Washington State Institute for Public Policy
Brief Alcohol Screening and Intervention of College Students (BASICS): A Harm Reduction Approach
Substance Abuse: Substance Abuse Early Intervention
Benefit-cost estimates updated December 2016.  Literature review updated May 2014.
College students recruited or referred are screened for "hazardous" drinking (not alcohol dependence.) Those reporting high rates of consumption receive one to two brief motivational sessions that include comparison of the students’ alcohol consumption relative to their peers. Interventions are typically delivered by graduate students or counselors.
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2015). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $410 Benefits minus costs $1,203
Participants $801 Benefit to cost ratio $17.61
Others $63 Chance the program will produce
Indirect $0 benefits greater than the costs 70 %
Total benefits $1,275
Net program cost ($72)
Benefits minus cost $1,203
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Crime $7 $0 $18 $4 $29
Labor market earnings associated with problem alcohol use $360 $792 $0 $11 $1,162
Property loss associated with problem alcohol use $0 $2 $3 $0 $5
Health care associated with problem alcohol use $44 $8 $41 $22 $114
Adjustment for deadweight cost of program $0 $0 $0 ($36) ($36)
Totals $410 $801 $63 $0 $1,275
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $72 2014 Present value of net program costs (in 2015 dollars) ($72)
Comparison costs $0 2014 Cost range (+ or -) 20 %
The average duration of the intervention in these studies was 1.5 hours. We assume the following: (1) 36% of screened students are eligible and agree to the intervention (per Carey et al., 2006); (2) screening takes 30 minutes to administer the screen, score, and identify those with hazardous drinking; and (3) graduate students or counselors receive $25 per hour (2014 dollars) to administer the screening and intervention.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Estimated Cumulative Net Benefits Over Time (Non-Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in non-discounted dollars to simplify the “break-even” point from a budgeting perspective. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Primary or secondary participant No. of effect sizes Treatment N Adjusted effect sizes (ES) and standard errors (SE) used in the benefit-cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
Regular smoking 1 118 0.000 0.205 19 0.000 0.308 21 0.000 1.000
Problem alcohol use 20 3296 -0.166 0.031 19 -0.023 0.047 21 -0.166 0.001
Cannabis use 1 118 0.000 0.205 19 0.000 0.308 21 0.000 1.000
Citations Used in the Meta-Analysis

Borsari, B., & Carey, K.B. (2000). Effects of a brief motivational intervention with college student drinkers. Journal of Consulting and Clinical Psychology, 68(4), 728-733.

Carey, K.B., Carey, M.P., Maisto, S.A., & Henson, J.M. (2006). Brief motivational interventions for heavy college drinkers: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 74(5), 943-54.

Chiauzzi, E., Green, T.C., Lord, S., Thum, C., & Goldstein, M. (2005). My Student Body: A High-Risk Drinking Prevention Web Site for College Students. Journal of American College Health, 53(6), 263.

Collins, S.E., Carey, K.B., & Sliwinski, M.J. (2002). Mailed personalized normative feedback as a brief intervention for at-risk college drinkers. Journal of Studies on Alcohol, 63(5), 559-567.

DiFulvio, G.T., Linowski, S.A., Mazziotti, J.S., & Puleo, E. (2012). Effectiveness of the brief alcohol and screening intervention for college students (BASICS) program with a mandated population. Journal of American College Health, 60(4), 269-280.

Dimeff, L.A. (1997). Brief intervention for heavy and hazardous college drinkers in a student primary health care setting (Doctoral dissertation). UMI No. 9819231.

Hansson, H., Rundberg, J., Zetterlind, U., Johnsson, K.O., & Berglund, M. (2006). An intervention program for university students who have parents with alcohol problems: a randomized controlled trial. Alcohol and Alcoholism (oxford, Oxfordshire), 41(6), 655-663.

Juarez, P., Walters, S.T., Daugherty, M., & Radi, C. (2006). A randomized trial of motivational interviewing and feedback with heavy drinking college students. Journal of Drug Education, 36(3), 233-246.

Kulesza, M., McVay, M.A., Larimer, M.E., & Copeland, A.L. (2013). A randomized clinical trial comparing the efficacy of two active conditions of a brief intervention for heavy college drinkers. Addictive Behaviors, 38(4), 2094-101.

Larimer, M.E., Turner, A.P., Anderson, B.K., Fader, J.S., Kilmer, J.R., Palmer, R.S., & Cronce, J.M. (2001). Evaluating a brief alcohol intervention with fraternities. Journal of Studies on Alcohol, 62(3), 370-380.

Marlatt, G.A., J.S. Baer, D.R. Kivlahan, L.A. Dimeff, M.E. Larimer, L.A. Quigley, J.M. Somers, and E. Williams. (1998). Screening and Brief Intervention for High-Risk College Student Drinkers: Results From a 2-Year Follow-Up Assessment. Journal of Consulting and Clinical Psychology, 66, 604-615.

Murphy, J.G., Duchnick, J.J., Vuchinich, R.E., Davison, J.W., Karg, R.S., Olson, A.M., . . . Coffey, T.T. (2001). Relative efficacy of a brief motivational intervention for college student drinkers. Psychology of Addictive Behaviors, 15(4), 373-379.

Neighbors, C., Larimer, M.E., & Weis, M.A. (2004). Targeting misperceptions of descriptive drinking norms: Efficacy of acomputer-delivered personalized normative feedback interventions. Journal of Consulting and Clinical Psychology, 72(3), 434-447.

Schaus, J. F., Sole, M. L., McCoy, T. P., Mullett, N., & O'Brien, M. C. (2009). Alcohol screening and brief intervention in a college student health center: A randomized controlled trial. Journal of Studies on Alcohol and Drugs, Suppl. 16, 131-141.

Turrisi, R., Larimer, M.E., Mallett, K.A., Kilmer, J.R., Ray, A.E., Mastroleo, N.R., Geisner, I.M., ... Montoya, H. (2009 A randomized clinical trial evaluating a combined alcohol intervention for high-risk college students. Journal of Studies on Alcohol and Drugs, 70(4), -67.

White, H.R., Morgan, T.J., Pugh, L.A., Celinska, K., Labouvie, E.W., & Pandina, R.J. (2006). Evaluating two brief substance-use interventions for mandated college students. Journal of Studies on Alcohol, 67(2) 309-17.

For more information on the methods
used please see our Technical Documentation.