Comparative Effectiveness of Brief Alcohol Interventions for College Students: Results from a Network Meta-Analysis

Abstract

Late adolescence is a time of increased drinking, and alcohol plays a predominant role in college social experiences. Colleges seeking to prevent students’ hazardous drinking may elect to implement brief alcohol interventions (BAIs). However, numerous manualized BAIs exist, so an important question remains regarding the comparative effectiveness of these different types of BAIs for college students. This study uses network meta-analyses (NMA) to compare seven manualized BAIs for reducing problematic alcohol use among college students. We systematically searched multiple sources for literature, and we screened studies and extracted data in duplicate. For the quantitative synthesis, we employed a random-effects frequentist NMA to determine the effectiveness of different BAIs compared to controls and estimated the relative effectiveness ranking of each BAI. A systematic literature search resulted in 52 included studies: On average, 58% of participants were male, 75% were binge drinkers, and 20% were fraternity/sorority-affiliated students. Consistency models demonstrated that BASICS was consistently effective in reducing students’ problematic alcohol use (ES range: g = − 0.23, 95%CI [− 0.36, − 0.16] to g = − 0.36, 95% CI [− 0.55, − 0.18]), but AlcoholEDU (g = − 0.13, 95%CI [− 0.22, − 0.04]), e-CHUG (g = − 0.35, 95%CI [− 0.45, − 0.05]), and THRIVE (g = − 0.47, 95%CI [− 0.60, − 0.33]) were also effective for some outcomes. Intervention rankings indicated that BASICS, THRIVE, and AlcoholEDU hold the most promise for future trials. Several BAIs appear effective for college students. BASICS was the most effective but is resource intensive and may be better suited for higher risk students; THRIVE and e-CHUG are less resource intensive and show promise for universal prevention efforts.

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Notes

  1. 1.

    SUCRA values include the uncertainty of the different effect estimates and probabilities of assuming another possible treatment ranking so are more stable than the probability of being the best treatment: SUCRA values are thus focused on in the text; however, both values are presented in Table 3.

  2. 2.

    Alfonso et al. 2012; Carey et al. 2011; Doumas et al. 2009; Horner 2010; Logan 2013; Terlecki 2011; Terlecki et al. 2011

  3. 3.

    Carey et al. 2011; Doumas et al. 2009; Horner 2010; Logan et al. 2015; Terlecki et al. 2011; Terlecki et al. 2015

  4. 4.

    Labrie et al. 2013; Schaus et al. 2009; Terlecki et al. 2015

  5. 5.

    Doumas et al. 2009; Horner 2010; Terlecki et al. 2011; Terlecki et al. 2015

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Funding

Funding for the original review was provided by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant 1R01AA020286-01A1 (2011–2015).

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Correspondence to Emily Alden Hennessy.

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S.G.’s spouse is a salaried-employee of Eli Lilly and Company, and owns stock. S.G. has accompanied his spouse on company-sponsored travel. All other authors declare no known conflicts of interest.

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Appendices

Appendix 1

Fig. 4
figure4

Assessment of small study bias: Contour enhanced funnel plots for networks in main analysis, split by timing and outcome

Table 4 Risk of bias
Table 5 Included studies characteristics
Table 6 Effect sizes for networks

Appendix 2 – Additional Networks

Frequency of Heavy Alcohol Use: 3–6 Months

Frequency of heavy alcohol use outcomes measured at 3–6 months post-intervention represents a limited network (Fig. 3a) of 12 studies comparing five interventions, and the AO-CT and ACT-CT groups. As the size of the nodes indicate, BASICS was compared the most, while the other interventions were only studied once, with the exception of e-CHUG, which was included in two trials. The most informative direct evidence in this network was for BASICS versus AO-CT as it contributed 20.9%, which is likely overly influential given the representation of other interventions in this network. Results from the consistency model indicate that relative to the AO-CT, no comparisons significantly reduced the frequency of heavy drinking (Table 7). The global test for inconsistency was non-significant (χ2 (1) = 0.03, p = 0.8518), and for the single loop available, there was no evidence of loop-specific inconsistency. Node-splitting also indicated that the consistency model fit well. When compared against all other interventions BASICS had the largest SUCRA value, followed by THRIVE (Table 8)

Table 7 Intervention effects compared to assessment-only control groups
Table 8 Treatment rankings

Sensitivity Analysis: Frequency of Heavy Alcohol Use, 3–6 Months

Sensitivity analysis replacing the measurement duration from one study (12.9 versus 25.8 weeks: Schaus et al. 2009) did not change the results (Tables 7 and 8)

Quantity of Alcohol Use During Peak Consumption: 3–6 Months

Quantity of alcohol use during peak consumption measured at 3–6 months post-intervention is a network (Fig. 3c) of 11 studies comparing five interventions and both types of control groups. As the size of the nodes indicate, BASICS was compared the most, while the other interventions were only studied once, with the exception of e-CHUG, which was included in four trials. The most informative direct evidence in this network was for e-CHUG versus AO-CT as it contributed 17.3%. Results from the consistency model indicate that relative to the AO-CT, no comparisons significantly reduced the quantity of alcohol consumption during peak drinking episodes (Table 7). The global test for inconsistency was non-significant (χ2 (2) = 1.73, p = 0.4216); however, one loop (BASICS, e-CHUG, ACT-CT) demonstrated significant inconsistency (IF = 1.04, 95% CI [0.42, 1.65]). Node-splitting, however, supported the consistency model. When compared against all other interventions BASICS had the largest SUCRA value, followed by Alcohol101 (Table 8)

Sensitivity Analysis: Quantity of Alcohol Use during Peak Consumption, 3–6 Months

Removing the two studies that utilized a mandated sample from this network (Alfonso et al. 2012; Horner 2010) resulted in removing one comparison involving e-CHUG and two comparisons of BASICS, but did not substantively change the model results (Tables 7 and 8). The global test for inconsistency remained non-significant (χ2 = 2.35, df = 2, p = 0.3086). There was, however, evidence of significant loop-specific inconsistency for two evidence loops: (1) BASICS, e-CHUG, ACT-CT, IF = 1.89, 95% CI [0.59, 3.20]) and (2) BASICS, e-CHUG, AO-CT, IF = 0.87, 95% CI [0.00, 2.17]

Sensitivity analysis replacing the measurement duration from three studies (12.9 versus 25.8 weeks: Labrie et al. 2013; Schaus et al. 2009; Terlecki et al. 2015) did not change the results of the effects compared to the AO-CT, but Alcohol101 and BASICS switched their rankings such that Alcohol 101 had the largest SUCRA value (Tables 7 and 8)

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Hennessy, E.A., Tanner-Smith, E.E., Mavridis, D. et al. Comparative Effectiveness of Brief Alcohol Interventions for College Students: Results from a Network Meta-Analysis. Prev Sci 20, 715–740 (2019). https://doi.org/10.1007/s11121-018-0960-z

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Keywords

  • Brief alcohol intervention
  • College students
  • Network meta-analysis