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Collaborative primary care for depression (general adult population)

Adult Mental Health: Depression
Benefit-cost methods last updated December 2023.  Literature review updated December 2016.
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Collaborative primary care for depression integrates behavioral health into the primary care setting to treat adult patients with major or minor depression, dysthymia, or subthreshold depression. In the collaborative care model, a care manager coordinates with a primary care provider and behavioral health care providers to develop and implement measurement-based treatment plans for individual patients. Care managers can be mental health providers (e.g. psychologists) or non-behavioral health specialists (e.g. registered nurses or social workers). Programs included in this review were intended for adult populations, age 18 and over. All programs were implemented in primary care settings, where patients received collaborative care for 3 to 36 months.

We report separate results for collaborative primary care programs for depression among older adults.
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 $4,625 Benefits minus costs $13,808
Participants $8,961 Benefit to cost ratio $15.03
Others $962 Chance the program will produce
Indirect $243 benefits greater than the costs 98%
Total benefits $14,792
Net program cost ($984)
Benefits minus cost $13,808

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
47 25 4094 -0.258 0.058 48 -0.134 0.071 50 -0.307 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
Major depressive disorder Labor market earnings associated with major depression $3,685 $8,681 $0 $0 $12,366
Health care associated with major depression $933 $264 $962 $466 $2,625
Mortality associated with depression $7 $16 $0 $269 $292
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($492) ($492)
Totals $4,625 $8,961 $962 $243 $14,792
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $834 2016 Present value of net program costs (in 2022 dollars) ($984)
Comparison costs $0 2016 Cost range (+ or -) 15%
Treatment cost estimates for this program reflect costs beyond treatment as usual. Costs are based on a weighted average of per-participants costs published in Adler et al. (2004), Katon et al. (1996); Katon et al. (1999), Rost et al. (2001), Simon et al. (2000); and Grochtdreis et al (2015). Cost-effectiveness of collaborative care for the treatment of depressive disorders in primary care: a systematic review. PLoS One 10(5): e0123078.
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

Adler, D.A., Bungay, K. M., Wilson, I. B., Pei, Y., Supran, S., Peckham, E., . . . Rogers, W. H. (2004). The impact of a pharmacist intervention on 6-month outcomes in depressed primary care patients. General Hospital Psychiatry, 26(3), 199-209.

Aragones, E., Lluis, P. J., Caballero, A., Lopez-Cortacans, G., Casaus, P., Maria, H. J., . . . Folch, S. (2012). Effectiveness of a multi-component programme for managing depression in primary care: A cluster randomized trial. The INDI project. Journal of Affective Disorders, 142(1-3), 297-305.

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).

Capoccia, K. L., Boudreau, D. M., Blough, D. K., Ellsworth, A. J., Clark, D. R., Stevens, N. G., . . . Sullivan, S. D. (2004). Randomized trial of pharmacist interventions to improve depression care and outcomes in primary care. American Journal of Health-System Pharmacy, 61(4), 364-372.

Datto, C. J., Thompson, R., Horowitz, D., Disbot, M., & Oslin, D. W. (2003). The pilot study of a telephone disease management program for depression. General Hospital Psychiatry, 25, 3.

Dietrich, A. J., Oxman, T. E., Williams, J. J. W., Schulberg, H. C., Bruce, M. L., Lee, P. W., Barry, S., ... Nutting, P. A. (2004). Re-engineering systems for the treatment of depression in primary care: Cluster randomised controlled trial. British Medical Journal, 329, 7466, 602.

Dobscha, S. K., Corson, K., Hickam, D. H., Perrin, N. A., Kraemer, D. F., & Gerrity, M. S. (2006) Depression decision support in primary care: A cluster randomized trial. Annals of Internal Medicine, 145(7), 477-487.

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.

Gensichen, J., von Korff, M., Peitz, M., Muth, C., Beyer, M., Gu?thlin, C., . . . Gerlach, F. M. (2009). Case management for depression by health care assistants in small primary care practices: a cluster randomized trial. Annals of Internal Medicine, 151(6), 369-378.

Hedrick, S. C., Chaney, E. F., Felker, B., Liu, C.-F., Hasenberg, N., Heagerty, P., . . . Katon, W. (2003). Effectiveness of collaborative care depression treatment in veterans' affairs primary care. Journal of General Internal Medicine, 18(1), 9-16.

Katon, W., Robinson, P., Von, K. M., Lin, E., Bush, T., Ludman, E., . . . Walker, E. (1996). A multi-faceted intervention to improve treatment of depression in primary care. Archives of General Psychiatry, 53(10), 924-932.

