Richard D. Della Penna, MD

To evaluate the Advanced Illness Coordinated Care Program (hereafter AICCP) for effects on health delivery among patients and caregivers, quality of life, advance planning, and health service utilization.

Prospective trial involving 532 patients and 185 caregivers. AICCP consisted of care coordination, health counseling, and education delivered in cooperation with physicians.

Patients with advanced disease and their caregivers were assigned to AICCP or usual care (UC). Data sources included self-report, medical record review, and health plan databases. Statistical analyses used t test, χ2 test, regression analysis, and analysis of variance.

Compared with those in UC, AICCP participants had improved communication and care concerning symptoms (P = .02), support in understanding and coping with their illness (

 <.001), support in managing family decision making (

 = .002), and help in accessing spiritual support (

 <.001). AICCP caregivers received more attention for emotional and spiritual needs (

 = .02). AICCP participants were 2.23 times more likely to formulate an advance directive (

 <.001]) and were 1.26 times more likely to agree to a do-not-resuscitate or do-not-intubate order (

 = .04). AICCP participants had on average 1.89 fewer inpatient admissions (

 = .045). There was no difference in 1-year survival (

AICCP improved communication and care delivery, advance planning, and do-not-resuscitate or do-not-intubate orders in a population at risk to use them. AICCP had fewer admissions. Coordination and health counseling seem matched for those coping with advancing illness.

An Advanced Illness Coordinated Care Program (hereafter AICCP) that included health counseling was developed for patients with advanced illness (congestive heart failure, end-stage pulmonary disease, end-stage renal disease, and cancer) in 3 settings of a multistate health plan.

AICCP resulted in improved communication about discomfort, support for decision making and problem resolution, and attention to caregiver needs.

AICCP also resulted in increased formulation of advance directives (5.5 months sooner), more agreement to do-not-resuscitate or do-not-intubate orders, and fewer inpatient admissions, with no difference in survival.

Specialized programming matched to the needs of patients coping with advanced disease improved health delivery and reduced admissions.

Systematic reviews about care coordination report beneficial quality outcomes of programs that coordinate care for individuals with chronic illnesses such as heart disease and diabetes mellitus.

This literature suggests that the effectiveness of coordination programs is most likely when such programs appropriately match the problems they are designed to address. The need for improved coordination of health services is well documented. This need will increase given the probability of fewer primary care providers, larger volume of patients with more needs, and a lack of reimbursement for coordination activities. Also, increased longevity will lead to more clinically complex patients needing coordination.

 Furthermore, care coordination by physician-led teams is recognized as a promising means to reform health system inefficiencies.

However, there has been less focus on coordination programs for populations with advanced illness.

The literature has sparse evidence of benefit from specialized programs for patients with advanced disease.

Therefore, we evaluated a health counseling and care coordination program matched to the needs of populations with advanced illness and delivered in cooperation with referring physicians.

The sample was drawn from the Kaiser Permanente health system. Participants had advanced stages of cancer, congestive heart failure, endstage pulmonary disease, or end-stage renal disease. For cancer, we included individuals with solid-tumor cancers with metastasis. For congestive heart failure, we included (1) individuals who had an ejection fraction of 30% or lower and persistent New York Heart Association (NYHA) functional class III or IV symptoms and were not a heart transplant candidate, (2) individuals who had severe aortic stenosis or severe mitral regurgitation and persistent NYHA functional class III or IV symptoms and were not a heart transplant candidate, or (3) individuals who had persistent NYHA functional class III or IV symptoms. We defined end-stage pulmonary disease as (1) severe chronic lung disease with progression to end stage (ie, disabling dyspnea at rest or during minimal exertion, poor or no response to bronchodilators, or decreased functional capacity) or (2) hypoxemia or hypercapnia on room air (ie, partial pressure of oxygen <56 mm Hg by arterial blood gases or arterial saturation <88% or partial pressure of carbon dioxide ≥51 mm Hg by arterial blood gases) or continuous oxygen. Finally, we included individuals on dialysis or individuals with a recommendation that dialysis should begin within 3 months who also had 1 or more hospitalizations or emergency department visits for complications of end-stage renal disease within the past year.

We excluded patients who (1) resided in intensive care or skilled nursing settings, (2) were enrolled in an end-oflife service, (3) did not speak English, or (4) were cognitively impaired.

 Once participants completed informed consents, they were asked to identify a surrogate who had knowledge of their health needs and services. Because most surrogates were spouses who helped patients, we describe them as caregivers to capture the larger role that they usually had in patients’ lives. A total of 185 caregivers completed interviews (

Although the study was initiated at 4 sites, 1 site withdrew from participation because they needed to shift study personnel to usual care (UC) service delivery. Resources to complete recruitment from this site were unavailable. We analyzed results with and without these participants.

The study was approved by institutional review boards at Kaiser Permanente and the University at Albany—State University of New York. Recruiters blinded to group assignment enrolled 532 patients. We assigned participants to the Advanced Illness Coordinated Care Program (hereafter AICCP) (n = 267) or to UC (n = 265).

 Four hundred three participants (198 in AICCP and 205 in UC) completed the baseline and posttest questionnaires. Of 129 participants who did not complete posttests, 88 died before the posttest, and 41 did not agree to complete the posttest.

AICCP is designed to help patients and families optimally manage living with advancing illness. The 6-session model has the following 3 components: (1) nondirective health counseling, (2) education, and (3) care coordination.

AICCP was delivered by social workers and a health educator with 16 hours of initial training and with 20 hours of follow-up. AICCP meetings were face to face, lasting a mean of 59.0 (SD = 22.1) minutes, including brief follow-up telephone contacts. The mean number of sessions was 4.9 (SD = 2.1) (range, 0-10 [mode, 6]), with 81.9% of patients completing 3 to 7 sessions. On average, caregivers attended 50% of sessions based on patient preference, caregiver availability, and need.

The topics covered across sessions were structured in a biopsychosocial 3-domain format, including the following: (1) health-related topics included but were not limited to understanding illness, treatment expectations, emerging symptoms, adherence to treatment recommendations, communication with health professionals, and advance planning; (2) coping with loss of role, functional capacity, or health status; evaluating whether situations are amenable to change or, if not, whether reactions to unchangeable situations are modifiable; and monitoring for anxiety or depression, interpersonal conflict, and existential concerns; and (3) caregiving concerns, maximizing health system benefits, home environmental modifications, home care, and long-term care planning.

This structure was delivered using a nondirective health counseling format, patient education, and care coordination. It facilitated recognition and normalization of the consequences of living with ongoing health problems in domains of function beyond physical health per se. It promoted identification of psychosocial needs and supports and facilitated initiation of discussions about ways to adapt to and compensate for losses induced by reduced health status.

An electronic Web tool operationalized each session of AICCP by providing a checklist of health education topics and tasks to be completed in interviews. For example, at the second meeting, coordinators introduced advance planning. If an expected task was not addressed at a specified meeting, coordinators were given pop-up reminders to complete them at subsequent meetings. Health education also included asneeded information about health-related benefits within their health system and their community.

The rationale for measurement selection was tripartite: (1) Among populations coping with life-limiting illness, in which medical interventions are unlikely to provide a return to full health, quality-of-life (QOL) variables take on importance. (2) Advance plans and do-not-resuscitate or do-notintubate (DNR/DNI) orders and their timing are relevant measures in a population at risk for needing such planning. (3) Experiences with health delivery and service utilization are important for health plan administrators to determine the feasibility of program adaptation.

Participants were administered functional and QOL outcome questionnaires at enrollment and 4 months later. The questionnaires included the Short Form Health Survey (SF-12), which measures perceived physical and mental health function. It has well-established validity and reliability.

 The McGill Quality of Life Questionnaire measured total QOL.

 The Social Provisions Scale assessed the provisions of social relationships (eg, attachment).

 The Functional Assessment of Chronic Illness Therapy—Spiritual Well-Being Scale measures spiritual wellbeing.

We included 11 of 25 items from the Revised Death Anxiety Scale, which were selected for inclusion as the most likely measures to be affected by AICCP.

