- Email: [email protected]
The Role of Medical Comorbidity in Outcome of Major Depression in Primary Care: The PROSPECT Study
The Role of Medical Comorbidity in Outcome of Major Depression in Primary Care The PROSPECT Study Hillary R. Bogner, M.D., M.S.C.E., Mark S. Cary, Ph.D. Martha L. Bruce, Ph.D., Charles F. Reynolds III, M.D. Benoit Mulsant, M.D., Thomas Ten Have, Ph.D. George S. Alexopoulos, M.D., and The PROSPECT Group
Objective: The authors described the influence of specific medical conditions on clinical remission and response of major depression (MDD) in a clinical trial evaluating a care-management intervention among older primary-care patients. Methods: Adults age 60 years and older were randomly selected and screened for depression. Participants were randomly assigned to Usual Care or to an Intervention with a depression care-manager offering algorithm-based care for MDD. In all, 324 adults meeting criteria for MDD were included in these analyses. Remission and response was defined by a score on the Hamilton Rating Scale for Depression ⬍10 and by a decrease from baseline of ⱖ50%, respectively. Medical comorbidity was ascertained through self-report. Cognitive impairment was defined by a score ⬍24 on the MiniMental State Exam (MMSE). Results: In Usual Care, rates of remission were faster in persons who reported atrial fibrillation (AF) than in persons who did not report AF and slower in persons who reported chronic pulmonary disease than in persons who did not report chronic pulmonary disease; rates of response were less stable in persons with MMSE ⬍24 than in those with MMSE ⱖ24. In the Intervention condition, none of the specific chronic medical conditions were significantly associated with outcomes for MDD. Conclusions: Because disease-specific findings were observed in persons who received Usual Care but not in persons who received more intensive treatment in the Intervention condition, our results suggest that the association of medical comorbidity and treatment outcomes for MDD may be determined by the intensity of treatment for depression. (Am J Geriatr Psychiatry 2005; 13:861–868)
Received February 10, 2005; revised June 20, 2005; accepted June 21, 2005. From the Dept. of Family Practice and Community Medicine, Univ. of Pennsylvania, Philadelphia, PA (HRB), the Center for Clinical Epidemiology and Biostatistics, Univ. of Pennsylvania (MSC, TTH), the Dept. of Psychiatry, Weill Medical College of Cornell University (MLB, GSA), and the Dept. of Psychiatry, Univ. of Pittsburgh School of Medicine (CFR, BM). Send correspondence and reprint requests to Hillary R. Bogner, M.D., M.S.C.E., Assistant Professor, Dept. of Family Practice and Community Medicine, Univ. of Pennsylvania, 3400 Spruce Street, 2 Gates Building, Philadelphia, PA 19104. e-mail: [email protected] 䉷 2005 American Association for Geriatric Psychiatry
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Medical Comorbidity and Depression
D
epression is one of the most common problems in primary-care settings. Depressed patients seen in primary care have a high prevalence of medical comorbidity.1 Specifically, investigators have documented the association of depression with: myocardial infarction (MI),2 heart failure,3 coronary artery disease,4 stroke,5 diabetes,6 cancer,7 dementia,8 and urinary incontinence.9 In particular, the strong association between depression (MDD) and cardiovascular disease has led to the conceptualization of a subtype of depression termed vascular depression.10 MDD accompanying medical illness poses a challenge to clinicians charged with care, but it also provides an opportunity to study the interplay of treatment for depression with medical illness.11 Previous studies have suggested an association between chronic medical conditions and treatment outcomes for MDD, although the evidence has been conflicting. The recovery rate for MDD has been found to be lower for patients with comorbid medical conditions,12,13 and the presence of physical illness has been associated with greater chronicity of depression.14,15 However, patients with comorbid medical conditions appear to respond to antidepressants as well as patients without comorbid medical conditions.16 Other investigations used the Cumulative Illness Rating Scale for Geriatrics (CIRS–G) to yield a quantitative measure of overall medical burden and to abstract specific cerebrovascular risk factor scores and found that, with intensive treatment, medical burden and cerebrovascular risk were not related to depression outcomes.17–19 Koike and colleagues20 found that quality-improvement programs for MDD improved depression outcomes for depressed primary-care patients with medical comorbidity. Whether a treatment intervention can overcome the influence of specific medical illnesses on the course of MDD remains unclear. In particular, information is lacking on the relationship of specific medical conditions to the outcomes of MDD in older primary-care patients in the context of an intervention trial. For the most part, the previous studies did not involve randomization. The PROSPECT Study (Prevention of Suicide in Primary-Care Elderly: Collaborative Trial) was a multisite effectiveness trial designed to assess the use of care-management on reducing major risk factors for suicide in late life, primarily the risk factor
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of depression.21 Depression care-management has been shown to be effective in a range of primarycare practices that serve patients with diverse sociodemographic and clinical characteristics.21–23 Because a depression care-manager providing care in primary-care practices is both costly and time-intensive, we sought to identify which patients with specific medical illnesses would benefit the most from the intervention. To provide a more detailed analysis of our data, we examined both remission of depression and response to depression treatment as outcomes. Remission, defined as an almost-asymptomatic state, is a critical clinical goal in the care of depression. Patients left with residual depressive symptoms have functional impairment, compromised quality of life, and high utilization of healthcare services.24 Moreover, remission is a stable clinical state with a lower risk for relapse than improvement of depression that leaves the patient with residual symptoms.25,26 Remission, therefore, is a more optimal goal than measures of response of depression, typically defined as a 50% reduction of symptoms since baseline, which is the focus of most pharmacological studies. Our analysis had two specific goals: The first goal was to identify medical conditions in an intervention trial associated with poor remission and response rates of MDD in elderly primary-care patients. Consistent with previous research on specific medical illnesses and treatment outcomes for depression.13 we hypothesized that older persons with specific medical illnesses with ongoing symptoms, for example, arthritis, often associated with ongoing pain, or heart failure, often associated with ongoing fatigue, would be less likely to achieve remission. The second goal was to examine whether the intervention of the PROSPECT study modified the impact of medical conditions on the outcomes of depression. Specifically, we hypothesized that the PROSPECT intervention would modify the effects of medical comorbidity seen in usual care, particularly among persons with medical conditions requiring ongoing symptom management. We recognize the exploratory nature of our analyses but took the opportunity afforded by the PROSPECT Study to probe the association of specific medical conditions on outcomes in a depression intervention trial.
