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Anger, Anxiety, and Depression as Risk Factors for Cardiovascular Disease: The Problems and Implications of Overlapping Affective Dispositions

March 2005
Psychological Bulletin 131(2):260-300

March 2005
131(2):260-300

DOI:10.1037/0033-2909.131.2.260

Source
PubMed

Authors:

Jerry Suls

National Cancer Institute (USA)

Several recent reviews have identified 3 affective dispositions--depression, anxiety, and anger-hostility--as putative risk factors for coronary heart disease. There are, however, mixed and negative results. Following a critical summary of epidemiological findings, the present article discusses the construct and measurement overlap among the 3 negative affects. Recognition of the overlap necessitates the development of more complex affect-disease models and has implications for the interpretation of prior studies, statistical analyses, prevention, and intervention in health psychology and behavioral medicine. The overlap among the 3 negative dispositions also leaves open the possibility that a general disposition toward negative affectivity may be more important for disease risk than any specific negative affect.

Conventional model of pathways between negative affects and heart disease.

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Conventional model of pathways between anger cluster and heart disease.

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continued )

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Model of pathways between anger cluster and heart disease (with measurement overlap).

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continued )

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Content uploaded by Jerry Suls

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Anger, Anxiety, and Depression as Risk Factors for Cardiovascular

Disease: The Problems and Implications of Overlapping

Affective Dispositions

Jerry Suls and James Bunde

University of Iowa

Several recent reviews (e.g., L. C. Gallo & K. Matthews, 2003; A. Rozanski, J. A. Blumenthal, & J.

Kaplan, 1999; R. Rugulies, 2002) have identified 3 affective dispositions— depression, anxiety, and

anger– hostility—as putative risk factors for coronary heart disease. There are, however, mixed and

negative results. Following a critical summary of epidemiological findings, the present article discusses

the construct and measurement overlap among the 3 negative affects. Recognition of the overlap

necessitates the development of more complex affect– disease models and has implications for the

interpretation of prior studies, statistical analyses, prevention, and intervention in health psychology and

behavioral medicine. The overlap among the 3 negative dispositions also leaves open the possibility that

a general disposition toward negative affectivity may be more important for disease risk than any specific

negative affect.

Keywords: hostility, depression, anxiety, cardiac disease, health psychology

Are disease-prone people both angry and depressed or angry but not

depressed, or is either of these states sufficient? This is a question that

deserves immediate attention. (H. S. Friedman & Booth-Kewley,

1987a, p. 552)

Since the time of the ancient Greeks, negative emotions have

been thought to play a role in the etiology of physical illness.

Appreciable empirical evidence is currently available (H. S. Fried-

man & Booth-Kewley, 1987a) in the form of prospective epide-

miological studies using psychometrically valid measures of af-

fective dispositions. The most extensive support for connections

between negative affective dispositions and disease comes from

epidemiological studies with coronary heart disease (CHD) as an

illness endpoint (Booth-Kewley & Friedman, 1987; Matthews,

1988; Rozanski, Blumenthal, & Kaplan, 1999). Several narrative

and quantitative reviews of dozens of descriptive epidemiological

studies have concluded that depression and anxiety predict CHD

morbidity and mortality, even after traditional CHD risk factors,

such as serum cholesterol, blood pressure, and smoking, are con-

trolled (see Gallo & Matthews, 2003; Hemingway & Marmot,

1999; Kubzansky & Kawachi, 2000; Rugulies, 2002). Anger,

hostility, and anger expression are associated with more mixed

findings (Krantz & McCeney, 2002), but prospective associations

with CHD have been found in some studies. Further, human and

animal model research has identified multiple behavioral, biolog-

ical, and physiologic pathways by which anger, anxiety, and de-

pression might contribute to the development and progression of

CHD (e.g., Grippo & Johnson, 2002; Krantz & Manuck, 1984).

Despite systematic evidence and identification of plausible be-

havioral, biologic, and physiologic mechanisms, however, this

area of inquiry elicits controversy (e.g., Angell, 1985). Disagree-

ment stems partly from inconsistencies in the literature, as noted

above. The controversy has recently been intensified by the find-

ings of the multicenter randomized clinical behavioral trial En-

hancing Recovery in Coronary Heart Disease (ENRICHD; P. G.

Kaufmann, 2003; Writing Committee for the ENRICHD Investi-

gators, 2003). This trial, involving cognitive– behavioral treatment

of depression in post-myocardial infarction (MI) patients, reduced

depression but not subsequent cardiac events. Although cognitive–

behavioral intervention for depression did not confer direct cardiac

benefits in patients with existing disease, the negative findings

have less bearing on the role of depression for CHD risk in initially

healthy samples. Furthermore, finding a null effect of treatment

fails to establish that depression plays no role in patients with

clinical disease (Frasure-Smith & Lespe´rance, 2003b). In fact,

dozens of descriptive epidemiological studies have found that

depression and other negative emotions predict CHD in both

clinical and nonclinical samples.

The present article draws attention to another problem in this

literature—the appreciable construct and measurement overlap

among anger, anxiety, and depression, which creates ambiguity

both for theory testing and for interpretation of available evidence.

The overlap is problematic because researchers have tended to

evaluate the effects of putative psychological risk factors for

physical disease by analyzing or measuring only a single psycho-

logical construct at a time. Kaplan (1995) cautioned, however, that

the single-factor approach ignores the clustering of psychosocial

risk factors for physical disease, which may act synergistically (see

also Rozanski et al., 1999). In this regard, Raynor, Pogue-Geile,

Kamarck, McCaffery, and Manuck (2002) found that depression

and hostility were intercorrelated and that a single common factor

Jerry Suls and James Bunde, Department of Psychology, University of

Iowa.

Work on this article was supported in part by American Heart Associ-

ation Grant in Aid GS96-44 and National Science Foundation Grant

BCS-99-10592.

Correspondence concerning this article should be addressed to Jerry

Suls, Department of Psychology, University of Iowa, East 11 Seashore

Hall, Iowa City, IA 52242-1407. E-mail: jerry-suls@uiowa.edu

2005, Vol. 131, No. 2, 260–300 0033-2909/05/$12.00 DOI: 10.1037/0033-2909.131.2.260

260

explained many of the observed correlations among the variables.

Frasure-Smith and Lespe´rance (2003b) observed that the assess-

ment of only a single psychological construct at a time makes it

“impossible to compare the prognostic importance of the different

concepts in the same individuals . . . to know whether any apparent

prognostic relationships are due to a specific psychological con-

cept or one or more hidden underlying dimensions” (p. 627).

In this article we consider the implications of the overlap among

three negative affective dispositions for the interpretation of ex-

isting evidence and for understanding the pathways between affect

and cardiovascular disease. The disposition to experience frequent

and intense episodes of negative emotions is our focus. Kop (1999)

has distinguished between chronic psychological risk factors (e.g.,

hostility and anxiety), which show considerable stability across

years and can be considered dispositional, and episodic risk fac-

tors, which last from several months to 2 years and tend to reoccur

(e.g., major depressive disorder). Although depression is consid-

ered as episodic, its recurrent nature suggests that at least some

persons have a depressogenic disposition and exhibit a habitual

dysphoria that is punctuated by major or minor depressive epi-

sodes (e.g., Judd & Akiskal, 2000). That is, the subclinical and

clinical symptoms of depression exhibit a chronicity that resem-

bles dispositions such as anger and anxiety. Consequently, we

conceptually treat depression like anger and anxiety for the pur-

poses of this review.

In addition to affective dispositions, acute outbursts of anger,

fear, or sadness and stressful events are also linked to the risk of

heart attack (e.g., Carroll, Ebrahim, Tilling, Macleod, & Smith,

2002; Kamarck & Jennings, 1991; Lear & Kloner, 1996; Mittle-

man et al., 1995). The main focus of this article, however, is not on

acute emotions. Our arguments do have implications for stressful

and emotional events that trigger MI or other manifestations of

heart disease, because affective dispositions, by definition, in-

crease the frequency of acute outbursts. Hence, the implications of

measurement and construct overlap of the negative affects for

understanding the impact of acute episodes on manifestations of

CHD are considered later in this article.

To provide a foundation for our arguments, we briefly describe

the biological causes that contribute to the development of CHD.

We follow this with a summary and critical analysis of relevant

epidemiological studies and a description of the behavioral, bio-

logical, and physiological pathways leading from affect to cardio-

vascular disease. We then review psychometric evidence regarding

the measurement and construct overlap of anger, anxiety, and

depression. We present more complex models of affect– disease

pathways that incorporate the overlap, followed by discussion of

the implications for the interpretation of epidemiological evidence.

In the final section we consider how increased recognition of the

overlap can advance current conceptualizations of and research on

the role of emotions in cardiovascular disease.

Biological Causes of Cardiovascular Disease

The general development of CHD begins with the accumulation

of lipid deposits on the artery walls, which, over time, grow into

fatty streaks (Guyton & Hall, 2000; Lilly, 2003; Ross, 1999). In

response to injury to the artery walls, inflammatory and reparatory

processes promote the deposit of lipoproteins into cellular struc-

tures within artery walls. Accumulation of these deposits causes a

thickening of the wall, the addition of fibrous material and calci-

fication that encroach on the artery opening (i.e., atherosclerosis).

As the lesions advance, blood flow is impeded, and oxygen supply

is reduced in the myocardium supplied by the vessel. Myocardial

ischemia occurs when the oxygen demand exceeds the supply,

eventuating in chest pain (i.e., angina pectoris). With advancing

disease, the calcified and fibrous cap may rupture; these exposed

tissues (thrombi) may be dislodged and cause near or complete

blockage of blood flow. This is associated with worsening chest

pain or death of heart muscle (MI). In addition, ischemia makes the

heart muscle unstable and promotes disturbances in heart rhythm

(arrhythmia). Myocardial contractions become chaotic in the se-

vere cases and can cause the cessation of circulation (i.e., cardiac

arrest), leading to sudden cardiac death (SCD). Traditional CHD

risk factors, such as smoking, hypertension, diabetes, lack of

physical exercise, obesity, serum cholesterol, age, and gender play

independent and overlapping roles in cardiopathogenesis by facil-

itating intimal injury or atherosclerosis, increasing oxygen de-

mand, and/or promoting coagulation and inflammatory processes

(Joynt, Whellan, & O’Connor, 2003; Stamler et al., 1999).

There are several manifestations of CHD, and these are impor-

tant because they are the outcomes of epidemiological studies.

Most commonly, CHD is manifested as a fatal or nonfatal MI. MI

is usually documented through medical tests, including special

enzyme tests, electrocardiogram recordings, and angiography. In

cases of nonfatal MI, the patient receives medication and/or may

undergo a surgical procedure in addition to being advised to make

lifestyle changes in diet, exercise, and smoking. SCD refers to

cardiac arrest; this need not be fatal, because some people are

resuscitated. Documented fatal or nonfatal MI and SCD are con-

sidered hard endpoints by epidemiologists. Another manifestation

of CHD, angina pectoris, is marked by reports of chest, shoulder,

and/or jaw pain. However, because chest pain can be a manifes-

tation of noncardiac causes (e.g., of a gastrointestinal nature), it is

not considered a hard outcome. In addition, some people report

chest pain because of somatic overconcern (Costa & McCrae,

1987). Hence, self-reports of chest pain are generally considered a

soft endpoint (e.g., Rudisch & Nemeroff, 2003; Rugulies, 2002).

Empirical Evidence for Affective Dispositions as CHD

Risk Factors

Statistical Outcomes

This empirical literature reports outcomes in a variety of statis-

tical formats, including relative risk ratios (RRs), hazard ratios,

odds ratios, and correlations. When the risk estimate was adjusted

for other traditional risk factors (which was the case for the

majority of studies), we report the most highly adjusted values in

the tables. The most commonly reported effect index in this

literature is the RR. RR refers to the ratio of the incidence (or

prevalence) of the disease in an exposed group to the incidence (or

prevalence) in the unexposed group. The RR of the unexposed

group is always 1.00, so an RR of 1.75 indicates that the exposed

Perceived social support was also measured by Raynor et al. (2002)

and was moderately correlated with both depression and anger. Although

social support also has emerged as a risk factor for CHD (e.g., Rozanski et

al., 1999), it is not considered further here. Social support has dispositional

elements, but it is conceptually distinct from personality or affective

disposition.

261

AFFECT, OVERLAP, AND HEART DISEASE

group is 75% more likely to develop the disease in question than

the unexposed group. An RR of 0.75 means that the rate of disease

in the exposed group is only about 75% of the rate in the unex-

posed group.

Prognostic Studies and Studies With Healthy Samples at

Baseline

In this area of research, studies of participants with known

cardiac disease who were followed for occurrence of subsequent

cardiac events (indicating progression of disease) are considered

prognostic studies. These are distinguished from studies of healthy

samples (i.e., without clinical CHD at baseline). The first set of

studies is concerned with the progression of disease, whereas the

second set is concerned with the development of CHD. Some

reviewers contend that evidence based on samples that are healthy

at baseline is stronger because there are no confounding effects of

illness (e.g., Hemingway & Marmot, 1999). Another reason to

consider healthy sample studies separately from samples with

clinical disease (i.e., prognostic studies) is that a risk factor that

initiates pathogenic processes may not necessarily also facilitate

the progression of existing disease (Scheier & Bridges, 1995).

Consequently, the results of studies of samples with clinical dis-

ease versus studies with persons who are initially healthy could

differ.

Follow-Up Criteria

In this review, we report only the results of the longest

follow-up in the tables for studies with healthy samples when there

were multiple reports involving the same sample with different

follow-ups. For prognostic studies, we include results of all

follow-ups in the tables because the length of follow-up may

interact with disease status. (In summarizing the data, however, we

did not count the same sample more than once.)

Nature of the Literature Review

The review of the literature was narrative in form. All relevant

studies were identified, and the tables present information about

sample features, length of follow-up, other risk factor adjustments,

outcome variables, and results. Although conducting a meta-

analysis of the relevant studies would have been optimal, there

were too few studies in some categories to make this feasible.

Study authors reported different kinds of analyses (and outcomes)

using different forms of classifications to make comparisons (e.g.,

most extreme deciles; top vs. bottom half) for depression; anxiety;

and anger, hostility, and anger expression. This makes the RRs (or

odds ratios, etc.) noncomparable across many studies. Adjustments

for other risk factors also vary widely across studies (and unad-

justed values are not uniformly reported). A meta-analysis would

have had to be restricted to only those studies that used the same

analyses and reported the requisite information, yielding a much

smaller, highly selective set of studies. In addition, certain cate-

gories of studies (e.g., anger and hostility in samples with known

clinical disease) are currently in short supply and consequently not

appropriate for meta-analysis. For literatures for which there was

a sufficient number of studies, recently published meta-analyses

already exist (for depression in initially healthy samples; Wulsin &

Singal, 2003; Rugulies, 2002; for certain types of anger; T. Q.

Miller, Smith, Turner, Guijarro, & Hallet, 1996; Myrtek, 2001).

The results of these recent meta-analyses are noted below.

Depression

Both the presence of depressive disorder, defined by clinical

interview, and the presence of depressive symptomatology, typi-

cally measured with questionnaire scales, have been studied as

predictors of CHD. The Diagnostic and Statistical Manual of

Mental Disorders third edition, revised (American Psychiatric

Association, 1987), and fourth edition (American Psychiatric As-

sociation, 1994) criteria for major depressive disorder include (a)

depressed mood, (b) markedly diminished interest or pleasure in

activities, (c) weight loss or gain (more than 5%), (d) insomnia or

hypersomnia, (e) psychomotor retardation or agitation, (f) fatigue

or loss of energy, (g) feelings of worthlessness or guilt, (h) dimin-

ished ability to concentrate or think, and (i) recurrent thoughts of

death. To receive a diagnosis of a major depressive disorder, the

individual must have five of the nine criteria, at least one of which

must be depressed mood or anhedonia, and the symptoms should

be exhibited for a minimum of 2 weeks. The symptoms also should

be deemed to have impaired daily functioning. Finally, depression

is not diagnosed in the case of grief or if the symptoms are due to

the direct physiological effects of a medical illness (e.g., hypothy-

roidism) or medication.

Milder mood disorders also have been studied as predictors of

CHD. One form is dysthymia, a chronic, low-level depression. To

receive this diagnosis, the person must experience depressed mood

and at least three of the following symptoms for most of the day,

nearly every day, for at least 2 years: poor appetite or overeating,

insomnia or hypersomnia, low energy, low self-esteem, poor con-

centration or difficulty making decisions, or feelings of hopeless-

ness. In this literature, another form of the disorder is minor

depression; to receive this classification, the individual must have

at least one of the core symptoms, and this symptom must be

present most of the day, almost every day, for at least 2 weeks and

must have resulted in some change in function or impairment in

daily activities (American Psychiatric Association, 1994).

Researchers who assess depression with self-report question-

naire measures have used instruments such as the Beck Depression

Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) and

the Center for Epidemiological Studies–Depression Scale (Rad-

loff, 1977). Investigators typically sum the responses to the items

and either treat depressive symptomatology as a continuous di-

mension or use quartiles or other means to classify high and low

scorers or establish caseness (e.g., Frasure-Smith, Lespe´rance, &

Talajic, 1995a).

Some depression reviews (e.g., Krantz & McCeney, 2002) have

also included related concepts such as vital exhaustion and hope-

lessness. Vital exhaustion (Appels, 1990; Appels & Mulder, 1988)

There is one exception in the tables. In keeping with some previous

reviewers (e.g., Rozanski et al., 1999), articles reporting results from the

Emotions and Prognosis Post-Infarct Project (EPPI) sample and those from

the Montreal Heart Attack Readjustment Trial (M-HART) plus EPPI

sample (Frasure-Smith & Lespe´rance, 2003a; Frasure-Smith et al., 1999,

2000; Lespe´rance, Frasure-Smith, Juneau, & Theroux, 2000) were counted

independently in drawing conclusions although the samples overlap. The

combined M-HART plus EPPI sample (n⫽896) consisted of the usual

care control arm of the M-HART (n⫽678) plus the EPPI participants.

262 SULS AND BUNDE

is defined by a lack of energy, increased irritability, and demor-

alization. This construct shares some features with depression, but

the two are not synonymous (e.g., vital exhaustion does not include

sadness, guilt, or feelings of worthlessness). Hopelessness (Ever-

son et al., 1996), which focuses on cognitive perceptions of effi-

cacy and pessimism, overlaps with the cognitive elements of

depression but makes no reference to somatic symptoms (e.g.,

weight loss or gain, fatigue). For the purpose of inclusiveness,

studies involving vital exhaustion and hopelessness are included in

the summary of studies. The conclusions we draw about the

association of depression and CHD would not be notably different

if results for vital exhaustion and hopelessness were excluded,

however.

Study Identification and Inclusion Criteria

Relevant prospective cohort studies were identified. These, un-

like cross-sectional and retrospective case-control studies, are not

subject to recall bias. To identify relevant studies, we conducted

MEDLINE and PsycINFO searches with the terms cardiac dis-

ease,myocardial infarction,heart attack,depression, and dyspho-

ria for all relevant articles published in English from 1966 to 2003.

In addition, we also identified relevant studies from the reference

sections of 21 prior narrative (Fleet & Beitman, 1998; Fleet,

Lavoie, & Beitman, 2000; Frasure-Smith & Lespe´rance, 1999;

Gallo & Matthews, 2003; Glassman & Shapiro, 1998; Hayward,

1995; Hemingway & Marmot, 1999; Ketterer, Mahr, & Goldberg,

2000; Krantz & McCeney, 2002; Kubzansky & Kawachi, 2000;

Kubzansky, Kawachi, Weiss, & Sparrow, 1998; Musselman,

Evans, & Nemeroff, 1998; Rozanski et al., 1999; Rudisch &

Nemeroff, 2003; Sirois & Burg, 2003; D. Smith, 2001; Strike &

Steptoe, 2004; Tennant & McLean, 2001; A. Thomas, Kalaria &

O’Brien, 2004; Wulsin, Valliant, & Wells, 1999; Zellweger, Os-

terwalder, Langewitz & Pfisterer, 2004) and 6 meta-analytic re-

views (Booth-Kewley & Friedman, 1987; Matthews, 1988; T. Q.

Miller et al., 1996; Myrtek, 2001; Rugulies, 2002; Wulsin &

Singal, 2003) published through February 2004.

In some research areas, there is the concern that null, small, or

contradictory effects might be systematically excluded from the

published literature. However, the long-standing controversy (An-

gell, 1985; R. Williams, Schneiderman, Relman, & Angell, 2002)

about the role of emotions and personality in physical health and

illness has actually encouraged the publication of both positive and

negative findings. Hence, publication bias probably is less of a

problem here than in other areas.

In summarizing the evidence, we attached greater importance to

prospective studies that included hard endpoints, such as medically

documented CHD and fatal or nonfatal MI and SCD. By relying

more heavily on hard endpoints (as have prior reviewers; Wulsin

& Singal, 2003), we eliminate study outcomes that include partic-

ipants who report chest pain (i.e., angina) because of somatic

overconcern (Costa & McCrae, 1987) or patients whose chest pain

might be due to noncardiac causes, such as gastric reflux. Our

summaries of the evidence also give more weight to studies that

adjusted for traditional CHD risk factors (e.g., Rozanski et al.,

1999; Wulsin & Singal, 2003). However, for the purpose of

inclusiveness, Tables 1–3 list all studies (with sample size, length

of follow-up, other risk factor adjustments, outcome variables, and

main results), whether they included hard or soft endpoints.

Healthy Populations at Baseline

Table 1 presents the results of 24 studies that assessed depres-

sion in populations without known CHD at baseline. Sample sizes

ranged from 730 to 9,563; follow-up ranged from 4 years to 15

years. As mentioned earlier, we focus on studies that reported hard

endpoints (i.e., cardiac death or documented disease, e.g., MI) in

summarizing and drawing conclusions. However, studies that re-

lied on reports of chest pain also are listed in the tables for the

purposes of inclusiveness.

Nineteen studies operationalized the outcome as either cardiac

death or documented MI. Ten studies found statistically significant

effects ( p⬍.05) for depression (designed as ⫹in Table 1,

Column 7), and 7 studies found significant effects in a subsample

(e.g., sex or age groups) or marginally significant effects ( ps⫽

.06 –.10) for the entire sample (designated as ⫾). Only 2 studies

reported null effects (designated as ⫺). In general, samples of

physically healthy persons at baseline showed that higher depres-

sion was associated with a greater risk of manifesting CHD.

Meta-analyses by Rugulies (2002) and Wulsin and Singal

(2003) of community samples (using slightly different criteria for

study inclusion) reinforce our conclusions. Both meta-analyses

reported that higher depression conferred an average relative risk

of 1.64 for cardiac death or documented cardiac disease.

Populations With Known CHD

Fifty-two prognostic studies were identified that assessed asso-

ciations between depression and progression of CHD (see Table

1). Samples sizes ranged from 87 to 5,057 patients, with follow-

ups ranging from 3 months to 10 years.

Of the 52 studies, 50 studies reported hard endpoints, but several

of these involved the same samples with different follow-up peri-

ods (e.g., Frasure-Smith & Lespe´rance, 2003b; Frasure-Smith,

Lespe´rance, Juneau, Talajic, & Bourassa, 1999; Frasure-Smith,

Lespe´rance, & Talajic, 1993; Frasure-Smith et al., 1995a, 1995b,

Frasure-Smith et al., 2000; Lane, Carroll, Ring, Beevers, & Lip,

2000a, 2000b, 2001; Lespe´rance, Frasure-Smith, & Talajic, 1996;

Lespe´rance, Frasure-Smith, Talajic, & Bourassa, 2002). On the

basis of only the 44 independent samples (and including only the

results from the longest follow-up), 24 studies found statistically

significant relations between depression and cardiac morbidity or

mortality in the entire sample. Another 5 studies found significant

risk in a subgroup or a marginally significant effect in the entire

sample. Fifteen studies found no statistically significant associa-

tion between depression and cardiac disease. Among the positive

studies, the relative risk conferred by depression ranged from 1.90

to 5.50. In light of the positive conclusions regarding depression and

CHD drawn by some prior reviewers (e.g., Gallo & Matthews, 2003;

Krantz & McCeney, 2002), the number of prognostic studies with

negative results may seem surprising. Carney and Freedland (2003),

however, have noted that some of the negative reports have small

sample sizes, low participation rates, and/or short follow-up intervals.

All of these features reduce the possibility of detecting effects.

Summary

Evidence favoring a connection between depression and subse-

quent development of CHD is strong in populations that were

(text continues on page 282)

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AFFECT, OVERLAP, AND HEART DISEASE

Table 1

Depression and Coronary Heart Disease (CHD)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples free of CHD at baseline

Ha¨llstro¨m et al.

(1986)

795 women 12.0 years Hamilton Rating Scale and

psychiatric interview

(DSM–III)

Age Angina coronary ECG,

RR ⫽5.40, p⬍.01; severity

of depression predicted

angina but not other

outcomes

(⫹/⫺)

Appels & Mulder

(1988)

3,877 men 4.2 years Vital exhaustion (Maastricht

Questionnaire)

Age, smoking, BP, cholesterol MI p⬍.01

(⫹)

Appels & Otten

(1992)

3,365 men 9.5 years One item, “At the end of the

day I am completely

exhausted mentally and

physically”

Age, smoking, BP, TSC, obesity,

marital status

Cardiac death HR at

10 mo. ⫽8.96 (1.84–43.57),

p⬍.01

20 mo. ⫽6.33 (1.61–24.92),

p⬍.01

30 mo. ⫽4.47 (1.40–14.22),

p⬍.05

40 mo. ⫽3.16 (1.19–8.39),

p⬍.05

After 40 mo. follow-up, ns

(⫹/⫺)

Anda et al. (1993) 2,832 adults,

NHEFS

12.4 years General Well-Being Schedule

(Depression subscale) and

Hopelessness item

Age, sex, race, education, marital

status, smoking, TSC, SBP,

BMI, alcohol, physical

inactivity

Fatal and nonfatal IHD RR:

Depression

Fatal IHD ⫽1.50 (1.00–

2.30), nonfatal IHD ⫽

1.60 (1.10–2.40)

Hopelessness

Fatal IHD ⫽2.10 (1.10–

3.90), nonfatal IHD ⫽

2.30 (1.20–4.70)

(⫹)

Aromaa et al.

(1994)

3,203 adults 6.6 years PSE, GHQ Age CHD mortality RR ⫽3.36, p⬍.05

(⫹)

Vogt et al. (1994) 2,573 adults, Kaiser

HMO

15.0 years Authors’ depression

index–items chosen to

approximate DSM–III

depression

Age, sex, subjective health

status, SES, smoking, length

of health plan membership

IHD NR

(⫺)

Simonsick et al.

(1995)

3,457 adults with

diagnosed HTN

from three sites,

EPESE

6.0 years CES-D Age, disability, diabetes, angina,

digitalis use, history of MI and

stroke

CV-related mortality p⬍.01 for Iowa women, all

others, ns

(⫹/⫺)

Barefoot & Schroll

(1996)

730 adults 27.0 years MMPI OBD subscale Age, sex, SBP, triglycerides,

smoking, sedentary work,

sedentary leisure

MI RR ⫽1.70 (1.23–2.34), p⬍

.01

(⫹)

Everson et al.

(1996)

2,428 men, KIHD 6.0 years Two hopelessness items Age, smoking, BP, cholesterol,

education, income, exercise,

alcohol, lipids, social support,

depression, perceived health,

disease history

MI, CV mortality RH:

MI ⫽2.05 (1.08–3.88),

CV mortality ⫽1.85

(0.94–3.64)

(⫹/⫺)

264 SULS AND BUNDE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples free of CHD at baseline (continued)

Pratt et al. (1996) 1,551 adults, ECA 13.0 years DIS (DSM–III) Age, sex, smoking, HTN,

diabetes, education, marital

status, psychiatric illness,

psychotropic medication

Self-reported MI OR:

Dysphoria ⫽2.06 (1.15–

3.72), major depression

⫽4.14 (1.48–11.62)

(⫹)

Wassertheil-Smoller

et al. (1996)

4,367 adults (with

systolic HTN)

4.5 years CES-D Age, race, education, activities of

daily living, smoking, history

of MI, stroke, diabetes

Fatal and nonfatal MI RR:

Dichotomous analysis: NR

Continuous analysis (five-

unit CES-D increase

during follow-up):

Women ⫽1.20 (0.97–

1.48), p⫽.09; men

⫽1.07 (0.83–1.38), p

⫽.81

(⫹/⫺)

Ford et al. (1998) 1,190 male medical

students,

Precursors Study

37.0 years From experts, evaluated self-

reported episode of

depression

Age, cholesterol, parental history

of premature MI, physical

inactivity, smoking, HTN,

diabetes

CHD and MI RR:

CHD ⫽2.12 (1.24–3.63), MI

⫽2.12 (1.11–4.06)

(⫹)

Hippisley-Cox et al.

(1998)

327 adults with

IHD and 897

age- and sex-

matched controls

Documented depression prior

to IHD diagnosis

Smoking, HTN, diabetes,

deprivation score

IHD OR:

Men ⫽2.75 (1.13–6.69), p

⫽.03; women ⫽NR

(⫹/⫺)

Mendes de Leon et

al. (1998)

2,391 adults,

EPESE

9.0 years CES-D Age, education, diabetes,

smoking, BP, exertional

angina, physical functioning

CHD incidence and

mortality

RR:

Women

Incidence ⫽1.01 (0.99–

1.03), mortality ⫽1.02

(0.99–1.05)

Men

Incidence ⫽0.98 (0.95–

1.01), mortality ⫽0.98

(0.95–1.01)

(⫹/⫺)

Penninx et al.

(1998)

3,701 young adults,

EPESE

4.0 years CES-D Age, sex, smoking, alcohol,

BMI, HTN, physical disability,

history of stroke, diabetes,

cancer

CHD mortality and

events

RR:

Chronic depression: NR

New depression

Men—Mortality ⫽1.75

(1.00–3.05), events ⫽

2.03 (1.28–3.24)

Women—NR

(⫹/⫺)

Schwartz et al.

