Washington State Institute for Public Policy
Collaborative primary care for depression
Adult Mental Health: Depression
Benefit-cost estimates updated December 2016.  Literature review updated May 2014.
A care manager collaborates with the primary care provider, mental health specialists, and other providers to develop treatment plans for each patient. The care manager manages these treatment plans and follows up with patients to ensure treatment adherence. Care managers predominantly focus their efforts on improving depression symptoms.
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 $2,388 Benefits minus costs $6,491
Participants $4,133 Benefit to cost ratio $8.99
Others $743 Chance the program will produce
Indirect $39 benefits greater than the costs 100 %
Total benefits $7,304
Net program cost ($812)
Benefits minus cost $6,491
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
Labor market earnings associated with major depression $1,788 $3,937 $0 $145 $5,870
Health care associated with major depression $600 $195 $743 $301 $1,840
Adjustment for deadweight cost of program $1 $1 $0 ($408) ($406)
Totals $2,388 $4,133 $743 $39 $7,304
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $787 2012 Present value of net program costs (in 2015 dollars) ($812)
Comparison costs $0 2012 Cost range (+ or -) 15 %
Per-participant costs include telephone contacts, in-person contacts, supervision & information support, screening, educational materials, and time spent with a primary care provider. Costs were obtained from Ell et al. (2010). Collaborative care management of major depression among low-income, predominantly Hispanic subjects with diabetes: A randomized controlled trial. Diabetes Care, 33(4), 706-713. Our cost estimate is based on the average number of telephone and in-person contacts from studies. There is a wide variation of cost, since the time the care manager spent with each patient varied.
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
Major depressive disorder 48 7151 -0.266 0.035 52 -0.130 0.038 54 -0.279 0.001
Health care costs 8 2551 0.079 0.039 56 0.039 0.043 58 0.079 0.044
Suicidal ideation 2 981 -0.297 0.091 71 -0.146 0.099 73 -0.305 0.001
Citations Used in the Meta-Analysis

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Bergho?fer, A., Hartwich, A., Bauer, M., Unu?tzer, J., Willich, S. N., & Pfennig, A. (2012). Efficacy of a systematic depression management program in high utilizers of primary care: A randomized trial. BMC Health Services Research, 12(298).

Blanchard, M. R., Waterreus, A., & Mann, A. H. (1995). The effect of primary care nurse intervention upon older people screened as depressed. International Journal of Geriatric Psychiatry, 10(4), 289-298.

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Finley, P. R., Rens, H. R., Pont, J. T., Gess, S. L., Louie, C., Bull, S. A., . . . Bero, L. A. (2003). Impact of a collaborative care model on depression in a primary care setting: A randomized controlled trial. Pharmacotherapy, 23(9), 1175-1185.

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Katon, W., Von, K. M., Lin, E., Simon, G., Walker, E., Unu¨tzer, J., Bush, T., ... Ludman, E. (1999). Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Archives of General Psychiatry, 56(12), 1109-15.

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Simon, G. E., VonKorff, M., Rutter, C., & Wagner, E. (2000). Randomised trial of monitoring, feedback, and management of care by telephone to improve treatment of depression in primary care. British Medical Journal, 320(7234), 550-554.

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Unutzer, J., Katon, W., Callahan, C. M., Williams, J. W., Hunkeler, E., Harpole, L., Hoffing, M., ... Lin, E. H. B. (2002). Collaborative Care Management of Late-Life Depression in the Primary Care Setting: A Randomized Controlled Trial. Journal- American Medical Association, 288, 2836-2845.

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