Centrally assisted collaborative telecare is a cost-effective strategy relative to usual care for treating posttraumatic stress disorder and depression in the Military Health System.
Objectives: Collaborative care is an effective approach for treating posttraumatic stress disorder (PTSD) and depression within the US Military Health System (MHS), but its cost-effectiveness remains unstudied. Our objective was to evaluate the costs and cost-effectiveness of centrally assisted collaborative telecare (CACT) versus optimized usual care (OUC) for PTSD and depression in the MHS.
Study Design: A randomized trial compared CACT with OUC. Routine primary care screening identified active-duty service members with PTSD or depression. Eligible participants (N = 666) were randomized to CACT or OUC and assessed at 3, 6, and 12 months. OUC patients could receive care management and increased behavioral health support. CACT patients could receive these services plus stepped psychosocial treatment and routine centralized team monitoring.
Methods: Quality-adjusted life-years (QALYs) were derived from the 12-Item Short Form Health Survey. Claims and case management data were used to estimate costs. Cost-effectiveness analyses were conducted from a societal perspective.
Results: Data from 629 patients (320 CACT and 309 OUC) with sufficient follow-up were analyzed. CACT patients gained 0.02 QALYs (95% CI, —0.001 to 0.03) relative to OUC patients. Twelve-month costs, including productivity, were $987 (95% CI, –$3056 to $5030) higher for CACT versus OUC. CACT was estimated to cost $49,346 per QALY gained compared with OUC over 12 months. There is a 58% probability that CACT is cost-effective at a $100,000/QALY threshold.
Conclusions: Despite its higher costs, CACT appears to be a cost-effective strategy relative to OUC for managing PTSD and depression in the MHS.
Am J Manag Care. 2018;24(2):91-98Takeaway Points
Centrally assisted collaborative telecare is a cost-effective strategy relative to usual care for treating posttraumatic stress disorder and depression in the Military Health System (MHS).
Posttraumatic stress disorder (PTSD), anxiety, and depression are common conditions in the US military. Prevalence estimates of postdeployment PTSD and depression range from 13% to 18%, and 28% of service members report severe symptoms of PTSD, anxiety, or depression.1,2 These problems cause suffering and impairment and contribute to military attrition, absenteeism, misconduct, and sick-call visits.3,4 Despite this, less than half of the serving military personnel affected receive military mental health services, which are often not timely or adequate.5,6 The military has attempted to better integrate mental health services into primary care, and the first Army integration approach began in 2007.7,8 Access to and quality of mental health services for military personnel has remained a recurring public policy concern, however.1,9 To address this, the Institute of Medicine has called for health system—level interventions to increase access to and continuity of mental health services in military and veteran populations.10
Collaborative care is an empirically supported method of extending and improving the reach, quality, and outcomes of care for common health conditions.11,12 In more than 80 randomized trials, collaborative care models have demonstrated improved outcomes among patients with depression and anxiety,12-14 depression-related suicidal ideation,15 depression and other chronic health conditions (eg, diabetes, asthma),16 and chronic pain.17,18 As of January 1, 2017, the Medicare fee schedule now reimburses for the delivery of collaborative care.
Recently, the first randomized controlled trial (RCT) of centrally assisted collaborative telecare (CACT) for PTSD and depression within the Military Health System (MHS) was completed.19,20 The STepped Enhancement of PTSD Services Using Primary Care (STEPS-UP) trial compared CACT with the Army’s preexisting program integrating behavioral health in primary care. CACT was effective in reducing the severity of PTSD and depressive symptoms in active military personnel using primary care, adding to the evidence supporting the use of collaborative care treatment models for mental illness in a range of settings and populations.20 However, no prior research has shown whether a collaborative care approach to treating PTSD and depression is cost-effective in the MHS, an important question given that the military spends more than $50 billion annually on healthcare for its nearly 10 million beneficiaries.21 The objective of this study was to evaluate the cost-effectiveness of treating patients with PTSD and depression using CACT compared with optimized usual care (OUC) in the MHS.
Trial Design and Treatment Protocol
The RCT study design has been published elsewhere in detail.19 Briefly, between February 2012 and September 2013, routine clinical screening in 18 Army primary care clinics at 6 military installations identified active-duty service members with 1) PTSD, depression, or both, and 2) access to Internet and email. Patients were excluded if they had current alcohol dependence, active suicidal ideation in the prior 2 months, planned geographic relocation within 6 months, or current duties in the participating clinic.20
Eligible participants (N = 666) were randomized to OUC (n = 334) or CACT (n = 332) to treat their symptoms for up to 12 months after enrollment. OUC patients received the standard integrated mental health approach for Army primary care clinics, which included increased access to mental health specialists and follow-up monitoring from a nurse care manager who tracked patients’ progress and provided status updates to primary care clinicians.8 CACT patients received the standard OUC services plus 1) stepped psychosocial treatment and 2) routine monitoring by a central mental health team with a centralized patient symptom registry. Nurse care managers who worked with CACT patients also received additional training in behavioral activation, problem solving, and motivational interviewing to provide additional support to patients.20 The study was approved by all affiliated institutional research review boards.
Health Outcome Assessment
We used data from patient surveys administered at baseline and 3-, 6-, and 12-month follow-ups to assess the primary outcomes of depression and PTSD, as well as health-related quality of life (QOL) and other secondary health outcomes. We assessed the severity of PTSD and depression symptoms using the PTSD Diagnostic Scale22,23 (PDS) and the Hopkins Symptom Checklist depression items (HSCL-20).24 We assessed QOL using the 12-Item Short Form Health Survey (SF-12).25 We derived quality-adjusted life-years (QALYs) from the Short-Form Six-Dimension utility index (SF-6D).26 Depression-free days (DFDs) were derived from the scored HSCL-20,27 and PTSD-free days (PFDs) were derived from the PDS.
Both arms of the study followed protocols that instructed the use of weekly caseload review calls between nurse coordinators and staff psychiatrists to review participating patients’ progress, as well as regular case management calls between the nurse coordinator and the patient and other phone, email, or text message contacts as needed. An electronic case management system was used to track all contacts and caseload review calls. Nurse coordinators in each intervention arm also underwent training and education sessions.
To estimate the cost of each of these intervention components, we multiplied the estimated hourly wage of each participating staff member (nurse coordinator and/or staff psychiatrist) by the number and average duration of each contact or training session, estimated from case management system data and interviews with nurse coordinators at each site. The CACT arm also included a centralized management team that coordinated intervention activities throughout the 12-month study period, composed of a half-time administrative assistant, a full-time psychologist, and a half-time nurse coordinator. We estimated the cost of these services using the salary of each staff member. All wage estimates were based on the General Schedule pay scale.20
We used claims data to assess the other (nonintervention) healthcare resources utilized within and outside of the MHS through the 12-month study period. These data contain information on medications, inpatient stays, emergency department visits, outpatient tests and procedures, outpatient visits, and telephone contacts. Utilization was recorded within the MHS and outside the MHS when reimbursed by TRICARE. For services provided within the MHS, costs were estimated from the given estimated full service cost, which includes resources used and estimated overhead; for services and medications provided outside of the MHS, costs were estimated from the total amount reimbursed by TRICARE. We excluded any services, such as certain telephone contacts, that were recorded in claims data but already accounted for in our analysis as an intervention component. Healthcare costs borne by the patient, including co-pays for services, were not included.
We used survey data from 3-, 6-, and 12-month follow-ups to estimate productivity costs over the course of the 12-month study. At each survey time point, we asked participants if they had missed an entire workday or part of a workday “due to a mental or physical health problem” in the previous 28 days. We extrapolated the stated number of lost workdays to cover the full period since the previous survey and estimated productivity costs by multiplying the estimated number of lost workdays by a daily personnel cost. We estimated personnel costs from the salaries reported at the study baseline plus fringe benefits.
We included patients who received the intervention and had at least 1 follow-up interval of both survey data and cost data in the primary economic evaluation analytic population. We excluded patients who did not receive any intervention or received the intervention but did not have both cost and survey data. For patients included in the analysis, we imputed missing 12-month cumulative QALY, utilization, and cost data using the fully conditional specification approach.28 Imputation models included available measures of cost and utilization; demographic and social characteristics, including age, race, gender, salary, marital status, and education; and clinical characteristics, including SF-12 scores and depression and/or PTSD status. Imputations were performed within each treatment arm. Five imputations were created, and results from each imputation were pooled using the rules outlined by Rubin.29 Specifically, we defined point estimates as the average of those from the 5 imputed datasets. The variance of the estimate was derived from both the within-imputation and between-imputation variances, where the former is the average of the variances of point estimates from the 5 imputed datasets.
Categorical data are reported as frequencies and were compared using χ2 or Fisher’s exact test statistics. Continuous demographic variables are reported as means and SDs and were compared with t test statistics. Continuous cost and utilization data are reported as means; the statistical significance of the difference in means between the 2 groups was evaluated using a 95% CI. Cost categories also report median values. All analyses were performed in SAS 9.4 (SAS Institute, Inc; Cary, North Carolina).
We examined the cost-effectiveness of CACT versus OUC over the 12-month study period. In our base case analysis, we included all intervention costs, other healthcare costs paid for by the MHS or TRICARE within and outside of the MHS, and productivity costs. The primary outcome assessed was the incremental cost-effectiveness ratio (ICER) in dollars per QALY gained, which was the difference in mean total costs between the 2 treatment arms over the 12-month study period divided by the difference in mean QALYs. We converted all costs to 2014 US dollars for analysis.
We ran 6 sensitivity analyses to examine the impact of assumptions used in our base case analysis. We generated cost-effectiveness acceptability curves to show the probability that either CACT or OUC would be considered the preferred intervention at a range of cost-effectiveness thresholds (Figure).29
Among the 666 patients enrolled in the study, 629 patients were assigned to an intervention arm (320 CACT and 309 OUC) and had at least 1 follow-up interval each of cost data and health outcome data. A total of 553 patients answered the survey at all 4 time points. Full 12-month cost and health outcome data were available for 459 patients, and multiple imputation was used to generate full QALY and cost data for the remaining 170 patients (27% of full analytic sample). Patients were well matched between study arms, with no statistically significant differences among them. The majority of patients in each arm were male (~80%) and married (63%), and mean ages were 31 and 32 years in the CACT and OUC arms, respectively (Table 1).26,30,31
Twelve-Month Health Outcomes
As reported in Engel et al, the primary PTSD and depression outcomes examined as part of the overall RCT showed small to moderate, but statistically significant, improvements over 12 months in the CACT group compared with the OUC group.20 These improvements were measured by the PDS (—2.53 lower scores for CACT vs OUC; 95% CI,–4.47 to –0.59) and HSCL-20 scores (–0.26; 95% CI, –0.41 to –0.11).20 Significant improvements were also observed in the CACT group versus OUC for the 12-month secondary RCT outcomes of physical symptom severity and mental health—related QOL, but not for other secondary health outcomes of suicidality, pain intensity and interference, alcohol misuse, and physical QOL.20
We found that over the 12-month follow-up, QALYs, as derived from the SF-6D, were 0.60 for CACT versus 0.59 for OUC, with a nonsignificant 0.02 QALY (95% CI, —0.001 to 0.03) gain for CACT relative to OUC. DFDs and PFDs also did not differ significantly between groups (Table 2).
Compared with patients randomized to OUC, patients in the CACT arm of the study received significantly more intervention resources, including care management contacts (7.25 vs 3.74) and other “as needed” contacts (3.97 vs 1.06) during the study period (P <.05 for difference in both) (Table 2). Patients in CACT were also reviewed more frequently in caseload review calls (9.9 times over the 12-month study period vs 1.5 times for OUC patients; P <.05), and CACT nurse coordinators received more training and education (Table 2). These resources, plus 12 months of central assistance, resulted in $1754 higher intervention costs for patients in the CACT arm compared with the OUC arm over the 12-month study period ($2743 vs $989; P <.05).
The utilization of other healthcare resources did not differ significantly between study arms, with the exception of nonintervention-related telephone contacts, which were significantly more common in the CACT intervention arm (6.0 vs 4.3 in the OUC arm) (Table 2). However, patients in the CACT arm had 3.0 fewer lost workdays (95% CI, —13.1 to 7.0) compared with patients in OUC, equating to a $1255 (95% CI, –$3961 to $1451) productivity gain for CACT versus OUC. Adding up intervention costs, other healthcare costs, and productivity costs across both CACT and OUC, total 12-month costs were $987 (95% CI, –$3056 to $5030) higher in CACT compared with OUC over the study period ($23,125 vs $22,138) (Table 3).
In the primary cost-effectiveness analysis, CACT was estimated to cost $49,346 per QALY gained compared with OUC (Table 4). At a US willingness to pay (WTP) cost-effectiveness threshold of $100,000/QALY, there is a 58% probability that CACT is the preferred intervention (Figure). This likelihood increases at higher WTP thresholds (74% at $200,000/QALY) and decreases at lower WTP thresholds (46% at $50,000/QALY).
Excluding productivity costs increased the cost of CACT relative to OUC, resulting in a ratio of $112,081/QALY (Table 4). Similarly, halving the number of patients followed by centralized management increased the ratio to $110,089/QALY. Other sensitivity analyses resulted in more favorable ICERs for CACT relative to OUC (Table 4).
The MHS is a sizable federal health system with a budget nearly as large as that of the Veterans Health Administration. The MHS invests heavily in behavioral health integration and infrastructure in primary care,32 ensuring that this study is timely. We have estimated that CACT costs $49,346/QALY gained and has a 58% probability of being cost-effective at a $100,000/QALY WTP threshold compared with OUC. These results were based on 12-month improvements in the PTSD and depression outcomes observed as part of the overall RCT for CACT versus OUC and a 0.02 (95% CI, —0.001 to 0.03) gain in QALYs. CACT had significantly higher intervention costs ($1754) over the 12-month period of intervention and follow-up. Total 12-month costs were $987 (95% CI, –$3056 to $5030) higher in the CACT versus the OUC arm of the study when intervention costs, other healthcare costs, and productivity costs were considered. Cost-effectiveness estimates appear robust to assumptions regarding the imputation of missing data, high-cost outliers, and OUC intervention costs.
These results are sensitive to the size of the patient caseload handled by the central assistance team used to support CACT. In contrast to patient-level treatments (eg, the cost-effectiveness analysis [CEA] of a medication or psychotherapy), CACT is a systems-level intervention requiring a multidisciplinary team with centralized administrative and clinical support. Significant research suggests that depression and anxiety outcomes improve when a primary care—based collaborative care systems approach is utilized to maximize adherence to existing clinical practice guidelines by using a care manager, valid and feasible measures of clinical status, and improved access to mental health specialist consultation.12 The STEPS-UP trial results have now shown that when a central assistance capability is used to improve clinics’ capacity to implement, monitor, and sustain collaborative care,20 patient outcomes improve. However, to utilize collaborative care resources most efficiently, a critical mass of patients is required to make the intervention economically feasible. Using a hypothetical scenario, we show that if the patient population in our study were reduced by half, the per patient cost of CACT relative to OUC would increase substantially due to the additional per-patient costs of centralized care, and the ICER of CACT versus OUC would increase from $49,346 to $110,089/QALY. Thus, a system planning to implement CACT would need to carefully project the size of the patient population and plan central resources accordingly.
In addition, the results were sensitive to our decision to include the monetary value of lost workdays due to health problems as a cost input, as recommended by the Second Panel on Cost-Effectiveness in Health and Medicine.33 We found that the higher costs in the CACT arm due to the higher costs of the intervention were diminished somewhat once the smaller number of days of missed work was taken into account. Excluding these productivity costs increased the ICER to $112,081/QALY, showing the importance of including all relevant costs in a CEA from a societal perspective.
The evidence for collaborative care is robust. Collaborative care has been demonstrated to be effective in more than 80 RCTs in various settings, which now include the MHS.20 Health economics studies have also consistently reported the model to be either cost saving or cost-effective. Although no previous studies have investigated collaborative care for mental illness in the MHS, several have investigated the use of similar models of care in a variety of civilian and Veterans Affairs healthcare settings and populations. These studies produced a range of estimates, which are generally consistent with our results. A systematic review34 of 11 cost-effectiveness studies accompanying RCTs of enhanced primary care for depression found that interventions based upon collaborative care/case management resulted in improved outcomes at a greater cost, but they were generally considered to be cost-effective (range, $15,463 to $36,467/QALY). Another study35 that reviewed existing cost-effectiveness studies of primary care depression treatments also found that collaborative care interventions are generally considered to be cost-effective (range, cost-saving to $105,819/QALY gained). Varying results are due to different forms of collaborative care interventions, different comparators, and different study populations.
The claims data we used included all services provided within the MHS and those reimbursed by TRICARE outside of the MHS. We were not able to track other services paid for by non-TRICARE third-party payers or by the patient. Previous study findings indicate that more than one-fifth of depression treatment expenses are paid for out-of-pocket in the civilian population,36 but no estimates are available regarding the expected extent of these expenditures in the military population. In addition, some study participants left the military and became ineligible for services before the end of the 12-month study.37 However, loss of MHS services did not differ significantly between the 2 treatment arms,37 and we used multiple imputation to include all patients who had minimal cost and health outcome follow-up data.
We were limited to a 12-month follow-up period and were not able to track cost or health outcomes beyond this time. Previous study results have suggested that higher up-front intervention costs associated with collaborative care may produce longer-term savings38 and that health effects may continue to be realized beyond the 12-month mark,39 indicating that our relatively short follow-up time period may have produced conservative cost-effectiveness estimates. Our RCT results found that the relative effectiveness of CACT improved over the 12-month period of follow-up.20
We used the SF-6D conversion of the SF-12 instrument to estimate QALY values. This utility conversion includes only 1 mental health question and may be limited in its sensitivity to changes in mental health functioning over time.40 In addition, our evaluation did not incorporate the impact the intervention may have had on the health of patients’ family members. Previous research has shown that mental health conditions, including depression, can have measurable impacts on other family members.41 Excluding spillover gains in family health that may result from treatment may lead to underestimation of the true value of interventions.42
Compared with OUC, use of the CACT costs $49,346/QALY gained for decreasing symptoms of PTSD and depression in active-duty service members in the MHS, with a 58% probability of being cost-effective at a $100,000/QALY threshold. The results of this study are timely and show that a stepped collaborative care approach may offer an economically sustainable way of improving the quality and outcomes of care for patients with PTSD and depression in the military.
The authors would like to thank Claude Setodji (RAND Corporation) and Norma Terrin (Tufts Medical Center) for their statistical support, Christine Eibner (RAND Corporation) for instrument development, and Mahlet Woldetsadik (RAND Corporation) and Brittany D’Cruz (Tufts Medical Center) for their research assistance.Author Affiliations: RAND Corporation (TAL, MK, LHJ, CCE), Arlington, VA; Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center (TAL), Boston, MA; School of Medicine, Tufts University (TAL), Boston, MA; Department of Psychiatry, Uniformed Services University of the Health Sciences (BB, MCF, CCE), Bethesda, MD; Deployment Health Clinical Center at the Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury (BB, MCF), Bethesda, MD.
Source of Funding: This study was supported by a Department of Defense Deployment Related Medical Research Program award (Grant DR080409). The award was a joint award to the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc (award W81XWH-09-2-0077), Research Triangle Institute (award W81XWH-09-2-0078), and RAND Corporation (award W81XWH-09-2-0079). The sponsor had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Defense, Uniformed Services University of the Health Sciences, National Institutes of Health, or any other agency or organization public or private.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (TAL, LHJ, BB, MCF, CCE); acquisition of data (TAL, LHJ, BB); analysis and interpretation of data (TAL, MK, BB, MCF, CCE); drafting of the manuscript (TAL, MK, LHJ, MCF, CCE); critical revision of the manuscript for important intellectual content (TAL, LHJ, MCF, CCE); statistical analysis (MK); provision of patients or study materials (BB, MCF); obtaining funding (LHJ, MCF, CCE); administrative, technical, or logistic support (TAL, MK, BB, MCF, CCE); and supervision (TAL).
Address Correspondence to: Tara A. Lavelle, Tufts Medical Center, 800 Washington St, Boston, MA 02111. Email: firstname.lastname@example.org.REFERENCES
1. Hoge CW, Castro CA, Messer SC, McGurk D, Cotting DI, Koffman RL. Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. N Engl J Med. 2004;351(1):13-22. doi: 10.1056/NEJMoa040603.
2. Tanielian TL, Jaycox LH, eds. Invisible Wounds of War: Psychological and Cognitive Injuries, Their Consequences, and Services to Assist Recovery. Washington, DC: RAND Corporation; 2008.
3. Hoge CW, Lesikar SE, Guevara R, et al. Mental disorders among U.S. military personnel in the 1990s: association with high levels of health care utilization and early military attrition. Am J Psychiatry. 2002;159(9):1576-1583. doi: 10.1176/appi.ajp.159.9.1576.
4. Hoge CW, Terhakopian A, Castro CA, Messer SC, Engel CC. Association of posttraumatic stress disorder with somatic symptoms, health care visits, and absenteeism among Iraq war veterans. Am J Psychiatry. 2007;164(1):150-153. doi: 10.1176/ajp.2007.164.1.150.