In addition, we used the AICCP questionnaire, which consists of 21 items designed to capture patients’ perspectives about their involvement in care and the effectiveness of healthcare professionals’ service delivery on AICCPsensitive care domains (eg, coordinating care). We conducted factor analysis on these items. We found 6 factors that reflect patient reports about their own effectiveness in communicating with healthcare professionals and their perceptions about the care they received. Six scores capture information about the effectiveness of communication in these subdomains: (1) The symptom communication and care score captures communication and care for physical and emotional discomfort (6 items [eigenvalue = 3.16, α = .79]). (2) The family communication and support score measures the effectiveness of patient communication with family about the nature and consequences of the patient’s health (4 items [eigenvalue = 2.85, α = .84]). (3) The illness understanding and coping support score measures how effectively that professionals communicate with patients about their illness, prepare them for illness sequelae, and assist them in coping with illness (4 items [eigenvalue = 2.33, α = .78]). (4) The family decision-making support score measures health professional support in family decision making and problem resolution (3 items [eigenvalue = 2.12, α = .77]). (5) The spiritual support score captures communication about spiritual support and professional efforts to facilitate patients’ obtaining help for those needs (2 items [eigenvalue = 1.90, α = .88]). (6) The advance planning score measures how effectively that health professionals discuss the development of advance plans and help patients formulate them (2 items [eigenvalue = 1.70, α = .77]).

We also measured caregivers’ experiences with the healthcare system using a revised version of the Surrogate Integrated Care Systems After Death Interview from the 

Toolkit of Instruments to Measure End-of-Life Care

In addition to the questions about advance planning on the AICCP questionnaire, a medical record review at 9 months assessed the frequency at which participants completed advance directives, including healthcare proxies and living wills. Also assessed was the frequency at which patients accepted DNR/ DNI orders.

We also measured health service utilization by collecting data on 7 categories of service use. These included inpatient admissions, emergency department visits, home health visits, outpatient visits, radiology tests, laboratory tests, and pharmacy prescriptions.

Patient and caregiver baseline characteristics were evaluated using 

 test (interval or ratio scale data) and 

 test (categorical data). Communication in health delivery, patient QOL, caregiver posttest interviews, and service utilization were analyzed using analysis of variance. We analyzed medical record—based advance planning data using 

 test, regression analysis, and Kaplan-Meier method. For descriptive purposes, we compared survival in AICCP and UC at 1 year using logistic regression analysis.

The electronic Web tool that guided the treatment protocol was also used to assess treatment integrity.

 Key AICCP tasks were delivered with high frequency (eg, 82.8% for advance planning and 98.4% for psychosocial monitoring). On average, participants completed AICCP in 5.

Of 532 participants, 129 did not complete posttest measures. There was a higher prevalence of cancer among noncompleters (55.8%) versus completers (41.4%) (

 = .01). Cancer deaths accounted for most of the nonresponse among diagnoses (56 cancer vs 32 other diagnoses; 

As already mentioned, 1 study site dropped out but had recruited 41 participants (19 AICCP and 22 UC). To provide a more complete data set and to depict outcomes from sites that fully implemented AICCP, the results summarized in the tables are from the 3 sites that completed the study. We also report how outcomes were affected by inclusion of the data from the dropped site.

Of 403 participants who completed posttest self-report questionnaires, patients in UC were significantly older by 2.4 years (

 <.001) (Table 1). We statistically controlled for these differences to reduce the likelihood that differences between AICCP and UC on these variables may account for observed results.

Of 185 caregivers completing questionnaires, more UC caregivers were female (

 <.001) (Table 1). Given that a high percentage of caregivers were spouses (71.1% in UC and 66.0% in AICCP), this is most likely a reciprocal effect of the group sex difference.

We compared the baseline QOL measures for AICCP and UC (

). We found differences for 5 of 13 measures: (1) attachment (

 = .03) (Social Provisions Scale), (3) spiritual well-being (

 = .03) (Functional Assessment of Chronic Illness Therapy—Spiritual Well-Being Scale), (4) family communication and support (

 = .03), and (5) family decision-making support (

 = .02) (AICCP questionnaire). Scores were higher for AICCP on all significant measures except family decision-making support, indicating that AICCP participants were functioning significantly better overall at baseline for these variables.

