Washington State Institute for Public Policy
Peer support: Addition of a peer specialist to the treatment team
Adult Mental Health: Serious Mental Illness
Benefit-cost estimates updated December 2016.  Literature review updated May 2014.
The programs examined in this analysis compared treatment teams with a peer specialist to treatment teams without a peer specialist. The treatment teams in this analysis provided services to individuals with serious mental illness or individuals receiving VA services for a psychiatric diagnosis.
BENEFIT-COST
META-ANALYSIS
CITATIONS
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 $794 Benefits minus costs ($2,887)
Participants $1,426 Benefit to cost ratio $0.17
Others $37 Chance the program will produce
Indirect ($1,667) benefits greater than the costs 9 %
Total benefits $590
Net program cost ($3,477)
Benefits minus cost ($2,887)
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 $3 $0 $5 $1 $8
Labor market earnings associated with employment $647 $1,424 $0 $0 $2,071
Health care associated with psychiatric hospitalization $145 $2 $33 $71 $250
Adjustment for deadweight cost of program $0 $0 $0 ($1,739) ($1,739)
Totals $794 $1,426 $37 ($1,667) $590
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $1,842 2011 Present value of net program costs (in 2015 dollars) ($3,477)
Comparison costs $0 2011 Cost range (+ or -) 10 %
The cost of treatment is the weighted average cost of peer services provided in the studies included in this analysis. The average number of service hours is estimated from Eisen et al., 2012, Felton et al., 1995, and Sledge et al., 2011 is higher than the average number of encounters with a peer specialist in Washington State as reported in Mercer (2013). The cost per encounter was estimated using the peer specialist reimbursement cost reported in Mercer, (2013). Felton et al., (1995). Consumers as peer specialists on intensive case management teams: Impact on client outcomes. Psychiatric Services, 46(10), 1037-1044. Sledge et al., (2011). Effectiveness of peer support in reducing readmissions of persons with multiple psychiatric hospitalizations. Psychiatric Services, 62(5), 541-544. Eisen et al., (2012). Outcome of a randomized study of a mental health peer education and support group in the VA. Psychiatric Services, 63(12), 1243-1246. Mercer, (2013). Behavioral health data book for the state of Washington for rates effective January 1, 2014.
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
Crime 1 36 0.000 0.243 46 0.000 0.000 47 0.000 1.000
Employment 1 78 0.386 0.133 46 0.000 0.000 47 0.386 0.004
Global functioning 1 78 0.685 0.135 46 0.000 0.000 47 0.685 0.001
Hospitalization (psychiatric) 7 2191 -0.064 0.123 46 0.000 0.000 47 -0.064 0.604
Homelessness 1 36 -0.138 0.243 46 0.000 0.000 47 -0.138 0.569
Psychiatric symptoms 3 274 0.044 0.080 46 0.000 0.000 47 0.044 0.552
Citations Used in the Meta-Analysis

Chinman, M., Oberman, R.S., Hanusa, B.H., Cohen, A.N., Salyers, M.P., … & Young, A.S. (2014). A cluster randomized trial of adding peer specialists to intensive case management teams in the veterans' health administration. The journal of behavioral health services & research, 1-13.

Craig, T., Doherty, I., Jamieson-Craig, R., Boocock, A., & Attafua, G. (2004). The consumer-employee as a member of a Mental Health Assertive Outreach Team I Clinical and social outcomes. Journal of Mental Health, 13(1), 59-69.

Eisen, S.V., Schultz, M.R., Mueller, L.N., Degenhart, C., Clark, J.A., Resnick, S.G., Christiansen, C.L., …, & Sadow, D. (2012). Outcome of a randomized study of a mental health peer education and support group in the VA. Psychiatric Services, 63(12), 1243-1246.

Felton, C.J., Stastny, P., Shern, D.L., Blanch, A., Donahue, S.A., Knight, E., & Brown, C. (1995). Consumers as peer specialists on intensive case management teams: Impact on client outcomes. Psychiatric Services, 46(10), 1037-1044.

Gordon, R.E., Edmunson, E., Bedell, J. & Goldstein, N. (1979). Reducing rehospitalization of state mental patients. Journal of the Florida Medical Association, 66(9), 927-933.

Landers, G.M., & Zhou, M. (2011). An analysis of relationships among peer support, psychiatric hospitalization, and crisis stabilization. Community Mental Health Journal, 47(1), 106-112.

Min, S.Y., Whitecraft, J., Rothbard, A.B., & Salzer, M.S. (2007). Peer support for persons with co-occurring disorders and community tenure: a survival analysis. Psychiatric Rehabilitation Journal, 30(3), 207-213.

Resnick, S.G., & Rosenheck, R.A. (2008). Integrating peer-provided services: a quasi-experimental study of recovery orientation, confidence, and empowerment. Psychiatric Services : a Journal of the American Psychiatric Association, 59(11), 1307-1314.

Sledge, W.H., Lawless, M., Sells, D., Wieland, M., O'Connell, M.J., & Davidson, L. (2011). Effectiveness of peer support in reducing readmissions of persons with multiple psychiatric hospitalizations. Psychiatric Services, 62(5), 541-544.

Tracy, K., Burton, M., Nich, C., & Rounsaville, B. (2011). Utilizing peer mentorship to engage high recidivism substance-abusing patients in treatment. The American Journal of Drug and Alcohol Abuse, 37(6), 525-531.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov