved. doi:10.1016/j.jadohealth.2006.11.138
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Theoretical framework for the relationship between truancy and the onset of drug use The role of the school in an adolescent’s life has been incorporated into most theories that describe adolescent problem behavior. For example, Catalano and Hawkins’ [12] social development model stresses the importance of school bonding as a critical component of pro-social development. Students who are not well bonded to school are more likely to follow an antisocial path through adolescence. As students become disengaged from school, one potential manifestation of this disengagement may be truancy, and the social development model would suggest that disengaged students are more likely to become involved with drug use. Cloward and Ohlin’s [13] strain theory provides a second potential explanation for the relationship between truancy and drug use. This framework hypothesizes that adolescents are more likely to engage in delinquency, including drug use, when they are experiencing a significant discrepancy between their personal aspirations and their perceived opportunities for achievement. Failure (or lack of success) in the school environment is often presumed as one of the major sources of strain. As students become disenfranchised with school, avoidance of school through actions such as truancy and self-medication through drug use may become more likely. Corroborating this line of reasoning, some research has suggested that drug use may be used as a coping mechanism to deal with stress associated with academic failure [14], and formal strain theories have incorporated similar notions of retreatist and emotional adaptations (to include drug use) as one of the major kinds of responses to strain. Thirdly, several seminal theories, including routine activities theory [15–17] and Stoolmiller’s concept of unsupervised wandering [18], propose that delinquent behavior is most likely to occur during periods of unstructured and unsupervised socializing with peers. These theories suggest that unstructured, unsupervised time with peers creates a setting that is more likely to propagate delinquency. Osgood et al [17] indicate that lack of adult authority figures limits the likelihood that adolescents will respond to social control pressures to behave in a pro-social manner, and exposure to delinquent peers in these types of settings may instigate delinquent behavior as well as make delinquent acts easier to carry out, and more rewarding. It is logical to believe that for some students and in some instances, truancy provides exactly this type of setting. When students are truant from school, they are much more likely to be unsupervised as well as unoccupied with pro-social activities. Data from the sample used in this study indicate that truant youth tend to skip school in pairs or groups (across waves, 70 –77% of truants said that they skipped school with other kids). Therefore, we hypothesize that truant students are more likely to initiate drug use at least in part because of the unstructured
and unsupervised opportunities with peers afforded by skipping school. Potential confounding variables of the relationship between truancy and initiation of drug use It is important to note that an observed relationship between truancy and onset of drug use could be spurious if some third variable(s) cause both truancy and onset of drug use. We identified four sets of potential confounding variables that may account for both truancy and initiation of drug use: these include school-related variables, peer-related variables, individual attitudes/beliefs, and family-related variables; each of these are detailed below. First, one might hypothesize that poor performance in school may lead to both truancy and drug use. As described earlier, ample evidence exists to suggest an association between school-related problems and drug use, both theoretically and from empirical studies. Second, the relationship between truancy and initiation of drug use may be confounded if association with delinquent peers causes a youth to both skip school and use substances. Several theories, including social learning theory [19], primary socialization theory [20], and peer cluster theory [21], emphasize the role of peers and social learning. Those with deviant peers may be more likely to obtain social rewards for involvement in deviance, including both truancy and drug use, and may learn and adopt attitudes favorable to delinquency. Third, truancy may not cause initiation of drug use, but rather students who have a propensity for delinquency may both skip school and use drugs [22]. This general propensity for deviance may be captured by a young person’s attitude toward delinquency. Finally, family-related variables may increase an adolescent’s involvement in both truancy and drug use. Indeed, a great deal of research has identified the importance of family on the pro-social development of youth in general [23,24] and with regard to substance use in particular [25]. While these family-related risk factors are known to increase the probability of drug use, it is quite likely that these variables may also increase the probability of truancy. We deal with the potential for a confounded effect between truancy and onset of drug use by adjusting for the potential confounding variables in each of the domains (e.g., school performance, school isolation, association with delinquent peers, personal delinquent values, parental monitoring, and family attachment). Of course, even if these identified potential confounders do, in fact, lead to both truancy and drug use, truancy could still have a legitimate and unique effect on initiation of drug use. To represent the broadest picture, we present two models for each initiation process. In the first, we look at the independent effect of truancy (as well as each potential confounder) on initiation. Second, we look at the effect of truancy in a full model that