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Bogner et al. METHODS The PROSPECT Study The PROSPECT Study compared a primary-care– based intervention with usual care in improving the outcomes of MDD. All study procedures were implemented with written informed consent from the Institutional Review Board of Cornell University, the University of Pittsburgh, and the University of Pennsylvania Schools of Medicine. Details of the study design of The PROSPECT Study are available elsewhere.21,27,28 All patients with a Center for Epidemiologic Studies Depression scale (CES–D)29 score ⬎20 were invited into the study, and, also, a 5% random sample of patients with lower scores were invited to participate. Participants who agreed to be part of the study were scheduled for an in-person interview at the primary-care practice site. Participating patients also were administered telephone assessments at 4 months and 8 months and an in-person interview at 1 year. Intervention Condition The intervention has been described in detail elsewhere.21 Briefly, the intervention comprised trained depression care-managers offering guideline-concordant recommendations to the primary-care physicians and helping patients with adherence to treatment. Patients who refused antidepressants and those who requested or required interpersonal psychotherapy (IPT) were offered IPT by the depression care-managers. In the Intervention condition, the cost of the first-line antidepressant, the selective serotonin reuptake inhibitor (citalopram), and the IPT was covered for the participants. The duration of the intervention was 2 years. Usual Care In Usual Care, physicians received patients’ depression diagnoses, informational materials on geriatric depression, and treatment guidelines for depression. However, no specific recommendations were given to physicians regarding individual patients except in psychiatric emergencies. Depression Diagnoses Trained research assistants assigned depression diagnoses to patients by use of the Structured Clinical Interview for Axis I DSM-IV Diagnoses (SCID).30 Study psychiatrists reviewed all
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the SCID ratings. Severity of depression was assessed with the 24-item Hamilton Rating Scale for Depression (Ham-D).31 Consistent with other geriatric depression studies,24 remission was defined as a HamD score lower than 10. For geriatric depression, a Ham-D score lower than 10 is often used because older patients often have chronic medical conditions resulting in somatic symptoms that are reflected in the Ham-D. In addition to the use of Ham-D score ⬍10 to define remission, we repeated the analyses, using a ⱖ50% change in the Ham-D score as a marker for response to treatment. Medical Comorbidity Persons were classified as having a medical comorbidity by self-report. The questionnaire used was based on the Charlson Comorbidity Index, supplemented by questions about common disabling conditions of late life.32 Participants were asked about myocardial infarction, heart failure, angina, angioplasty or coronary artery bypass surgery, atrial fibrillation, stroke, peripheral vascular disease, high blood pressure, diabetes, cancer, chronic pulmonary disease, peptic ulcer disease, and joint disease. Cognitive status was assessed with the Mini-Mental State Exam (MMSE), which is a short standardized mental status examination that has been widely used for clinical and research purposes.33 MMSE scores were analyzed as a dichotomous variable, with scores less than 24 representing cognitive impairment. Total medical burden was calculated by adding up the number of medical conditions. The median number of medical conditions in our sample was 4, and persons with 4 or more conditions were considered to have high medical burden. Data Analysis Our data analysis proceeded in two phases corresponding to the two aims of the study. In the first phase, we estimated the remission and response rates for depression in patients with specific chronic medical conditions as well as in patients with high medical burden. An estimate of association (odds ratio [OR]) along with corresponding standard error and p value (two-tailed) was produced for individual chronic medical conditions, with remission of depression (Ham-D ⬍10) and depression response (change in Ham-D score ⱖ50%) as the outcome at 4,
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Medical Comorbidity and Depression 8, and 12 months, according to group assignment. These analyses were based on longitudinal models with random effects for clustering by patient, practice, or practice pairs. For all longitudinal and depression outcomes, clustering by practice and pairs of practice was negligible and did not affect the analysis. All models were adjusted for baseline Ham-D score and for the presence or absence of suicidal ideation. We augmented this analysis with additional analyses adjusting for age, gender, and ethnicity. We calculated the omnibus test statistic representing a test of the statistical significance of the time ⳯ group interaction.34,35 In the second phase, we were interested not only in whether the intervention was effective in the face of chronic medical illness, but also in whether the intervention modified the relationship between chronic medical conditions and remission of depression. To accomplish this aim, we introduced terms representing the interaction between presence of a chronic medical condition and the intervention condition into separate models for each medical condition. Both the PROC NLMIXED and the GLIMMIX macros in SAS were used to apply two- and threelevel random-effects models, respectively, for binary outcomes. We provide unadjusted p values and p values adjusted for multiple comparisons within groups of medical conditions by dividing the p value by the number of medical conditions in each group (the method of Bonferroni).36 For our analyses adjusted for multiple comparisons, we set alpha at 0.05, recognizing that tests of statistical significance are approximations that serve as aids to interpretation and inference.
RESULTS Study Sample The results of screening and enrollment for The PROSPECT Study have been described in detail elsewhere.21 Our study sample included 396 persons who met criteria for MDD. Seventy-two people were excluded because they did not complete a 4-month follow-up visit, leaving a sample size of 324 for this analysis. The mean age of our study sample was 69.5 years, with a standard deviation (SD) of 7.7 years. The age range was 60 to 90 years; 240 of the participants
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(74.1%) were women. The self-identified ethnic groups of the participants consisted of 233 white (68.8%), 93 African Americans (28.7%), and 8 American Indians, Hispanics, or Asians (2.5%). Medical Comorbidity and Clinical Remission and Response There were differences in the rates of remission between persons with fewer than four medical conditions versus persons with four-or-more medical conditions (omnibus v2[3]⳱10.84; p⳱0.013) with 40% remission at 4 months, 42% at 8 months, and 53% at 1 year, versus 23%, 35%, and 45%, respectively. The rates of response did not differ significantly between groups. However, looking at total number of conditions in this way obscures any differences in outcomes between specific disorders, as shown in Table 1. Medical Comorbidity and Clinical Remission According to Group Assignment Corresponding to our first aim, we evaluated the association of specific medical conditions and clinical remission (Ham-D ⬍10) according to group assignment by use of multiple logistic regression. The results for the Usual Care practices presented in Table 2 demonstrate significant omnibus trends for only two chronic medical conditions (atrial fibrillation and chronic pulmonary disease). Older adults in Usual Care who had atrial fibrillation were significantly more likely to achieve remission at 4 months and 8 months than were older adults who were in Usual Care and who did not have atrial fibrillation. The small number of persons with atrial fibrillation precludes the estimation of a stable relative odds estimate for 12 months in Usual Care. In models that controlled for age, gender, and ethnicity, the association between atrial fibrillation and clinical remission remained significant (omnibus v2[3]⳱15.71; p⳱0.001, adjusted for multiple comparisons: p⳱0.008). Older adults with chronic pulmonary disease, in Usual Care, were less likely to achieve remission at 4 months and 8 months, but were more likely to achieve remission at 12 months than were older adults without chronic pulmonary disease who were in Usual Care. In models controlling for age, gender, and ethnicity, the association between chronic pulmonary disease and clinical remission remained statistically significant (omnibus v2[3]⳱13.08; p⳱0.004; adjusted for multiple comparisons: p⳱0.024).
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Bogner et al. The results for remission (Ham-D ⬍10) for the intervention practices presented in the middle columns of Table 2 demonstrate that in the Intervention practices, none of the chronic medical conditions had a statistically significant influence on the remission rate. In other words, persons with chronic medical conditions were no more or less likely to remit than were persons without chronic medical conditions in the Intervention group. Corresponding to our second aim, we examined each interaction between intervention assignment and specific medical condition. Our results, presented in the right-hand column of Table 2, after correction for multiple comparisons, did not yield any statistically significant interactions between the presence of specific medical illnesses and the intervention condition. The interaction between atrial fibrillation and intervention assignment did not remain statistically
TABLE 1.
significant after adjustment for multiple comparisons (omnibus v2[3]⳱12.17; p⳱0.007; adjusted for multiple comparisons: p⳱0.056). Medical Comorbidity and Response According to Group Assignment The next set of analyses parallels the previous section; however, the outcome now is change in Ham-D scores ⱖ50%. Results for response were similar to results for remission discussed previously. Here, we highlight the differences from remission. Older adults with MMSE ⬍24, in Usual Care, were more likely to achieve remission at 4 months and 8 months but were less likely to maintain it at 12 months. The findings related to MMSE score remained significant in the models adjusting for age, gender, and ethnicity (omnibus v2[3]⳱12.43; p⳱0.006; adjusted for multiple
Clinical Remission for Patients With Major Depression (MDD) by Specific Conditions (Nⴔ324) Remission of Depression (Ham-D ⬍10) % (N)
Medical Comorbidity
Presence of Disease
4-Month
8-Month
12-Month
present absent present absent present absent present absent present absent present absent present absent present absent
38% (58) 31% (258) 27% (22) 33% (302) 23% (95) 36% (224) 34% (47) 32% (272) 36% (22) 33% (289) 29% (82) 33% (237) 26% (108) 36% (211) 32% (220) 33% (98)
47% (53) 38% (237) 33% (21) 40% (278) 33% (82) 42% (211) 44% (43) 38% (250) 54% (24) 38% (263) 35% (74) 41% (219) 42% (96) 38% (197) 39% (198) 39% (94)
52% (42) 49% (217) 44% (18) 50% (249) 43% (76) 52% (186) 44% (36) 50% (226) 65% (17) 48% (240) 48% (62) 50% (200) 48% (84) 51% (178) 49% (180) 51% (82)
present absent present absent present absent present absent present absent present absent
22% (72) 35% (246) 24% (42) 33% (275) 24% (100) 36% (219) 17% (35) 34% (282) 28% (192) 39% (121) 26% (31) 33% (293)
33% (67) 41% (225) 38% (40) 39% (251) 40% (85) 39% (208) 28% (32) 41% (258) 38% (178) 41% (111) 31% (26) 40% (273)
43% (56) 52% (205) 46% (37) 50% (223) 55% (73) 48% (189) 53% (30) 49% (230) 48% (154) 52% (104) 44% (25) 50% (242)
Vascular conditions Myocardial infarction Heart failure Angina Angioplasty or bypass surgery Atrial fibrillation Stroke Peripheral vascular disease High blood pressure Non-vascular conditions Diabetes Cancer Chronic pulmonary disease Peptic ulcer disease Joint disease MMSE ⬍24
Note: Data were gathered from The PROSPECT Study. Ham-D: Hamilton Rating Scale for Depression; MMSE: Mini-Mental State Exam. Column percent represents percentage of the total number available who remitted in that category. Number in parentheses represents the total number of patients available for analysis at that time-point.
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Medical Comorbidity and Depression TABLE 2.
Relative Odds Ratio (OR) for Remission (Ham-D ⬍10) of Major Depression (MDD) for Selected Medical Conditions by Group Assignment Group Assignment Usual Care
Medical Condition Vascular conditions Myocardial infarction Heart failure Angina Angioplasty or Bypass surgery Atrial fibrillation Stroke Peripheral vascular disease High blood pressure Non-vascular conditions Diabetes Cancer Chronic pulmonary disease Peptic ulcer disease Joint disease MMSE ⬍24
Intervention
v2[3]; p
4 Months
8 Months
12 Months
v2[3]; p
4 Months
8 Months
12 Months
Interaction v2; p
3.73, 0.29 3.59, 0.31 3.41, 0.33 1.91, 0.59 14.59, 0.002 0.57, 0.90 4.16, 0.24 0.62, 0.89
3.87 0.68 0.34 2.28 1.45 0.73 0.18 0.54
4.95 0.29 0.23 3.57 6.94 0.52 0.82 0.80
3.33 10.76 0.66 1.10 ** 0.95 0.61 0.93
0.34, 0.95 0.98, 0.81 7.56, 0.06 0.44, 0.93 2.15, 0.54 1.18, 0.76 4.68, 0.20 0.22, 0.97
0.99 0.38 0.17 0.59 1.17 0.61 0.61 1.08
1.26 1.02 0.83 0.94 2.86 0.92 2.62 1.26
0.74 0.41 0.60 0.69 0.32 1.38 1.46 1.36
2.09, 0.55 3.77, 0.29 2.94, 0.40 1.40, 0.70 12.17, 0.007 0.47, 0.93 1.74, 0.63 0.43, 0.93
6.78, 0.079 1.61, 0.66 12.20, 0.007 3.41, 0.33 4.28, 0.23 1.97, 0.58
0.09 0.26 0.27 0.23 0.30 1.74
0.47 0.60 0.77 0.37 0.40 0.61
0.16 0.52 9.04 1.85 1.31 0.29
4.91, 0.18 0.06, 0.99 4.49, 0.21 2.82, 0.42 3.90, 0.27 2.13, 0.55
0.30 1.13 0.33 0.20 0.35 0.21
0.61 1.27 1.45 0.30 1.03 0.38
1.73 1.13 1.13 1.88 0.44 0.74
4.19, 0.24 1.01, 0.80 5.33, 0.15 0.02, 0.99 3.44, 0.33 2.93, 0.40
Note: Data were gathered from The PROSPECT Study. Ham-D: Hamilton Rating Scale for Depression; MMSE: Mini-Mental State Exam. v2[3] represents an omnibus test. OR estimates are adjusted for baseline Ham-D and suicidal ideation. **The number of cases of atrial fibrillation was too small to yield a stable OR estimate.
comparisons: p⳱0.036). The results for change in Ham-D ⱖ50% demonstrated no statistically significant associations of specific physical illnesses with change in Ham-D scores in the Intervention condition. There were no statistically significant interactions between the presence of specific medical illnesses and intervention condition in the set of analyses when change in Ham-D ⱖ50% was the outcome.
DISCUSSION The principal finding of this study was that chronic pulmonary disease was associated with low remission rates of depression during the early phases of follow-up in older patients receiving Usual Care, whereas atrial fibrillation was associated with improved outcomes in Usual Care. Comorbid cognitive impairment was associated with a less stable response in Usual Care. Importantly, no medical illness or cognitive disorder significantly influenced the remission and response rates of depression in patients in primary-care practices implementing PROSPECT’s intervention. The Intervention approach appears, at least in part, to overcome the effects of medical comorbidity.
