Substance use is prevalent among school-age children, with up to 70% having experimented with substances, and the rates of abuse have been steady at approximately 6% from 1996 – 1999. There are numerous situations that influence the likelihood of a child using a substance, ranging from school to home to peers, with each offering both risk and protective factors. Drug abuse education programs vary widely, and their implementation can take many forms, with interactive, multimodal approaches working the best.
Substance abuse is prevalent in today’s school-age children. Historically, the rates of use rose from the mid to late 1970’s, leveled out in the 1980’s and began rising again in the 1990’s (Johnson, O’Malley, and Bachman, 1995). Overall, the numbers of students meeting the criteria for having a substance abuse disorder (SUD) remained fairly steady between 1996 and 1999 with youth in both years meeting the DSM-III-R criteria for a substance abuse disorder at a rate of 6.2% in a general community sample (Kandel, Johnson, & Bird, 1999; Rohde, Lewinsohn, & Seely, 1996).
The rates, however, rise sharply when specific populations are considered. With environmental factors, children involved in the juvenile justice system had a lifetime prevalence rate of a SUD of 62.1% and those involved with child welfare a lifetime prevalence of 19.2%. When looking at mental/emotional factors, children had a lifetime prevalence rate of 40.8% of a SUD if they were being seen for mental health issues, and a lifetime prevalence rate of 23.6% if they were classified as having a serious emotional disturbance (Aarons, Brown, Hough, Garland & Wood, 2001).
The most troubling figure, however, is the trend in which teens are asked if they would ever try illegal drugs. This number has been decreasing, with 86% saying they would never try illegal drugs in 1995, 51% in 1996, and 46% in 1997 (Bruner & Fishman, 1998).
The most abused substance is alcohol in adolescents, followed by cigarettes and marijuana (Johnson, O’Malley and Bachman, 1994). Crome (1997) found that at least 30% of secondary school students drink alcohol regularly, with 10% drinking more than moderately. 10 – 20% of the students smoke cigarettes regularly, 70% have tried at least one illicit drug, with 2.5% using an illicit drug at least weekly. When only a single month is looked at, 33% of 12th graders and 9% of 8th graders reported being drunk at least one time in the past month in the study conducted by Johnston, O’Malley, & Bachman (1999). They also found that 23% of high-school seniors and 10% of 8th graders reported using marijuana in the last month. This is an increase of 9% and 7% over the last 8 years. Cigarette usage in the last month also increased, to 35% of seniors and 18% of 8th graders. This is an increase of 7 and 4% respectively over the last 8 years.
Factors Contributing to Substance Use
School and Employment Factors
Nutbeam and Aaro (1991) found in their studies that general dissatisfaction with school increased the likelihood that school-age children would be to smoke on a weekly basis, as does academic stress (Hee-Soon, Yeanghee, Jung-Ja, 1995). Conversely, Oakley, Biannen, and Dodd (1992) found that scholastic satisfaction had beneficial effects on the decrease in smoking behaviors. Karatzias, Power and Swanson (2001) found that school stress was the factor that most accurately predicted a student’s likelihood to try alcohol.
Students who have decided that school is not an appropriate role, or one that what do not wish to undertake are also more likely to be involved in substance use. High school seniors who expected to attend college had a significantly lower rate of substance use than those who did not expect to attend college (Johnston, O’Malley, & Bachman, 1985). Hawkins, Catalano and Miller (1992) also found a negative relationship between the level of attachment and commitment to school and the levels of substance use. Academic achievement has also been shown to have a direct relationship with substance use. In their study, Maguin and Loeber (1996) found that there was a significantly greater risk for substance use among those students who achieved poorly academically.
The more that adolescents work during the school year, the greater their risk of substance abuse, according to Valois (2000). Those who work in excess of fifteen hours per week have higher rates of substance abuse, from binge drinking to marijuana to cocaine usage.
Family and Environmental Factors
Oakley, Biannen and Dodd (1992) found that general stress and uncertainty could lead to an increase in smoking behaviors in adolescents and children. More specifically, Baer, Garmezy, McLaughlin, Pokorny and Wernick (1987) found a positive correlation between the levels of alcohol use and abuse and the amount of daily stress and conflict within the family. One example of familial stress and uncertainty is pointed to in that children from less-intact families (single parent household, single parent with multiple partners, drug/alcohol dependence in a family member) have higher rates of substance use (Gabel, Stallings, Young, Schmitz, Crowley & Fulker, 1998). Self-esteem was found by Karatzias, Power and Swanson (2001) to be the factor that had the greatest influence on whether a student smoked cigarettes regularly. They also found peer self-esteem most accurately provided the prediction of whether a student would drink alcohol on a regular basis, as well as whether the student would try illicit drugs.
The home is also the primary source of alcohol for the school-age child, and where they will begin to draw their views as to its appropriate use. Sons of alcoholic men have a 25% chance of becoming an alcoholic themselves, in part because of genetics and in part because of the family acceptability of drinking to excess (Cloninger, 1983). The family view on the use of alcohol will play a large part in the development of the child’s views toward it’s use. If the family accepts and encourages drinking to excess, and/or if an older sibling encourages this, the child is more likely to develop a problem than in the family who drinks alcohol in moderation (Kandel, 1985). Additionally, it has been shown that older siblings are frequently a source of alcohol and drugs for younger siblings (Needle, McCubbin, Wilson, & Reineck, 1986)
While the average age of first use of alcohol is 13.1 years, use at age 9 and younger is becoming increasingly common (SAMSHA, 1999). Kandel (1985) points out that 30% of children in grades 4-6 reported they had received a lot of pressure from classmates to drink a beer. Santor, Messervey and Kusumakar (2000) found that two types of peer influence were at work with students and their risk for substance use. The first is the desire to be popular and to fit in. This internalized peer pressure was found to be less of a risk factor than the external peer pressure of actually being urged or pressured by peers to act in a certain way, such as participating in substance use.
While the views the child has on drinking may be initially set by the family, they are reinforced through the peer group, with whom most drinking, and nearly all illicit substance abuse occurs. The teenage population uses substances much as adults do – for peer acceptance in some cases, and in some cases to reduce social inhibitions for high-risk opportunities such as sexual activity (Hawkins, 1982).
The negative peer relationship problems that children experience can also lead to substance use problems. Woodward and Fergusson (1999) found in their longitudinal study that children who had the most problems at age 9 with peer relationships as measured by peer rejection, social isolation, perceived social incompetence were up to almost 9 times more likely to use substances at age 18 than those students with the fewest peer interaction problems.
With the high increase of the lifetime prevalence rate of children who have serious emotional disturbances and/or are being seen for a mental health issue over those in a community sample (23.6%, 40.8% vs. 6.2% respectively), it becomes evident that there is a relationship between emotional state and SUD.
It has been known that there is a high rate of dual-diagnosis between those with mental health disorders and SUD’s. This is often caused by the self-treatment of the mental illness with a substance (Kendall, Sherman & Bigelow, 1995), and can also be related to the development of peer relationships with antisocial peers, which increases the risk of substance experimentation as noted in Oxford, Harachi, Catalano and Abbott (2001). Additionally, Kandel and Chen (2000) found a higher risk for those children with a mental disorder to progress from experimentation into problematic usage of substances than those in a community sample.
Temperament has been linked to the eventual development of SUD’s in adolescents. Temperament is a set of characteristics that are present from birth and may form the basis for the personality. There are a number of temperament clusters that have been shown to increase the risk of an SUD. The first is behavioral disinhibition as shown by undercontrol, impulsivity, and aggression (Windle & Windle, 1993). Wills, Vaccara and McNamara (1994) also identified the novelty- or sensation-seeking temperament as being at a higher risk. The final cluster that has been shown to have relevance is the “difficult” temperament, which is characterized by a high activity level, social withdrawal, arrythmicity, and rigidity (Windle, 1991).
Most preventions strategies focus on the protective factors that help the student to resist the use of substances. These factors are items that can have an effect, directly or indirectly on limiting their substance involvement. They serve as a buffer to risk, and enhance appropriate responses to risks.
Some of the better known protective factors, some modifiable, some not, include intelligence, problem solving ability, social facility, positive self esteem, supportive family relationships, positive role models, and affect regulations (Glantz & Sloboda, 1999).
The Collaborative to Advance Social and Emotional Learning (CASEL) has developed a 17 skill/attitude competency list which they consider to be the basis for effective intervention programs, and is a comprehensive set of protective factors (Payton, Wardlaw, Graczyk, Bloodworth, Tompsett, & Weissberg, 2000). These are divided into four major areas: awareness of self and others, positive attitudes and values, responsible decision making, and social interaction skills. These key factors are:
- Awareness of Self and Others
- Awareness of feelings
- Management of feelings
- Constructive sense of self
- Perspective taking
- Positive Attitudes and Values
- Personal responsibility
- Respect for others
- Social responsibility
- Responsible Decision Making
- Problem identification
- Social norm analysis
- Adaptive goal setting
- Problem solving
- Social Interaction Skills
- Active listening
- Expressive communication
- Help seeking
Types of Prevention
There are currently two schools of thought regarding the prevention of substance abuse. The first is the prevention of the use of substances, while the second is aimed at the delaying of the onset of the use of substances.
While it is certainly a more admirable goal for us to attempt to prevent the use of substances altogether, it is not one that is reasonably attainable. We can continue to strive to prevent as many children as possible from using substances, and should keep this as our goal. However, there are children who will still use them.
Hanna and Grant (1999) make a compelling argument that efforts should be aimed at delaying the onset of initiation of substance experimentation and use. This is due to a number of factors, not the least of which is that the earlier children initiate substance use, the more likely they will experience substance abuse or misuse problems during adolescence or childhood (Anthony & Petronis, 1995; Hawkins, Graham, Maguin, Abbott, Hill & Catalano, 1997).
Delaying Onset of Substance Use
Oxford, Harachi, Catalano and Abbott (2001) noted that the early-onset (before age 13) were highly related to the family structure. Positive, prosocial family processes were found to have served as inhibitors to early involvement in substance use, while association with antisocial peers enhanced the likelihood of involvement with substances.
The prosocial family processes acted directly on the involvement in a number of ways. Increased monitoring of a child’s whereabouts and setting of limits, rules and guidelines for behavior were seen as positive interventions. Additionally, actions that increased attachment in the family during the elementary school years increased the chance that the child would not become involved with substances.
Indirectly, the prosocial family processes acted to decrease the risk of substance use, as strong family management by the parents decreased the chances, opportunities, and often desires, for the child to become involved with antisocial peers. Thus, the results are two-fold. Not only is there a positive influence, but the lessening of the chances of a negative influence.
Substance Use Prevention
Student focused interventions are those which are dependent on teaching to the students. They generally have a set curriculum, and a set number of weeks they are taught. They are designed to address and strengthen the protective factors in the children, in order that they can make better choices, resist peer pressure, form better attachments, and increase their social skills, for example.
In the mediational approach to the prevention of substance abuse, prevention efforts are aimed at changing the outcome (substance abuse) by changing one of the precursors to the behavior. These precursors can be referred to as mediators, risk- or protective-factors.
Hansen (1992) identified 12 basic mediating variables to prevent substance abuse:
- Normative beliefs about substance abuse prevalence and acceptability
- Lifestyle incongruity
- Beliefs about consequences
- Commitment to not using substances
- Social pressure resistance skills
- Stress management skills
- Alternatives to substance use
- Decision making skills
- Goal setting skills
- Social Skills of assertiveness, communication, and interpersonal problem solving
- Assistance skills (for helping peers resolve conflict and problems)
The four strongest mediators were found to be normative beliefs about substance abuse prevalence and acceptability, manifest commitment to avoid drug abuse, beliefs about consequences, and lifestyle incongruence (McNeal & Hansen, 1999). Varying levels of support for the effectiveness of the other 8 factors has come from other studies, especially among ethnically diverse students (Botvin, Baker, Dusenbery, Tortu, & Botvin, 1990).
Cumulative strategies model.
The cumulative strategies model for drug abuse prevention is one in which a minimum of two prevention strategies are used jointly, and each strategy targets at least two of the factors that places children at high risk for drug abuse (Horn & Kolbo, 2000).
The principles of this strategy are to: intervene early; focus on multiple rather than single risk factors; offer comprehensive, risk focused, as opposed to isolated problem-solving strategies; and address a range of cognitive, behavioral and psychological risk factors.
This program focuses on third grade students, younger than most programs, and carries into the fourth grade year through the development of the peer mentor program in which the fourth grade students act as mentors to the new participants. The main risk factors which are focused on are self-esteem, social skills, and attitudes towards substance use, and attachment to school, all of which act as protective factors toward substance abuse.
Holistically focused interventions are those that are focused on only on the student but the family and the teachers as well. There is a curriculum taught to the students, as in the student focused models, but also a curriculum is often taught to the parents through an outreach program or a series of parent classes. The teachers themselves have their own classes and methods in which they are taught, and they begin to use alternate teaching/behavior modification methods. This is truly an immersion system for the students.
In the Seattle Project, as outlined by Hawkins, Catalano, Kosterman, Abbot and Hill (1999), all three portions of a holistically focused intervention were involved. There were full interventions provided in grades 1-6 in the elementary schools.
The teachers received 5 days of in-service training each year, and the parents were offered parenting classes when their children were in grades 1-3 and 5-6. These were not a single class that all parents attended, but rather individual classes at each grade level with developmentally appropriate parenting skills being taught. The children were taught through their daily activities appropriate skills for social interactions.
When examined at age 18, it was found that these children, not only in substance abuse scales, but also in the areas of violence and sexual activity, had lower lifetime prevalence rates.
Midwestern prevention program.
The midwestern prevention program was developed as a holistic program encompassing an even broader range of methods. This intervention program included training for community leaders and had mass media components as well, and was aimed at both high- and low-risk youth. In the schools that were targeted in this program, at a 3-year follow-up, students had lower rates of tobacco and marijuana usage, but no difference in alcohol usage (Johnson, Pentz, & Weber, 1990).
Prevention Program Effectiveness
Adolescents’ Views of Prevention Programs
As the one’s being targeted by prevention programs, the opinion of the adolescent may be the most valuable as to what works and what doesn’t. Lisnov, Harding, and Safer (1998) compared three types of prevention programs and the adolescents’ perceptions of them.
The first was the interactive school based program, such as Project DARE which involves police officers coming into the schools over a period of weeks with a set program, and Captain Clean which is a musical theater program followed by an interactive discussion and role-playing session. The second was media strategies such as television ads and testimonials by famous people. The final approach was simply mass signs such as billboards and ads on public transportation.
The teens rated the interactive programs much higher than either the media or sign approaches, and the media approaches higher than signs. The students needed the interactive approach to have the intervention be effective, rather than a repetition of the same information.
research Oriented Views of Prevention Programs
Tobler and Stratton (1997) found in their meta-analysis of over 120 prevention programs that the most effective methods of prevention were those that met a number of specific criteria, with the most important being that they were interactive, and that they had fidelity. This is similar to the adolescent views that programs such as Project DARE and Captain Clean were the most effective, as they allowed for interaction between the presenters and the students, and were not simply didactic in nature. The authors note that the fidelity of the programs is important is also well taken, so that the programs are consistent and continuous in nature.
Additionally, it was found that the programs do not work equally well across all groups of students. As we are not a homogenous society, no single approach will work with all situations and all students. Therefore, before beginning on a course of action, the population must be appropriately matched with a prevention program.
- Aarons, G. A., Brown, S. A., Hough, R. L., Garland, A. F., & Wood, P. A. (2001). Prevalence of adolescent substance use disorders across five sectors of care. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 419-464.
- Anthony, J. C., & Petronis, K. R. (1995). Early-onset drug use and risk of later drug problems. Drug and Alcohol Dependency, 40, 9-15.
- Baer, P. E., Garmezy, L. B., McLaughlin, R. J., Pokorny, A. D. & Wernick, M. J. (1987). Stress, coping, family conflict, and adolescent alcohol use. Journal of Behavioral Medicine, 10, 449-466.
- Botvin, G. J., Baker, E., Dusenbury, L., Tortu, S., & Botvin, E. (1990). Preventing adolescent drug abuse through a multimodal cognitive-behavioral approach: Results of a three-year study. Journal of Consulting and Clinical Psychology, 58, 437-446.
- Bruner, A. B., & Fishman, M. (1998). Adolescents and illicit drug use. The Journal of the American Medical Association, 280, 597-602.
- Cloninger, C. R. (1983). Genetic and environmental-factors in the development of alcoholism. Journal of Psychiatry Treatment and Evaluation, 5, 487-496.
- Crome, I. B. (1997). Editorial: Young people and substance problems – from image to imagination. Drugs: Educational Policy, 4, 107-116.
- Gabel, S., Stallings, M. C., Young, S. E., Schmitz, S., Crowley, T. J., & Fulker, D. W. (1998). Family variables in substance-misusing male adolescents: The importance of maternal disorder. American Journal of Drug and Alcohol Abuse, 24, 61-84.
- Glantz M. D., & Sloboda, Z. (1999). Analysis and reconceptualization of resilience. In M. Glantz & J. Johnson (Eds.), Resilience and development: Positive life adaptations (pp. 109-126). New York: Kluwer Academic/Plenum.
- Hanna, E. Z. & Grant, B. F. (1999). Parallels to early onset alcohol use in the relationship of early onset smoking with drug use and DSM-IV drug and depressive disorders: Findings from the National longitudinal Epidemiological Survey. Alcoholism: Clinical and Experimental Research, 23, 513-522.
- Hansen, W. B. (1992). School-based substance abuse prevention: a review of the state of the art in curriculum, 1980-1990. Health Education Research, 7, 403-430.
- Hawkins, R. O. (1982). Adolescent alcohol abuse: A review. Journal of Developmental and Behavioral Pediatrics, 3, 83-87.
- Hawkins, J. D., Catalona, R. F., Kosterman, R., Abbott, R., & Hill, K. G. (1999). Preventing adolescent health-risk behaviors by strengthening protection during childhood. Archives of Pediatric and Adolescent Medicine, 153, 226-244.
- Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64-105.
- Hawkins, J. D., Graham, J. W., Maguin, E., Abbott, R. D., Hill, K. G., & Catalano, R. F. (1997). Exploring the effects of age of alcohol use initiation and psychosocial risk factors on subsequent alcohol misuse. Journal of Studies of Alcohol, 58, 280-290.
- Hee-soon, J., Yeanghee, S., Jung-Ja, N. (1995). Cigarette smoking among Korean adolescents: Prevalence and correlates. Adolescence, 30, 631-642.
- Horn, K. A., & Kolbo, J. R. (2000). Using the cumulative strategies model for drug abuse prevention: A small group analysis of the choices program. American Journal of Health Studies, 16, 7-23.
- Johnson, C. A., Pentz, M. A., & Weber, M. D. (1990). Relative effectiveness of comprehensive community programming for drug abuse prevention with high-risk and low-risk adolescents. Journal of Consulting and Clinical Psychology, 58, 447-456.
- Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1985). Use of licit and illicit drugs by America’s high school students: 1975-1984. Rockville, MD: National Institute on Drug Abuse.
- Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1995). National survey results on drug use from the monitoring the future study 1975 – 1993. Rockville, MD: National Institute on Drug Abuse.
- Johnston, L. D, O’Malley, P. M., & Bachman, J. G. (1995). National survey results on drug use from the monitoring the future study 1975 – 1994. Rockville, MD: National Institute on Drug Abuse.
- Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1999). The monitoring the future national survey results on adolescent drug use. Overview of key findings, 1999. Bethesda, MD: National Institute on Drug Abuse.
- Kandel, D. B. (1985). On processes of peer influence in adolescent drug use: A developmental perspective. Advances in Alcohol and Substance Abuse, 4, 139-163.
- Kandel, D. B., & Chen, K. (2000). Types of marijuana users by longitudinal course. Journal of Studies on Alcohol, 61, 367-378.
