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Examine long-term prescription drug misuse outcomes from three RCTs of brief universal preventive interventions conducted during middle school.
Study 1 tested the Iowa Strengthening Families Program (ISFP); 22 schools participated, with pretesting at grade 6 (1993) and outcomes measured at age 25. Study 2 evaluated a revised ISFP, renamed Strengthening Families Program: For Parents and Youth 10–14—SFP 10–14, plus the school-based Life Skills Training (SFP 10–14 + LST); 24 schools participated, with pretesting at grade 7 (1998) and outcomes at ages 21–25. Study 3 examined SFP 10–14 plus one of three school-based interventions selected from a menu (SFP 10–14 + School Program); 28 schools participated, with pretesting at grade 6 (2002) and outcomes at 12th grade. Self-reported outcomes were Prescription Opioid Misuse (POM) and Lifetime Prescription Drug Misuse Overall (PDMO).
Study 1: ISFP showed significant effects on POM and PDMO, Relative Reduction Rates (RRRs) of 65%, and comparable benefits for higher- and lower-risk subgroups. Study 2: SFP 10–14 + LST showed significant or marginally-significant effects on POM/PDMO across all ages; higher-risk participants showed stronger effects (RRRs 43–79%). Study 3: significant results were found for POM/PDMO (RRRs 20–21%); higher-risk and lower-risk participants showed comparable outcomes.
Brief universal interventions have potential for public health impact by reducing prescription drug misuse among adolescents and young adults.
The Center for Disease Control (CDC) and the Office of National Drug Control Policy (ONDCP) have declared that prescription drug misuse is epidemic;1 a Surgeon General Expert Panel on Prescription Drug Abuse convened in 2011 has called for universal preventive intervention. Both CDC data and expert panel findings demonstrate that prescription drug misuse (hereafter PDM) is a major public health problem. Among adolescents and young adults (ages 12 – 25) the only illicit drug that is abused more frequently is marijuana.2–4 Nationally, in 2010, the rate of lifetime PDM for ages 12–18 was 22%. For ages 18 to 25 it was 26%;2,4 notably, adolescent PDM can be somewhat higher in rural compared with urban/suburban areas.5
Negative health consequences of escalating PDM include increased injuries and hospitalizations, physical and sexual assaults, STIs, and poisoning deaths; longer-term physical health problems include brain damage and learning disability.1,6–8 Moreover, PDM is highly comorbid with psychiatric disorders (e.g., depressive and anxiety disorders), as well as with alcohol or drug use disorders.9–12 Generally, individuals who misuse prescription drugs are more likely to use other illicit drugs13,14 and, especially in combination with opiate misuse, engage in more violence.15 Finally, PDM is associated with problem behaviors among high school and college students (e.g., suspension or expulsion, driving after drinking, or being a passenger with a drunk driver).16,17
Observing that the most common sources of prescription narcotics are friends and relatives,1,4,16,17 and noting the limited uptake of preventive interventions that effectively address the problem, the Surgeon General Panel recommended that a range of evidence-based preventive interventions targeting PDM be developed, tested, and broadly implemented.18,19 The authors, however, could find no randomized controlled studies of community-based, universal preventive interventions that targeted PDM and established long-term effects. An alternative approach entails community-based interventions that were not specifically designed to address PDM but, rather, substance misuse more generally.14,20,21 Programs addressing the problem as part of a broader intervention to reduce youth risky behaviors could be effective and efficient (i.e., they could reduce the need for multiple programs targeting individual substances). Among universal interventions not specifically directed toward PDM, it is important to consider ones that: (1) effectively address multiple risk and protective factors common among many different types of substance misuse; (2) have demonstrated cross-over effects on diverse outcomes, such as conduct problems and health-risking sexual behaviors; and (3) have shown positive effects among higher-risk populations.19 These features characterize the interventions evaluated for this report.
Guided by etiological research, a number of substance misuse preventive interventions have been designed to reduce risk and enhance protective factors by modifying family and school environments or building youth competencies.22 These interventions have been shown to produce a wide range of positive outcomes (e.g., enhanced youth life skills and academic performance, improved parenting and family functioning, reduced youth health-risking sexual behaviors, substance misuse, and conduct problems), up to 10 years past baseline.23
The current report summarizes results of three RCTs testing universal preventive interventions implemented during middle school. Originally, the trials were designed to test universal interventions addressing other outcomes; adding PDM measures allowed us to examine these outcomes long term. As noted earlier, few studies have tested universal prevention programs on PDM, and no studies have examined results 6–14 years past program implementation into late adolescence and young adulthood—an important developmental stage when PDM can affect many areas of functioning.10,16 The three trials extend or replicate analyses of a family-focused program, either standing alone (Study 1) or implemented in combination with school-based programs (Studies 2 and 3). Analyses of Study 1 and 2 PDM outcomes at earlier ages are summarized in a previous report.24 For this report, Studies 1 and 2 (a) examined longer-term outcomes from young adult follow-up assessments, and (b) evaluated risk-related moderation of outcomes to determine whether there were comparable or stronger effects for higher-risk subsamples. Study 3 examined the same outcomes among late adolescents (Grade 12) in more recent cohorts, with “real-world” intervention implementation that entailed programming organized and delivered by local community teams.
All studies were cluster randomized controlled trials of universal preventive interventions implemented in rural communities or small towns (populations of up to ~ 50,000). Study 1, initiated in 1993, tested a family-focused intervention alone; Study 2, initiated in 1997, tested a combination of a family-focused and a school-based intervention. Study 3, initiated in 2002, was designed to examine a delivery system for evidence-based universal family-focused and school-based interventions selected from a menu. In all cases, IRB approval for human subject procedures was obtained.
22 schools from communities smaller than 8,500 residents and with over 15% of school district students eligible for the free- or reduced-cost lunch program were randomly assigned to the Iowa Strengthening Families Program (ISFP) or a minimal contact control condition. 446 families of 6th graders participated in pretesting (53% of those recruited); sample representativeness was confirmed. Of these, the majority were White and from dual-parent families (98% and 86% respectively), averaging 3 children. Details on sample characteristics and participation are presented in Table 1 and Figure 1a, respectively.
Students completed written questionnaires during in-home interviews in grades 6–12. Following high school, students completed written questionnaires and telephone interviews. Consent forms were signed by parents, for their adolescents under age 18, and by participants over 18.
After pretesting, facilitators implemented the interventions at participating schools. The ISFP included six two-hour curricular sessions involving concurrent youth and parent segments, followed by a family skill-building segment. The program concluded with a conjoint family session.22,25 49% of pretested intervention group families attended at least one ISFP session.
7th graders participating were from 24 schools in districts with enrollments below 1,200 students, of which 20% or more were eligible for the free- or reduced-cost school lunch program. Schools were matched and randomly assigned to a revised version of ISFP (renamed the Strengthening Family Program: For Parents and Youth 10–14), and the classroom-based Life Skills Training (LST) program (SFP 10–14 + LST), or a minimal-contact group. Details on sample characteristics and participation are presented in Table 1 and Figure 1b, respectively.
Data were collected via 40–45 minute machine-scored questionnaires administered in classrooms, first in the Fall of 7th grade, and then annually in the Spring, from grades 7 to 12. A combination of active and passive consent procedures allowed students, or parents on their behalf, to refuse participation. Following high school, students completed written questionnaires and telephone interviews.
SFP 10–14 was implemented in the same way as ISFP in Study 1. In addition, booster sessions were completed one year following the initial seven sessions and in the 11th grade in some schools. The LST26, 27 curriculum was delivered in 15 class periods by a trained teacher, followed by booster sessions during the next school year and in the 11th grade in some schools. Intervention condition families who participated in baseline home interviews (n=226) were actively recruited for the SFP 10–14 sessions; 129 (57%) of them attended at least one session. Approximately 90% of intervention condition students attended LST sessions.
Two consecutive cohorts of 6th graders were recruited from 28 school districts (ranging in size from approximately 1,300 to 5,200 students, and with at least 15% eligible for the free- or reduced-cost school lunch program) in Iowa and Pennsylvania. Matched (blocked) on school district size and geographic location, districts were randomly assigned to the intervention or control condition.28 11,960 or 90% of all eligible 6th graders participated in the pretest. Details on sample characteristics and participation are presented in Table 1 and Figure 1c, respectively.
Intervention implementation was guided by the PROSPER (PROmoting School-community-university Partnerships to Enhance Resilience) model for delivery of evidence-based programs, involving a partnership linking community teams, public schools, and the Cooperative Extension System of land-grant universities.29,30 Consent procedures allowed students, or parents on their behalf, to refuse participation. Participating students completed machine-scored questionnaires during school class periods.
From the menu of programs offered by the PROSPER Partnership Delivery System, all 14 community teams selected the SFP 10–14 program and delivered it to two successive cohorts of families of 6th graders. Teams also selected one of the three school-based curricula from the PROSPER menu to be implemented with the two cohorts during 7th grade. Four teams each selected LST and Project Alert, and six chose All Stars. Summary descriptions of the three school-based programs are available online at http://www.ppsi.iastate.edu/publicationsupplements/PF217/programs.pdf. All programs focused on fostering better understanding of the norms and behaviors regarding substance misuse, peer-resistance skills, and self-management.31
SFP 10–14 implementation was similar to that in Studies 1 and 2; 1,064 families participated in at least one session. While family programming reached 17% of eligible students, the school-based curriculum, delivered by teachers during designated class periods, reached nearly all eligible students.
PDM was assessed with several questions beginning in 10th grade in Studies 1 and 2 and at pretest in Study 3. In Studies 1 and 2, respondents were asked about lifetime use of barbiturates, tranquilizers, amphetamines, and narcotics. The question format for specific substances (barbiturates, tranquilizers, amphetamines, narcotics) was: “Have you ever used ___________, not under a doctor’s orders?” Reponses were coded 0 = No and 1 = Yes. To assess Prescription Drug Misuse Overall (PDMO), a composite index measure was created and coded such that 0 = No prescription drug misuse and 1 = Misuse of at least one type of the listed prescription drugs. In addition to the PDMO, Prescription Opioid Misuse (POM) was separately analyzed because opioids are the most frequently abused prescription drugs, with the highest percentage of misuse among 18–25 year olds.1,14
For Study 3 assessment of PDMO, questions similar to those in Studies 1 and 2 were employed at the 12th grade data collection. The questions in Study 3 covered two different, but overlapping and related circumstances of prescription drug use, with the first focusing on the most common prescription pain relievers, as follows: (1) “Have you ever used Vicodin, Percocet, or Oxycontin (not prescribed by a doctor)?” An index of lifetime PDMO utilized this item plus the responses to four additional questions about lifetime use of any prescription tranquilizer, sedative, stimulant, or pain reliever, “without a prescription of your own.” Respondents who answered “Yes” to any question were coded 1; those who answered “No” to all were coded 0.
All analyses were conducted within an intent-to-treat framework to minimize self-selection bias. In the rare instances in which a student moved from a school district in one study condition into a school district in another study condition, they were dropped from the analysis to preserve randomization and minimize potential confounding that might result from the influences of peers that received a different intervention exposure. Lifetime substance use items were corrected for consistency, so that if a participant answered “Yes” to any item, it was scored as a “Yes” in each of the following assessments. Because questionnaire items differed between adolescence and young adulthood, items were corrected only from post high-school forward in Studies 1 and 2.
The analytic strategy employed was dependent on cell size, as determined by sample size and rates of PDM. For samples with larger cell sizes (Studies 2 and 3), a multi-level analysis was conducted (based on clustering at the time of randomization). Conversely, due to the small cell sizes in Study 1, within-school dependency was difficult to evaluate; further, a multilevel analysis to address the nested structure of the data was not viable and Fisher’s Exact Test32 was used to assess intervention-control differences. For tests of risk moderation with dichotomous outcomes, SAS PROC GLIMMIX was used. Because we hypothesized a directional intervention effect and previous research has not found any evidence of negative intervention effects on substance use variables across multiple variables and time points in these RCTs, P-values were based on one-tailed tests; however, exact values are reported so that the corresponding two-tailed results can be readily deduced.
Tests to establish sample representativeness and pretest equivalence, as well as to rule out differential attrition were conducted for earlier reports.33– 35 No significant Condition × Attrition interaction effects were found for any sociodemographic variables between the pretest and the follow-up waves in the three studies. In addition, analyses using lifetime marijuana use as a proxy variable were conducted (no measures of PDM were available at the baseline assessments for Studies 1 and 2); no significant differential attrition by condition was found.