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includes all potential confounders. While inclusion of the confounders does not allow the “causal effect” of truancy to be assessed (only a randomized study of the effect of truancy could make such claims), it does help us to better understand the unique contribution of truancy after adjusting for these other salient factors. Methods Sample The Denver Youth Survey (DYS) is based on a probability sample of households in high-risk neighborhoods of Denver, CO. These neighborhoods were selected on the basis of their social ecology in terms of population and housing characteristics. Only socially disorganized neighborhoods with high official crime rates (top one third) were included. The participants in the DYS include 1528 children and youth who lived in one of the randomly selected households and were 8, 10, 12, 14, or 16 years of age in 1988, and one of their guardians. The young people were interviewed annually from 1988 until 1992. The retention rate was over 90%. The analyses presented here focus on ages 12 to 15, to examine school experiences during the time adolescents are legally required to be in school. Only 1 of the 5 cohorts (the cohort born in 1976) was assessed each year from ages 12 to 15. Therefore, the analyses are restricted to this cohort (N ⫽ 304). In total, 54.6% of the individuals in the cohort are male. The subsample is also ethically diverse (9.5% are White, 33.2% are African American, and 47.7% are Hispanic). Measures The primary independent variable of interest, truancy, was measured by self-report. To thoroughly examine this variable, we constructed and utilized two forms of the variable. At each measurement occasion, students reported the number of times they had “skipped school without an excuse” during the previous year. The students also reported more detailed information about the least serious and most serious incidence of truancy (i.e., the length of time that they were out of school for the least serious occasion of truancy and for the most serious occasion of truancy). Using these items, a variable was constructed that approximated the number of days that each student had missed school during a period of 1 year. This specification of the variable represented the first format. Because the variable is skewed, a natural log transformation was applied. For the second format of the truancy variable, we used the available information to construct a categorical variable made up of six categories: non-truant, class skipper (skipped school but never missed more than one class at a time), minor truant (missed 3 or fewer days of school during the year), moderate truant (missed 4 –9 days of school during the year), chronic truant (missed 10 –35 days of school—the legal
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definition of chronic truancy in Colorado is 10 or more days), and severe truant (missed 36 or more days of school—about once per week on average). To assess the unique effect of truancy, several control and potential confounding variables were assessed. First, both gender (coded as 1 for male, 0 for female) and race were included (as dummy variables comparing African American students, Hispanic students, and students of other ethnicities/races to White students) in all models. School performance was represented by a five-item scale, ranging from 1 to 5, including measures of academic performance and commitment to school work (coefficient alpha ⫽ .67). Social isolation at school represented the average of three items (e.g., “I often feel like nobody at school cares about me”). All items were measured on a five-point Likert scale ranging from “definitely disagree” to “definitely agree.” Coefficient alpha ⫽ .65. Personal attitude toward delinquency included 10 items (e.g., How wrong is it to purposely damage others’ property?), each measured on a four-point scale (very wrong, wrong, a little bit wrong, not wrong at all), coefficient alpha ⫽ .90. Friends’ attitudes toward delinquency included 16 items (e.g., During the past year, how many of your friends broke into a building to steal something?), each measured on a five-point scale (none of them, few of them, half of them, most of them, all of them). Coefficient alpha ⫽ .91. Parental/caregiver monitoring was made up of seven items indicating the extent to which the primary caregiver was aware of the students whereabouts, friendships, and activities (coefficient alpha ⫽ .55). All items ranged from 1 to 3, with 3 indicating better monitoring. Attachment to family was measured by an 11-item scale that assessed the extent to which the student and his/her family had a strong bond with one another (coefficient alpha ⫽ .77). All items ranged from 1 to 5, with 5 indicating better attachment. Means and standard deviations (SDs) for all independent variables are presented in Table 1. The dependent variable of interest was onset of drug use (alcohol, tobacco, and marijuana). Each year, the students reported the number of times in the past year that they had used each of these drugs. We limited alcohol use to consumption that was equal to or greater than one drink, to exclude sips or use that may be part of a family affair or religious service. At the first interview, students who had already initiated use of each respective drug indicated their age at first use. Using this information, an age of first use was constructed for each individual in the sample for each drug. Analysis Our research, question of interest concerned the association between truancy and initiation of alcohol, tobacco, and marijuana use. We utilized a type of survival model called a discrete-time proportional odds model [26] to analyze the
.48 .52 .55 .58 .39 .35 .38 .42 2.40 2.49 2.50 2.43 Age Age Age Age
12 13 14 15
81.3 67.7 51.9 41.6
6.5 10.2 10.8 12.9
6.4 12.6 17.1 20.7
2.6 3.9 7.5 9.1
1.0 3.9 8.7 8.7
2.3 1.7 4.0 7.0
3.99 3.93 3.75 3.60
.35 .58 .66 .70
1.53 1.62 1.79 1.85 .77 .70 .71 .71 2.33 2.14 2.17 2.13
.52 .53 .78 .75
1.32 1.44 1.51 1.63
.19 .48 .44 .54
3.73 3.74 3.67 3.57
Mean SD Mean Mean SD Mean
SD
Mean
SD
Mean
SD
Family attachment Parental monitoring Delinquent peers School isolation School performance Non-truant
Class skipper
Minor truant
Moderate truant
Chronic truant
Severe truant
Potential confounders Truancy (percentage in each group)
Table 1 Univariate statistics for independent variables
SD
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Delinquent values
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data. A separate series of models was tested for each of the three drugs. Truancy and each of the potential confounders were treated as contemporaneous time-dependent covariates (i.e., their values change within person over time), while gender and race were treated as time-independent covariates. Missing data To appropriately handle missing data, we employed multiple imputation (MI). The imputation was carried out using IVEware [27]. The amount of missing data was relatively small and was primarily related to the inability to locate respondents for a certain interview and/or refusal to participate. The range of missing observations across the variables considered in this paper ranged from a low of 0% to a high of 12.5%. In total, 10 imputed datasets were created. All analyses were performed on each of the imputed datasets, and the parameter estimates were then combined using the procedures outlined by Rubin [28]. Results We started by fitting an unconditional survival model for each drug, that is, a survival model that included only the time indicators (one time indicator for each age). The hazard function for first use of alcohol, tobacco, and marijuana use estimated from these models is presented in Figure 1. Each function describes the probability that a student would initiate use of a particular drug between the age j-1 and age j assessment, given that he or she had never used the drug coincident with or prior to the age j-1 assessment. A gentle increase in the hazard of initiation was observed over time for tobacco and marijuana, but a more rapid increase was observed for alcohol. The survival functions for each drug were also calculated. While the hazard function assesses the unique risk of initiation at each age, the survival function cumulates the risk of initiation at each age to assess the probability that a randomly selected adolescent will survive (i.e., not initiate use of a particular drug) through time period j. The probability that a randomly selected student in the sample would have not used marijuana through age 15 was .66. This same probability was .41 for initiation of alcohol use and .69 for initiation of tobacco use. After specifying unconditional discrete time survival models for each drug, we extended the models by including the time-independent control variables (gender and race), the time-dependent covariate of interest (truancy), and the time-dependent potential confounders. First, we tested the univariate effect of truancy (in the logged format described in the measurement section) and each potential confounder (i.e., besides the covariate of interest, the independent variables included just the time variables, gender, and race). The results of these models are presented under the heading Univariate Effects, Model 1 in Table 2 (for alcohol), Table 3 (for tobacco) and Table 4 (for marijuana). The univariate effect
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Figure 1. Hazard function for onset of drug use.
of truancy is a significant predictor of onset of all three drugs. The odds of initiation of alcohol use were 1.99 times higher, the odds of initiation of tobacco use were 1.65 times higher, and the odds of initiation of marijuana use were 2.33 times higher for each one unit increase in the log number of days truant. To increase our understanding of the effect size and more thoroughly examine the effect, we also considered
a second form of the univariate effect of truancy, in which truancy was specified as a categorical variable. Because of the low frequency of severe truants, we combined the chronic and severe categories. The categorical format of truancy was examined by including four dummy coded indicators to compare class skippers, minor truants, moderate truants, and chronic truants to non-truants, respectively.
Table 2 Effect of truancy on first use of alcohol Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.69 ⫺.46 ⫺.21 1.22 .35 .15 ⫺.36
.09 .17 .14 .16 .20 .27 .21
7.36 ⫺2.73 ⫺1.44 7.68 1.77 .57 ⫺1.75
1.99 .63 .81 3.37 1.43 1.17 .70
1.65 .45 .61 2.47 .96 .68 .47
Est.
1.04 1.58 2.12 2.26
t
OR
⫹
2.38 .88 1.08 4.60 2.11 1.99 1.04
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.15 ⫺2.33 ⫺1.69 ⫺1.57 .13 ⫺.80 ⫺.33 ⫺.14 .53 ⫺.04 ⫺.48 1.12 ⫺.23 .59 ⫺.28
⫹
.29 .23 .20 .22 .21 .37 .34 .44 .10 .21 .17 .18 .26 .32 .26
⫺7.34 ⫺10.13 ⫺8.45 ⫺7.24 .59 ⫺2.16 ⫺.98 ⫺.32 5.23 ⫺.20 ⫺2.82 6.14 ⫺.87 1.83 ⫺1.09
.12 .10 .18 .21 1.13 .45 .72 .87 1.69 .96 .62 3.07 .80 1.81 .75
.07 .06 .12 .14 .75 .22 .37 .37 1.39 .63 .44 2.15 .48 .96 .45
.21 .15 .27 .32 1.73 .93 1.39 2.05 2.06 1.46 .86 4.39 1.32 3.41 1.25
.37 .29 .44 .38
2.53 4.86 3.75 4.38
2.57 4.01 5.13 5.35
1.24 2.29 2.18 2.53
5.33 7.03 12.07 11.33
Model 4 .35 .28 .40 .33
2.97 5.71 5.31 6.78
2.83 4.85 8.33 9.56
1.43 2.82 3.81 4.98
5.64 8.34 18.19 18.35
.94 1.39 1.64 1.68
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
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Table 3 Effect of truancy on first use of tobacco Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.50 ⫺.46 ⫺.01 .96 .23 ⫺.35 ⫺.45
.09 .19 .20 .18 .24 .34 .22
5.52 ⫺2.45 ⫺.07 5.42 .93 ⫺1.02 ⫺2.04
1.65 .63 .99 2.61 1.25 .71 .64
1.38 .44 .67 1.85 .78 .36 .42
Est.
.97 1.42 1.72 2.05
t
OR
⫹
1.98 .91 1.46 3.70 2.01 1.38 .98
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.82 ⫺3.00 ⫺2.64 ⫺2.67 ⫺.11 ⫺1.10 ⫺.52 ⫺.33 .35 .04 ⫺.18 .79 ⫺.30 .01 ⫺.25
⫹
.38 .36 .29 .26 .33 .48 .38 .51 .11 .23 .23 .21 .29 .40 .27
⫺7.46 ⫺8.41 ⫺9.14 ⫺10.18 ⫺.35 ⫺2.28 ⫺1.37 ⫺.64 3.29 .16 ⫺.80 3.78 ⫺1.05 .01 ⫺.93
.06 .05 .07 .07 .89 .33 .59 .72 1.42 1.04 .83 2.20 .74 1.01 .78
.03 .02 .04 .04 .47 .13 .28 .26 1.15 .66 .53 1.46 .42 .46 .46
.12 .10 .13 .12 1.69 .86 1.25 1.96 1.76 1.63 1.30 3.32 1.30 2.20 1.32
.46 .39 .55 .53
1.99 3.26 2.38 2.93
2.51 3.56 3.69 4.78
1.01 1.66 1.26 1.68
6.23 7.63 10.84 13.61
Model 4 .46 .38 .51 .48
2.10 3.79 3.37 4.25
2.65 4.14 5.59 7.76
1.07 1.99 2.06 3.02
6.58 8.65 15.20 19.97
.92 1.27 1.31 1.56
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
The results of these analyses are presented under Univariate Effects, Model 2 in Tables 2– 4. The estimates indicate that all types of truants have a higher odds of initiation as compared to non-truants. Next, we tested multivariate models that included all potential confounders. The results of these models are reported under the heading Full Model, Model 3 in Tables 2– 4. After adjusting for all potential confounders, the effect of truancy was diminished, yet remained robust. Holding constant gender, race, and all potential confounders, the odds of initiation of alcohol use was 1.69 times higher, the odds of initiation of tobacco use was 1.42 times higher, and the odds of initiation of marijuana use was 1.93 times higher for each one unit increase in the log number of days truant. Model 4 presents the results of the multivariate models that considered the categorical measure of truancy in the same way as described above for the univariate model. Although not shown in the tables, these models also adjusted for all covariates and potential confounders. The estimates indicate that, even after adjusting for potential confounders, all types of truants have a higher odds of initiation as compared to non-truants. To ensure that the proportionality assumption was not violated, time by covariate interactions were included for all time-independent and time-dependent covariates. The only
variable that resulted in a significantly better fit when allowed to vary across time was involvement with delinquent peers. The parameter estimates indicated that the effect of delinquent peer association on the initiation of tobacco and marijuana use was more robust at the earlier ages (i.e., ages 12 and 13). No time by delinquent peer association effects were noted for alcohol initiation. Allowing the effect of involvement with delinquent peers to vary across time did not significantly change the effect of truancy on initiation of tobacco use (odds ratio [OR] ⫽ 1.43, 95% confidence interval [CI]: 1.16, 1.76) or marijuana use (OR ⫽ 1.89, 95% CI: 1.49, 2.39). In addition, we assessed the extent to which the effect of truancy differed by gender and race by including a series of interaction variables. No gender or race differences were found for initiation of alcohol or marijuana use. However, one difference was identified for initiation of tobacco. Specifically, the effect of truancy on initiation of tobacco use was more robust for boys as compared to girls (interaction effect: b ⫽ .53, standard error [SE] ⫽ .24, p ⬍ .05). It is important to note that the small sample size limits are able to detect small differences over time and/or small differences by race and gender. Therefore, it is possible that more differences exist but are not detected in this study. Although many of the potential confounders do not have
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Table 4 Effect of truancy on first use of marijuana Parameter estimate
Univariate effects
Full model
Model 1
Model 3
Est.
SE
t
OR
95% CI ⫺
Age 12 Age 13 Age 14 Age 15 Male African American Hispanic Other ethnicity Log of truancy School performance School isolation Delinquent peers Delinquent values Parental monitoring Family attachment
.85 ⫺.71 ⫺.11 1.28 .42 ⫺.64 ⫺.51
.11 .20 .18 .17 .23 .30 .21
7.65 ⫺3.58 ⫺.58 7.48 1.85 ⫺2.14 ⫺2.40
2.33 .49 .90 3.58 1.52 .53 .60
1.88 .33 .63 2.56 .97 .29 .40
Est.
