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Washington State Institute for Public Policy
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Lifestyle interventions to prevent diabetes: Shorter-term programs with group-based counseling

Health Care: Obesity and Diabetes
Benefit-cost methods last updated December 2023.  Literature review updated February 2017.
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All lifestyle diabetes prevention programs target individuals at high risk for developing type 2 diabetes, providing them with counseling and other support. Programs in this specific category are shorter-term, lower-cost, group-based counseling programs provided in community settings (e.g., YMCA's, churches).
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2022). 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 $6,198 Benefits minus costs $21,433
Participants $10,195 Benefit to cost ratio $41.79
Others $3,986 Chance the program will produce
Indirect $1,579 benefits greater than the costs 78%
Total benefits $21,958
Net program cost ($525)
Benefits minus cost $21,433

^WSIPP’s benefit-cost model does not monetize this outcome.

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. See Estimating Program Effects Using Effect Sizes for additional information.

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 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
ES SE Age ES SE Age ES p-value
50 7 490 -0.156 0.101 53 -0.048 0.101 60 -0.218 0.002
50 7 763 -0.292 0.074 50 n/a n/a n/a -0.292 0.001
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
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Weight change Labor market earnings associated with diabetes $3,864 $9,102 $0 $0 $12,965
Health care associated with diabetes $2,330 $1,083 $3,986 $1,165 $8,563
Mortality associated with diabetes $5 $11 $0 $677 $692
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($263) ($263)
Totals $6,198 $10,195 $3,986 $1,579 $21,958
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $440 2014 Present value of net program costs (in 2022 dollars) ($525)
Comparison costs $0 2014 Cost range (+ or -) 10%
These programs typically last for up to one year. Per-participant costs are based on a 2014 Washington Department of Health Diabetes Epidemic and Action Report (p. 133), accessed from:
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.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative 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 discounted dollars. 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.

Citations Used in the Meta-Analysis

Ackermann, R.T., Finch, E.A., Brizendine, E., Zhou, H., & Marrero, D.G. (2008). Translating the Diabetes Prevention Program into the community. The DEPLOY Pilot Study. American Journal of Preventive Medicine, 35(4), 357-63.

Katula, J.A., Vitolins, M.Z., Rosenberger, E.L., Blackwell, C.S., Morgan, T.M., Lawlor, M.S., & Goff, D.C.J. (2011). One-year results of a community-based translation of the Diabetes Prevention Program: Healthy-Living Partnerships to Prevent Diabetes (HELP PD) Project. Diabetes Care, 34(7), 1451-7.

Kulzer, B., Hermanns, N., Gorges, D., Schwarz, P., & Haak, T. (2009). Prevention of diabetes self-management program (PREDIAS): effects on weight, metabolic risk factors, and behavioral outcomes. Diabetes Care, 32(7), 1143-6.

Ma, J., Yank, V., Xiao, L., Wilson, S.R., Rosas, L.G., Stafford, R.S., & Lavori, P.W. (2013). Translating the diabetes prevention program lifestyle intervention for weight loss into primary care: A randomized trial. Jama Internal Medicine, 173(2), 113-121.

Mason, C., Foster-Schubert, K.E., Imayama, I., Kong, A., Xiao, L., Bain, C., Campbell, K.L., ... McTiernan, A. (2011). Dietary weight loss and exercise effects on insulin resistance in postmenopausal women. American Journal of Preventive Medicine, 41(4), 366-75.

Moore, S.M., Hardie, E.A., Hackworth, N.J., Critchley, C.R., Kyrios, M., Buzwell, S.A., & Crafti, N.A. (2011). Can the onset of type 2 diabetes be delayed by a group-based lifestyle intervention? A randomised control trial. Psychology and Health, 26(4), 485-499.

Ockene, I.S., Tellez, T.L., Rosal, M.C., Reed, G.W., Mordes, J., Merriam, P.A., Olendzki, B.C., ... Ma, Y. (2012). Outcomes of a Latino community-based intervention for the prevention of diabetes: the Lawrence Latino Diabetes Prevention Project. American Journal of Public Health, 102(2), 336-42.

Parikh, P., Simon, E.P., Fei, K., Looker, H., Goytia, C., & Horowitz, C.R. (2010). Results of a pilot diabetes prevention intervention in East Harlem, New York City: Project HEED. American Journal of Public Health, 100(Suppl 1), S232-S239.