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Original Article |

Collaborative Depression Care Management and Disparities in Depression Treatment and Outcomes FREE

Yuhua Bao, PhD; George S. Alexopoulos, MD; Lawrence P. Casalino, MD, PhD; Thomas R. Ten Have, PhD; Julie M. Donohue, PhD; Edward P. Post, MD, PhD; Bruce R. Schackman, PhD; Martha L. Bruce, PhD, MPH
[+] Author Affiliations

Author Affiliations: Departments of Public Health (Drs Bao, Casalino, and Schackman) and Psychiatry (Drs Alexopoulos and Bruce), Weill Cornell Medical College, New York, New York; Division of Biostatistics, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia (Dr Ten Have); Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Donohue); and Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, and Health Services Research and Development, VA, Ann Arbor (Dr Post).


Arch Gen Psychiatry. 2011;68(6):627-636. doi:10.1001/archgenpsychiatry.2011.55.
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The PROSPECT intervention substantially reduced disparities by patient education but did not mitigate racial/ethnic disparities in depression treatment and outcomes. Incorporation of culturally tailored strategies in DCM models may be needed to extend their benefits to minorities.

Figures in this Article

Context  Collaborative depression care management (DCM), by addressing barriers disproportionately affecting patients of racial/ethnic minority and low education, may reduce disparities in depression treatment and outcomes.

Objective  To examine the effects of DCM on treatment disparities by education and race/ethnicity in older depressed primary care patients.

Design  Analysis of data from the randomized controlled trial Prevention of Suicide in Primary Care Elderly: Collaborative Trial (PROSPECT).

Setting  Twenty primary care practices.

Participants  A total of 396 individuals 60 years or older with major depression. We conducted model-based analysis to estimate potentially differential intervention effects by education, independent of those by race/ethnicity (and vice versa).

Intervention  Algorithm-based recommendations to physicians and care management by care managers.

Main Outcome Measures  Antidepressant use, depressive symptoms, and intensity of DCM over 2 years.

Results  The PROSPECT intervention had a larger and more lasting effect in less-educated patients. At month 12, the intervention increased the rate of adequate antidepressant use by 14.2 percentage points (pps) (95% confidence interval [CI], 1.7 to 26.4 pps) in the no-college group compared with a null effect in the college-educated group (−9.2 pps [95% CI, −25.0 to 2.7 pps]); at month 24, the intervention reduced depressive symptoms by 2.6 pps on the Hamilton Depression Rating Scale (95% CI, −4.6 to −0.4 pps) in no-college patients, 3.8 pps (95% CI, −6.8 to −0.4) more than in the college group. The intervention benefitted non-Hispanic white patients more than minority patients. Intensity of DCM received by minorities was 60% to 70% of that received by white patients after the initial phase but did not differ by education.

Conclusions  The PROSPECT intervention substantially reduced disparities by patient education but did not mitigate racial/ethnic disparities in depression treatment and outcomes. Incorporation of culturally tailored strategies in DCM models may be needed to extend their benefits to minorities.

Trial Registration  clinicaltrials.gov Identifier for PROSPECT: NCT00279682

Figures in this Article

Racial/ethnic minority status and lower socioeconomic status are associated with substantially lower use of mental health services.14 Minority and low–socioeconomic status patients are more likely to delay initial treatment of mental disorders5 and are less likely to seek treatment from mental health specialists68 or to receive minimally adequate care for depression and other mental disorders once care starts.1,4,8,9

Quality improvement efforts, by “lifting all boats,” may reduce, exacerbate, or have neutral effects on ethnic and socioeconomic disparities in health and health care.1013 It has been argued that to reduce disparities, quality improvement programs need to address barriers to effective treatment that disproportionately affect vulnerable patient populations.11,14

Collaborative depression care management (DCM) programs are multifaceted quality improvement interventions that redesign care processes to incorporate evidence-based depression treatment guidelines and components of the Chronic Care Model.15,16 More than 30 randomized controlled trials have shown that collaborative DCM improves depression outcomes, patient adherence to treatment, and satisfaction with care.1719 A central element of these interventions is a care manager who serves as a physician extender and conducts patient assessment, education, follow-ups, and care coordination.20 These activities address barriers to appropriate depression treatment in primary care, including stigma, avoidance of mental health specialty treatment, limited knowledge of depression and its treatment, poor self-management and treatment adherence, poor physician-patient communication, and competing demands during a brief clinical encounter. Although collaborative DCM programs are generally not designed to target minority and low-education patients, because the aforementioned barriers disproportionately affect these patients,6,7,2124 DCM has the potential to reduce race/ethnicity and education-related disparities. However, most DCM programs have not specifically addressed culture-specific beliefs about depression25,26 and preferences for its treatment21,27,28 or the compromised clinician-patient interactions as a result of cultural discordance29; they might provide less benefit for minority or low-education patients, thereby increasing disparities.

The Prevention of Suicide in Primary Care Elderly: Collaborative Trial (PROSPECT), a major randomized controlled trial of DCM, demonstrated effectiveness of the intervention in reducing suicidal ideation and depression symptoms in older depressed primary care patients.30,31 In this study, we examined whether the PROSPECT intervention benefited minority and low-education patients to a greater extent and, thus, reduced disparities in depression treatment and outcomes. We also examined whether differential effects of the intervention were associated with differences in the intensity of care management received. We hypothesized that the PROSPECT intervention reduced education-related disparities to a greater extent than disparities related to race/ethnicity. The rationale for this hypothesis was that the intervention addressed poor patient self-management and treatment adherence but lacked strategies culturally tailored to specific ethnic populations.

The PROSPECT intervention used depression treatment guidelines tailored to the elderly and lasted for 2 years. We analyzed the research interview data and intervention documentation of the PROSPECT over 24 months to examine (1) potentially differential intervention effects, by patient education and race/ethnicity, on use and dose adequacy of antidepressant therapy and depressive symptoms and (2) differences in the intensity of DCM received.

SETTING AND PARTICIPANTS

The PROSPECT trial recruited 20 primary care practices from greater New York City, Philadelphia, and Pittsburgh. Between May 1, 1999, and August 31, 2001, the study sampled 79% of patients 60 years or older with upcoming appointments to participate in the study. Initial eligibility criteria included the ability to give informed consent, a Mini-Mental State Examination32 score of 18 or higher, and the ability to communicate in English. Eligible patients were screened for depression using the Centers for Epidemiologic Studies Depression Scale (CES-D).33The study invited all patients with a CES-D score higher than 20 and a 5% random sample of patients with lower scores (to reduce the bias associated with false negatives) to enroll in the study. To increase screen sensitivity, patients with a CE S-D score of 20 or less who were not selected were invited if they responded positively to supplemental questions regarding previous episodes or treatment of depression.31 Of the 1226 patients who were enrolled and completed the baseline research interview, 396 were determined to meet the clinical criteria for major depression and 203 for minor depression. The remaining patients (n = 627) did not meet the clinical criteria for minor or major depression and were not the target of the intervention.

Previous studies examining overall PROSPECT intervention effects found consistent effects on process and clinical outcomes (including those considered in this analysis) in patients with major depression at baseline but no advantage in patients with minor depression.30,31 We, thus, focused on patients with baseline major depression in this study. We also report findings of a parallel analysis of the patient sample with minor depression to determine whether null effects were, in fact, masking important intergroup differences.

RANDOMIZATION AND INTERVENTION

The PROSPECT study adopted a practice randomization design: practices paired by urban vs rural or suburban location, academic affiliation, size, and racial/ethnic composition of the patient population were randomly assigned to intervention or usual care within pairs. The PROSPECT depression care managers offered targeted and timed recommendations to primary care physicians based on a treatment algorithm. The algorithm recommended a first-line trial of a selective serotonin reuptake inhibitor (citalopram), but physicians could prescribe other medications if clinically indicated; when a patient declined medication therapy, the physician could recommend interpersonal psychotherapy provided by the care manager. Practice-based care managers collaborated with physicians and supervising psychiatrists in recognizing depression; providing guideline-based treatment recommendations; monitoring patient clinical status, medication adverse effects, and adherence; and providing psychotherapy. Care managers interacted with patients in person or by telephone at scheduled intervals or when clinically necessary. Physicians of practices randomized to the usual care group received videotaped and printed information on late-life depression and were informed by letter if a patient met the criteria for a depression diagnosis.

MEASURES
Outcome 1: Antidepressant Use

Patients were asked to bring all the medications they were currently taking to each follow-up assessment. The interviewers recorded the name, dosage, and prescribed frequency of administration for each medication. The intensity of antidepressant treatment was classified based on the modified composite antidepressant score.34 In this analysis, we generated dichotomous measures of engagement in antidepressant therapy (composite antidepressant score >0 vs 0) and antidepressant treatment with adequate dosage (composite antidepressant score ≥3 vs <3) at each assessment point in the first year. Clinical appropriateness of continued treatment beyond the first year may depend on whether patients responded to medication in the earlier phase of treatment and on whether patients were experiencing recurrent depression35 and, therefore, may or may not indicate favorable clinical outcomes.

Outcome 2: Depressive Symptoms

Severity of depression was assessed at each follow-up research interview using the 24-item Hamilton Depression Rating Scale (HDRS),36 which ranges from 0 to 75, with higher scores indicating greater severity.

Outcome 3: DCM Intensity

The outcome of DCM intensity applies only to intervention patients. We quantified the intensity of DCM received using (1) the number of care manager contacts with the patient, the patient's primary care physician, and the patient's family members and (2) care manager time spent on patient assessment, medication management, and care coordination. We derived this information based on the PROSPECT intervention checklist forms that care managers completed periodically to document intervention activities. Care managers recorded the date when a form was filled out but not the dates when patient contacts and other collaborative activities occurred. We, therefore, aggregated measures of DCM intensity by intervals consistent with the follow-up research interviews, that is, 1 to 4, 5 to 8, 9 to 12, 13 to 18, and 19 to 24 months.

MAIN INDEPENDENT VARIABLES: EDUCATION AND RACE/ETHNICITY

Patient education and race/ethnicity were based on response to baseline research interviews. We defined the 2 education groups (no college vs some college) based on self-reported years of education (≤12 vs >12 years). We categorized patients into “minority” vs non-Hispanic white (“white”; n = 262). Minorities included patients who considered themselves “Hispanic descendents” (n = 17) and those who reported their racial identity as non-Hispanic African American (n = 111), Asian or Pacific Islander (n = 2), or other (n = 4). The small sample size of minority groups other than African American did not allow us to consider them separately. We, however, conducted a sensitivity analysis by excluding non–African American minorities and, thus, performing an African American vs white comparison.

STATISTICAL ANALYSIS

For the analysis of antidepressant use and depressive symptoms, we grouped patients by the random assignment status of the practice (DCM intervention vs usual care) and their educational level and race/ethnicity. We generated descriptive statistics by these patient groups over time. We further conducted a model-based analysis to estimate potentially differential intervention effects by education, independent of those by race/ethnicity (and vice versa). In this sample, although patients with a lower educational level were disproportionately of ethnic minority (and vice versa), there is still substantial variation in race/ethnicity in each education group (and variation in educational achievement in each racial/ethnic group) (Table 1). Such variation allowed us to estimate ethnic (educational) differences in outcomes of interest while holding constant patient education (race/ethnicity).

Table Graphic Jump LocationTable 1. Baseline Patient Characteristics by Education and Race/Ethnicitya

We estimated longitudinal mixed-effects logistic models for antidepressant use and linear models for HDRS scores. Each model included the main effects of and 2- and 3-way interactions between the following dichotomous variables: intervention (vs usual care) status, time or follow-up assessment points (4, 8, 12, 18, and 24 months), and education (no-college vs college) and race/ethnicity (minority vs white) indicators. This specification allowed us to estimate the intent-to-treat effect of the PROSPECT intervention by education and race/ethnicity throughout the 2-year follow-up. To correct for any imbalances in baseline sociodemographic and clinical characteristics between patient groups, we adjusted for patient sex, baseline age, marital status (married vs not), living arrangement (living alone vs with someone), HDRS score, indicator of suicidal ideation,37 and Charlson comorbidity score.38 In addition, we controlled for the dependent variable measured at baseline in each model (eg, an indicator of adequate antidepressant use at baseline in the longitudinal model of adequate antidepressant use). This adjustment served, in part, to mitigate differences in the baseline outcome between education and ethnic groups and between intervention and usual care patients in each patient group.

All these mixed-effects models included a random intercept at the patient level to account for correlations between longitudinal outcomes of the same patient. Consistent with findings of previous analyses of the PROSPECT data30,31 and approaches adopted in other DCM studies, such as Partners in Care (PIC),39 clustering by practice was negligible and was not specified in the final models. With the maximum likelihood estimation of these models, group differences in dropout and missing data were accounted for under the missing-at-random assumption.40,41 We derived estimates of intervention effects (in terms of changes in the probability of antidepressant use and in HDRS score) by education and race/ethnicity at each follow-up assessment. We derived empirical standard errors of all estimates using a bootstrap method that resampled clusters of observations by patient.

The analysis of DCM intensity used data from intervention patients only. Given the overdispersed (ie, variance exceeds the mean) count data nature of the measures, we conducted a negative binomial regression of each measure using a panel data set containing 5 intervals for each individual. We derived incident rate ratios (IRRs) for no-college vs college and minorities vs whites. We included in each model dichotomous indicators of PROSPECT sites to control for any intersite differences in the practice of DCM and DCM documentation. Each model also controlled for the patient's HDRS score at the beginning of each study period (ie, 1-4 months, 5-8 months, etc). Because the DCM protocol calls for varying intensity of care management for patients with different needs, with this adjustment, we derived between-group differences in DCM intensity conditional on severity of depression. Robust standard errors of the IRRs were derived by specifying clusters at the patient level.4244 All the analyses were conducted using a commercially available software program (STATA, version 10; StataCorp LP, College Station, Texas).

The PROSPECT protocol received full review and approval from the institutional review boards of all 3 institutions involved. Written informed consent was obtained from all the participants.

Sociodemographic and clinical characteristics were largely balanced between intervention and usual care patients in the overall PROSPECT sample.30,31 The present investigation focused on the 396 patients with major depression at baseline (214 in the intervention group and 182 in the usual care group) (Figure 1). An examination of patient characteristics in each education and ethnic group considered revealed some differences by intervention status (Table 1). In particular, in minority patients, the rate of adequate antidepressant use was much higher in the intervention group (37%) than in the usual care group (22%) at baseline. Although all the analyses controlled for the outcome of interest at baseline, results pertaining to minority patients should be interpreted with caution because of the relatively small sample size of this group.

Place holder to copy figure label and caption
Figure 1.

Study flow diagram. CES-D indicates Centers for Epidemiologic Studies Depression Scale.

Graphic Jump Location
GROUP DIFFERENCES IN INTERVENTION EFFECTS
Antidepressant Use

We present herein results pertaining to adequate antidepressant use. Results regarding any antidepressant use were qualitatively similar. Descriptive results indicate a strong intervention effect that increased adequate antidepressant use in all patient groups; patients with no college education saw more sustained benefits in later months compared with college-educated patients (Figure 2A); minority intervention patients lagged behind white patients in the early months, achieved a comparable rate at 12 months, but dropped off thereafter (Figure 2B).

Place holder to copy figure label and caption
Figure 2.

Adequate antidepressant (AD) use (A and B) and Hamilton Depression Rating Scale (HDRS) scores (C and D) by practice randomization assignment and either patient education (A and C) or race/ethnicity (B and D): descriptive results. Sample sizes shown are based on the number of patient observations with nonmissing values for a given measure. Int. indicates intervention; UC, usual care.

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For comparison by education, adjusted results indicated a consistent and more definitive pattern than was suggested by descriptive results (Table 2). At months 4 and 8, intervention effects on adequate antidepressant use were slightly stronger in no-college than college-educated patients, but differences in the intervention effects did not attain statistical significance. At month 12, however, the intervention increased the rate of adequate antidepressant use by 14.2 percentage points (pps) (95% confidence interval (CI), 1.7 to 26.4 pps) in the no-college group but had no statistically significant effect in the college-educated group (−9.2 pps [95% CI, −25.0 to 2.7 pps]). Differences in the 2 intervention effects indicate that the intervention increased adequate antidepressant use in no-college patients by 23.4 pps (95% CI, 5.5 to 43.7 pps) more than it did in college-educated patients (Table 2). Differences in the intervention effects between the 2 education groups did not achieve statistical significance at months 18 and 24.

Table Graphic Jump LocationTable 2. Adjusted Intervention Effects on Adequate AD Use and HDRS Scores by Patient Education and Race/Ethnicity

For race/ethnicity comparisons, adjusted results suggest a different pattern than is indicated by the descriptive analysis. At month 4, the intervention raised the rate of adequate antidepressant use to a similar extent in minority patients (10.6 pps [95% CI, −2.2 to 25.1 pps]) and white patients (12.6 pps [95% CI, 3.3 to 21.9 pps]), although the effect was not statistically significant in minorities. Starting at month 8, the intervention effect became null in minority patients, whereas it remained strong in white patients, leading to a minority-white difference in intervention effects of −17.4 pps (95% CI, −41.1 to 2.7 pps) at month 12 and −19.3 pps (95% CI, −40.6 to 3.3 pps) at month 24.

Depressive Symptoms

Descriptive results of the HDRS over 24 months indicate overall declines in depression symptoms in all patient groups (Figure 2C and D). Under usual care, patients with no college education demonstrated progressively smaller declines than did college-educated patients; under intervention, however, no-college patients displayed a somewhat more rapid decline in symptoms overall and a particularly large decline in the last 6 months compared with college-educated patients (Figure 2C). Although minority patients under usual care experienced a similar course of depression as did white patients, under intervention, minorities saw a slower decline in the first 4 months and an unstable trajectory thereafter, leading to a widened gap in HDRS scores between racial/ethnic groups at month 24 (Figure 2D).

Adjusted results were largely consistent with descriptive comparisons (Table 2). The DCM intervention had a larger and more lasting effect on the HDRS score in the no-college group than in college-educated patients. For example, at month 24, the intervention reduced HDRS scores in no-college patients by 3.8 pps (95% CI, −6.8 to −0.4 pps) more than it did in the college-educated group. The intervention had comparable effects between minority and white patients in the early phase; by month 18, however, it had ceased to benefit minority patients, whereas for white patients, the intervention effect still amounted to a 2.3-pps reduction in HDRS score at month 24 (95% CI, −4.0 to −0.1 pps).

DCM INTENSITY BY PATIENT GROUPS

For all education and racial/ethnic groups in the intervention arm, DCM was most intensive in the first 4 months, declined markedly in the next 4 months, and assumed a milder downward trend thereafter (Table 3). There were no statistically significant differences in DCM intensity by educational attainment. Minority/white IRRs in DCM intensity were close to 1.0 in the first 4 months but trended below 1.0 thereafter. For example, the minority/white IRR associated with care manager contacts with the patient was 0.8 (P = .23) during months 4 to 8 and 0.6 (P = .02) during months 8 to 12; IRRs associated with care manager time on patient assessment, medication management, and care coordination during several intervals between months 4 and 18 were also statistically significantly below 1.0. In contrast, care managers had 3 to 4 times as many contacts with family members of minority patients in year 2 as they did with families of white patients (P ≤ .03).

Table Graphic Jump LocationTable 3. Care Manager Contacts and Time Over 24 Months by Patient Education and Race/Ethnicity

The sensitivity analysis excluding non–African American minorities (n = 23) (and, thus, performing an African American vs non-Hispanic white comparison) generated findings similar to those of the main analysis. For example, for depression symptoms at 24 months, the intervention reduced the mean HDRS score by 4.1 pps more in the no-college group compared with in the college-educated group (95% CI, −7.3 to −0.7 pps) but by 4.0 pps less in African American patients compared with in non-Hispanic white patients (95% CI, 0.2 to 7.4 pps).

In the secondary analysis focusing on patients with minor depression at baseline, we did not find statistically significant effects of the intervention in most cases considered. The exceptions were with the adequate antidepressant use outcome at month 12, where the no-college group achieved an intervention effect of 43.0 pps (95% CI, 13.2 to 67.3 pps), leading to a no-college–college difference in intervention effects of 38.1 pps (95% CI, 3.4 to 67.6 pps); for the same outcome at month 12, non-Hispanic white patients achieved an intervention effect of 33.3 pps (95% CI, 8.8 to 48.5 pps), but the racial/ethnic difference in intervention effects did not achieve statistical significance. Given that these findings were consistent with what we found in the main analysis, we focus the following discussion on results pertaining to patients with major depression at baseline.

The PROSPECT intervention was effective in improving care process and clinical outcomes in the general patient population with late-life depression30,31 and in each patient group considered in this analysis. Our results suggest that in patients of a given race/ethnicity (minority or white), the intervention had a larger and more lasting effect in less-educated patients compared with patients with a college education. Meanwhile, in patients of the same educational level, the intervention did not benefit racial/ethnic minority patients nearly as much as it did non-Hispanic white patients. As a result, the intervention narrowed or closed the gap between education groups in antidepressant use and depressive symptoms seen in usual care but did not mitigate ethnic disparities in either outcome.

To our knowledge, this is the first investigation to demonstrate that the impact of a collaborative DCM program in reducing disparities associated with patient education differs from the program's effect on disparities associated with race/ethnicity. The present findings indicate that the PROSPECT intervention, and, in particular, the longitudinal care management and coordination by a care manager based on a clinical protocol, effectively addressed barriers pertaining to patient self-management and treatment adherence, which disproportionately affected patients with lower education regardless of race/ethnicity. In addition, low education was found to be associated with a less proactive and assertive care-seeking style and a perceived lack of interest and ability to adhere to medical advice by physicians.45 Given their experience with compromised physician-patient communication, patients with lower education in PROSPECT may have responded more to the personal attention provided by the depression care manager and the ensuing therapeutic alliance. This is consistent with the present findings of comparable intensity of DCM between the 2 education groups but more beneficial effect of the intervention in the less educated.

The lack of additional benefits to minority patients (compared with white patients) may reflect the lack of culture-specific strategies to maximize the potential of the intervention to minority patients. We found a substantially lower intensity of DCM received by minority patients after the initial intervention period. This observation offers 1 explanation for the less favorable intervention effects in minorities and speaks to the inadequacy of a generic DCM program in retaining minority patients so that they can take full advantage of DCM. We found that family members of minority patients had greater involvement in DCM compared with those of white patients. This is consistent with previous findings46,47 that family values regarding togetherness and interdependence are endorsed more in Hispanic and African American individuals than in white individuals. The lack of explicit consideration of family involvement in the PROSPECT protocol (eg, specifying circumstances where it is most critical or effective to engage family members) may partly explain the lack of additional benefits to minority patients.

Similar to most other DCM interventions, the PROSPECT protocol did not explicitly use a participatory shared decision-making style that attends to the patients' culture and related beliefs. Previous studies46,48 have found that Hispanic and African American individuals tend to attribute depression to difficulty in life circumstances and stressors, deemphasizing medical etiology. Insufficient attention to such differences and a lack of strategies to help patients articulate and subsequently adjust their beliefs may have rendered DCM unable to realize its full potential for minority patients.

Previous studies have examined whether the DCM interventions tested in the PIC study39 and in the Improving Mood-Promoting Access to Collaborative Treatment (IMPACT) study49 improved care across ethnic groups5053 and reduced outcome disparities compared with usual care.5153 The DCM program in PIC featured accommodations for minority patients, including culture-sensitive study materials, translation (for Spanish-speaking patients), and bilingual and culturally trained clinicians.51,52 Studies5153 using data from PIC found minorities (but not white patients) to benefit markedly from the intervention in self-reported depression outcomes, leading to reduced disparities. In addition, the disparity-mitigating effects found in PIC were largely attributable to the quality improvement program that specifically supported psychotherapy, a treatment modality preferred by minority patients.21,54,55

The DCM intervention in the IMPACT study did not have explicit cultural accommodations other than reference of elderly from different ethnic backgrounds in the educational video and written materials.50 One study50 based on the IMPACT data found significant intervention effects on rates of depression care, depression severity, and health-related functional impairments in ethnic minority participants that were similar to those observed in white participants. Another study56 found that low-income patients in the IMPACT trial experienced similar benefits as patients with higher incomes. Although the IMPACT and PROSPECT interventions were designed to ensure access to high-quality depression care, they differed regarding a few specific features. PROSPECT offered citalopram, psychoeducation, and adherence enhancement sessions as first-line treatment. Interpersonal therapy was offered to patients who declined medication therapy, although many patients who initially declined medication therapy initiated antidepressant therapy later in the study, suggesting that the intervention was somewhat effective in changing patient preference and choice over time. Other types of pharmacotherapy were available to those who did not tolerate or respond to citalopram and were selected by study psychiatrists on the basis of a guideline tailored to the individual's history of response and stage of depression.57 IMPACT offered behavioral activation to all participants and a choice between medication management and problem-solving therapy. The selection of medication was based on physician judgment and was not limited to 1 class of medication. Although the different effects by race/ethnicity for PROSPECT vs IMPACT may be attributable to multiple domains across which the 2 interventions differed, 1 possible area for future investigation is whether problem-solving therapy, as adopted in IMPACT, better helps older patients cope with life circumstances and stressors and, therefore, is especially relevant to minority patients, who tend to attribute depression more to social and environmental, rather than medical, factors.

All published PIC and IMPACT analyses examined intervention effects along 1 dimension (race/ethnicity or income) at a time; none examined DCM intervention effects by patient income or education independent of those by race/ethnicity. Because minority patients disproportionately have a lower income or education, ethnic differences in intervention effects (or lack thereof) reported in these studies reflected the net impact of race/ethnicity and income or education on the intervention effects. Therefore, findings from these studies may have overstated the impact of the intervention on depression care and outcomes in ethnic minority patients and are not necessarily at odds with the present findings. Put another way, the seeming conflict between the present findings and those from previous studies may, in part, be real, resulting from the fact that specific designs of the PIC and IMPACT interventions have resulted in more culturally sensitive processes of care compared with PROSPECT and may, in part, be illusory, resulting from the confounding of education and minority status in the IMPACT and PIC studies.

Differences in the composition of minority patients among the 3 trials warrant caution in directly comparing findings across studies: the minority sample was predominantly African American in PROSPECT in contrast with Latino in PIC and a more balanced mix of African American, Latino, and other ethnicities in IMPACT.

The present study has limitations. In each subgroup examined, not all key characteristics were balanced between the intervention and usual care samples (Table 1). The main strategy used was to control for the outcome of interest at baseline, in addition to baseline characteristics, in all analyses, enabling us to compare (adjusted) intervention effects between groups. Also, the present findings regarding minority patients largely apply to African Americans and not to other racial/ethnic populations; findings regarding racial/ethnic differences may be unstable because of the small sample size of minority patients.

In conclusion, the PROSPECT intervention substantially reduced disparities by patient education but did not mitigate ethnic disparities in antidepressant use and depressive symptoms. Adding culturally tailored strategies to collaborative DCM models may be needed to extend their benefits to minority patients. Possible strategies include, but are not limited to, helping patients articulate culturally specific beliefs and attitudes and engaging patients in treatment planning and adjustment; identifying areas of treatment in which family members may be instrumental and engaging them as collaborators; and incorporating an explicit shared decision-making component whereby patients and physicians exchange information and experiences to arrive at a mutually agreed-on treatment goal and plan.

Correspondence: Yuhua Bao, PhD, Department of Public Health, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065 (yub2003@med.cornell.edu).

Accepted for Publication: January 6, 2011.

Author Contributions: Dr Bao had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Bao, Alexopoulos, and Bruce. Acquisition of data: Alexopoulos, Ten Have, Post, and Bruce. Analysis and interpretation of data: Bao, Alexopoulos, Casalino, Donohue, Post, Schackman, and Bruce. Drafting of the manuscript: Bao. Critical revision of the manuscript for important intellectual content: Bao, Alexopoulos, Casalino, Ten Have, Donohue, Post, Schackman, and Bruce. Statistical analysis: Bao and Ten Have. Obtained funding: Bao, Alexopoulos, and Bruce. Administrative, technical, and material support: Alexopoulos. Study supervision: Bao, Casalino, Donohue, Post, Schackman, and Bruce.

Financial Disclosure: Dr Bao has received research grants from the National Institute of Mental Health (NIMH) and Pfizer. Dr Alexopoulos has received research grants from the NIMH, Cephalon, and Forest Laboratories; has served as a consultant on the scientific advisory boards of Forest Laboratories and sanofi-aventis; holds stock in Johnson & Johnson; and has served on speakers' bureaus for Forest Laboratories, Eli Lilly & Co, Bristol-Meyers Squibb, Pfizer, and Janssen. Drs Ten Have, Donohue, and Post have received research grants from the NIMH. Dr Bruce has received grant funding from the NIMH and is a consultant to Medispin Inc, a medical education company.

Funding/Support: The NIMH funded the PROSPECT study. Dr Bao was supported by the Pfizer Scholar's Grant in Health Policy, the NIMH Advanced Center for Interventions and Services Research at Weill Cornell Medical College (P30 MH085943), mentored research scientist career development award K01 MH090087 from the NIMH and Drew/UCLA Project Export, National Center on Minority Health and Health Disparities, P20MD000182. Dr Donohue was supported by grant KL2 RR-024154 from the National Center for Research Resources, a component of the National Institutes of Health, the National Institutes of Health Roadmap for Medical Research, and the NIMH (R34 MH082682). Dr Schackman was supported by the Advanced Center for Interventions and Services Research at Weill Cornell. Dr Post was supported by grant K23 MH01879 from the NIMH.

Role of the Sponsors: The National Institutes of Health and Pfizer Inc had no role in the design and conduct of this study; in the collection, management, analysis, and interpretation of the data; or in the preparation, review, and approval of the manuscript.