1.37 2.15 2.68 2.76
t
OR
⫹
2.90 .72 1.29 5.01 2.37 .95 .91
Model 2 Class skipper Minor truant (1–3 days) Moderate truant (4–9 days) Chronic truant (10⫹ days)
SE
95% CI ⫺
⫺2.43 ⫺2.96 ⫺3.20 ⫺2.93 .05 ⫺.42 ⫺.25 ⫺.90 .66 ⫺.20 ⫺.39 1.00 ⫺.38 ⫺.35 ⫺.09
⫹
.37 .33 .30 .28 .28 .44 .42 .64 .12 .24 .23 .21 .30 .35 .28
⫺6.63 ⫺9.02 ⫺10.69 ⫺10.59 .17 ⫺.94 ⫺.60 ⫺1.42 5.52 ⫺.84 ⫺1.72 4.68 ⫺1.26 ⫺.99 ⫺.31
.09 .05 .04 .05 1.05 .66 .78 .40 1.93 .82 .68 2.72 .69 .71 .92
.04 .03 .02 .03 .60 .28 .34 .12 1.53 .51 .44 1.79 .38 .35 .53
.18 .10 .07 .09 1.83 1.57 1.77 1.41 2.44 1.31 1.06 4.13 1.23 1.41 1.59
.52 .41 .52 .50
2.32 4.77 4.06 4.21
3.35 6.98 8.15 8.21
1.21 3.14 2.96 3.08
9.28 15.52 22.44 21.86
Model 4 .51 .39 .46 .44
2.66 5.49 5.83 6.25
3.93 8.58 14.61 15.80
1.44 3.99 5.93 6.65
10.75 18.47 36.00 37.57
1.21 1.94 2.10 2.10
Notes: Est. is the log odds of initiation. CI ⫽ confidence interval. The values under OR for Age 12–15 are odds, not odds ratios. The univariate effects are adjusted for each covariate separately after adjusting for gender and race. The estimates at the bottom of the table (Models 2 and 4) consider a categorical form of the truancy measure in which each type of truant is compared to non-truants.
a significant direct effect on initiation after adjusting for all other variables in the model, it is important to note that some of the identified potential confounders may in fact act as more distal predictors of initiation. For example, poor family attachment and poor monitoring are both significant predictors of initiation of marijuana use when assessed independently; however, once truancy and the other potential confounders are included in the model, the direct effect of these parent variables no longer exists. It is reasonable to hypothesize that poor family attachment and poor monitoring may lead to truancy, which then leads to initiation of drug use. That is, these family variables may have important indirect effects. There is support in the literature for this type of mediated effect [29 –31]. A similar argument could be made for the indirect effect of school performance on initiation via truancy. It also should be noted that some of the identified potential confounders could actually be influenced by truancy. For example, it is possible that involvement in truancy leads to increased association with delinquent peers, increased endorsement of delinquent values, or a decline in academic performance. To the extent that this is true, then controlling for these variables may attenuate the true effect of truancy. Future studies designed to unpack causal ordering of these
types of relationships is necessary to more fully understand how truancy affects initiation of drug use.
Discussion In this paper, we have demonstrated the effect of truancy on initiation of drug use among urban adolescents who grew up in socially disorganized neighborhoods of Denver, CO. Consistent with our hypothesis, truancy significantly increased the odds of initiation of drug use after adjusting for gender, race, and potential confounders. Our own theoretical orientation for the best explanation of the observed relationship follows the thinking of Osgood and colleagues [16,17] and Stoolmiller [18]. These researchers have shown that delinquent behavior (including drug use) is especially likely to occur in situations of unsupervised, unstructured time with peers. The results in this paper corroborate these findings. For many adolescents and in many situations, truancy provides a context for initiation because of the unstructured and unsupervised time that it provides. Given this orientation, we would hypothesize that truancy demonstrated by adolescents who skip school for nondelinquent reasons (e.g., to help a family member) would not put them at increased risk for initiation of drug use. Unfortunately,
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we do not have adequate data to know for sure if this is the correct mechanism to explain the relationship. A future study that more closely tracks both truancy and drug use that takes place during truant days is necessary. Given the results presented in this paper, along with previous studies that have demonstrated the harmful consequences of truancy, more research into the etiology and prevention of truancy is necessary. Although more work is needed, some research suggests that programs aimed at reducing truancy may have a deterrent effect on delinquency, including drug use. In one study, Garry [32] reported that shoplifting arrests declined by 60% and purse snatching dropped by nearly 50% when intensive truancy sweeps were conducted by the police. Thinking proactively about adolescent truancy, Office of Juvenile Justice and Delinquency Prevention (OJJDP), in conjunction with the Department of Education, started the Truancy Reduction Demonstration Program. Funding several programs across the country, these initiatives have allowed for the identification of a set of critical components that are predictive of positive outcomes for children and families [33]. These critical components include collaboration between key agencies (e.g., schools, communities, juvenile authorities); creation of a context of support; family involvement in efforts; comprehensive approaches that seek to remedy the root causes of truancy; and use of incentives and sanctions. Besides these efforts, several other recent studies have reported on the effectiveness of school and community based truancy prevention programs [34,35]. These programs have demonstrated encouraging results. However, much more work needs to be done to understand why students skip school and how best to prevent it. Given that truancy may play a salient role in adolescent drug use, truancy prevention is an extremely important topic for future research. A commitment to developing and testing truancy prevention efforts is imperative. Limitations The sample for this study includes urban youth who grew up in socially disorganized neighborhoods of Denver, CO. Future research is necessary to determine if these results can be generalized to other types of youth. In this study, data were collected in one-year intervals. Our research question of interest may have been better addressed by assessing the lagged effect of truancy on initiation of drug use. This type of analysis would have ensured that truancy occurred before the onset of drug use. Because our hypothesis posits that the effect of truancy is largely due to the environment that is afforded by skipping school (unmonitored, unstructured time with peers), it does not make sense to think that the effect of unmonitored, unstructured time would lead to the onset of drug use one year later. That is, it is likely that the effect occurs much more proximally.
Despite these limitations, this study provides important evidence that truancy may play an important role in the onset of drug use. While this study cannot point to causal effects, the robust effect persists even after adjusting for salient potential confounding variables. This important study highlights the need for more focus on a behavior (i.e., truancy) that has been given relatively little attention. Acknowledgment This research was supported by grant K01 DA01781001A1 (PI: Kimberly L. Henry) from the National Institute on Drug Abuse and grant 96-MU-FX-0017 (PI: David Huizinga) from the Office of Juvenile Justice and Delinquency Prevention, US Department of Justice. Points of view or opinions in this article are those of the authors and do not necessarily represent official positions or policies of the National Institute of Drug Abuse or the Office of Juvenile Justice and Delinquency Prevention. References [1] Johnston LD, Bachman, JG, O’Malley PM, et al. Monitoring the Future: A Continuing Study of American Youth. 2003 [computer file]. Ann Arbor, MI: Inter-University Consortiom for Political and Social Research, 2004. [2] Wehlage GG, Rutter RA. Dropping out: How much do schools contribute to the problem? Teach Coll Rec 1986;87:374 –92. [3] Kandel DB, Logan JA. Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. Am J Public Health 1984;74:660 – 6. [4] Office of Applied Studies. Summary of findings from the 2000 National Household Survey on drug abuse. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2001. [5] Ellickson PL, Hays RD, Bell RM. Stepping through the drug use sequence: Longitudinal scalogram analysis of initiation and regular use. J Abnorm Psychol 1992;101:441–51. [6] Anthony JC, Petronis KR. Early-onset drug use and risk of later drug problems. Drug Alcohol Depen 1995;40:9 –15. [7] Hawkins JD, Graham JW, Maguin E, et al. Exploring the effects of age alcohol use initiation and psychosocial risk factors on subsequent alcohol misuse. J Stud Alcohol 1997;8:280 –90. [8] Kandel DB, Yamaguchi K, Chen K. Stages of progression in drug involvement from adolescence to adulthood: Further evidence for the gateway theory. J Stud Alcohol 1992;53:447–57. [9] Grant BF, Dawson DA. Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. J Subst Abuse 1997;9:103–10. [10] Brook JS, Balka EB, Whiteman M. The risks for late adolescence of early adolescent marijuana use. Am J Public Health 1999;89: 1549 –54. [11] National Institute on Drug Abuse Research Report Series. Marijuana Abuse. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2002. [12] Catalano RF, Hawkins JD. The social development model: A theory of antisocial behavior. In: Hawkins JD (ed). Delinquency and Crime: Current Theories. New York, NY: Cambridge University Press, 1996: 149 –97. [13] Cloward RA, Ohlin LE. Delinquency and Opportunity: A Theory of Delinquent Gangs. New York, NY: Free Press, 1960.
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