Controlling for baseline scores, age, and sex, we found posttest differences favoring AICCP on 5 of 6 AICCP questionnaire scores (Table 2). AICCP providers and patients experienced (1) improved symptom communication and care for physical and emotional discomfort (

 = .02), (2) improved support from providers in understanding and coping with illness (

 = .01), (3) improved support from providers in managing family decision making and problem resolution (

 = .002), (4) significantly more help in coordinating needs for spiritual support (

 <.001), and (5) acquisition of more information about and implementation of advance planning (

We also examined the caregivers’ evaluations of their experiences with the healthcare system (

). Because of nonnormal data, we used nonparametric statistics to analyze caregiver data. Compared with UC caregivers, AICCP caregivers reported receiving significantly more attention for emotional and spiritual needs (

 = .04). The difference remained significant when controlling for caregiver sex differences (β = .12, SE = .05; 

We found differences between the 2 groups in advance directives and in physician orders (DNR/DNI) (

). To capture postenrollment differences, only participants who had not completed an advance planning document at baseline were included in the analyses. AICCP participants were 2.23 times more likely than UC participants to formulate an advance directive (47.0% AICCP vs 21.1% UC; Wald

 <.001) and 1.26 times more likely to agree to DNR/DNI (40.2% AICCP vs 31.8% UC; Wald

AICCP participants developed advance plans sooner. Specifically, 21.0% of UC participants formulated advance directives by 237 days, whereas 21.0% of AICCP participants formulated directives by 73 days (5.5 months sooner) (logrank test

We compared baseline differences between AICCP and UC in 7 areas of healthcare utilization (

). Results indicated no difference in service utilization between groups before the intervention.

We also compared AICCP and UC on posttest differences, controlling for baseline variables, age, and sex (Table 5). In AICCP, less intensive and less expensive services tended to be used more frequently (eg, outpatient services), whereas in UC more intensive and more expensive services tended to be used more frequently (inpatient services and emergency department visits). This posttest pattern of utilization differences was significant when controlling for pretest baseline differences (Wilks

 = .45) but did not remain significant with the inclusion of age and sex covariates. However, AICCP participants on average had 1.89 fewer inpatient admissions (2.44 AICCP vs 4.33 UC; 

= .045) when controlling for baseline admissions, age, and sex.

There was no significant difference in 1-year survival between the groups (Wald

 = .80). After 1 year, 32.5% of UC participants and 32.2% of AICCP participants were deceased.

Articles

This prospective trial suggests that specialized care coordination and health counseling for patients coping with advanced stages of 4 life-limiting illnesses can be beneficial.


Effectiveness of Care Coordination and Health Counseling in Advancing Illness

: To determine the long-term effects on total healthcare costs of the Improving Mood: Promoting Access to Collaborative Treatment (IMPACT) program for late-life depression compared with usual care.

: Randomized controlled trial with enrollment from July 1999 through August 2001. The IMPACT trial, conducted in primary care practices in 8 delivery organizations across the United States, enrolled 1801 depressed primary care patients 60 years or older. Data are from the 2 IMPACT sites for which 4-year cost data were available. Trial enrollment across these 2 health maintenance organizations was 551 patients.

: Participants were randomly assigned to the IMPACT intervention (n = 279) or to usual primary care (n = 272). Intervention patients had access to a depression care manager who provided education, behavioral activation, support of antidepressant medication management prescribed by their regular primary care provider, and problem-solving treatment in primary care for up to 12 months. Care managers were supervised by a psychiatrist and a primary care provider. The main outcome measures were healthcare costs during 4 years.

: IMPACT participants had lower mean total healthcare costs ($29 422; 95% confidence interval, $26 479-$32 365) than usual care patients ($32 785; 95% confidence interval, $27 648-$37 921) during 4 years. Results of a bootstrap analysis suggested an 87% probability that the IMPACT program was associated with lower healthcare costs than usual care.