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.

Katzelnick, D. J., Simon, G. E., Pearson, S. D., Manning, W. G., Helstad, C. P., Henk, H. J., . . . Kobak, K. A. (2000). Randomized trial of a depression management program in high utilizers of medical care. Archives of Family Medicine, 9(4), 345-351.

Klinkman, M. S., Bauroth, S., Fedewa, S., Kerber, K., Kuebler, J., Adman, T., & Sen, A. (2010). Long-term clinical outcomes of care management for chronically depressed primary care patients: A report from the depression in primary care project. Annals of Family Medicine, 8(5), 387-396.

Landis, S. E., Gaynes, B. N., Morrissey, J. P., Vinson, N., Ellis, A. R., & Domino, M. E. (2007). Generalist care managers for the treatment of depressed medicaid patients in North Carolina: A pilot study. BMC Family Practice, 8(1), 7-11.

Lin, E.H., VonKorff, M., Russo, J., Katon, W., Simon, G.E., Unützer, J., . . . Ludman, E. (2000). Can depression treatment in primary care reduce disability? A stepped care approach. Archives of Family Medicine, 9(10), 1052-1058.

Menchetti, M., Sighinolfi, C., Di Michele, V., Peloso, P., Nespeca, C., Bandieri, P.V., . . . Berardi, D. (2013). Effectiveness of collaborative care for depression in Italy. A randomized controlled trial. General Hospital Psychiatry, 35(6), 579-586.

Richards, D. A., Lovell, K., Gilbody, S., Gask, L., Torgerson, D., Barkham, M., . . . Richardson, R. (2008). Collaborative care for depression in UK primary care: A randomized controlled trial. Psychological Medicine, 38(2), 279-287.

Richards, D. A., Hill, J. J., Gask, L., Lovell, K., Chew-Graham, C., Bower, P., . . . Barkham, M. (2013). Clinical effectiveness of collaborative care for depression in UK primary care (CADET): Cluster randomised controlled trial. British Medical Journal (Clinical Research Ed.), 347.

Rost, K., Nutting, P., Smith, J., Werner, J., & Duan, N. (2001). Improving Depression Outcomes in Community Primary Care Practice. A Randomized Trial of the QuEST Intervention. Journal of General Internal Medicine, 16(3), 143-149.

Schoenbaum, M., Unutzer, J., Sherbourne, C., Duan, N., Rubenstein, L. V., Miranda, J., . . . Wells, K. (2001). Cost-effectiveness of practice-initiated quality improvement for depression: results of a randomized controlled trial. Jama : the Journal of the American Medical Association, 286(11), 1325-30.

Shippee, N. D., Shah, N. D., Angstman, K. B., DeJesus, R. S., Wilkinson, J. M., Bruce, S. M., & Williams, M. D. (2013). Impact of collaborative care for depression on clinical, functional, and work outcomes: A practice-based evaluation. The Journal of Ambulatory Care Management, 36(1),13-23

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.

Simon, G. E., Ludman, E. J., Tutty, S., Operskalski, B., & Von, K. M. (2004). Telephone psychotherapy and telephone care management for primary care patients starting antidepressant treatment: a randomized controlled trial. Journal of the American Medical Association, 292(8), 935-42.

Smit, A., Kluiter, H., Conradi, H. J., van der Meer, K., Tiemens, B. G., Jenner, J. A., . . . Ormel, J. (2006). Short-term effects of enhanced treatment for depression in primary care: Results from a randomized controlled trial. Psychological Medicine, 36(1), 15-26.

Swindle, R. W., Rao, J. K., Helmy, A., Plue, L., Zhou, X. H., Eckert, G. J., & Weinberger, M. (2003). Integrating clinical nurse specialists into the treatment of primary care patients with depression. International Journal of Psychiatry in Medicine, 33(1), 17-37.

Uebelacker, L. A., Marootian, B. A., Tigue, P., Haggarty, R., Primack, J. M., & Miller, I. W. (2011). Telephone depression care management for Latino Medicaid health plan members: A pilot randomized controlled trial. The Journal of Nervous and Mental Disease, 199(9), 678-683.

Wells, K. B., Sherbourne, C., Schoenbaum, M., Duan, N., Meredith, L., Unu?tzer, J., . . . Rubenstein, L. V. (2000). Impact of disseminating quality improvement programs for depression in managed primary care: a randomized controlled trial. JAMA : The Journal of the American Medical Association, 283(2), 212-220.