5. Hepner KA, Sloss EM, Roth CP, et al. Quality of Care for PTSD and Depression in the Military Health System: Phase I Report. Santa Monica, CA: RAND Corporation; 2016.
6. Hoge CW, Grossman SH, Auchterlonie JL, Riviere LA, Milliken CS, Wilk JE. PTSD treatment for soldiers after combat deployment: low utilization of mental health care and reasons for dropout. Psychiatr Serv. 2014;65(8):997-1004. doi: 10.1176/appi.ps.201300307.
7. Belsher BE, Curry J, McCutchan P, et al. Implementation of a collaborative care initiative for PTSD and depression in the Army primary care system. Soc Work Ment Health. 2014;12(5-6):500-522. tandfonline.com/doi/abs/10.1080/15332985.2014.897673. Published October 23, 2014. Accessed January 15, 2016.
8. Wong EC, Jaycox LH, Ayer L, et al. Evaluating the Implementation of the Re-Engineering Systems of Primary Care Treatment in the Military (RESPECT-Mil). Santa Monica, CA: RAND Corporation; 2015.
9. Institute of Medicine. Returning Home From Iraq and Afghanistan: Assessment of Readjustment Needs of Veterans, Service Members, and Their Families. Washington, DC: National Academies Press; 2013.
10. Institute of Medicine. Treatment for Posttraumatic Stress Disorder in Military and Veteran Populations: Final Assessment. Washington, DC: National Academies Press; 2014.
11. Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS. Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. Am J Psychiatry. 2012;169(8):790-804. doi: 10.1176/appi.ajp.2012.11111616.
12. Archer J, Bower P, Gilbody S, et al. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:CD006525. doi: 10.1002/14651858.CD006525.pub2.
13. Roy-Byrne P, Craske MG, Sullivan G, et al. Delivery of evidence-based treatment for multiple anxiety disorders in primary care: a randomized controlled trial. JAMA. 2010;303(19):1921-1928. doi: 10.1001/jama.2010.608.
14. Rollman BL, Belnap BH, Mazumdar S, et al. A randomized trial to improve the quality of treatment for panic and generalized anxiety disorders in primary care. Arch Gen Psychiatry. 2005;62(12):1332-1341. doi: 10.1001/archpsyc.62.12.1332.
15. Alexopoulos GS, Reynolds CF 3rd, Bruce ML, et al; PROSPECT Group. Reducing suicidal ideation and depression in older primary care patients: 24-month outcomes of the PROSPECT study. Am J Psychiatry. 2009;166(8):882-890. doi: 10.1176/appi.ajp.2009.08121779.
16. Watson LC, Amick HR, Gaynes BN, et al. Practice-based interventions addressing concomitant depression and chronic medical conditions in the primary care setting: a systematic review and meta-analysis. J Prim Care Community Health. 2013;4(4):294-306. doi: 10.1177/2150131913484040.
17. Kroenke K, Krebs EE, Wu J, Yu Z, Chumbler NR, Bair MJ. Telecare collaborative management of chronic pain in primary care: a randomized clinical trial. JAMA. 2014;312(3):240-248. doi: 10.1001/jama.2014.7689.
18. Dobscha SK, Corson K, Perrin NA, et al. Collaborative care for chronic pain in primary care: a cluster randomized trial. JAMA. 2009;301(12):1242-1252. doi: 10.1001/jama.2009.377.
19. Engel CC, Bray RM, Jaycox LH, et al. Implementing collaborative primary care for depression and posttraumatic stress disorder: design and sample for a randomized trial in the U.S. military health system. Contemp Clin Trials. 2014;39(2):310-319. doi: 10.1016/j.cct.2014.10.002.
20. Engel CC, Jaycox LH, Freed MC, et al. Centrally assisted collaborative telecare for posttraumatic stress disorder and depression among military personnel attending primary care: a randomized clinical trial. JAMA Intern Med. 2016;176(7):948-956. doi: 10.1001/jamainternmed.2016.2402.
21. Department of Defense. Evaluation of the TRICARE program: access, cost and quality: fiscal year 2015 report to Congress. Military Health System website. health.mil/Reference-Center/Reports/2015/02/28/Evaluation-of-the-TRICARE-Program-Fiscal-Year-2015-Report-to-Congress. Published February 28, 2015. Accessed January 10, 2018.