: Compared with usual primary care, the IMPACT program is associated with a high probability of lower total healthcare costs during a 4-year period.

Evidence-based collaborative care management programs for depression in primary care have been shown to be more effective and more cost-effective than care as usual.

This study demonstrates that during a 4-year period older adults with depression and comorbid medical disorders who participated in a collaborative care program had lower total healthcare costs than those in care as usual.

The findings add to the arguments for widely implementing such programs and for policies that facilitate coverage and reimbursement for such evidence-based care management programs.

Major depression and dysthymic disorder (chronic depression) are common in older adults. In addition to causing impairment of functioning and quality of life, depression in late life has been associated with substantial increases in total healthcare costs.

 The Improving Mood: Promoting Access to Collaborative Treatment (IMPACT) trial

 enrolled 1801 depressed older primary care patients from 8 healthcare systems in a randomized controlled trial of a collaborative care management program for depression compared with care as usual. Participants from each organization were randomly assigned to collaborative care or to care as usual. 

 reported that the collaborative care program was substantially more effective than care as usual in reducing depression and in improving physical and social function. Intervention patients continued to have significantly less depression than patients in usual care even at the 24-month follow-up, 12 months after the end of the intervention program.4 Analyses from the IMPACT trial5 found the collaborative care program to be substantially more cost-effective than care as usual. IMPACT participants experienced 107 more depression-free days during a 24-month period than patients assigned to care as usual. During the initial study year, total healthcare costs (including the costs of the IMPACT intervention) were slightly higher among the intervention group than among control subjects, but a slight decrease in costs among the intervention group compared with usual care patients was observed in the second year, suggesting that an initial investment in better depression care may result in long-term cost savings.

In this article, we report long-term (4-year) effects of collaborative care for late-life depression on total healthcare costs from a payer&#8217;s perspective. Our findings are based on cost data available from 2 participating group-model health maintenance organizations.

Participants were identified by systematic depression screening or were referred by primary care physicians. Inclusion criteria included being aged 60 years or older, meeting criteria for current major depression or dysthymia on the Structured Clinical Interview for the 

Diagnostic and Statistical Manual of Mental Disorders

 and planning to continue using 1 of the participating primary care medical offices during the next year. Exclusion criteria included current alcohol abuse, severe cognitive dysfunction, acute risk of suicide, and a history of bipolar disorder or psychosis. Patients were recruited into the study between July 1999 and August 2001. During this time, 2% to 3% of the older population served by the participating medical offices were enrolled in the IMPACT trial.

 Eligible patients who agreed to participate in the study were randomly assigned to the IMPACT intervention or to usual care. 

The number of dropouts because of death or disenrollment by the end of each year in the 2 centers participating in the present study were 17, 13, 57, and 27, for years 1 through 4, respectively, without significant differences in the number of dropouts between intervention and usual care patients. The resulting sample sizes were 534, 521, 464, and 437 in years 1 through 4, respectively. All available cost information was included in the analyses. For example, if a patient died between year 2 and year 3, this patient&#8217;s year 1 and year 2 costs were included in the analyses.

Patients randomly assigned to receive usual care were told that they met the criteria for major depression or dysthymia and were encouraged to follow up with their primary care provider for treatment. The primary care provider was also notified of the diagnosis. Patients with this condition were eligible to receive all treatments routinely provided for depression (antidepressant medication, supportive counseling by their physician, and self- or physician-referral to specialty mental healthcare).

For each cost category, we provide the mean cost during the 4-year period for the intervention and usual care groups. The difference in mean costs between these 2 groups and its 95% confidence interval are provided. We also generated 1000 bootstrap samples to estimate the probability that total healthcare costs were lower in the intervention group than in the usual care group during the 4-year time frame. The probability was estimated by dividing the number of bootstrap samples in which the intervention group had higher total healthcare costs than the usual care group by 1000. The analyses were performed using SAS 9.1 (SAS Institute Inc, Cary, NC).

