Medical comorbidity and health-related quality of life in bipolar disorder across the adult age span

Journal of Affective Disorders 86 (2005) 47 – 60 www.elsevier.com/locate/jad

Research report

Medical comorbidity and health-related quality of life in bipolar disorder across the adult age span Howard H. Fenna,*, Mark S. Bauerb, Lori Alshulerc, Denise R. Evansd, William O. Williforde,f, Amy M. Kilbourneg, Thomas P. Beresfordh,i, Gail Kirke, Margaret Stedmanh,i, Louis Fioreh,i for the VA Cooperative Study #430 Team a

Adjunct Clinical Associate Professor of Psychiatry Stanford University; Veterans Affairs Palo Alto Health Care System-Building 348-Menlo Park Division, 795 Willow Road, Menlo Park, CA 94025-6328 94025, United States b Providence VAMC and Brown University, United States c West Los Angeles (CA) and UCLA, United States d Augusta VAMC and Georgia Medical College, United States e Perry Point Cooperative Studies Coordinating Center, Perry Point, MD, United States f University of Maryland, College Park, MD, United States g Pittsburgh VAMC and University of Pittsburgh, United States h Denver VAMC and University of Colorado, United States i Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston, MA, United States Received 26 May 2004; accepted 9 December 2004

Abstract Background: Little is known about medical comorbidity or health-related quality of life (HRQOL) in bipolar disorder across the adult age span, especially in public sector patients. Methods: We obtained cross-sectional demographic, clinical, and functional ratings for 330 veterans hospitalized for bipolar disorder with Mini-Mental State score z27 and without active alcohol/substance intoxication or withdrawal, who had had at least 2 prior psychiatric admissions in the last 5 years. Structured medical record review identified current/lifetime comorbid medical conditions. SF-36 Physical (PCS) and Mental (MCS) Component Scores, measured physical and mental HRQOL. Univariate and multivariate analyses addressed main hypotheses that physical and mental function decrease with age with decrements due to increasing medical comorbidity. Results: PCS decreased (worsened) with age; number of current comorbid medical diagnoses, but not age, explained the decline. Older individuals had higher (better) MCS, even without controlling for medical comorbidity. Multivariate analysis indicated association of MCS with age, current depressed/mixed episode, number of past-year depressive episodes, and current

* Corresponding author. Tel.: +1 650 493 5000x26141; fax: +1 650 614 9842. E-mail address: [email protected] (H.H. Fenn). 0165-0327/$ - see front matter. Published by Elsevier B.V. doi:10.1016/j.jad.2004.12.006

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anxiety disorder, but not with medical comorbidity, number of past-year manic episodes, current substance disorder or lifetime comorbidities. Limitations: This cross-sectional design studied a predominantly male hospitalized sample who qualified for and consented to subsequent randomized treatment. Conclusions: Medical comorbidity is associated with lower (worse) physical HRQOL, independent of age. Surprisingly, younger rather than older subjects reported lower mental HRQOL. This appears due in part to more complex psychiatric presentations, and several mechanisms are discussed. Both results suggest that age-specific assessment and treatment may enhance HRQOL outcome. Published by Elsevier B.V. Keywords: Bipolar disorder; Medical comorbidity; Health-related quality of life; Anxiety disorders; Substance disorders

1. Introduction Medical conditions tend to be under-recognized in psychiatric clinics (Koranyi, 1979), state mental health systems (Koran et al., 1989) and general psychiatric inpatient units (Hall et al., 1981; D’Ercole et al., 1991). Among those with mixed psychiatric diagnoses, an association has been found between under-diagnosis of medical comorbid illness and increased risk of poorer quality medical care and greater mortality, especially in older patients (Felker and Yazel, 1996; Zubenko et al., 1997a,b; Druss et al., 2001). Patients with psychiatric disorders in general and depressive symptoms in particular have an increased risk of poor functional outcome, reduced quality of life, and physical compromise in the presence of medical comorbidity (Booth et al., 1998; Penninx et al., 1998). In primary care settings, mental disorders have been correlated with greater impairment in health-related quality of life (HRQOL) and distinctive patterns of impairment (Spitzer et al., 1995). The combined effect of depressive symptoms and medical conditions has been found to be more than additive in reducing function in those late middle-aged and older (Ormel et al., 1998). Little is known, however, about the relationship between medical comorbidity, HRQOL, and function at different ages in bipolar disorder (Shulman et al., 1992). Early research demonstrated that individuals with bipolar disorder and medical comorbidity may have a poorer response to psychiatric treatment (Black et al., 1988), and two recent studies have demonstrated an association between obesity and poor functional outcome (Fagiolini et al., 2002, 2003). Importantly, the impact of age per se has not been

addressed in prior studies. We are also aware of no data on populations treated in the public sector. Moreover, prevalence rates of medical illness in bipolar disorder have not been reliably delineated. The few studies of patients with any medical comorbidity in bipolar affective disorder have yielded widely varying rates, between 2.7% and 75%, likely due in part to sample differences and variability in assessment methodology (Ghadirian and Engelsmann, 1985; Black et al., 1988; Winokur et al., 1993; Gierz and Jeste, 1993; Strakowski et al., 1991). Data on prevalence of specific comorbid medical conditions are limited and also show substantial variability (e.g., Lilliker, 1980; Ritchie et al., 1996; Cassidy et al., 1999; Regenold et al., 2002; Fagiolini et al., 2003). There are several reasons why better understanding of the interaction of age and medical comorbidity in bipolar affective disorder may improve its assessment and treatment. As mentioned above, reduced physical function with age or illness may increase propensity to depression, or vice versa (Broadhead et al., 1990; Musselman et al., 1998; Penninx et al., 1998). Certain medical comorbidities in bipolar disorder may delineate separate subgroups, such as those with cerebrovascular disease (e.g., Krauthammer and Klerman, 1978; Berrios and Bakshi, 1991; Shulman et al., 1992; Young and Klerman, 1992; Fujikawa et al., 1995; Zubenko et al., 1997a,b; Young, 1997; Krishnan et al., 1997; Hays et al., 1998). Medications for bipolar disorder may interact with drugs used to treat concurrent medical conditions among older patients (Cozza and Armstrong, 2001). Finally, comorbid medical conditions such as renal or hepatic disease may lead to

H.H. Fenn et al. / Journal of Affective Disorders 86 (2005) 47–60

alteration in the pharmacodynamics, pharmacokinetics, or toxicity of relevant drugs (Bauer, 2003). We therefore investigated overall prevalence rates of current and lifetime medical illness and their relationship to function in bipolar affective disorder at different adult ages. We used a dataset of 330 well-characterized bipolar inpatients from the Department of Veterans Affairs (VA) Cooperative Study #430 to systematically determine frequencies of medical comorbidity across the adult age span. We then examined the relationship of medical comorbidity to health-related quality of life (HRQOL) at different ages, as assessed by the 36-item Short-Form from the Medical Outcomes Study (SF-36; Stewart et al., 1988). Specifically, we hypothesized: 1. Prevalence of medical comorbidity in bipolar disorder increases with age. 2. Physical and mental HRQOL decrease with age. 3. Decrements in physical and mental HRQOL with age are not significant after controlling for number of current medical comorbidities. That is, decrements in physical and mental HRQOL with age in bipolar disorder are associated with medical comorbidity rather than factors inherent in bipolar illness or age.

2. Methods 2.1. Sample Subjects were recruited as part of VA Cooperative Study #430, bReducing the Efficacy-Effectiveness Gap in Bipolar Disorder,Q an 11-site randomized controlled trial of an easy access treatment program vs. usual VA care (Bauer, 2001; Bauer et al., 2001). This study recruited a sample with a wide age range and minimal inclusion and exclusion criteria in order to represent as closely as possible the population of veterans with bipolar disorder seen in VA clinical practice. Subjects were recruited as inpatients, randomized at time of discharge, and then followed prospectively for three years. This paper reports baseline data at entry to the study.

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2.2. Selection criteria Inclusion: ! Diagnosis of bipolar disorder type I or II by DSMIV (APA, 2000) criteria. All psychiatric and medical comorbidities allowed except as specified below. ! Index episode of manic, major depressed, or mixed episode by DSM-IV criteria requiring hospitalization on an acute psychiatric ward. ! At least 2 hospitalizations on acute psychiatric wards more than 3 months apart over the prior 5 years. Exclusion: ! Moderate to severe dementia with a Mini Mental State Examination (MMSE; Folstein et al., 1975) score of V26. ! Unresolved substance intoxication or withdrawal (these individuals were approached after the resolution of these syndromes). ! Hospitalization on chronic or acute psychiatric wards for z6 months in the past year. ! Ongoing enrollment in mental health programs with a mobile outreach component in which clinical caregivers deliver services to the patient in the community. ! Terminal medical illness with b3 years expected longevity. ! Unable or unwilling to give informed consent or in other ways unable to complete study requirements. ! Participation in other concurrent experimental mental health or medical-surgical treatment protocol. Participants were assessed between 1/1/97 and 12/ 31/00. Those randomized did not differ from eligible refusers in gender, homelessness, number of psychiatric hospitalizations, or number of comorbid medical diagnoses, but were somewhat younger (46.6F10 vs. 48.0F10.3, p=0.03). 2.3. Assessment battery Intake assessment included informed consent as approved by each site’s Institutional Review Board

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H.H. Fenn et al. / Journal of Affective Disorders 86 (2005) 47–60

followed by the Structured Clinical Interview for DSM-IV (SCID; First et al., 1996) and a battery of interview and self-report instruments. SCID inter-rater reliability (including differentiation of bipolar from substance-induced symptoms) was good to excellent across the sites, including for bipolar diagnosis (Crame´r’s V: 0.98), index episode polarity (0.83), psychosis (0.65), comorbid current (0.93–0.97) and lifetime (0.91–0.98) anxiety disorders and current (0.94) and lifetime (1.00) substance use disorders. The Medical Outcome Study Short-Form 36 (SF36; Stewart et al., 1988) was used as the main measure of subjective HRQOL. The instrument has been used successfully in many studies of affectively ill patients (e.g., Wells et al., 1989; Leidy et al., 1998) including several studies of bipolar disorder (e.g., Cooke et al., 1996; Vojta et al., 2001). It is a 36-item self-report that assesses the impact of physical and mental symptoms on healthrelated HRQOL including, for example, pain, ability to function in a series of daily tasks, vigor, and one’s self-perceptions of overall health compared to others. A series of specific subscales can be generated, and the instrument can also be scored according to two bcomponentsQ that separately summarize HRQOL due to physical and mental symptoms; these are, respectively, the Physical and Mental Component Scores (PCS, MCS). Subscales and components are scored 0–100 (higher scores represent better HRQOL) and normalized so that 50F10 represents the meanFS.D. for the general population. Because we were interested in overall physical and mental HRQOL, we chose to analyze the PCS and MCS rather than the more focused SF-36 subscales.

chart discharge summaries from 10 years, and outpatient progress notes from one year, prior to the index hospitalization. Each diagnosis was categorized as either bActive,Q bPast, or bNot NotedQ (because there was no certainty that diagnoses not mentioned in the chart were truly not present, any not documented were categorized simply as Not Noted). Differentiation between Active and Past was based on physician judgment (e.g., hypertension noted 5 and 2 years prior and treated but not noted during the index admission would be considered Active, while cholecystitis 3 years ago with cholecystectomy would be categorized as Past). While there is no gold standard against which to assess this structured, physicianimplemented chart review, inter-rater reliability was established by an independent review of 7 preselected tracer conditions; average inter-rater agreement was 96.0F0.1% (range 80–100%). We also wished to determine whether this focused, structured chart review would over-diagnose medical conditions and thus overestimate the medical burden in this population. We therefore compared chart extraction data with diagnostic data on the same participants in the central VA National Patient Care Database in Austin, TX, covering inpatient and outpatient contacts for the identical timeframe covered by the chart extraction. For six conditions with prevalence of at least 9% in this sample, sensitivity averaged 79.0% (range 72–89%) and specificity 92.3% (80–99%); predictive values positive and negative were, respectively, 68.8% (58– 92%) and 96.1% (91–99%). Thus the chart review has reasonable psychometrics and, if anything, would under- rather than overestimate medical comorbidity prevalence rates.

2.4. Structured medical chart extraction methodology 2.5. Data analysis Existing medical chart extraction methodologies were reviewed and an instrument for standardized extraction was developed based on the Cumulative Illness Rating Scale (Linn et al., 1968; Conwell et al., 1993). Iterative group discussion among physician–investigators yielded a mutually exclusive and exhaustive grouping of medical diagnoses by organ system. The instrument was piloted, revised, and then released to sites with telephone-based training. It was completed using the electronic and paper

Chart review data were available for 290 of 330 participants (exclusions were due to IRB requirements). Those with and without chart reviews did not differ in terms of several key clinical characteristics (Table 1). Categorical analyses were performed with chi-square tests, while continuous variable analyses were either t-tests or analysis of variances. The Kruskal–Wallis nonparametric test was used for the analysis of the number of major manic (or depressive)

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Table 1 Sample characteristics

Age Female % Minoritya % Bipolar Ib Index mood episode depressed or mixed Median # depressive episodes past year Median # manic episodes past year Number of medical diagnoses in index hospitalization discharge summary PCS MCS

Total sample (n=330)

Chart review sample (n=290)

Sample without chart review (n=40)

45.9F10.0 30 (9%) 81 (25%) 286 (88%) 211 (65%) 3 3 1.9F1.8

46.0F10.1 27 (9%) 67 (35%) 249 (87%) 189 (66%) 3 3 1.9F1.8

45.5F8.7 3 (8%) 14 (23%) 37 (95%) 22 (61%) 3 2 1.7F1.6

44.4F12.3 27.8F12.5

44.7F12.4 28.8F12.3

42.3F11.7 28.1F13.6

a

Trend: v 2(1)=2.7, p=0.10; minority includes the four US federally identified ethnic categories: African-American, Hispanic, AsianAmerican/Pacific Islander, Native American/Eskimo/Inuit. b Trend: v 2(1)=2.1, p=0.15.

episodes last year. A backward stepwise linear regression procedure was used to determine which variables had significant levels of association with the MS-36 variable.

3.3. Hypothesis 2: HRQOL at different ages

3. Results

As predicted, unadjusted PCS was lower (worse) in older age groups. (Fig. 2a: F(5)=3.7, p=0.003;). Surprisingly, unadjusted MCS was higher (better) in older age groups, even without controlling for medical comorbidity (Fig. 2b: F(5)=6.2, pb0.001).

3.1. Prevalence of medical comorbidity in bipolar affective disorder

3.4. Hypothesis 3: correlates of physical and mental HRQOL across the life span

Current and lifetime medical disorders were common across all organ systems. Table 2 summarizes diagnoses by organ system, including the most salient diagnoses within each organ system. As summarized in Table 3, current comorbidity was found in 81%. Only 5.9% of subjects were free of lifetime comorbidity of sufficient severity to be recorded in the medical record. Participants had a median of 2 current (quartiles: 1–3) and 4 lifetime (quartiles: 2–6) comorbidities.

The age-related decrement in PCS was examined with the pre-planned multivariable analysis using age and number of current medical comorbidities as predictors. Coefficient for the number of medical comorbidities, but not age, was significant (medical comorbidities: F(1)=4.8, p=0.03; age F(5)=1.2, p=0.3; overall r 2=0.28 ). Thus the decrement in physical function appears due to effects of comorbid medical conditions, not age per se. The unexpected finding that MCS increases with increasing age required revision of this portion of Hypothesis 3 analytic plan. We therefore conducted a multiple linear regression using MCS as the dependent variable with age, number of current medical comorbidities, and several psychiatric characteristics that have been related to HRQOL in all-age samples as independent variables; the latter include: depressed/mixed episode at intake, number of depressive or manic episodes in the past year, any current or lifetime

3.2. Hypothesis 1: medical comorbidity across the life span As hypothesized, the prevalence of both current and lifetime medical comorbidity increases with age (Fig. 1, respectively: F(5)=19.8, pb0.001; F(5)=18.1, pb0.001). Trends for the two rates can be superimposed, with the latter shifted toward higher rates as might be expected from lifetime vs. current diagnoses.

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H.H. Fenn et al. / Journal of Affective Disorders 86 (2005) 47–60 Table 2 (continued)

Table 2 Prevalence rates of major comorbid medical conditions Medical disorder Any autoimmune Rheumatoid arthritis Systemic lupus erythematosis (SLE) Any cardio/cerebrovascular Stroke Coronary artery disease (CAD) Procedure for CAD including CABG, stent, or other Hypertension (HTN) Cardiac arrythmias Any dermatologic Any endocrine Diabetes mellitus type I Diabetes mellitus type II Hyperthyroidism Hypothyroidism Any eyes, ears, nose, throat (EENT) disease Any gastrointestinal Esophageal varices Peptic ulcer disease Alcohol gastritis Pancreatisis, alcoholic Any genitourinary Benign prostatic hypertrophy Incontinence Prostate or genitourinary Cancer Any hematologic Any hepatic Hepatitis B Hepatitis C Cirrhosis, alcoholic HIV/AIDS AIDS HIV+ without clinical AIDS Dyslipidemias including hypercholesterolemia, hyperlipidemia, hypertriglyceridemia Any musculoskeletal Degenerative disc disease Fractures Osteoarthritis Any neurologic Head trauma with loss of consciousness Head trauma without loss of consciousness Peripheral neuropathies Seizure disorder Tardive dyskinesia Any pulmonary

Current 4 (1.4%) 1 (0.3%) 1 (0.3%)

Medical disorder

Lifetime 7 (2.4%) 1 (0.3%) 1 (0.3%)

101 4 28 6

(34.8%) (1.4%) (9.7%) (2.1%)

141 8 35 11

(48.6%) (2.8%) (12.1%) (3.8%)

74 8 20 66 4 24 1 29 26

(25.6%) (2.7%) (6.9%) (22.8%) (1.4%) (8.3%) (0.3%) (10.0%) (9.0%)

94 11 59 83 5 24 5 40 67

(32.5%) (3.7%) (20.3%) (28.6%) (1.7%) (8.3%) (1.7%) (13.8%) (23.1%)

52 2 13 1 2 26 14 5 2 11 49 9 39 5 4 2 2 65

(17.9%) (0.7%) (4.5%) (0.3%) (0.7%) (9.0%) (4.8%) (1.7%) (0.7%) (3.8%) (16.9%) (3.1%) (13.5%) (1.7%) (1.4%) (0.7%) (0.7%) (23.4%)

114 3 36 2 5 62 22 8 5 25 61 13 45 6 4 2 3 81

(39.3%) (1.0%) (12.4%) (0.6%) (1.7%) (21.4%) (7.6%) (2.7%) (1.7%) (8.6%) (21.0%) (4.5%) (15.6%) (2.0%) (1.4%) (0.7%) (1.0%) (28.9%)

68 10 4 33 50 3

(23.4%) (3.5%) (1.4%) (11.4%) (17.2%) (1.0%)

3 (1.0%) 4 12 5 38

(1.4%) (4.2%) (1.7%) (13.1%)

144 12 30 46 110 22

(49.7%) (4.2%) (10.4%) (15.9%) (37.9%) (7.6%)

18 (6.2%) 7 24 7 70

(2.4%) (8.4%) (2.4%) (24.1%)

Current

Asthma Chronic obstructive pulmonary disease (COPD) or emphysema Sleep apnea Any renal Chronic renal insufficiency

Lifetime

9 (3.1%) 23 (8.0%)

16 (5.5%) 26 (9.0%)

5 (1.7%) 7 (2.4%) 5 (1.7%)

10 (3.4%) 19 (6.6%) 7 (2.4%)

substance use disorder, and any current or lifetime anxiety disorder. Table 4 summarizes the distribution of these characteristics by decade. Significant univariate age effects are seen in the probability of presenting in a depressed/mixed episode at intake and having a current or lifetime substance or anxiety disorder, but not the number of depressive or manic episodes during the year prior to intake. Multiple linear regression analysis of MCS using these variables indicated that lower values were significantly associated with younger age ( F=11.2, p=0.001), presentation in a depressed/mixed episode ( F=33.5, pb0.001), number of depressive episodes in the prior year ( F=5.0, p=0.026), and current anxiety disorder ( F=4.6, p=0.032), but not the other variables (overall adjusted r 2=0.28). Table 5 further describes the relationship of age to specific substance and anxiety comorbidities. Significant age effects are seen for current obsessive– compulsive disorder (OCD) and cannabis use disorders, with trends for all other current comorbidities to Table 3 Frequency distribution of medical comorbidities Number of medical comorbid conditions

Current

Lifetime

0 1 2 3 4 5 6 7 8 9 10 N10 Total

55 56 57 48 28 18 8 7 3 2 2 6 290

17 21 42 33 43 37 27 24 14 3 5 24 290

(19.0%) (19.3%) (19.7%) (16.6%) (9.7%) (6.2%) (2.8%) (2.4%) (1.0%) (0.7%) (0.7%) (2.1%) (100%)

(5.9%) (7.2%) (14.5%) (11.4%) (14.8%) (12.8%) (9.3%) (8.3%) (4.8%) (1.0%) (1.7%) (8.3%) (100%)

H.H. Fenn et al. / Journal of Affective Disorders 86 (2005) 47–60 20

Lifetime Medical Comorbidities

Current Medical Comorbidities

20

15

10

5

0

53

30

or

31 le

to

ss

41 40

to

51 50

to

61 60

to

71 70

15

10

5

0 +

30

or

31 le

to

ss

41 40

Age in Years

to

51 50

to

61 60

to

71 70

+

Age in Years

Fig. 1. Current and lifetime medical comorbidity across the adult age span. Panels a and b depict, respectively, the number of current and lifetime comorbid medical diagnoses by decade ( pb0.001 for both).

acute affective episode and willing to undergo subsequent treatment randomization. Cell size for the age extremes of V30 and z70 years of age was relatively small, even in this large sample, so that the estimates may be somewhat unstable. Treatment trial criteria excluded subjects with an MMSE score V26. Thus these data derive from a sample relatively free of effects of comorbid cognitive deficits, which appear more frequently among older individuals with bipolar disorder (Berrios and Bakshi, 1991; Shulman and Herrmann, 1999). Potential impact of sample exclusions on results is described in detail below (Section 4.4).

be higher in younger age groups. Notably, the same pattern of higher lifetime comorbidity rates persists in younger participants, despite their having lived fewer years than older participants.

4. Discussion 4.1. Limitations of the study

60

60

40

40

Mental SF-36

Physical SF-36

Subjects who entered the study were predominantly male, self-selected users of VHA healthcare services, sufficiently ill to be hospitalized for an

20

0

30

or

31 le

ss

to

41 40

to

51 50

Age in Years

to

61 60

to

71 70

20

0 +

30

or

31 le

ss

to

41 40

to

51 50

to

61 60

to

71 70

+

Age in Years

Fig. 2. Physical and mental HRQOL across the adult age span. Panels a and b depict, respectively, SF-36 PCS ( p=0.003) and MCS ( pb0.001) scores by decade.

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H.H. Fenn et al. / Journal of Affective Disorders 86 (2005) 47–60

Table 4 Univariate age effects on selected illness characteristics by decadea Age by decade Y

V 30, N=13 31– 40, N=69 41–50, N=128 51– 60, N = 51 61–70, N=23 N70, N = 6

MCS # Current medical comorbidities Index episode depressed or mixed # Depressive episodes past year (median) # Manic episodes past year (median) Any current substance use disorder Any lifetime substance use disorder Any current anxiety disorder Any lifetime anxiety disorder

32.5F14.1 2.5F1.6

25.9F11.3 2.2F1.2

27.3F11.4 3.0F1.9

29.9F12.3 3.6F3.0

36.7F13.0 4.4F3.6

48.0F12.0 F(5) = 6.1 11.0F3.5 F(5) = 17.9

0 (0.0%)

38 (55.1%)

86 (67.2%)

32 (62.7%)

7 (30.4%)

3 (50.0%)

v 2(5) = 32.9 b0.001

2

3

4

3

1

1.5

KW = 7.8

0.17

2

3

3

3

2

2.5

KW = 3.8

0.60

8 (61.5%)

28 (40.6%)

44 (34.4%)

16 (31.4%)

3 (13.0%)

0 (0.0%)

v 2(5) = 13.7

0.018

9 (69.2%)

47 (68.1%)

105 (82.0%)

34 (66.7%)

14 (60.9%)

1 (16.7%)

v 2(5) = 18.2

0.003

4 (30.8%)

30 (43.5%)

58 (45.3%)

17 (33.3%)

2 (8.7%)

0 (0.0%)

v 2(5) = 16.7

0.005

6 (46.2%)

32 (46.4%)

63 (49.2%)

22 (43.1%)

2 (8.7%)

0 (0.0%)

v 2(5) = 18.5

0.002

a

Statistic

Univariate p-value b0.001 b0.001

Sample includes all those with structured chart review (n=290); see Methods for details.

In terms of assessment, a combination of crosssectional (e.g., SCID, SF-36) and retrospective longitudinal (e.g., SCID, medical chart extraction) data were used. Although the SF-36 of HRQOL has been validated in older individuals (Lyons et al., 1994; Brazier et al., 1996), it has only begun to be used among older patients with serious mental illnesses such as schizophrenia (Sciolla et al., 2003) or bipolar disorder (Cooke et al., 1996; Vojta et al., 2001).

Moreover, MCS is heavily weighted toward symptoms commonly found in depressive and anxiety disorders (e.g., lack of energy, nervousness), so that some correlation with current depression and anxiety may be suspected on this basis alone. Despite these limitations, this is one of the first studies to comprehensively assess medical comorbidity via structured chart review in a large multisite population of individuals with bipolar disorder.

Table 5 Specific anxiety and substance use disorders by decadea Independent variable: age by decadeY

V30, N=14

31– 40, N=78

41–50, N=145

51–60, N=60

Current PTSD OCD Panic disorder Alcohol use disorders Cocaine use disorders Cannabis use disorders

1 3 2 7 0 2

(7.1%) (21.4%) (14.3%) (50.0%) (0%) (14.3%)

20 11 18 24 10 9

(25.6%) (14.1%) (23.1%) (30.8%) (12.8%) (11.5%)

42 13 29 36 11 9

(29.0%) (9.0%) (20.0%) (24.8%) (7.6%) (6.2%)

17 1 6 14 1 0

(28.3%) (1.7%) (10.0%) (23.3%) (1.7%) (0%)

2 0 1 3 0 0

Lifetime PTSD OCD Panic disorder Alcohol use disorders Cocaine use disorders Cannabis use disorders

2 3 3 9 2 2

(14.3%) (21.4%) (21.4%) (64.3%) (14.3%) (14.3%)

23 12 19 44 25 26

(29.5%) (15.4%) (24.4%) (56.4%) (32.1%) (33.3%)

46 19 34 99 32 36

(31.7%) (13.1%) (23.4%) (68.3%) (22.1%) (24.8%)

20 2 8 34 5 9

(33.3%) (3.3%) (13.3%) (56.7%) (8.3%) (15.0%)

2 0 1 16 0 1

a

Sample includes all those with SCID interview (n=328); see Methods for details.

61–70, N=25

N70, N=6

Statistic

p-value

(8.0%) (0%) (4.0%) (12.0%) (0%) (0%)

0 0 0 0 0 0

(0%) (0%) (0%) (0%) (0%) (0%)

v 2(5) = 9.8 v 2(5) = 12.6 v 2(5) = 9.3 v 2(5) = 10.2 v 2(5) = 10.5 v 2(5) = 11.6

0.080 0.027 0.097 0.071 0.062 0.041

(8.0%) (0%) (4.0%) (64.0%) (0%) (4.0%)

0 0 0 1 0 0

(0%) (0%) (0%) (16.7%) (0%) (0%)

v 2(5) = 10.4 v 2(5) = 11.2 v 2(5) = 9.2 v 2(5) = 9.5 v 2(5) = 20.9 v 2(5) = 14.8

0.064 0.048 0.100 0.091 0.001 0.011

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4.3. Medical illness, physical HRQOL, and age

This is also one of the first studies to assess the impact of comorbidity on health-related quality of life in a group of severely mentally ill patients treated in the public sector. Prior studies of medical comorbidity burden in patients with serious mental illness have been limited in sample size or did not assess conditions via a systematic chart review (Kilbourne et al., 2004; Gierz and Jeste, 1993). Finally, the sample was selected in the context of an effectiveness study that enrolled participants representative of the VA population of individuals with bipolar disorders treated in the Veterans’ Affairs Health Care System. (Bauer et al., 2001). As a result, those with comorbid medical and psychiatric conditions were well represented.

While medical comorbidity is highly prevalent, it was equally striking, though expected, that both current and lifetime prevalence increased with increasing age (Fig. 1). Additionally, physical HRQOL, assessed by PCS, declined with age, as it does in individuals with schizophrenia (Friedman et al., 2002; Sciolla et al., 2003). However, it is of interest that the decrement in PCS was associated with medical comorbidity, but not with age per se. This indicates that decrements in physical HRQOL in this population are not an inevitable aspect of aging, and that adequate attention to medical comorbidity at any age may have tangible impact on HRQOL.

4.2. Prevalence of medical comorbidity

4.4. Mental HRQOL in younger versus older cohorts

Medical comorbidity is highly prevalent and wideranging in this population. Eighty-one percent had some active medical comorbidity at the time of hospitalization, and over half had 2 or more active disorders (Table 3). Thus adequate screening for such disorders will likely result in a high yield, and treatment planning should take these comorbid diagnoses into account. The very high prevalence of medical comorbidity and its impact on HRQOL suggests that integrated medical-psychiatric treatment systems will be useful for optimizing outcome in this population. High rates of several specific disorders were notable, and resemble rates in the database study of veterans with bipolar disorder by Kilbourne et al. (2004), including those for coronary artery disease (9.7% vs. 10.6%), hypertension (25.6% vs. 34.8%), dyslipidemias (23.4% vs. 22.6%), and thyroid disease (10.3% vs. 7.0%). Rates for diabetes mellitus (9.7% vs. 17.2%) were somewhat lower in this study, while rates for hepatitis C (13.5% vs. 5.9%) were somewhat higher. Of interest, in a parallel database study by our group on the entire VA service user population, rates of hepatitis C and hypothyroidism were significantly elevated in those with bipolar disorder compared to others (Fiore et al., 2004), likely due to, respectively, elevated rates of drug use disorders (Strakowski and Sax, 1998; Bauer et al., in press) and lithium exposure (Kleiner et al., 1999).

It was notable that MCS was actually higher rather than lower in older age groups. Multivariate analysis indicated that lower (worse) MCS was associated with younger age, likelihood of presenting in a depressed/ mixed episode, number of prior-year depressive episodes, and presence of a current anxiety disorder. Two aspects of this finding stand out. First, depressive and anxious features were strongly associated with lower MCS. This is not surprising, given the heavy weighting of the MCS for depressed and anxious features. This is consistent with our earlier findings in a similar multi-site sample (Vojta et al., 2001)—though the reverse, higher MCS in (hypo)manic individuals, does not appear to hold (Vojta et al., 2001). Second, it is notable that, even after controlling for these characteristics, older age was still significantly associated with higher (better) MCS. While crosssectional analysis cannot establish that mental HRQOL improves with age (see also item 4 below), these data can help to inform future cross-sectional and longitudinal studies. We considered five possible interpretations for the finding: 1) reporting bias of physical symptoms, 2) selection bias, 3) illness natural history, 4) accommodation and better sense of well-being with age, and 5) cohort effects. (1) Older subjects may under-report physical symptoms, as has been found in studies of human immune deficiency virus (Piette et al., 1995; Zingmond et al., 2003). However, older patients do tend to

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report lower physical HRQOL scores compared to younger patients (e.g., Piette et al., 1995; Wenzel et al., 1999), and lower physical compared to mental HRQOL scores (Ware et al., 1994; Coons et al., 2000). Older subjects in our study did have lower (worse) PCS scores, which one would not expect if under-reporting were present. (2) Older individuals with worse mental function may simply not be represented in this dataset for any of several reasons. Those with worse mental function as measured by the MCS may have a higher mortality rate at younger ages, thus leaving a subgroup of bsuper-survivorsQ in old age. Studies in other populations indicate that comorbid conditions, especially alcohol, tobacco, and illicit drug dependence, may confer excess mortality and lead to earlier death in afflicted individuals (e.g., Black, 1998; Harris and Barraclough, 1998; Vaillant, 2003). Longitudinal data are clearly needed to clarify this issue. Similarly, those with worse function may be under-represented in this sample because: (a) though alive, they do not seek treatment; or (b) though seeking treatment, they are not hospitalized; or (c) though hospitalized, they do not consent to randomization. Recall that subjects who had evident cognitive deficits (MMSE V26) were excluded from the sample, and that the sample that consented to randomization was 1.4 years younger than the eligible refusers. It is conceivable that including these individuals would have resulted in lower MCS scores in older individuals. (3) Some have suggested that bipolar disorder lessens in severity with age (Broadhead and Jacoby, 1990; Dhingra and Rabins, 1991), which could explain the lack of decline in MCS with age. However, other researchers have found little evidence that either acute symptomatology or chronicity of bipolar disorder is reduced with age (Meeks, 1999). Bipolar illness in older age groups is complicated by increased incidence of cognitive dysfunction (Savard et al., 1980; Berrios and Bakshi, 1991) that may not remit even with remission of symptoms (McKay et al., 1995; Rubinsztein et al., 2000; Zubieta et al., 2001). Moreover, episode frequency has been reported to increase over the course of the illness (e.g., Perris, 1966; Post et al., 2001), while risk of recurrence appears to remain stable at least through age 70 (Angst et al., 2003). Other long-term analyses show no relationships of age to outcome at 10 or 15 years of follow-up (Coryell et

al., 1998; Turvey et al., 1999; Judd et al., 2001). Thus it is unlikely that bipolar disorder lessens in severity with age. Our data also indicate no decrement in episode frequency across decades, although depressed/ mixed presentation at index hospitalization is relatively less likely in the older decades. (4) While the illness itself may or may not lessen in severity, the ability of an individual to adapt to its symptoms, its subjective experience, and its social effects may improve with age. The work of Vaillant and others (e.g., Vaillant and Mukamal, 2001; Mroczek and Kolarz, 1998) describes the dynamic course of development through adult life. Normal development in adults, as in children, is based on a normally functioning brain that allows the individual to meet and manage the challenges of each stage of life. While the acute pathophysiological processes of mania or depression may suspend normal functioning, there is no theoretical reason to suspect that stabilized bipolar patients are less likely to progress through adult development. One study, for example, notes that effective psychological adaptive mechanisms lost during an acute depressive episode return with successful treatment (Akkerman et al., 1992). Some studies have also found that older patients recall a fewer number of negative images compared with positive and neutral images. (Charles et al., 2003). Importantly, recent studies of serious medical illnesses, including end stage renal disease (Rebollo et al., 2001), breast cancer (Kroenke et al., 2004) and coronary heart disease (Denollet et al., 2000; Lavie and Milani, 2004) suggest that older individuals may experience less of a reduction of health-related quality of life than younger patients and therefore report less of a decrement in MCS. These data from the present study, while by no means establishing that better adaptation to bipolar illness comes with age, are consistent with this general notion. They certainly do not argue the opposite case. (5) The younger age group may represent a cohort that has a more depressed/mixed form of the illness and/or a course more complicated by psychiatric comorbidity, which in turn leads to lower mental HRQOL. There are strong age effects on both current and lifetime anxiety disorders (Table 5), with higher rates in younger decades. Importantly, the higher lifetime prevalence rates in younger decades–despite fewer years lived–underscore the concern that these

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younger decades represent a psychiatrically more complex cohort, as discussed in the next section. 4.5. Specific psychiatric comorbidities across the adult age span Lifetime prevalence rates by decade for specific psychiatric comorbidities (Tables 4 and 5) indicate significant age effects for obsessive–compulsive disorder, cocaine use disorders, and cannabis use disorders. Trends for higher rates of alcohol use disorders and post-traumatic stress disorder in younger decades are also notable. As noted above, it is possible that these differences represent cohort effects since lifetime, and not just current, comorbidity is higher in younger ages despite these individuals having had less time to accumulate comorbid diagnoses. The pattern for current comorbidities is similar. With the exception of alcohol use disorders, all these comorbidities have a notable inflection point in the decade of Vietnam era veterans (ages 51–60), with lifetime rates higher in those this age or younger (Table 5). It will be of interest to determine whether similar patterns occur in other non-veteran and nonhospitalized populations. It is ominous that societal or other factors may have contributed to cohort effects that yield progressively more disabling bipolar disorder, but this possibility merits further inquiry. 4.6. Summary It is clear that multiple, active medical comorbidities are the rule rather the exception among individuals with bipolar disorder, that this medical burden increases significantly with age, and that it impacts physical HRQOL. These data further indicate that reduced physical HRQOL is not an inevitable correlate of aging, but that attention to medical comorbidities will likely minimize decrements in HRQOL. Therefore comprehensive screening and integrated treatment of such individuals across medical and mental health sectors is likely to improve both physical and mental HRQOL. The intriguing age-related finding of higher mental HRQOL in older individuals suggests future avenues of investigation. These will ultimately require longitudinal, within-subjects follow up over relatively long periods of time.

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A complementary line of investigation focusing on younger individuals is also warranted, since they appear to have a more complex psychiatric presentation and lower mental HRQOL. Overall, then, attention to age-specific aspects of adult bipolar disorder appears likely to improve assessment and treatment of this population.

Acknowledgements This study was supported in part by VA Cooperative Study #430: bReducing the Efficacy-Effectiveness Gap in Bipolar Disorder.Q These data were presented at the International Conference on Bipolar Disorders at Pittsburgh, Pennsylvania on June 13, 2003 and at the American Association of Geriatric Psychiatry in Baltimore, MD on February 21, 2004. The CSP #430 Study Team includes: Denise Evans, M.D., Augusta (GA) VAMC; George Jaskiw, M.D., Cleveland (OH) VAMC; Janet Tekell, M.D., Steven Eilers, M.D. Dallas (TX) VAMC; Thomas Beresford, M.D. Denver (CO) VAMC; John Crayton, M.D. Hines (IL) VAMC; Aimee Mayeda, M.D. Indianapolis (IN) VAMC; George R. Brown, M.D. Mountain Home (TN) VAMC; Howard Fenn, M.D. Palo Alto (CA) VAMC; Hagop Akiskal, M.D., Richard Hauger, M.D. San Diego (CA) VAMC; John Jachna, M.D. Tucson (AZ) VAMC; Lori Altshuler, M.D. West Los Angeles (CA) VAMC; William O. Williford, Ph.D. Perry Point Cooperative Studies Coordinating Center; Henry A. Glick, Ph.D., Bruce Kinosian, M.D., Economic Analysis Office (University of Pennsylvania); Mark S. Bauer, M.D., Linda McBride, MSN, Nancy Shea, RN, MS, Study ChairTs Office (Providence (RI) VAMC).

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