22. Foa E. The Posttraumatic Diagnostic Scale (PDS) Manual. Minneapolis, MN: National Computer Systems; 1996.
23. Foa EB, Cashman L, Jaycox L, Perry K. The validation of a self-report measure of posttraumatic stress disorder: the Posttraumatic Diagnostic Scale. Psychol Assess. 1997;9(4):445. doi: 10.1037/1040-35220.127.116.115.
24. Williams JW Jr, Stellato CP, Cornell J, Barrett JE. The 13- and 20-item Hopkins Symptom Checklist Depression Scale: psychometric properties in primary care patients with minor depression or dysthymia. Int J Psychiatry Med. 2004;34(1):37-50. doi: 10.2190/U1B0-NKWC-568V-4MAK.
25. Bjorner J, Turner-Bowker D. SF-36 and SF-12 health surveys. In: Kattan MW, ed. Encyclopedia of Medical Decision Making. Thousand Oaks, CA: SAGE Publications Inc; 2009:1031-1037.
26. Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851-859. doi: 10.1097/01.mlr.0000135827.18610.0d.
27. Vannoy SD, Arean P, Unützer J. Advantages of using estimated depression-free days for evaluating treatment efficacy. Psychiatr Serv. 2010;61(2):160-163. doi: 10.1176/ps.2010.61.2.160.
28. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res. 2007;16(3):219-242. doi: 10.1177/0962280206074463.
29. Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc. 1996;91(434):473-489. doi: 10.2307/2291635.
30. Lang AJ, Stein MB. An abbreviated PTSD checklist for use as a screening instrument in primary care. Behav Res Ther. May 2005;43(5):585-594. doi: 10.1016/j.brat.2004.04.005.
31. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. Sep 2001;16(9):606-613.
32. Hoge CW, Ivany CG, Brusher EA, et al. Transformation of mental health care for U.S. soldiers and families during the Iraq and Afghanistan wars: where science and politics intersect. Am J Psychiatry. 2016;173(4):334-
343. doi: 10.1176/appi.ajp.2015.15040553.
33. Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine [erratum in JAMA. 2016;316(18):1924. doi: 10.1001/jama.2016.15518]. JAMA. 2016;316(10):1093-1103. doi: 10.1001/jama.2016.12195.
34. Gilbody S, Bower P, Whitty P. Costs and consequences of enhanced primary care for depression: systematic review of randomised economic evaluations. Br J Psychiatry. 2006;189:297-308. doi: 10.1192/bjp.bp.105.016006.
35. Glied S, Herzog K, Frank R. Review: the net benefits of depression management in primary care. Med Care Res Rev. 2010;67(3):251-274. doi: 10.1177/1077558709356357.
36. Soni A. Statistical brief #377: trends in use and expenditures for depression among US adults age 18 and older, civilian noninstitutionalized population, 1999 and 2009. Agency for Healthcare Research and Quality website. meps.ahrq.gov/data_files/publications/st377/stat377.pdf. Published July 2012. Accessed January 10, 2018.
37. Belsher BE, Jaycox LH, Freed MC, et al. Mental health utilization patterns during a stepped, collaborative care effectiveness trial for PTSD and depression in the Military Health System. Med Care. 2016;54(7):706-713. doi: 10.1097/MLR.0000000000000545.
38. Katon W, Russo J, Von Korff M, et al. Long-term effects of a collaborative care intervention in persistently depressed primary care patients. J Gen Intern Med. 2002;17(10):741-748. doi: 10.1046/j.1525-1497.2002.11051.x.
39. Wells K, Sherbourne C, Schoenbaum M, et al. Five-year impact of quality improvement for depression: results of a group-level randomized controlled trial. Arch Gen Psychiatry. 2004;61(4):378-386. doi: 10.1001/archpsyc.61.4.378.
40. Donald-Sherbourne C, Unützer J, Schoenbaum M, et al. Can utility-weighted health-related quality-of-life estimates capture health effects of quality improvement for depression? Med Care. 2001;39(11):1246-1259.
41. Prosser LA, Lamarand K, Gebremariam A, Wittenberg E. Measuring family HRQoL spillover effects using direct health utility assessment. Med Decis Making. 2015;35(1):81-93. doi: 10.1177/0272989X14541328.
42. Wittenberg E, Prosser LA. Health as a family affair. N Engl J Med. 2016;374(19):1804-1806. doi: 10.1056/NEJMp1604456.