The IMPACT sample of depressed older adults included 551 patients enrolled in the 2 health plans with available long-term cost data. Study participants in this long-term cost analysis (

) were clinically and sociodemographically diverse but were generally representative of the larger IMPACT sample,

 without significant differences between the intervention and control groups. The mean (SD) age of participants was 72.7 (7.8) years. Most (72%) were women, 9% reported belonging to a racial/ethnic minority group, 47% were married or living with a partner, and 89% were high school graduates. More than 80% had Medicare coverage and prescription drug coverage. All patients met Structured Clinical Interview for the 

 (Fourth Edition) criteria for major depression or dysthymia, and 46% of the sample met criteria for both. Thirty percent showed some evidence of mild cognitive impairment on a 6-item screen. On average, patients reported a moderate amount of functional impairment on a composite index of the disability score by Sheehan et al,14 and 64% reported physical pain that caused functional impairment. Participants endorsed a mean of 3.1 chronic medical conditions (from a list of 10).

Intervention patients had 4-year mean total healthcare costs of $29 422 (95% confidence interval, $26 479-$32 365), and usual care patients had mean total healthcare costs of $32 785 (95% confidence interval, $27 648-$37 921), representing a cost savings among intervention patients of $3363 (95% confidence interval, $-9282 to $2557) per patient on average during 4 years (

). Intervention patients had lower healthcare costs than usual care patients in every cost category observed (outpatient and inpatient mental health specialty costs, outpatient and inpatient medical and surgical costs, pharmacy costs, and other outpatient costs). Intervention costs for IMPACT participants during the initial 12 months were $522 (95% confidence interval, $495-$550) per patient and are included in the total cost estimates.

Long-term cost analyses from this trial suggest that the IMPACT program for late-life depression is associated with a high probability of cost savings during a 4-year period. Earlier cost-effectiveness analyses from this trial

 showed slightly higher costs for intervention patients compared with usual care patients in the initial year (the year that intervention services were provided) and somewhat lower costs in the second year of the study. The present long-term cost analysis (extending the earlier follow-up period by an additional 24 months) suggests that cost savings observed after the conclusion of the 12-month intervention continued in subsequent years, resulting in a high probability of lower total healthcare costs among intervention patients than control patients during 4 years. Lower costs were observed for the intervention group in each cost category examined, a finding that is consistent with results of observational studies

 suggesting that depression is associated with higher healthcare costs across various cost categories.

The confidence intervals for the estimates of differences in healthcare costs include 0, which means that observed cost differences are not significant at 

 <.05. We used bootstrapping, a procedure of generating random samples by resampling the observation from the study data (in this case 1000 times), to simulate the distribution of the data and to compute parameter estimates (eg, mean costs) based on estimates from the bootstrap samples. Given the wide variation in healthcare costs, bootstrapping is often used to estimate the probability of intervention vs control cost differences in studies with small samples. We recorded the results derived from the 1000 bootstrap samples and divided the number of cost-saving bootstrap samples by 1000 to estimate the probability that a similar study would yield a cost-saving result. An 87% probability suggests that we have substantial but not absolute confidence that a study like ours would find the IMPACT intervention to be cost-saving compared with care as usual.

To our knowledge, our study provides the longest-term follow-up data to date on healthcare costs associated with collaborative care programs for depression. Other studies that have provided long-term follow-up data include the study by Simon and colleagues,

 which reported lower total healthcare costs among patients with depression and diabetes mellitus receiving collaborative care during a 2-year follow-up period, and a long-term follow-up study by Katon and colleagues

 among primary care patients with depression, which had similar findings. Our findings of cost savings are consistent with a replication study

 (using a different sample of depressed patients) completed by 1 of 2 participating health plans after the IMPACT study ended. This study reported a cost savings of approximately $1117 associated with IMPACT care during a 12-month follow-up period.

Limitations of this study include the fact that our long-term cost data are limited to 2 of the organizations participating in the original trial. We report long-term (4 year) cost findings on all study participants from the 2 participating health plans for whom such data were available. The sample is drawn from an insured, largely educated population of white race/ethnicity, and further research should be performed among lowerincome, underinsured populations predominantly of minority races/ethnicities to see if our findings are generalizable to such populations. However, the patients at the 2 participating study sites were generally representative of the overall IMPACT sample,

 and intervention effects on clinical outcomes and costs during 2 years of patient follow-up were consistent across each of the 8 participating healthcare organizations, including the 2 examined herein.