- Kandel, D. B., Johnson, J. G., & Bird, H. R. (1999). Psychiatric comorbidity among adolescents with substance use disorders: Finding from the MECA study. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 693-699.
- Karatzias, A., Power, K. G., & Swanson, V. (2001). Predicting use and maintenance of use of substances in scottish adolescents. Journal of youth and adolescence, 30, 465-484.
- Kendall, J., Sherman, M. & Bigelow, G. (1995). Psychiatric symptoms in polysubstance abusers: Relationship to race, sex and age. Addictive behaviors, 20, 685-690.
- Lisnov, L., Harding, C. G., Safer, L. A. (1998). Adolescents’ perceptions of substance abuse prevention strategies. Adolescence, 33, 301-311.
- Maguin, E., & Loeber, R. (1996). Academic performance and delinquency. In M. Tonry (Ed.), Crime and justice: Vol. 20. A review of research (pp. 145-264). Chicago: University of Chicago Press.
- McNeal, R. B., & Hansen, W. B. (1999). Developmental patterns associated with the onset of drug use: Changes in postulated mediators during adolescence. Journal of Drug Issues, 29, 381-400.
- Needle, R., McCubbin, H., Wilson, M., & Reineck, R. (1986). Interpersonal influences in adolescent drug use: The role of older siblings, parents, and peers. International Journal of the Addictions, 21, 739-766.
- Nutbeam, D., & Aaro, L. F. (1991). Smoking and pupils attitudes towards school: The implications for health education with young people. Results from the WHO study of health behavior among schoolchildren. Health education Research Theory and Practice, 6, 331-338.
- Oakley, A., Biannen, J., & Dodd, K. (1992). Schools and smoking: School features and variations in cigarette smoking by children and teachers. British Journal of Educational Psychology, 55, 34-44.
- Oxford, M. L., Harachi, T. W., Catalano, R. F., & Abbot, R. D. (2001). Preadolescent predictors of substance initiation: A test of both the direct and mediated effect of family social control factors on deviant peer associations and substance initiation. American Journal of Drug and Alcohol Abuse, 27, 599-617.
- Payton, J. W., Wardlaw, D. M., Craczyk, P. A., Bloodworth, M. R., Tompsett, C. J., & Weissberb, R. P. (2000). Social and emotional learning: A framework for promoting mental health and reducing risk behavior in children and youth. Journal of School Health, 70, 179-193.
- Rohde, P., Lewinsohn, P. M., & Seely, J. R. (1996). Psychiatric comorbidity with problematic alcohol use in high school students. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 101-109.
- SAMHSA (1999). National household survey on drug abuse: Main findings 1997. Rockville, MD: Substance Abuse and Mental Health Services Administration.
- Santor, D. A., Messervey, D., & Kusumakar, V. (2000). Measuring peer pressure, popularity, and conformity in adolescent boys and girls: Predicting school performance, sexual attitudes and substance abuse. Journal of Youth and Adolescence, 29, 163-183.
- Tobler, N. S., & Stratton, H. S. (1997). Effectiveness of school-based drug prevention programs: A meta-analysis of the research. Journal of Primary Prevention, 18, 71-128.
- Valois, R. F. (2000). Association between employment and substance abuse behaviors among public high school adolescents. The Journal of the American Medical Association, 283, 180-189.
- Wills, T. A., Vaccarro, D., & McNamara, G. (1994). Novelty seeking, risk taking, and related constructs as predictors of adolescent substance use: An application of Cloniger’s theory. Journal of Substance Abuse, 6, 1-20.
- Windle, M. (1991). The difficult temperament in adolescence: Associations with substance use, family support, and problem behaviors. Journal of Clinical Psychology, 47, 310-315.
- Windle, M. & Windle, R. C. (1993). The continuity of behavioral expression among disinhibited and inhibited childhood subtypes. Clinical Psychology Review, 13, 741-761.
- Woodward, L. J., & Fergusson, D. M. (1999). Childhood peer relationship problems and psychosocial adjustment in late adolescence. Journal of Abnormal Child Psychology, 27, 87-104.