(1998)

2,960 adults 3.0 years CES-D short form Age, gender, BP, smoking,

alcohol intake, prescription

medicine use, self-rated health

Fatal and nonfatal MI RR ⫽2.23 (1.34–3.71)

(⫹)

(table continues)

265

AFFECT, OVERLAP, AND HEART DISEASE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples free of CHD at baseline (continued)

Sesso et al. (1998) 1,305 men, NAS 7.0 years MMPI-2 Depression Clinical

Scale

Age, BMI, smoking, BP,

cholesterol, family history,

alcohol use

Nonfatal MI and CHD

mortality

RR:

Nonfatal MI ⫽2.40 (0.74–

7.85), p⫽.19; nonfatal

MI and CHD mortality

⫽1.69 (0.70–4.05), p⫽

.29

(⫺)

Whooley &

Browner (1998)

7,518 women 6.0 years GDS short form Age, history of MI or stroke,

diabetes, HTN, COPD,

perceived health, cognitive

function, smoking

CHD mortality HR ⫽1.70 (1.00–3.00), p⫽

.06

(⫹/⫺)

Cole et al. (1999) 5,053 men, College

Alumni Health

Study

12.0 years One item: “How often do

you experience sense of

exhaustion (except after

exercise)?”

Age, hours of sleep, report of

insomnia, use of sleeping pills

and tranquilizers, BMI,

physical activity, alcohol,

smoking, report of physician-

diagnosed HTN, diabetes, and

depression

CHD mortality RR ⫽2.06 (0.98–4.36)

(⫹/⫺)

Ariyo et al. (2000) 4,493 adults 6.0 years CES-D short form Age, sex, race, HTN, smoking,

physical inactivity, physical

illness, alcohol, diabetes,

cholesterol, triglycerides,

marital status

CHD and MI HR:

CHD ⫽1.11 (1.01–1.22), p

⫽.03; MI ⫽1.12

(0.97–1.29), p⫽.12

(⫹/⫺)

Ferketich et al.

(2000)

5,007 women,

2,886 men,

NHANES I

8.3 years CES-D Age, race, HTN, smoking,

exercise, diabetes, BMI,

poverty index

Fatal and nonfatal

CHD events

RR:

Women

Fatal ⫽0.74 (0.40–1.48),

nonfatal ⫽1.73 (1.11–

2.68), p⬍.05.

Men

Fatal ⫽2.34 (1.54–3.56),

nonfatal ⫽1.71

(1.14–2.56)

(⫹/⫺)

Penninx et al.

(2001)

2,397 adults 4.0 years CES-D and DIS for DSM–III

major depression

Age, sex, smoking, alcohol,

BMI, BP, HTN, education,

history of stroke, diabetes,

lung disease, cancer

Cardiac mortality RR:

Major depression ⫽3.90

(1.40–10.90), minor

depression ⫽1.50

(0.90–2.60)

(⫹)

Clouse et al. (2003) 76 women with

diabetes

10.0 years DSM–III depression Age, duration of diabetes, BMI,

glycosylated hemoglobin,

HTN, hyperlipidemia, tobacco

use

CHD HR ⫽5.20 (1.40–18.90), p⫽

.01

(⫹)

Prescott et al.

(2003)

4,084 men, 5,479

women,

Copenhagen City

Heart Study

5.0–7.0 years Vital Exhaustion

Questionnaire

Age, BMI, WHR, SBP, TSC,

HDL, diabetes, family history

of IHD smoking, physical

activity, alcohol, education,

and household income

IHD HR ⫽2.20 (1.53–3.17), p⬍

.01

(⫹)

266 SULS AND BUNDE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline

Ruberman et al.

(1984)

2,320 male post-MI

patients, BHAT

3.0 years Authors’ three-item

depression index

Age, smoking, BHAT risk class,

HR, digitalis use, CHF,

myocardial summary,

ventricular arrhythmia,

treatment group, angina,

smoking, education, life

circumstances, social isolation,

life stress–social isolation

summary

Mortality NR

(⫺)

G. J. Kennedy et al.

(1987)

88 patients with

arrhythmia or

syncope

18.0 mo. Assigned to one of four

profiles for cardiac surgery

patients—depressed gauged

as most distressed

None Mortality r⫽.19, p⬍.01

(⫹)

Silverstone (1987) 108 post-MI

patients

Not reported Montgomery–Asberg Rating

Scale

None Mortality, myocardial

arrest, reinfarction

p⬍.01

(⫹)

Carney et al. (1988) 52 patients

undergoing

cardiac

catheterization

1.0 year DIS (DSM–III) Controlled separately for severity

of CHD, LVEF, and smoking

Major cardiac events

(MI, CABG,

angioplasty,

mortality)

rs between .22 and .36; ps⬍

.02

(⫹)

Wiklund et al.

(1988)

201 male post-MI

patients

Maximum of 100 mo. Authors’ measure of

emotional state (from

interviews and

questionnaires)

None Mortality, nonfatal

reinfarction, and

total cardiac events

(⫺)

Schleifer et al.

(1989)

282 post-MI

patients

3.0–4.0 mo. Schedule for Affective

Disorders and

Schizophrenia (RDC

criteria for depression)

None Medical events (chest

pain,

rehospitalization for

cardiac disease,

reinfarction) and

death

(⫺)

Ahern et al. (1990) 502 patients post-

MI and

arrhythmia,

CAPS

1.0 year BDI Prior MI, LVEF, beta blocker

and/or digitalis use,

transmurality in qualify MI,

runs of ventricular premature

complexes on the 24-hr ECG

at baseline

Death or cardiac arrest p⫽.04

(⫹)

Ladwig et al.

(1991)

560 male post-MI

patients

6.0 mo. German self-report

questionnaire (emotional

isolation, vulnerability, and

manifest depression scales)

Age, recurrent infarction, late

potentials, dyspnea, ECG

Cardiac death OR:

Low vs. moderate ⫽2.80,

low vs. high ⫽4.90, p

⫽.07

(⫹/⫺)

Berkman et al.

