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Collaborative primary care for depression with comorbid medical conditions (older adult population)

Adult Mental Health: Depression
Benefit-cost estimates updated May 2017.  Literature review updated December 2016.
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Collaborative primary care integrates behavioral health into the primary care setting to treat older adult patients, age 50 and over, with all levels of depression (i.e. major or minor depression or dysthymia) and comorbid health conditions including diabetes and hypertension. 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 older adult populations. All programs were implemented in primary care settings, where patients received collaborative care for 1 to 12 months.

We report separate results for collaborative primary care programs for depression among adults with comorbid medical conditions.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). 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 $692 Benefits minus costs $1,392
Participants $225 Benefit to cost ratio $3.42
Others $856 Chance the program will produce
Indirect $194 benefits greater than the costs 82 %
Total benefits $1,968
Net program cost ($575)
Benefits minus cost $1,392
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 $0 $0 $0 $135 $135
Health care associated with major depression $692 $225 $856 $347 $2,120
Adjustment for deadweight cost of program $0 $0 $0 ($287) ($287)
Totals $692 $225 $856 $194 $1,968
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $576 2016 Present value of net program costs (in 2016 dollars) ($575)
Comparison costs $0 2016 Cost range (+ or -) 20 %
Treatment cost estimates for this program reflect costs beyond treatment as usual. Costs are based on a weighted average of per-participant costs for included studies. Based on Blanchard et al. (1995), Chew-Graham et al. (2007), and McCusker et al. (2008), we estimate provider hours, apply the mean hourly wage estimate for Washington State reported by the Bureau of Labor Statistics (September 2016) for the appropriate provider, and increase wages by a factor of 1.441 to account for the cost of employee benefits. These studies average 6.5 behavioral health nurse hours per participant. We use reported per-participant costs from Unutzer et al. (2002).
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.

^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.

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 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
Blood sugar^ 1 128 -0.020 0.162 69 n/a n/a n/a -0.020 0.902
Major depressive disorder 3 262 -0.483 0.110 69 -0.251 0.135 71 -0.483 0.001

Citations Used in the Meta-Analysis

Bogner, H.R., & de Vries, H.F. (2008). Integration of depression and hypertension treatment: A pilot randomized controlled trial. Annals of Family Medicine, 6(4), 295-301.

Bogner, H.R., & de Vries, H.F. (2010). Integrating type 2 diabetes mellitus and depression treatment among African Americans; a randomized controlled pilot trial. The Diabetes Educator, 36(2), 284-292.

Williams, J.W.J., Katon, W., Lin, E.H., Nöel, P.H., Worchel, J., Cornell, J., . . . IMPACT Investigators. (2004). The effectiveness of depression care management on diabetes-related outcomes in older patients. Annals of Internal Medicine, 140(12), 1015-24.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov