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Collaborative primary care for depression with comorbid medical conditions (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 integrates behavioral health into the primary care setting to treat adult patients with all levels of depression (i.e. major or minor depression or dysthymia) and comorbid health conditions including diabetes, heart disease, acute coronary syndrome, hypertension, or stroke. 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 12 months.

We report separate results for collaborative primary care programs for depression among older adults with comorbid medical conditions.
 
ALL
BENEFIT-COST
META-ANALYSIS
CITATIONS
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 $3,173 Benefits minus costs $8,690
Participants $5,426 Benefit to cost ratio $8.85
Others $1,021 Chance the program will produce
Indirect $177 benefits greater than the costs 100%
Total benefits $9,796
Net program cost ($1,107)
Benefits minus cost $8,690

^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
Major depressive disorder
Clinical diagnosis of major depression or symptoms measured on a validated scale.
 58 12 1616 -0.357 0.050 59 -0.186 0.061 61 -0.357 0.001
Global functioning^
How well individuals (typically those who are developmentally disabled or seriously mentally ill) have adapted to activities of daily living.
 58 3 684 -0.157 0.084 59 n/a n/a n/a -0.157 0.061
Systolic blood pressure^
The blood pressure in the arteries when the heart beats. This is the first (or top) number in a blood pressure reading.
 58 2 133 -0.274 0.178 59 n/a n/a n/a -0.274 0.125
Blood sugar (HbA1c)^
Measure of average blood sugar over 10-12 weeks.
 58 2 242 -0.151 0.163 59 n/a n/a n/a -0.151 0.354
LDL cholesterol^
“Bad cholesterol” that is associated with higher risk of cardiovascular disease.
 58 1 98 -0.185 0.220 59 n/a n/a n/a -0.185 0.400
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 $2,183 $5,142 $0 $0 $7,324
Health care associated with major depression $989 $280 $1,021 $494 $2,783
Mortality associated with depression $2 $4 $0 $235 $242
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($553) ($553)
Totals $3,173 $5,426 $1,021 $177 $9,796
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $938 2016 Present value of net program costs (in 2022 dollars) ($1,107)
Comparison costs $0 2016 Cost range (+ or -) 15%
Treatment cost estimates for this program reflect costs beyond treatment as usual. When available, we use the average cost of the program reported by the studies, weighted by treatment sample sizes. Average program costs were obtained from Davidson et al. (2013), Ell et al. (2010), Katon et al. (2010), and Katon et al. (2004).
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

Bogner, H.R., de Vries, H.F., Kaye, E. M., & Morales, K.H. (2013). Pilot trial of a licensed practical nurse intervention for hypertension and depression. Family Medicine, 45(5), 323-329.

Coventry, P., Lovell, K., Dickens, C., Bower, P., Chew-Graham, C., McElvenny, D., . . . Gask, L. (2015). Integrated primary care for patients with mental and physical multimorbidity: cluster randomized controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease. BMJ, 350, h638.

Davidson, K.W., Rieckmann, N., Clemow, L., Schwartz, J. E., Shimbo, D., Medina, V., . . . Burg, M.M. (2010). Enhanced depression care for patients with acute coronary syndrome and persistent depressive symptoms: Coronary psychosocial evaluation studies randomized controlled trial. Archives of Internal Medicine, 170(7), 600-608.

Davidson, K. W., Bigger, J. T., Burg, M. M., Duer-Hefele, J., Medina, V., Newman, J. D., . . . Vaccarino, V. (2013). Centralized, stepped, patient preference-based treatment for patients with post-acute coronary syndrome depression: CODIACS vanguard randomized controlled trial. JAMA Internal Medicine, 173(11), 997-1004.

Ell, K., Katon, W., Xie, B., Lee, P. J., Kapetanovic, S., Guterman, J., & Chou, C. P. (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.

Katon, W., Russo, J., Lin, E. H., Schmittdiel, J., Ciechanowski, P., Ludman, E., . . . Von Korff, M. (2012). Cost-effectiveness of a multicondition collaborative care intervention: a randomized controlled trial. Archives of General Psychiatry, 69(5), 506-514.

Katon, W.J., Von Korff, M., Lin, E. H., Simon, G., Ludman, E., Russo, J., . . . Bush, T. (2004). The Pathways Study: A randomized trial of collaborative care in patients with diabetes and depression. Archives of General Psychiatry, 61(10), 1042-1049.

Katon, W.J., Lin, E.H., Von, K.M., Ciechanowski, P., Ludman, E. J., Young, B., . . . McCulloch, D. (2010). Collaborative care for patients with depression and chronic illnesses. The New England Journal of Medicine, 363(27), 2611-2620.

Morgan, M.A.J., Coates, M.J., Dunbar, J.A., Schlicht, K., Reddy, P., & Fuller, J. (2013). The TrueBlue model of collaborative care using practice nurses as case managers for depression alongside diabetes or heart disease: A randomised trial. British Medical Journal Open, 3(1).

Rollman, B.L., Belnap, B.H., LeMenager, M.S., Mazumdar, S., Houck, P.R., Counihan, P.J., . . . Reynolds, C.F. (2009). Telephone-delivered collaborative care for treating post-CABG depression: A randomized controlled trial. JAMA : The Journal of the American Medical Association, 302(19), 2095-2103.

Simon, G.E., Katon, W.J., Lin, E.H., Rutter, C., Manning, W.G., Von, K.M., . . . Young, B.A. (2007). Cost-effectiveness of systematic depression treatment among people with diabetes mellitus. Archives of General Psychiatry, 64(1), 65-72.

Vera, M., Perez-Pedrogo, C., Huertas, S.E., Reyes-Rabanillo, M.L., Juarbe, D., Huertas, A., . . . Chaplin, W. (2010). Collaborative care for depressed patients with chronic medical conditions: A randomized trial in Puerto Rico. Psychiatric Services, 61(2), 144-150.

Williams, L.S., Kroenke, K., Bakas, T., Plue, L.D., Brizendine, E., Tu, W., & Hendrie, H. (2007). Care management of poststroke depression: A randomized, controlled trial. Stroke, 38(3), 998-1003.

Wu, B., Jin, H., Vidyanti, I., Lee, P.J., Ell, K., & Wu, S. (2014). Collaborative depression care among Latino patients in diabetes disease management, Los Angeles, 2011-2013. Preventing Chronic Disease, 11, E148.