 Earlier analyses of the program&#8217;s effectiveness suggest that participants of minority race/ethnicity benefited as much from the intervention as those of white race/ethnicity,

 and similar investigations in low-income countries show that collaborative care programs for depression are not only effective but also cost-effective in such settings.

 Study participants who died or disenrolled from the health plan during the 4-year follow-up period contributed cost data for the periods during which such data were available, but we do not have access to complete cost data for a small number of participants who left the health plan before the end of the 4- year follow-up period. Other limitations include the fact that we do not have long-term clinical follow-up data on depression outcomes with which to conduct cost-effectiveness analyses, but findings from an earlier study4 that examined differences between intervention and control patients at 18 and 24 months (12 months after the end of the intervention) suggest that the clinical benefits of the intervention continue well beyond the original 1-year intervention period. Our cost analyses are from a payer&#8217;s perspective and do not consider other societal costs, including costs of informal healthcare and other services, or other indirect costs.

More than 35 randomized controlled trials have established that collaborative care for depression is more effective and more cost-effective than usual care.19 Findings from our study suggest that, for older adults with depression, collaborative care not only produces substantial clinical improvements but also carries a high likelihood of long-term cost savings compared with care as usual.

We acknowledge the contributions and support of patients, primary care providers, and staff at the study coordinating center and at the participating study sites, which include Group Health Cooperative of Puget Sound in cooperation with the University of Washington and Kaiser Permanente of Southern California. We also acknowledge programming support by Heather Ladd, MS.

: From the Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine (JU, WJK, M-YF, DP), and Center for Health Studies, Group Health Cooperative (EHBL), Seattle; National Institute of Mental Health, Bethesda, Md (MCS); and Kaiser Permanente, Oakland, Calif (RDDP).

: This study was supported by grants 98297-G and 2004-0001 from the John A. Hartford Foundation, by grant 98-3138B from the California Health Care Foundation, and by grant 1 K24 MH 069471-01 from the National Institute of Mental Health. The opinions expressed in the article are not necessarily the opinions of

the National Institute of Mental Health, the National Institute of Health, or

2. Katon WJ, Lin E, Russo J, UnÃ¼tzer J

. Increased medical costs of a population-based sample of depressed elderly patients. 

5. Katon WJ, Schoenbaum M, Fan MY, et al.

 Cost-effectiveness of improving primary care treatment of late-life depression. 

. Cost-effectiveness and net benefit of enhanced treatment of depression for older adults with diabetes and depression. 

9. Hegel MT, Imming J, Cyr-Provost M, Hitchcock-Noel P, Arean P, UnÃ¼tzer J

. Role of allied behavioral health professionals in a collaborative stepped care treatment model for depression in primary care: project IMPACT. 

Problem-Solving Treatment of Primary Care (PSTPC): ATreatment Manual for Depression Project IMPACT

. Hanover, NH: Dartmouth Medical School; 2003.

13. Harpole LH, Stechuchak KM, Saur CD, Steffens DC, UnÃ¼tzer J, Oddone E.

 Implementing a disease management intervention for depression in primary care: a random work sampling study. 

15. Katon W, Russo J, Von Korff M, et al.

 Long-term effects of a collaborative care intervention in persistently depressed primary care patients. 

17. Arean PA, Ayalon L, Hunkeler E, et al

. Improving depression care for older, minority patients in primary care. 

19. Gilbody S, Bower P, Fletcher J, Richards D, Sutton AJ

. Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. 

Older adults with depression and comorbidities who participated in a 12-month collaborative care program had lower total healthcare costs over 4 years than those in care as usual.

Richard D. Della Penna, MD

Articles

Effectiveness of Care Coordination and Health Counseling in Advancing Illness

Long-term Cost Effects of Collaborative Care for Late-life Depression