(1992)

194 post-MI older

adults, EPESE

6.0 mo. CES-D None Mortality NR

(⫺)

Legault et al.

(1992)

92 post-MI patients 12.0 mo. BDI Age, sex, SES Cardiac morbidity

(incidence of cardiac

events, anginal

symptoms,

prescription of

anginal medications)

(⫺)

(table continues)

267

AFFECT, OVERLAP, AND HEART DISEASE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline (continued)

Frasure-Smith et al.

(1993)

222 post-MI

patients, EPPI

6.0 mo. DIS (DSM–III–R) Warfarin prescription, lack of

close friends, Killip class,

previous MI

Cardiac mortality HR ⫽3.44 (2.25–4.63)

(⫹)

Frasure-Smith et al.

(1995a)

222 post-MI

patients, EPPI

12.0 mo. BDI Previous MI, Killip class,

premature ventricular

contractions

Cardiac mortality OR ⫽6.64 (1.76–25.09), p⬍

.01

(⫹)

Frasure-Smith et al.

(1995b)

222 post-MI

patients, EPPI

12.0 mo. BDI Previous MI, prescription of

ACE inhibitors, previous

depression, anxiety

Cardiac events (fatal

and nonfatal MI,

admission for

unstable angina,

arrhythmic deaths,

survived cardiac

arrests)

OR ⫽1.99 (0.92–4.31), p⫽

.08

(⫹)

Lespe´rance et al.

(1996)

222 post-MI

patients, EPPI

18.0 mo. DIS for Major Depression

(DSM–III–R)

None Cardiac mortality OR ⫽3.96 (1.50–10.50), p⬍

.01

(⫹)

Frasure-Smith et al.

(1999)

896 post-MI

patients, M-

HART, EPPI

12.0 mo. BDI Age, Killip class, Gender ⫻

LVEF, Gender ⫻Non-Q-wave

MI, Gender ⫻Smoking

Cardiac mortality OR ⫽3.66

(⫹)

Frasure-Smith et al.

(2000)

887 post-MI

patients, M-

HART, EPPI

12.0 mo. BDI Age, Killip class, Sex ⫻Non-Q-

wave MI, Sex ⫻LVEF, Sex

⫻Smoking

Cardiac mortality p⬍.01

(⫹)

Lespe´rance et al.

(2002)

896 post-MI

patients, M-

HART, EPPI

5.0 years BDI Age, sex, education, marital

status, lack of close friends,

smoking, history of treatment

for HTN, diabetes, prior MI,

thrombolysis, Killip class, Q-

wave MI, LVEF,

revascularization, beta

blockers, ACE inhibitors

Cardiac mortality

nonfatal MI

HR:

CHD deaths ⫽4.32 (2.40–

7.75), p⬍.01; CHD

deaths or nonfatal MI ⫽

2.36 (1.49–3.73), p⬍

.01

(⫹)

Frasure-Smith &

Lespe´rance

(2003a)

896 post-MI

patients, M-

HART, EPPI

5.0 years BDI Age, sex, education, smoking,

prior MI, thrombolytic

treatment, Q-wave MI, Killip

class, revascularization, LVEF,

prescription of hypoglycemic

agents (for nondiabetics) and

beta blockers

Cardiac-related

mortality

HR ⫽1.46 (1.18–1.79), p⬍

.01

(⫹)

Jenkinson et al.

(1993)

1,376 patients with

suspected MI

3.0 years Authors’ depression

categorization

Age Mortality NR

(⫺)

Aromaa et al.

(1994)

435 adults with

CHD or

cerebrovascular

disease

6.6 years GHQ, PSE Age CHD mortality RR ⫽5.52, p⬍.05

(⫹)

268 SULS AND BUNDE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline (continued)

Kop et al. (1994) 127 post-PTCA

patients

18.0 mo. Maastricht Questionnaire

(vital exhaustion)

Severity of CHD,

hypercholesterolemia

New cardiac events

(cardiac death, MI,

CABG, repeat

PTCA, increase in

coronary athero-

sclerosis, new

anginal complaints

with documented

ischemia)

OR ⫽2.34, p⫽.06

(⫹/⫺)

Ladwig et al.

(1994)

377 post-MI

patients

6.0 mo. German self-report

questionnaire (emotional

isolation, vulnerability, and

psychotic tendency scales)

Age, social class status, recurrent

infarction, rehabilitation,

cardiac events, helplessness

Angina RR ⫽2.31 (1.11–4.80)

(⫹)

Allison et al. (1995) 381 adults referred

to cardiac

rehabilitation

(unstable angina,

MI, PTCA,

CABG)

6.0 mo. SCL-90–R Depression CABG, diabetes, EF, previous

cardiac event, smoking at

index event, use of beta

blocker, PTCA, continued

smoking

Cardiovascular

rehospitalization, any

recurrent event

(⫺)

Appels et al. (1995) 105 PTCA patients 18.0 mo. Maastricht Questionnaire

(vital exhaustion)

No. diseased vessels before

PTCA, hypercholesterolemia,

duration of complaints before

PTCA

New documented

cardiac events

OR ⫽3.07, p⫽.04

(⫹)

Denollet et al.

(1995)

105 post-MI men 3.8 years MBHI Premorbid Pessimism

and Future Despair scales

None Cardiac mortality p⬍.01 (nonsignificant in model

with personality type,

previous MI, low exercise

tolerance, anterior MI,

smoking, age)

(⫹)

Oxman et al. (1995) 232 patients

undergoing

CABG, aortic

valve

replacement, or

both

6.0 mo. HAM-D None Mortality p⫽.07

(⫹/⫺)

Barefoot et al.