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PubMed Link to Article
US Department of Health and Human Services, Mental Health: Culture, Race, and Ethnicity—A Supplement to Mental Health: A Report of the Surgeon General.  Rockville, MD US Dept of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services2001;
Wang  PSLane  MOlfson  MPincus  HAWells  KBKessler  RC Twelve-month use of mental health services in the United States: results from the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62 (6) 629- 640
PubMed Link to Article
Wang  PSBerglund  POlfson  MPincus  HAWells  KBKessler  RC Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62 (6) 603- 613
PubMed Link to Article
Gallo  JJMarino  SFord  DAnthony  JC Filters on the pathway to mental health care, II: sociodemographic factors. Psychol Med 1995;25 (6) 1149- 1160
PubMed Link to Article
Pingitore  DSnowden  LSansone  RAKlinkman  M Persons with depressive symptoms and the treatments they receive: a comparison of primary care physicians and psychiatrists. Int J Psychiatry Med 2001;31 (1) 41- 60
PubMed Link to Article
Cabassa  LJZayas  LHHansen  MC Latino adults' access to mental health care: a review of epidemiological studies. Adm Policy Ment Health 2006;33 (3) 316- 330
PubMed Link to Article
Prins  MAVerhaak  PFSmolders  MLaurant  MGvan der Meer  KSpreeuwenberg  Pvan Marwijk  HWPenninx  BWBensing  JM Patient factors associated with guideline-concordant treatment of anxiety and depression in primary care. J Gen Intern Med 2010;25 (7) 648- 655
PubMed Link to Article
Smedley  BDedSmith  ARedNelson  ARed Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare.  Washington, DC National Academies of Science2003;
Casalino  LP Individual Physicians or Organized Processes: How Can Disparities in Clinical Care Be Reduced?  Washington, DC National Academy of Social Insurance2005;
Beach  MCGary  TLPrice  EGRobinson  KGozu  APalacio  ASmarth  CJenckes  MFeuerstein  CBass  EBPowe  NRCooper  LA Improving health care quality for racial/ethnic minorities. BMC Public Health 2006;6 (1) 104
PubMed Link to Article
King  RKGreen  ARTan-McGrory  ADonahue  EJKimbrough-Sugick  JBetancourt  JR A plan for action: key perspectives from the racial/ethnic disparities strategy forum. Milbank Q 2008;86 (2) 241- 272
PubMed Link to Article
Kilbourne  AMSwitzer  GHyman  KCrowley-Matoka  MFine  MJ Advancing health disparities research within the health care system: a conceptual framework. Am J Public Health 2006;96 (12) 2113- 2121
PubMed Link to Article
Wagner  EH The role of patient care teams in chronic disease management. BMJ 2000;320 (7234) 569- 572
PubMed Link to Article
Wagner  EHAustin  BTVon Korff  M Organizing care for patients with chronic illness. Milbank Q 1996;74 (4) 511- 544
PubMed Link to Article
Gilbody  SBower  PFletcher  JRichards  DSutton  AJ Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med 2006;166 (21) 2314- 2321
PubMed Link to Article
Neumeyer-Gromen  ALampert  TStark  KKallischnigg  G Disease management programs for depression: a systematic review and meta-analysis of randomized controlled trials. Med Care 2004;42 (12) 1211- 1221
PubMed Link to Article
Williams  JW  JrGerrity  MHolsinger  TDobscha  SGaynes  BDietrich  A Systematic review of multifaceted interventions to improve depression care. Gen Hosp Psychiatry 2007;29 (2) 91- 116
PubMed Link to Article
Schulberg  HCBryce  CChism  KMulsant  BHRollman  BBruce  MCoyne  JReynolds  CF  IIIPROSPECT Group, Managing late-life depression in primary care practice: a case study of the Health Specialist's role. Int J Geriatr Psychiatry 2001;16 (6) 577- 584
PubMed Link to Article
Cooper-Patrick  LPowe  NRJenckes  MWGonzales  JJLevine  DMFord  DE Identification of patient attitudes and preferences regarding treatment of depression. J Gen Intern Med 1997;12 (7) 431- 438
PubMed Link to Article
Cruz  MPincus  HAHarman  JSReynolds  CF  IIIPost  EP Barriers to care-seeking for depressed African Americans. Int J Psychiatry Med 2008;38 (1) 71- 80
PubMed Link to Article
Sirey  JABruce  MLAlexopoulos  GSPerlick  DAFriedman  SJMeyers  BS Stigma as a barrier to recovery. Psychiatr Serv 2001;52 (12) 1615- 1620
PubMed Link to Article
Zylstra  RGSteitz  JA Public knowledge of late-life depression and aging. J Appl Gerontol 1999;1863- 76
Link to Article
Kleinman  AM Depression, somatization and the “new cross-cultural psychiatry.” Soc Sci Med 1977;11 (1) 3- 10
PubMed Link to Article
Kleinman  A Rethinking Psychiatry: From Cultural Category to Personal Experience.  New York, NY Free Press1988;
Neighbors  HWJackson  JS The use of informal and formal help: four patterns of illness behavior in the black community. Am J Community Psychol 1984;12 (6) 629- 644
PubMed Link to Article
Peifer  KLHu  TVega  W Help seeking by persons of Mexican origin with functional impairments. Psychiatr Serv 2000;51 (10) 1293- 1298
PubMed Link to Article
Cooper-Patrick  LGallo  JJGonzales  JJVu  HTPowe  NRNelson  CFord  DE Race, gender, and partnership in the patient-physician relationship. JAMA 1999;282 (6) 583- 589
PubMed Link to Article
Alexopoulos  GSReynolds  CF  IIIBruce  MLKatz  IRRaue  PJMulsant  BHOslin  DWTen Have  TPROSPECT 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
PubMed Link to Article
Bruce  MLTen Have  TRReynolds  CF  IIIKatz  IISchulberg  HCMulsant  BHBrown  GKMcAvay  GJPearson  JLAlexopoulos  GS Reducing suicidal ideation and depressive symptoms in depressed older primary care patients: a randomized controlled trial. JAMA 2004;291 (9) 1081- 1091
PubMed Link to Article
Folstein  MFFolstein  SEMcHugh  PR “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12 (3) 189- 198
PubMed Link to Article
Radloff  LS The CES-D: a self-report depression rating scale for research in the general population. Appl Psychol Meas 1977;1385- 401
Link to Article
Alexopoulos  GSMeyers  BSYoung  RCKakuma  TFeder  MEinhorn  ARosendahl  E Recovery in geriatric depression. Arch Gen Psychiatry 1996;53 (4) 305- 312
PubMed Link to Article
Depression Guideline Panel, Depression in Primary Care.  Rockville, MD US Dept of Health and Human Services, Agency for Health Care Policy and Research1993;
Hamilton  M A rating scale for depression. J Neurol Neurosurg Psychiatry 1960;2356- 62
PubMed Link to Article
Beck  ATBrown  GKSteer  RA Psychometric characteristics of the Scale for Suicide Ideation with psychiatric outpatients. Behav Res Ther 1997;35 (11) 1039- 1046
PubMed Link to Article
Charlson  MEPompei  PAles  KLMacKenzie  CR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40 (5) 373- 383
PubMed Link to Article
Wells  KBSherbourne  CSchoenbaum  MDuan  NMeredith  LUnützer  JMiranda  JCarney  MFRubenstein  LV Impact of disseminating quality improvement programs for depression in managed primary care: a randomized controlled trial. JAMA 2000;283 (2) 212- 220
PubMed Link to Article
Laird  NM Missing data in longitudinal studies. Stat Med 1988;7 (1-2) 305- 315
PubMed Link to Article
Siddique  JBrown  CHHedeker  DDuan  NGibbons  RDMiranda  JLavori  PW Missing data in longitudinal trials, part B: analytic issues. Psychiatr Ann 2008;38 (12) 793- 801
PubMed Link to Article
Huber  PJ The behavior of maximum likelihood estimates under non-standard conditions. Proceedings of the Fifth Berkley Symposium on Mathematical Statistics and Probability. Berkley University of California Press1967;221- 233
White  H A heteroskedasticity-consistent convariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48817- 830
Link to Article
White  H Maximum likelihood estimation of misspecified models. Econometrica 1982;501- 25
Link to Article
van Ryn  MBurke  J The effect of patient race and socio-economic status on physicians' perceptions of patients. Soc Sci Med 2000;50 (6) 813- 828
PubMed Link to Article
Cabassa  LJLester  RZayas  LH “It's like being in a labyrinth.” J Immigr Minor Health 2007;9 (1) 1- 16
PubMed Link to Article
Chesla  CAFisher  LMullan  JTSkaff  MMGardiner  PChun  KKanter  R Family and disease management in African-American patients with type 2 diabetes. Diabetes Care 2004;27 (12) 2850- 2855
PubMed Link to Article
Alverson  HSDrake  RECarpenter-Song  EAChu  ERitsema  MSmith  B Ethnocultural variations in mental illness discourse: some implications for building therapeutic alliances. Psychiatr Serv 2007;58 (12) 1541- 1546
PubMed Link to Article
Unützer  JKaton  WCallahan  CMWilliams  JW  JrHunkeler  EHarpole  LHoffing  MDella Penna  RDNoël  PHLin  EHAreán  PAHegel  MTTang  LBelin  TROishi  SLangston  CIMPACT Investigators, Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA 2002;288 (22) 2836- 2845
PubMed Link to Article
Areán  PAAyalon  LHunkeler  ELin  EHTang  LHarpole  LHendrie  HWilliams  JW  JrUnützer  JIMPACT Investigators, Improving depression care for older, minority patients in primary care. Med Care 2005;43 (4) 381- 390
PubMed Link to Article
Miranda  JDuan  NSherbourne  CSchoenbaum  MLagomasino  IJackson-Triche  MWells  KB Improving care for minorities: can quality improvement interventions improve care and outcomes for depressed minorities? results of a randomized, controlled trial. Health Serv Res 2003;38 (2) 613- 630
PubMed Link to Article
Wells  KSherbourne  CSchoenbaum  MEttner  SDuan  NMiranda  JUnützer  JRubenstein  L Five-year impact of quality improvement for depression: results of a group-level randomized controlled trial. Arch Gen Psychiatry 2004;61 (4) 378- 386
PubMed Link to Article
Wells  KBSherbourne  CDPMiranda  JPTang  LPBenjamin  BMSDuan  NP The cumulative effects of quality improvement for depression on outcome disparities over 9 years: results from a randomized, controlled group-level trial. Med Care 2007;45 (11) 1052- 1059
PubMed Link to Article
Cooper  LAGonzales  JJGallo  JJRost  KMMeredith  LSRubenstein  LVWang  NYFord  DE The acceptability of treatment for depression among African-American, Hispanic, and white primary care patients. Med Care 2003;41 (4) 479- 489
PubMed
Dwight-Johnson  MSherbourne  CDLiao  DWells  KB Treatment preferences among depressed primary care patients. J Gen Intern Med 2000;15 (8) 527- 534
PubMed Link to Article
Areán  PAGum  AMTang  LUnützer  J Service use and outcomes among elderly persons with low incomes being treated for depression. Psychiatr Serv 2007;58 (8) 1057- 1064
PubMed Link to Article
Mulsant  BHAlexopoulos  GSReynolds  CF  IIIKatz  IRAbrams  ROslin  DSchulberg  HCPROSPECT Study Group, Pharmacological treatment of depression in older primary care patients: the PROSPECT algorithm. Int J Geriatr Psychiatry 2001;16 (6) 585- 592
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