Figure 2a presents results at age 25. 4.7% of ISFP participants, as compared with 13.5% of control participants, reported lifetime POM (χ2(1) = 7.116, Fisher’s Exact P = 0.006); and 5.4% of ISFP participants, versus 15.5% of control participants, reported lifetime PDMO (χ2(1) = 8.252, Fisher’s Exact P = 0.003). Relative Reduction Rates (RRR) were 65% for both POM and PDMO. For risk moderation analyses, the higher-risk subsample was defined as those who had initiated use of alcohol, cigarettes, or marijuana at baseline—approximately 20%. The interaction term, Risk Status × Condition, was not significant, suggesting comparable intervention benefits across risk-related subgroups.
Figure 2b.1 and 2b.2 present Study 2 results at ages 21, 22, and 25; numerical results are summarized in Table 2. POM rates were higher for the control condition across all time points; P-values for tests of intervention effects with the full sample ranged from P = 0.022 at age 21 to P = 0.078 at age 25; RRRs ranged from 32% to 60%. For PDMO, results were similar, with P-values ranging from P = 0.015 at age 21 to P = 0.064 at age 25; RRRs ranged between 33% and 62% across those time points.
In Study 2, there were higher levels of substance initiation at baseline compared to Study 1; for this reason, the higher-risk subgroup included those who had initiated use of 2 out of 3 substances at baseline--alcohol, cigarettes, and marijuana. Approximately 20% were classified as higher risk. Significant risk moderation was found for both PMO and PDMO at all time points; thus, the results for both the full sample and the higher-risk subsample are presented. Regarding effects for the intervention vs. control higher-risk subsamples, POM results ranged from P = 0.005 at age 21 to P = 0.057 at age 25, with RRRs between 43% and 79%. For PDMO, P-values ranged from P = 0.004 at age 21 to P = 0.038 at age 25; RRRs were between 47% and 79%.
Results for Study 3 at 12th grade are presented in Figure 2c. Results indicated that 22.1% of the intervention condition participants and 27.8% of the control condition participants reported lifetime POM; F(1,12) = 5.45, P = 0.019, RRR = 21%. For PDMO, 23.1% of intervention condition participants and 29.0% of control condition participants reported lifetime initiation; F(1,12) = 5.83, P = 0.016, RRR = 20%. There was no evidence of risk-related moderation (in this case, the higher-risk subsample was defined by initiation of one of the gateway substances--alcohol, cigarette, or marijuana--at baseline; approximately 29% were classified as higher risk).
Between the earlier 2008 report24 and these extension and replication analyses, epidemologic data confirm worsening trends in PDM among adolescents and young adults. This public health problem is underscored by the CDC, the ONDCP, and the Surgeon General’s urgent calls to address the epidemic, especially considering its wide range of deleterious health consequences. Because this public health problem is growing,1–4,14 the need to identify effective interventions has become increasingly important.20,21 As noted, however, beyond the earlier work by the authors’ research group, no randomized controlled studies could be found that established long-term effects for community-based, general population preventive interventions on PDM outcomes.
The results of the current extension and replication analyses are noteworthy, indicating the long-term effectiveness of the tested interventions on lifetime PDM across three randomized studies. A key point of relevance to a national strategy for prevention of PDM is that none of the interventions had content specific to the prevention of PDM; the observed intervention effects likely were obtained by addressing general risk and protective factors for substance misuse targeted by the family and school preventive interventions. In this connection it is important to note that the interventions also have shown longitudinal effects on a range of other substance misuse and problem or skill behaviors, and have evidence supporting economic benefits.23,24,33,34,36,37
It is encouraging that the risk-related moderation analyses for all three trials showed that the intervention effects for the higher-risk subgroups were comparable to or stronger than those for the lower-risk subgroups. Study 2 findings suggest that even for those participants who had initiated substance use early—prior to intervention implementation—the interventions were effective in decreasing the expected levels of more serious prescription drug misuse later. It is important to recall that Study 2 was initiated when participants were in the 7th grade rather than the 6th grade, as in the other studies. By that time, a larger percentage already had initiated either alcohol or cigarette use. Although the lack of relevant data render the interpretation speculative, the stronger effects for the higher-risk subsamples could be at least partially a result of their inclination to value and engage more fully in the individual and family prevention program activities, motivated by their early exposure to substance use.37
Study 2 shows a pattern of results indicating limited additional initiation of PDM among higher-risk control group participants following the age 21 assessment point, suggesting a possible ceiling effect. It is not yet clear whether misuse in the higher-risk intervention subsample will reach that same or a lower ceiling rate at a later point in time. Additional planned follow-ups with the Study 2 and Study 3 samples will examine this trend.