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Our results are not wholly consistent with our initial hypotheses. Summarizing our findings relating to our first aim, we observed four distinct patterns for the effect of medical conditions on depression outcomes. Most conditions had no impact on outcomes. Atrial fibrillation in Usual Care was associated with improved outcomes; however, chronic pulmonary disease in Usual Care was associated with delayed remission. Finally, cognitive impairment in Usual Care was associated with a less stable response, with outcomes comparable to those for other patients over the short term, but with a worse response over time. By contrast, the depression outcomes for patients in the Intervention condition did not significantly differ among persons with and without specific medical illnesses, regardless of the outcome criterion. Overall, the findings support the hypothesis that specific medical conditions may influence depression outcomes in Usual Care, but the mechanisms have yet to be fully understood. There may be unmeasured biological, psychological, and social mechanisms that are important in explaining the effect of medical conditions with ongoing symptoms versus medical conditions presenting as episodic events on treatment outcomes for depression in Usual Care. The differences in depression outcomes might be due to the different nature of the medical conditions, with atrial
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Bogner et al. fibrillation presenting as episodic events, whereas chronic pulmonary disease has ongoing symptoms. The second aim of our research was to examine whether the assignment to PROSPECT’s Intervention condition or Usual Care modified the relationship between the presence of a chronic medical illness and outcomes for depression. There were no significant interactions present after correction for multiple comparisons. In general, our findings seem to indicate that the effects of medical conditions are apparent in the Usual Care sample, but that they are not significant in the Intervention condition. For example, a history of atrial fibrillation appeared to increase the rate of remission in Usual Care patients up to that observed in the Intervention condition. Cardiac disease may represent a “wake-up call” for Usual Care patients and providers, leading them to focus more carefully on the treatment of depression. In contrast, the delayed response in chronic pulmonary disease may have occurred because chronic pulmonary disease interfered with the management of depression. Also, the loss of response in individuals with cognitive impairment in Usual Care may have also occurred because cognitive impairment interfered with the management of depression beyond the initial treatment phase. Consistent with these explanations, the lack of significant effects of the illnesses in Intervention patients suggests that the Intervention can, at least in part, overcome the effects of medical comorbidity. Our results are consistent with other studies that found an attenuation of the effect of medical comorbidity on depression outcomes in depression interventions using measures of total medical comorbidity, rather than specific medical conditions.17–19 Our observations require further study in focused investigations of mechanisms of intervention effects in specific medical conditions. Our results must be considered in the context of some potential study limitations. First, we obtained our results only from primary-care sites in greater New York City, Philadelphia, and Pittsburgh, where patients may not be representative of other primarycare practices in the United States. Second, there is the potential for all the sources of error associated with retrospective interview data, including imperfect recall and response bias. Study data are based on self-reports, including the data on medical comorbidity. Identification of medical comorbidity is complex, and each method used to ascertain the presence of
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medical illnesses has limitations. Patients may not know or be able to recall their diagnoses and may misuse terms; however, medical records are often incomplete. For example, relying on medical records may miss persons who received health care in more than one health system. Third, we were not able to take into account the severity level of the specific medical condition. However, we address the cumulative effect of medical conditions by examining rates of remission between persons with fewer than four medical conditions versus persons with four or more medical conditions. Fourth, selection bias is a potential limitation because, although the larger project was based on a random sample of primary-care patients, the data on medical comorbidity and clinical remission of depression consisted of all who were selected for the larger project, agreed to participate, and had complete information. Fifth, treatment may have changed frequently, both in the Intervention and the Usual Care practices, and our analyses do not account for change in treatment. Finally, we recognize that this is a post-hoc analysis, and there is also the potential role of chance because of the small number of participants in some subgroups, such as those with atrial fibrillation. Late-life depression often presents in primary-care patients with medical comorbidity. We found that certain chronic medical conditions may play a role in depression outcomes among elderly primary-care patients, but the effect of specific medical comorbidity may be overcome by care management. Our results suggest that trained depression-care managers, using algorithm-based care, can attenuate the effects of medical comorbidity. Therefore, such interventions can improve the quality of care for late-life depression, where medical comorbidity is common. Interventions designed to improve depression treatment, to be sustainable and acceptable to physicians and patients, must account for the medical comorbidity that commonly accompanies depression in older persons. Identifying patients with specific chronic medical conditions that will benefit most from special attention may be key to improving outcomes for depression in primary care. We should consider integration of treatment for depression with the treatment for specific medical conditions. This work was supported by NIMH grants R01 MH59366, R01 MH59380, and R01 MH59381; addi-
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Medical Comorbidity and Depression tional small grants came from Forest Laboratories and The John D. Hartford Foundation. Dr. Bogner was supported
by an NIMH Mentored Patient-Oriented Research Career Development Award (MH67671-01).
References 1. Wells KB, Rogers W, Burnam A, et al: How the medical comorbidity of depressed patients differs across healthcare settings: results from the Medical Outcomes Study. Am J Psychiatry 1991; 148:1688–1696 2. Frasure-Smith N, Lesperance F, Gravel G, et al: Social support, depression, and mortality during the first year after myocardial infarction. Circulation 2000; 101:1919–1924 3. Gottlieb SS, Khatta M, Friedmann E, et al: The influence of age, gender, and race on the prevalence of depression in heart failure patients. J Am Coll Cardiol 2004; 43:1542–1549 4. Steffens DC, O’Connor CM, Jiang WJ, et al: The effect of major depression on functional status in patients with coronary artery disease. J Am Geriatr Soc 1999; 47:319–322 5. Whyte EM, Mulsant BH, Vanderbilt J, et al: Depression after stroke: a prospective epidemiological study. J Am Geriatr Soc 2004; 52:774–778 6. Grandinetti A, Kaholokula JK, Crabbe KM, et al: Relationship between depressive symptoms and diabetes among native Hawaiians. Psychoneuroendocrinology 2000; 25:239–246 7. Holland JC, Romano SJ, Heiligenstein JH, et al: A controlled trial of fluoxetine and desipramine in depressed women with advanced cancer. Psycho-oncology 1998; 7:291–300 8. Reichman WE, Coyne AC: Depressive symptoms in Alzheimer’s disease and multi-infarct dementia. J Geriatr Psychiatry Neurol 1995; 8:96–99 9. Bogner HR, Gallo JJ, Sammel MD, et al: Urinary incontinence and psychological distress among community-dwelling older adults. J Am Geriatr Soc 2002; 50:489–495 10. Alexopoulos GS, Meyers BS, Young RC, et al: “Vascular depression” hypothesis. Arch Gen Psychiatry 1997; 54:915–922 11. Alexopoulos GS, Buckwalter K, Olin J, et al: Comorbidity of latelife depression: an opportunity for research on mechanisms and treatment. Biol Psychiatry 2002; 52:543–558 12. Keitner GI, Ryan CE, Miller IW, et al: Twelve-month outcome of patients with major depression and comorbid psychiatric or medical illness (compound depression). Am J Psychiatry 1991; 148:345–350 13. Oslin DW, Datto CJ, Kallan MJ, et al: Association between medical comorbidity and treatment outcomes in late-life depression. J Am Geriatr Soc 2002; 50:823–828 14. Swindle RWJ, Cronkite RC, Moos RH: Risk factors for sustained nonremission of depressive symptoms: a 4-year follow-up. J Nerv Ment Dis 1998; 186:462–469 15. Akiskal HS: Factors associated with incomplete recovery in primary depressive illness. J Clin Psychiatry 1982; 43:266–271 16. Papakostas GI, Petersen T, Iosifescu DV, et al: Axis III disorders in treatment-resistant major depressive disorder. Psychiatry Res 2003; 118:183–188 17. Reynolds CF III, Frank E, Perel JM, et al: Nortriptyline and IPT as maintenance therapies for recurrent major depression: a randomized, controlled trial in patients older than 59 years. JAMA 1999; 281:39–45 18. Miller MD, Lenze EJ, Dew MA, et al: The effect of cerebrovascular
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risk factors on depression treatment outcome in late life depression. Am J Geriatr Psychiatry 2002; 10:592–589 19. Miller MD, Paradis CF, Houck PR, et al: Chronic medical illness in ambulatory elders with recurrent major depression: effects on acute treatment outcome. Am J Geriatr Psychiatry 1996; 4:281– 290 20. Koike AK, Unu ¨ tzer J, Wells KB: Improving the care for depression in patients with comorbid medical illness. Am J Psychiatry 2002; 159:1738–1745 21. Bruce ML, Ten Have TR, Reynolds CF III, et al: Reducing suicidal ideation and depressive symptoms in depressed older primarycare patients: a randomized, controlled trial. JAMA 2004; 291:1081–1091 22. Harpole LH, Stechuchak KM, Saur CD, et al: Implementing a disease-management intervention for depression in primary care: a random work sampling study. Gen Hosp Psychiatry 2003; 25:238–245 ¨ tzer J, Katon W, Callahan CM, et al: Collaborative care man23. Unu agement of late-life depression in the primary-care setting: a randomized, controlled trial. JAMA 2002; 288:2836–2845 24. Lecrubier Y: How do you define remission? Acta Psychiatr Scand 2002; (suppl):415:7–11 25. Judd LL, Akiskal HS, Maser JD, et al: Major depressive disorder: a prospective study of residual subthreshold depressive symptoms as predictor of rapid relapse. J Affect Disord 1998; 50:97–108 26. Thase ME, Entsuah AR, Rudolph RL: Remission rates during treatment with venlafaxine or selective serotonin reuptake inhibitors. Br J Psychiatry 2001; 178:234–241 27. Mulsant BH, Alexopoulos GS, Reynolds CF III, et al: Pharmacological treatment of depression in older primary-care patients: the PROSPECT algorithm. Int J Geriatr Psychiatry 2001; 16:585–592 28. Schulberg HC, Bryce C, Chism K, et al: Managing depression in late life in primary care practice: a case study of the health specialist’s role. Int J Geriatr Psychiatry 2001; 16:577–584 29. Radloff LS: The CES–D Scale: a self-report depression scale for research in the general population. Applied Psychol Meas 1977; 1:385–401 30. Spitzer RL, Gibbon M, Williams JB: Structured Clinical Interview for Axis I DSM-IV Disorders (SCID). Washington, DC, American Psychiatric Association Press, Inc., 1995 31. Hamilton M: A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56–62 32. Charlson ME, Pompei P, Ales KL, et al: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis 1987; 40:373–383 33. Folstein MF, Folstein SE, McHugh PR: “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189–198 34. Winer BJ: Statistical Principles in Experimental Design, 2nd Ed. New York, McGraw-Hill, 1971, chap. 4 35. Miliken GA, Johnson DE: Analysis of Messy Data, Vol 1: Designed Experiments. New York, Van Nostrand, 1984, chap. 26, 27 36. Oakes M: Statistical Inference. Chestnut Hill, Massachusetts, Epidemiological Resources, 1990
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Objective: The authors described the influence of specific medical conditions on clinical remission and response of major depression (MDD) in a clinical trial evaluating a care-management intervention among older primary-care patients. Methods: Adults age 60 years and older were randomly selected and screened for depression. Participants were randomly assigned to Usual Care or to an Intervention with a depression care-manager offering algorithm-based care for MDD. In all, 324 adults meeting criteria for MDD were included in these analyses. Remission and response was defined by a score on the Hamilton Rating Scale for Depression ⬍10 and by a decrease from baseline of ⱖ50%, respectively. Medical comorbidity was ascertained through self-report. Cognitive impairment was defined by a score ⬍24 on the MiniMental State Exam (MMSE). Results: In Usual Care, rates of remission were faster in persons who reported atrial fibrillation (AF) than in persons who did not report AF and slower in persons who reported chronic pulmonary disease than in persons who did not report chronic pulmonary disease; rates of response were less stable in persons with MMSE ⬍24 than in those with MMSE ⱖ24. In the Intervention condition, none of the specific chronic medical conditions were significantly associated with outcomes for MDD. Conclusions: Because disease-specific findings were observed in persons who received Usual Care but not in persons who received more intensive treatment in the Intervention condition, our results suggest that the association of medical comorbidity and treatment outcomes for MDD may be determined by the intensity of treatment for depression. (Am J Geriatr Psychiatry 2005; 13:861–868)
Received February 10, 2005; revised June 20, 2005; accepted June 21, 2005. From the Dept. of Family Practice and Community Medicine, Univ. of Pennsylvania, Philadelphia, PA (HRB), the Center for Clinical Epidemiology and Biostatistics, Univ. of Pennsylvania (MSC, TTH), the Dept. of Psychiatry, Weill Medical College of Cornell University (MLB, GSA), and the Dept. of Psychiatry, Univ. of Pittsburgh School of Medicine (CFR, BM). Send correspondence and reprint requests to Hillary R. Bogner, M.D., M.S.C.E., Assistant Professor, Dept. of Family Practice and Community Medicine, Univ. of Pennsylvania, 3400 Spruce Street, 2 Gates Building, Philadelphia, PA 19104. e-mail: [email protected] 䉷 2005 American Association for Geriatric Psychiatry
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Medical Comorbidity and Depression
D
epression is one of the most common problems in primary-care settings. Depressed patients seen in primary care have a high prevalence of medical comorbidity.1 Specifically, investigators have documented the association of depression with: myocardial infarction (MI),2 heart failure,3 coronary artery disease,4 stroke,5 diabetes,6 cancer,7 dementia,8 and urinary incontinence.9 In particular, the strong association between depression (MDD) and cardiovascular disease has led to the conceptualization of a subtype of depression termed vascular depression.10 MDD accompanying medical illness poses a challenge to clinicians charged with care, but it also provides an opportunity to study the interplay of treatment for depression with medical illness.11 Previous studies have suggested an association between chronic medical conditions and treatment outcomes for MDD, although the evidence has been conflicting. The recovery rate for MDD has been found to be lower for patients with comorbid medical conditions,12,13 and the presence of physical illness has been associated with greater chronicity of depression.14,15 However, patients with comorbid medical conditions appear to respond to antidepressants as well as patients without comorbid medical conditions.16 Other investigations used the Cumulative Illness Rating Scale for Geriatrics (CIRS–G) to yield a quantitative measure of overall medical burden and to abstract specific cerebrovascular risk factor scores and found that, with intensive treatment, medical burden and cerebrovascular risk were not related to depression outcomes.17–19 Koike and colleagues20 found that quality-improvement programs for MDD improved depression outcomes for depressed primary-care patients with medical comorbidity. Whether a treatment intervention can overcome the influence of specific medical illnesses on the course of MDD remains unclear. In particular, information is lacking on the relationship of specific medical conditions to the outcomes of MDD in older primary-care patients in the context of an intervention trial. For the most part, the previous studies did not involve randomization. The PROSPECT Study (Prevention of Suicide in Primary-Care Elderly: Collaborative Trial) was a multisite effectiveness trial designed to assess the use of care-management on reducing major risk factors for suicide in late life, primarily the risk factor