Study flow diagram. CES-D indicates Centers for Epidemiologic Studies Depression Scale.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Adequate antidepressant (AD) use (A and B) and Hamilton Depression Rating Scale (HDRS) scores (C and D) by practice randomization assignment and either patient education (A and C) or race/ethnicity (B and D): descriptive results. Sample sizes shown are based on the number of patient observations with nonmissing values for a given measure. Int. indicates intervention; UC, usual care.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Patient Characteristics by Education and Race/Ethnicitya
Table Graphic Jump LocationTable 2. Adjusted Intervention Effects on Adequate AD Use and HDRS Scores by Patient Education and Race/Ethnicity
Table Graphic Jump LocationTable 3. Care Manager Contacts and Time Over 24 Months by Patient Education and Race/Ethnicity

References

Alegría  MChatterji  PWells  KCao  ZChen  CNTakeuchi  DJackson  JMeng  XL Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv 2008;59 (11) 1264- 1272
PubMed Link to Article
Keyes  KMHatzenbuehler  MLAlberti  PNarrow  WEGrant  BFHasin  DS Service utilization differences for Axis I psychiatric and substance use disorders between white and black adults. Psychiatr Serv 2008;59 (8) 893- 901
PubMed Link to Article
US Department of Health and Human Services, Mental Health: Culture, Race, and Ethnicity—A Supplement to Mental Health: A Report of the Surgeon General.  Rockville, MD US Dept of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services2001;
Wang  PSLane  MOlfson  MPincus  HAWells  KBKessler  RC Twelve-month use of mental health services in the United States: results from the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62 (6) 629- 640
PubMed Link to Article
Wang  PSBerglund  POlfson  MPincus  HAWells  KBKessler  RC Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62 (6) 603- 613
PubMed Link to Article
Gallo  JJMarino  SFord  DAnthony  JC Filters on the pathway to mental health care, II: sociodemographic factors. Psychol Med 1995;25 (6) 1149- 1160
PubMed Link to Article
Pingitore  DSnowden  LSansone  RAKlinkman  M Persons with depressive symptoms and the treatments they receive: a comparison of primary care physicians and psychiatrists. Int J Psychiatry Med 2001;31 (1) 41- 60
PubMed Link to Article
Cabassa  LJZayas  LHHansen  MC Latino adults' access to mental health care: a review of epidemiological studies. Adm Policy Ment Health 2006;33 (3) 316- 330
PubMed Link to Article
Prins  MAVerhaak  PFSmolders  MLaurant  MGvan der Meer  KSpreeuwenberg  Pvan Marwijk  HWPenninx  BWBensing  JM Patient factors associated with guideline-concordant treatment of anxiety and depression in primary care. J Gen Intern Med 2010;25 (7) 648- 655
PubMed Link to Article
Smedley  BDedSmith  ARedNelson  ARed Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare.  Washington, DC National Academies of Science2003;
Casalino  LP Individual Physicians or Organized Processes: How Can Disparities in Clinical Care Be Reduced?  Washington, DC National Academy of Social Insurance2005;
Beach  MCGary  TLPrice  EGRobinson  KGozu  APalacio  ASmarth  CJenckes  MFeuerstein  CBass  EBPowe  NRCooper  LA Improving health care quality for racial/ethnic minorities. BMC Public Health 2006;6 (1) 104
PubMed Link to Article
King  RKGreen  ARTan-McGrory  ADonahue  EJKimbrough-Sugick  JBetancourt  JR A plan for action: key perspectives from the racial/ethnic disparities strategy forum. Milbank Q 2008;86 (2) 241- 272
PubMed Link to Article
Kilbourne  AMSwitzer  GHyman  KCrowley-Matoka  MFine  MJ Advancing health disparities research within the health care system: a conceptual framework. Am J Public Health 2006;96 (12) 2113- 2121
PubMed Link to Article
Wagner  EH The role of patient care teams in chronic disease management. BMJ 2000;320 (7234) 569- 572
PubMed Link to Article
Wagner  EHAustin  BTVon Korff  M Organizing care for patients with chronic illness. Milbank Q 1996;74 (4) 511- 544
PubMed Link to Article
Gilbody  SBower  PFletcher  JRichards  DSutton  AJ Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med 2006;166 (21) 2314- 2321
PubMed Link to Article
Neumeyer-Gromen  ALampert  TStark  KKallischnigg  G Disease management programs for depression: a systematic review and meta-analysis of randomized controlled trials. Med Care 2004;42 (12) 1211- 1221
PubMed Link to Article
Williams  JW  JrGerrity  MHolsinger  TDobscha  SGaynes  BDietrich  A Systematic review of multifaceted interventions to improve depression care. Gen Hosp Psychiatry 2007;29 (2) 91- 116
PubMed Link to Article
Schulberg  HCBryce  CChism  KMulsant  BHRollman  BBruce  MCoyne  JReynolds  CF  IIIPROSPECT Group, Managing late-life depression in primary care practice: a case study of the Health Specialist's role. Int J Geriatr Psychiatry 2001;16 (6) 577- 584
PubMed Link to Article
Cooper-Patrick  LPowe  NRJenckes  MWGonzales  JJLevine  DMFord  DE Identification of patient attitudes and preferences regarding treatment of depression. J Gen Intern Med 1997;12 (7) 431- 438
PubMed Link to Article
Cruz  MPincus  HAHarman  JSReynolds  CF  IIIPost  EP Barriers to care-seeking for depressed African Americans. Int J Psychiatry Med 2008;38 (1) 71- 80
PubMed Link to Article
Sirey  JABruce  MLAlexopoulos  GSPerlick  DAFriedman  SJMeyers  BS Stigma as a barrier to recovery. Psychiatr Serv 2001;52 (12) 1615- 1620
PubMed Link to Article
Zylstra  RGSteitz  JA Public knowledge of late-life depression and aging. J Appl Gerontol 1999;1863- 76
Link to Article
Kleinman  AM Depression, somatization and the “new cross-cultural psychiatry.” Soc Sci Med 1977;11 (1) 3- 10
PubMed Link to Article
Kleinman  A Rethinking Psychiatry: From Cultural Category to Personal Experience.  New York, NY Free Press1988;
Neighbors  HWJackson  JS The use of informal and formal help: four patterns of illness behavior in the black community. Am J Community Psychol 1984;12 (6) 629- 644
PubMed Link to Article
