|Benefit-Cost Summary Statistics Per Participant|
|Taxpayers||$1,147||Benefits minus costs||($1,820)|
|Participants||$2,206||Benefit to cost ratio||$0.48|
|Others||$35||Chance the program will produce|
|Indirect||($1,690)||benefits greater than the costs||26 %|
|Net program cost||($3,518)|
|Benefits minus cost||($1,820)|
|Detailed Monetary Benefit Estimates Per Participant|
|Benefits from changes to:1||Benefits to:|
|Labor market earnings associated with employment||$1,001||$2,204||$0||$0||$3,205|
|Health care associated with psychiatric hospitalization||$145||$2||$33||$73||$252|
|Adjustment for deadweight cost of program||$0||$0||$0||($1,763)||($1,763)|
|Detailed Annual Cost Estimates Per Participant|
|Annual cost||Year dollars||Summary|
|Program costs||$1,842||2011||Present value of net program costs (in 2016 dollars)||($3,518)|
|Comparison costs||$0||2011||Cost range (+ or -)||10 %|
|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 of Program Effects|
|Outcomes measured||Treatment Age||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|
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.