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of depression.21 Depression care-management has been shown to be effective in a range of primarycare practices that serve patients with diverse sociodemographic and clinical characteristics.21–23 Because a depression care-manager providing care in primary-care practices is both costly and time-intensive, we sought to identify which patients with specific medical illnesses would benefit the most from the intervention. To provide a more detailed analysis of our data, we examined both remission of depression and response to depression treatment as outcomes. Remission, defined as an almost-asymptomatic state, is a critical clinical goal in the care of depression. Patients left with residual depressive symptoms have functional impairment, compromised quality of life, and high utilization of healthcare services.24 Moreover, remission is a stable clinical state with a lower risk for relapse than improvement of depression that leaves the patient with residual symptoms.25,26 Remission, therefore, is a more optimal goal than measures of response of depression, typically defined as a 50% reduction of symptoms since baseline, which is the focus of most pharmacological studies. Our analysis had two specific goals: The first goal was to identify medical conditions in an intervention trial associated with poor remission and response rates of MDD in elderly primary-care patients. Consistent with previous research on specific medical illnesses and treatment outcomes for depression.13 we hypothesized that older persons with specific medical illnesses with ongoing symptoms, for example, arthritis, often associated with ongoing pain, or heart failure, often associated with ongoing fatigue, would be less likely to achieve remission. The second goal was to examine whether the intervention of the PROSPECT study modified the impact of medical conditions on the outcomes of depression. Specifically, we hypothesized that the PROSPECT intervention would modify the effects of medical comorbidity seen in usual care, particularly among persons with medical conditions requiring ongoing symptom management. We recognize the exploratory nature of our analyses but took the opportunity afforded by the PROSPECT Study to probe the association of specific medical conditions on outcomes in a depression intervention trial.
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Bogner et al. METHODS The PROSPECT Study The PROSPECT Study compared a primary-care– based intervention with usual care in improving the outcomes of MDD. All study procedures were implemented with written informed consent from the Institutional Review Board of Cornell University, the University of Pittsburgh, and the University of Pennsylvania Schools of Medicine. Details of the study design of The PROSPECT Study are available elsewhere.21,27,28 All patients with a Center for Epidemiologic Studies Depression scale (CES–D)29 score ⬎20 were invited into the study, and, also, a 5% random sample of patients with lower scores were invited to participate. Participants who agreed to be part of the study were scheduled for an in-person interview at the primary-care practice site. Participating patients also were administered telephone assessments at 4 months and 8 months and an in-person interview at 1 year. Intervention Condition The intervention has been described in detail elsewhere.21 Briefly, the intervention comprised trained depression care-managers offering guideline-concordant recommendations to the primary-care physicians and helping patients with adherence to treatment. Patients who refused antidepressants and those who requested or required interpersonal psychotherapy (IPT) were offered IPT by the depression care-managers. In the Intervention condition, the cost of the first-line antidepressant, the selective serotonin reuptake inhibitor (citalopram), and the IPT was covered for the participants. The duration of the intervention was 2 years. Usual Care In Usual Care, physicians received patients’ depression diagnoses, informational materials on geriatric depression, and treatment guidelines for depression. However, no specific recommendations were given to physicians regarding individual patients except in psychiatric emergencies. Depression Diagnoses Trained research assistants assigned depression diagnoses to patients by use of the Structured Clinical Interview for Axis I DSM-IV Diagnoses (SCID).30 Study psychiatrists reviewed all
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the SCID ratings. Severity of depression was assessed with the 24-item Hamilton Rating Scale for Depression (Ham-D).31 Consistent with other geriatric depression studies,24 remission was defined as a HamD score lower than 10. For geriatric depression, a Ham-D score lower than 10 is often used because older patients often have chronic medical conditions resulting in somatic symptoms that are reflected in the Ham-D. In addition to the use of Ham-D score ⬍10 to define remission, we repeated the analyses, using a ⱖ50% change in the Ham-D score as a marker for response to treatment. Medical Comorbidity Persons were classified as having a medical comorbidity by self-report. The questionnaire used was based on the Charlson Comorbidity Index, supplemented by questions about common disabling conditions of late life.32 Participants were asked about myocardial infarction, heart failure, angina, angioplasty or coronary artery bypass surgery, atrial fibrillation, stroke, peripheral vascular disease, high blood pressure, diabetes, cancer, chronic pulmonary disease, peptic ulcer disease, and joint disease. Cognitive status was assessed with the Mini-Mental State Exam (MMSE), which is a short standardized mental status examination that has been widely used for clinical and research purposes.33 MMSE scores were analyzed as a dichotomous variable, with scores less than 24 representing cognitive impairment. Total medical burden was calculated by adding up the number of medical conditions. The median number of medical conditions in our sample was 4, and persons with 4 or more conditions were considered to have high medical burden. Data Analysis Our data analysis proceeded in two phases corresponding to the two aims of the study. In the first phase, we estimated the remission and response rates for depression in patients with specific chronic medical conditions as well as in patients with high medical burden. An estimate of association (odds ratio [OR]) along with corresponding standard error and p value (two-tailed) was produced for individual chronic medical conditions, with remission of depression (Ham-D ⬍10) and depression response (change in Ham-D score ⱖ50%) as the outcome at 4,
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Medical Comorbidity and Depression 8, and 12 months, according to group assignment. These analyses were based on longitudinal models with random effects for clustering by patient, practice, or practice pairs. For all longitudinal and depression outcomes, clustering by practice and pairs of practice was negligible and did not affect the analysis. All models were adjusted for baseline Ham-D score and for the presence or absence of suicidal ideation. We augmented this analysis with additional analyses adjusting for age, gender, and ethnicity. We calculated the omnibus test statistic representing a test of the statistical significance of the time ⳯ group interaction.34,35 In the second phase, we were interested not only in whether the intervention was effective in the face of chronic medical illness, but also in whether the intervention modified the relationship between chronic medical conditions and remission of depression. To accomplish this aim, we introduced terms representing the interaction between presence of a chronic medical condition and the intervention condition into separate models for each medical condition. Both the PROC NLMIXED and the GLIMMIX macros in SAS were used to apply two- and threelevel random-effects models, respectively, for binary outcomes. We provide unadjusted p values and p values adjusted for multiple comparisons within groups of medical conditions by dividing the p value by the number of medical conditions in each group (the method of Bonferroni).36 For our analyses adjusted for multiple comparisons, we set alpha at 0.05, recognizing that tests of statistical significance are approximations that serve as aids to interpretation and inference.
RESULTS Study Sample The results of screening and enrollment for The PROSPECT Study have been described in detail elsewhere.21 Our study sample included 396 persons who met criteria for MDD. Seventy-two people were excluded because they did not complete a 4-month follow-up visit, leaving a sample size of 324 for this analysis. The mean age of our study sample was 69.5 years, with a standard deviation (SD) of 7.7 years. The age range was 60 to 90 years; 240 of the participants
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(74.1%) were women. The self-identified ethnic groups of the participants consisted of 233 white (68.8%), 93 African Americans (28.7%), and 8 American Indians, Hispanics, or Asians (2.5%). Medical Comorbidity and Clinical Remission and Response There were differences in the rates of remission between persons with fewer than four medical conditions versus persons with four-or-more medical conditions (omnibus v2[3]⳱10.84; p⳱0.013) with 40% remission at 4 months, 42% at 8 months, and 53% at 1 year, versus 23%, 35%, and 45%, respectively. The rates of response did not differ significantly between groups. However, looking at total number of conditions in this way obscures any differences in outcomes between specific disorders, as shown in Table 1. Medical Comorbidity and Clinical Remission According to Group Assignment Corresponding to our first aim, we evaluated the association of specific medical conditions and clinical remission (Ham-D ⬍10) according to group assignment by use of multiple logistic regression. The results for the Usual Care practices presented in Table 2 demonstrate significant omnibus trends for only two chronic medical conditions (atrial fibrillation and chronic pulmonary disease). Older adults in Usual Care who had atrial fibrillation were significantly more likely to achieve remission at 4 months and 8 months than were older adults who were in Usual Care and who did not have atrial fibrillation. The small number of persons with atrial fibrillation precludes the estimation of a stable relative odds estimate for 12 months in Usual Care. In models that controlled for age, gender, and ethnicity, the association between atrial fibrillation and clinical remission remained significant (omnibus v2[3]⳱15.71; p⳱0.001, adjusted for multiple comparisons: p⳱0.008). Older adults with chronic pulmonary disease, in Usual Care, were less likely to achieve remission at 4 months and 8 months, but were more likely to achieve remission at 12 months than were older adults without chronic pulmonary disease who were in Usual Care. In models controlling for age, gender, and ethnicity, the association between chronic pulmonary disease and clinical remission remained statistically significant (omnibus v2[3]⳱13.08; p⳱0.004; adjusted for multiple comparisons: p⳱0.024).
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Bogner et al. The results for remission (Ham-D ⬍10) for the intervention practices presented in the middle columns of Table 2 demonstrate that in the Intervention practices, none of the chronic medical conditions had a statistically significant influence on the remission rate. In other words, persons with chronic medical conditions were no more or less likely to remit than were persons without chronic medical conditions in the Intervention group. Corresponding to our second aim, we examined each interaction between intervention assignment and specific medical condition. Our results, presented in the right-hand column of Table 2, after correction for multiple comparisons, did not yield any statistically significant interactions between the presence of specific medical illnesses and the intervention condition. The interaction between atrial fibrillation and intervention assignment did not remain statistically
TABLE 1.
significant after adjustment for multiple comparisons (omnibus v2[3]⳱12.17; p⳱0.007; adjusted for multiple comparisons: p⳱0.056). Medical Comorbidity and Response According to Group Assignment The next set of analyses parallels the previous section; however, the outcome now is change in Ham-D scores ⱖ50%. Results for response were similar to results for remission discussed previously. Here, we highlight the differences from remission. Older adults with MMSE ⬍24, in Usual Care, were more likely to achieve remission at 4 months and 8 months but were less likely to maintain it at 12 months. The findings related to MMSE score remained significant in the models adjusting for age, gender, and ethnicity (omnibus v2[3]⳱12.43; p⳱0.006; adjusted for multiple
Clinical Remission for Patients With Major Depression (MDD) by Specific Conditions (Nⴔ324) Remission of Depression (Ham-D ⬍10) % (N)
Medical Comorbidity
Presence of Disease
4-Month
8-Month
12-Month
present absent present absent present absent present absent present absent present absent present absent present absent
38% (58) 31% (258) 27% (22) 33% (302) 23% (95) 36% (224) 34% (47) 32% (272) 36% (22) 33% (289) 29% (82) 33% (237) 26% (108) 36% (211) 32% (220) 33% (98)
47% (53) 38% (237) 33% (21) 40% (278) 33% (82) 42% (211) 44% (43) 38% (250) 54% (24) 38% (263) 35% (74) 41% (219) 42% (96) 38% (197) 39% (198) 39% (94)
52% (42) 49% (217) 44% (18) 50% (249) 43% (76) 52% (186) 44% (36) 50% (226) 65% (17) 48% (240) 48% (62) 50% (200) 48% (84) 51% (178) 49% (180) 51% (82)
present absent present absent present absent present absent present absent present absent
22% (72) 35% (246) 24% (42) 33% (275) 24% (100) 36% (219) 17% (35) 34% (282) 28% (192) 39% (121) 26% (31) 33% (293)
33% (67) 41% (225) 38% (40) 39% (251) 40% (85) 39% (208) 28% (32) 41% (258) 38% (178) 41% (111) 31% (26) 40% (273)
43% (56) 52% (205) 46% (37) 50% (223) 55% (73) 48% (189) 53% (30) 49% (230) 48% (154) 52% (104) 44% (25) 50% (242)
Vascular conditions Myocardial infarction Heart failure Angina Angioplasty or bypass surgery Atrial fibrillation Stroke Peripheral vascular disease High blood pressure Non-vascular conditions Diabetes Cancer Chronic pulmonary disease Peptic ulcer disease Joint disease MMSE ⬍24
Note: Data were gathered from The PROSPECT Study. Ham-D: Hamilton Rating Scale for Depression; MMSE: Mini-Mental State Exam. Column percent represents percentage of the total number available who remitted in that category. Number in parentheses represents the total number of patients available for analysis at that time-point.
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Medical Comorbidity and Depression TABLE 2.