Peifer  KLHu  TVega  W Help seeking by persons of Mexican origin with functional impairments. Psychiatr Serv 2000;51 (10) 1293- 1298
PubMed Link to Article
Cooper-Patrick  LGallo  JJGonzales  JJVu  HTPowe  NRNelson  CFord  DE Race, gender, and partnership in the patient-physician relationship. JAMA 1999;282 (6) 583- 589
PubMed Link to Article
Alexopoulos  GSReynolds  CF  IIIBruce  MLKatz  IRRaue  PJMulsant  BHOslin  DWTen Have  TPROSPECT 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
PubMed Link to Article
Bruce  MLTen Have  TRReynolds  CF  IIIKatz  IISchulberg  HCMulsant  BHBrown  GKMcAvay  GJPearson  JLAlexopoulos  GS Reducing suicidal ideation and depressive symptoms in depressed older primary care patients: a randomized controlled trial. JAMA 2004;291 (9) 1081- 1091
PubMed Link to Article
Folstein  MFFolstein  SEMcHugh  PR “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12 (3) 189- 198
PubMed Link to Article
Radloff  LS The CES-D: a self-report depression rating scale for research in the general population. Appl Psychol Meas 1977;1385- 401
Link to Article
Alexopoulos  GSMeyers  BSYoung  RCKakuma  TFeder  MEinhorn  ARosendahl  E Recovery in geriatric depression. Arch Gen Psychiatry 1996;53 (4) 305- 312
PubMed Link to Article
Depression Guideline Panel, Depression in Primary Care.  Rockville, MD US Dept of Health and Human Services, Agency for Health Care Policy and Research1993;
Hamilton  M A rating scale for depression. J Neurol Neurosurg Psychiatry 1960;2356- 62
PubMed Link to Article
Beck  ATBrown  GKSteer  RA Psychometric characteristics of the Scale for Suicide Ideation with psychiatric outpatients. Behav Res Ther 1997;35 (11) 1039- 1046
PubMed Link to Article
Charlson  MEPompei  PAles  KLMacKenzie  CR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40 (5) 373- 383
PubMed Link to Article
Wells  KBSherbourne  CSchoenbaum  MDuan  NMeredith  LUnützer  JMiranda  JCarney  MFRubenstein  LV Impact of disseminating quality improvement programs for depression in managed primary care: a randomized controlled trial. JAMA 2000;283 (2) 212- 220
PubMed Link to Article
Laird  NM Missing data in longitudinal studies. Stat Med 1988;7 (1-2) 305- 315
PubMed Link to Article
Siddique  JBrown  CHHedeker  DDuan  NGibbons  RDMiranda  JLavori  PW Missing data in longitudinal trials, part B: analytic issues. Psychiatr Ann 2008;38 (12) 793- 801
PubMed Link to Article
Huber  PJ The behavior of maximum likelihood estimates under non-standard conditions. Proceedings of the Fifth Berkley Symposium on Mathematical Statistics and Probability. Berkley University of California Press1967;221- 233
White  H A heteroskedasticity-consistent convariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48817- 830
Link to Article
White  H Maximum likelihood estimation of misspecified models. Econometrica 1982;501- 25
Link to Article
van Ryn  MBurke  J The effect of patient race and socio-economic status on physicians' perceptions of patients. Soc Sci Med 2000;50 (6) 813- 828
PubMed Link to Article
Cabassa  LJLester  RZayas  LH “It's like being in a labyrinth.” J Immigr Minor Health 2007;9 (1) 1- 16
PubMed Link to Article
Chesla  CAFisher  LMullan  JTSkaff  MMGardiner  PChun  KKanter  R Family and disease management in African-American patients with type 2 diabetes. Diabetes Care 2004;27 (12) 2850- 2855
PubMed Link to Article
Alverson  HSDrake  RECarpenter-Song  EAChu  ERitsema  MSmith  B Ethnocultural variations in mental illness discourse: some implications for building therapeutic alliances. Psychiatr Serv 2007;58 (12) 1541- 1546
PubMed Link to Article
Unützer  JKaton  WCallahan  CMWilliams  JW  JrHunkeler  EHarpole  LHoffing  MDella Penna  RDNoël  PHLin  EHAreán  PAHegel  MTTang  LBelin  TROishi  SLangston  CIMPACT Investigators, Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA 2002;288 (22) 2836- 2845
PubMed Link to Article
Areán  PAAyalon  LHunkeler  ELin  EHTang  LHarpole  LHendrie  HWilliams  JW  JrUnützer  JIMPACT Investigators, Improving depression care for older, minority patients in primary care. Med Care 2005;43 (4) 381- 390
PubMed Link to Article
Miranda  JDuan  NSherbourne  CSchoenbaum  MLagomasino  IJackson-Triche  MWells  KB Improving care for minorities: can quality improvement interventions improve care and outcomes for depressed minorities? results of a randomized, controlled trial. Health Serv Res 2003;38 (2) 613- 630
PubMed Link to Article
Wells  KSherbourne  CSchoenbaum  MEttner  SDuan  NMiranda  JUnützer  JRubenstein  L Five-year impact of quality improvement for depression: results of a group-level randomized controlled trial. Arch Gen Psychiatry 2004;61 (4) 378- 386
PubMed Link to Article
Wells  KBSherbourne  CDPMiranda  JPTang  LPBenjamin  BMSDuan  NP The cumulative effects of quality improvement for depression on outcome disparities over 9 years: results from a randomized, controlled group-level trial. Med Care 2007;45 (11) 1052- 1059
PubMed Link to Article
Cooper  LAGonzales  JJGallo  JJRost  KMMeredith  LSRubenstein  LVWang  NYFord  DE The acceptability of treatment for depression among African-American, Hispanic, and white primary care patients. Med Care 2003;41 (4) 479- 489
PubMed
Dwight-Johnson  MSherbourne  CDLiao  DWells  KB Treatment preferences among depressed primary care patients. J Gen Intern Med 2000;15 (8) 527- 534
PubMed Link to Article
Areán  PAGum  AMTang  LUnützer  J Service use and outcomes among elderly persons with low incomes being treated for depression. Psychiatr Serv 2007;58 (8) 1057- 1064
PubMed Link to Article
Mulsant  BHAlexopoulos  GSReynolds  CF  IIIKatz  IRAbrams  ROslin  DSchulberg  HCPROSPECT Study Group, Pharmacological treatment of depression in older primary care patients: the PROSPECT algorithm. Int J Geriatr Psychiatry 2001;16 (6) 585- 592
PubMed Link to Article

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For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
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