Finally, of the three RCTs, the Study 3 replication tested the most readily “real world” implementation system, grounded in an existing infrastructure and guided by local community teams. The programming was administered with high implementation quality, which is likely essential for accomplishing positive, generalizable results from universal family and school preventive interventions. All studies benefitted from effective partnerships among schools and communities that overcame barriers to sustained, high-quality implementation.38–41 In addition, the Study 3 implementation system is the most broadly transportable and sustainable, partially because it is built into the functioning local system and has demonstrated cost efficiencies.
The degree to which the findings will generalize to populations with different geographic (e.g., suburban, urban) or ethnic compositions is not yet known. The authors recommend replication with additional types of populations.
Because relatively small numbers of participants per condition reported PDM, specific estimates of use rates are somewhat sensitive to small changes in numbers of users. This concern is partially abated by the larger sample sizes in Studies 2 and 3, and by the pattern of positive findings across studies and across time.
Also noteworthy is the relatively higher rate of PDM in Study 3. It is not clear to what degree this may be due to a period or cohort effect reflecting increased rates of PDM, more generally, or is due in part to differences in measures and data collection methods.
It is useful to consider the generally positive pattern of findings across the three trials, including the indications of practical significance suggested by RRRs. All trials were effectiveness studies, implemented through community-university partnerships, and have ecological validity, especially Study 3. Even under the more “real world” conditions of Study 3, RRRs were 20% or greater for both POM and PDMO. In concrete terms, if such rates held in general population implementation, for every 100 12th graders who reported lifetime PDM in the non-intervention population, there would be only 80 in the intervention population reporting such misuse.
It also is important to consider how the magnitude of RRRs varied somewhat across studies and, within Study 2, across time. In part, this variability may be due to some combination of the range of time periods of the studies, the sensitivity of the RRRs to the level of use rates, varying characteristics of the sampled populations, and differences in the wording of the measures.
In general, the prevention trials reported herein have provided useful information in support of future dissemination of the tested interventions for greater public health impact;22,42 as suggested earlier, the PROSPER replication study (Study 3) is especially instructive in this regard. The PROSPER delivery system was designed to scale-up for population impact, consistent with advocacy for the creation of a nationwide Primary Care Cooperative Extension Service of the type suggested in healthcare reform legislation.43,44
In conclusion, the extended and replicated findings from these studies underscore earlier findings about the benefits of relatively brief, cost effective universal interventions implemented with quality in early adolescence.
We gratefully acknowledge the PPSI and PRC staff, the Data Acquisition Units, the adolescent/young adult participants, and the schools that participated in the projects.
Funding/Support: Work on this paper was supported by the National Institute on Drug Abuse (grants DA013709, DA10815, DA007029), co-funding from the National Institute on Alcohol Abuse and Alcoholism (grant AA14702), and the National Institute of Mental Health (grant MH49217).
Role of the Sponsors: The funding organizations were not involved in the design or the conduct of the studies, data management, analysis or interpretation of the data, or in the preparation, review, or approval of the manuscript.
Author Contributions: Dr. Spoth had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design, primary writing: Spoth. Acquisition of data: Spoth, Shin, Redmond, Greenberg, Feinberg. Analysis and interpretation of data: Trudeau, Shin, Spoth. Drafting of the manuscript: Spoth and Trudeau. Critical revision of the manuscript for important intellectual content: Spoth, Ralston, Redmond, Greenberg, Feinberg. Obtained funding: Spoth, Redmond, Greenberg.
Financial Disclosures: None.
Conflict of interest
The authors have no conflict of interest to report.
Human Participant Protection
This study was approved by Iowa State University’s institutional review board.
Supplemental Material: Available at http://www.ppsi.iastate.edu/publicationsupplements/PF217/programs.pdf
Richard Spoth, Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa.
Linda Trudeau, Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa.
Chungyeol Shin, Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa.
Ekaterina Ralston, Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa.
Cleve Redmond, Partnerships in Prevention Science Institute, Iowa State University, Ames, Iowa.
Mark Greenberg, Prevention Research Center, The Pennsylvania State University, University Park, Pennsylvania.
Mark Feinberg, Prevention Research Center, The Pennsylvania State University, University Park, Pennsylvania.