Relative Odds Ratio (OR) for Remission (Ham-D ⬍10) of Major Depression (MDD) for Selected Medical Conditions by Group Assignment Group Assignment Usual Care
Medical Condition Vascular conditions Myocardial infarction Heart failure Angina Angioplasty or Bypass surgery Atrial fibrillation Stroke Peripheral vascular disease High blood pressure Non-vascular conditions Diabetes Cancer Chronic pulmonary disease Peptic ulcer disease Joint disease MMSE ⬍24
Intervention
v2[3]; p
4 Months
8 Months
12 Months
v2[3]; p
4 Months
8 Months
12 Months
Interaction v2; p
3.73, 0.29 3.59, 0.31 3.41, 0.33 1.91, 0.59 14.59, 0.002 0.57, 0.90 4.16, 0.24 0.62, 0.89
3.87 0.68 0.34 2.28 1.45 0.73 0.18 0.54
4.95 0.29 0.23 3.57 6.94 0.52 0.82 0.80
3.33 10.76 0.66 1.10 ** 0.95 0.61 0.93
0.34, 0.95 0.98, 0.81 7.56, 0.06 0.44, 0.93 2.15, 0.54 1.18, 0.76 4.68, 0.20 0.22, 0.97
0.99 0.38 0.17 0.59 1.17 0.61 0.61 1.08
1.26 1.02 0.83 0.94 2.86 0.92 2.62 1.26
0.74 0.41 0.60 0.69 0.32 1.38 1.46 1.36
2.09, 0.55 3.77, 0.29 2.94, 0.40 1.40, 0.70 12.17, 0.007 0.47, 0.93 1.74, 0.63 0.43, 0.93
6.78, 0.079 1.61, 0.66 12.20, 0.007 3.41, 0.33 4.28, 0.23 1.97, 0.58
0.09 0.26 0.27 0.23 0.30 1.74
0.47 0.60 0.77 0.37 0.40 0.61
0.16 0.52 9.04 1.85 1.31 0.29
4.91, 0.18 0.06, 0.99 4.49, 0.21 2.82, 0.42 3.90, 0.27 2.13, 0.55
0.30 1.13 0.33 0.20 0.35 0.21
0.61 1.27 1.45 0.30 1.03 0.38
1.73 1.13 1.13 1.88 0.44 0.74
4.19, 0.24 1.01, 0.80 5.33, 0.15 0.02, 0.99 3.44, 0.33 2.93, 0.40
Note: Data were gathered from The PROSPECT Study. Ham-D: Hamilton Rating Scale for Depression; MMSE: Mini-Mental State Exam. v2[3] represents an omnibus test. OR estimates are adjusted for baseline Ham-D and suicidal ideation. **The number of cases of atrial fibrillation was too small to yield a stable OR estimate.
comparisons: p⳱0.036). The results for change in Ham-D ⱖ50% demonstrated no statistically significant associations of specific physical illnesses with change in Ham-D scores in the Intervention condition. There were no statistically significant interactions between the presence of specific medical illnesses and intervention condition in the set of analyses when change in Ham-D ⱖ50% was the outcome.
DISCUSSION The principal finding of this study was that chronic pulmonary disease was associated with low remission rates of depression during the early phases of follow-up in older patients receiving Usual Care, whereas atrial fibrillation was associated with improved outcomes in Usual Care. Comorbid cognitive impairment was associated with a less stable response in Usual Care. Importantly, no medical illness or cognitive disorder significantly influenced the remission and response rates of depression in patients in primary-care practices implementing PROSPECT’s intervention. The Intervention approach appears, at least in part, to overcome the effects of medical comorbidity.
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Our results are not wholly consistent with our initial hypotheses. Summarizing our findings relating to our first aim, we observed four distinct patterns for the effect of medical conditions on depression outcomes. Most conditions had no impact on outcomes. Atrial fibrillation in Usual Care was associated with improved outcomes; however, chronic pulmonary disease in Usual Care was associated with delayed remission. Finally, cognitive impairment in Usual Care was associated with a less stable response, with outcomes comparable to those for other patients over the short term, but with a worse response over time. By contrast, the depression outcomes for patients in the Intervention condition did not significantly differ among persons with and without specific medical illnesses, regardless of the outcome criterion. Overall, the findings support the hypothesis that specific medical conditions may influence depression outcomes in Usual Care, but the mechanisms have yet to be fully understood. There may be unmeasured biological, psychological, and social mechanisms that are important in explaining the effect of medical conditions with ongoing symptoms versus medical conditions presenting as episodic events on treatment outcomes for depression in Usual Care. The differences in depression outcomes might be due to the different nature of the medical conditions, with atrial
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Bogner et al. fibrillation presenting as episodic events, whereas chronic pulmonary disease has ongoing symptoms. The second aim of our research was to examine whether the assignment to PROSPECT’s Intervention condition or Usual Care modified the relationship between the presence of a chronic medical illness and outcomes for depression. There were no significant interactions present after correction for multiple comparisons. In general, our findings seem to indicate that the effects of medical conditions are apparent in the Usual Care sample, but that they are not significant in the Intervention condition. For example, a history of atrial fibrillation appeared to increase the rate of remission in Usual Care patients up to that observed in the Intervention condition. Cardiac disease may represent a “wake-up call” for Usual Care patients and providers, leading them to focus more carefully on the treatment of depression. In contrast, the delayed response in chronic pulmonary disease may have occurred because chronic pulmonary disease interfered with the management of depression. Also, the loss of response in individuals with cognitive impairment in Usual Care may have also occurred because cognitive impairment interfered with the management of depression beyond the initial treatment phase. Consistent with these explanations, the lack of significant effects of the illnesses in Intervention patients suggests that the Intervention can, at least in part, overcome the effects of medical comorbidity. Our results are consistent with other studies that found an attenuation of the effect of medical comorbidity on depression outcomes in depression interventions using measures of total medical comorbidity, rather than specific medical conditions.17–19 Our observations require further study in focused investigations of mechanisms of intervention effects in specific medical conditions. Our results must be considered in the context of some potential study limitations. First, we obtained our results only from primary-care sites in greater New York City, Philadelphia, and Pittsburgh, where patients may not be representative of other primarycare practices in the United States. Second, there is the potential for all the sources of error associated with retrospective interview data, including imperfect recall and response bias. Study data are based on self-reports, including the data on medical comorbidity. Identification of medical comorbidity is complex, and each method used to ascertain the presence of
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medical illnesses has limitations. Patients may not know or be able to recall their diagnoses and may misuse terms; however, medical records are often incomplete. For example, relying on medical records may miss persons who received health care in more than one health system. Third, we were not able to take into account the severity level of the specific medical condition. However, we address the cumulative effect of medical conditions by examining rates of remission between persons with fewer than four medical conditions versus persons with four or more medical conditions. Fourth, selection bias is a potential limitation because, although the larger project was based on a random sample of primary-care patients, the data on medical comorbidity and clinical remission of depression consisted of all who were selected for the larger project, agreed to participate, and had complete information. Fifth, treatment may have changed frequently, both in the Intervention and the Usual Care practices, and our analyses do not account for change in treatment. Finally, we recognize that this is a post-hoc analysis, and there is also the potential role of chance because of the small number of participants in some subgroups, such as those with atrial fibrillation. Late-life depression often presents in primary-care patients with medical comorbidity. We found that certain chronic medical conditions may play a role in depression outcomes among elderly primary-care patients, but the effect of specific medical comorbidity may be overcome by care management. Our results suggest that trained depression-care managers, using algorithm-based care, can attenuate the effects of medical comorbidity. Therefore, such interventions can improve the quality of care for late-life depression, where medical comorbidity is common. Interventions designed to improve depression treatment, to be sustainable and acceptable to physicians and patients, must account for the medical comorbidity that commonly accompanies depression in older persons. Identifying patients with specific chronic medical conditions that will benefit most from special attention may be key to improving outcomes for depression in primary care. We should consider integration of treatment for depression with the treatment for specific medical conditions. This work was supported by NIMH grants R01 MH59366, R01 MH59380, and R01 MH59381; addi-
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Medical Comorbidity and Depression tional small grants came from Forest Laboratories and The John D. Hartford Foundation. Dr. Bogner was supported
by an NIMH Mentored Patient-Oriented Research Career Development Award (MH67671-01).
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