(1996)

1,250 adults with

CHD

15.2 years Zung Depression Scale LVEF, ECG abnormalities, no.

vessels with more than 75%

narrowing, indicators of

myocardial damage, treatment

(medical vs. surgical)

Cardiac mortality p⬍.01

(⫹)

Mendes de Leon et

al. (1996)

149 patients after

successful PTCA

18.0 mo. Maastricht Questionnaire

(vital exhaustion)

Age, smoking, HTN, prior MI,

history of hyper-

cholesterolemia, duration of

complaints, residual stenosis

after PTCA, anger

Recurrent events RR ⫽2.57

(⫹)

Carinci et al. (1997) 2,449 post-MI

patients

6.0 mo. Cognitive–Behavioral

Assessment Hospital Form

questionnaire

None Mortality HR ⫽1.70 (0.90–3.10), p⫽

.08

(⫹/⫺)

(table continues)

269

AFFECT, OVERLAP, AND HEART DISEASE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline (continued)

S.A. Thomas et al.

(1997)

308 post-MI

patients with

asymptomatic

ventricular

arrhythmia

taking nonactive

medication,

CAST

1.6 years Zung Depression Scale LVEF, diabetes Mortality NR

(⫺)

Denollet &

Brutsaert (1998)

87 post-MI patients 7.9 years MBHI Pessimism and

Despair scales

None Cardiac mortality OR ⫽4.30 (1.40–13.30), p⫽

.01

(⫹); nonsignificant in model

with Type D

personality, LVEF, three

vessel disease, poor

exercise tolerance,

history of previous MI,

smoking after MI

Ketterer et al.

(1998)

144 men with

diagnostic

coronary

angiograms

59.7 mo. Ketterer Stress Symptoms

Frequency Checklist—

Depression

None Mortality, new MI,

revascularization

Inverse relationship, p⬍.01

(⫺)

Perski et al. (1998) 171 CABG patients 3.0 years Nottingham Health Profile Lipid ratio (LDL/HDL), SBP Cardiac events RR ⫽1.89 (1.04–3.55), p⬍

.04

(⫹)

Irvine et al. (1999) 671 post MI

patients,

CAMIAT

2.0 years BDI Previous MI, CHF, social

participation, social network

contacts, dyspnea/fatigue

SCD, cardiac mortality NR

(⫺)

M.W. Kaufmann et

al. (1999)

331 post-MI

patients

1.0 year DIS depression LVEF, prior MI, CHF, CABG,

previous stroke, diabetes, age,

HTN, family history of CHD

Mortality NR

(⫺)

Pirraglia et al.

(1999)

237 CABG patients 6.0 mo. CES-D None MI, major cardiac

outcomes, ICU

complications

(⫺)

Scheier et al.

(1999)

247 CABG patients 6.0 mo. CES-D short form Sex, TSC Surgery and CHD-

rehospitalization

OR ⫽1.78 (0.96–3.30)

(⫹/⫺)

Denollet et al.

(2000)

319 patients with

CHD

5.0 years GMS depression None Cardiac events and

revascularization

OR ⫽2.30 (1.20–4.20), p⫽

.01

(⫹); nonsignificant in model

with Type D

personality, LVEF, and

age

Hermann et al.

(2000)

5,057 patients

referred for

exercise testing

(48.5% with

documented

CHD)

5.7 years HADS depression Sex, age, exercise endurance,

exercise work load, recent

coronary angiography,

maximum SBP during

exercise, history of CABG,

history of MI, positive

exercise ECG, HADS anxiety

Mortality OR:

Documented CHD ⫽1.21

(1.04–1.42), p⬍.03;

CHD not documented ⫽

1.29 (1.02–1.63), p⬍

.05

(⫹)

270 SULS AND BUNDE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline (continued)

Horsten et al.

(2000)

292 women

postcoronary

event

FemCorRisk

5.0 years Nine-item depression

questionnaire derived from

Pearlin Depression Scale

Age, diagnosis at index event,

symptoms of heart failure,

diabetes, HDL, severity of

angina, smoking, sedentary

lifestyle, BMI, SBP, history of

HTN

Recurrent cardiac event HR ⫽1.90 (1.02–3.60), p⬍

.05

(⫹)

Lane et al. (2000a) 288 post-MI

patients

1.0 year BDI None Recurrent cardiac

events

(⫺)

Lane et al. (2000b) 288 post-MI

patients

4.0 mo. BDI None Cardiac mortality NR

(⫺)

Lane et al. (2001) 288 post-MI

patients

1.0 year BDI None Cardiac mortality NR

(⫺)

Lespe´rance et al.

(2000)

430 patients with

unstable angina

1.0 year BDI ECG evidence of ischemia,

LVEF, no. of diseased vessels

Cardiac events OR ⫽6.73 (2.43–18.64), p⬍

.01

(⫹)

Mayou et al. (2000) 347 post-MI

patients

MONICA

1.0 year HADS depression Significant baseline predictors of

mortality

Cardiac mortality NR

(⫺)

Welin et al. (2000) 275 post-MI

patients

10.0 years Zung Depression Scale Sex, LV failure, ventricular

dysrhythmia, social support

Cardiac mortality HR ⫽3.16 (1.38–7.25)

(⫹)

Baker et al. (2001) 158 CABG patients 25.0 mo. Depression Anxiety Stress

Scale

None Mortality OR ⫽6.40 (1.18–32.98), p⬍

.05

(⫹)

Bush et al. (2001) 271 post-MI

patients

4.0 mo. BDI LVEF, diabetes Mortality RR ⫽3.50, p⬍.05

(⫹)

Connerrey et al.

(2001)

309 CABG patients 12.0 mo. U.S. NIMH diagnostic

interview for major

depression (DSM–IV)

Sex, living alone, LVEF, length

of hospital stay, complexity of

surgical procedures, NYHA

class

Adverse cardiac

outcomes (angina,

hospital admission

for CHF, MI, PTCA,

CABG, nonfatal MI,

cardiac death)

RR ⫽2.30 (1.17–4.56), p⬍

.01

(⫹)

Penninx et al.

(2001)

450 adults with

CHD

4.0 years CES-D and DIS for DSM-III

major depression

Age, sex, smoking, alcohol,

BMI, BP, HTN, education,

history of stroke, diabetes,

lung disease, cancer

Cardiac mortality RR:

Major depression ⫽3.00

(1.10–7.80), minor

depression ⫽1.60

(1.00–2.70)

(⫹)

Saur et al. (2001) 416 CABG

patients, QMMI

6.0 mo. SF-36 Mental Health Scale Age, sex, diabetes, obesity,

COPD, dialysis, severity of

CHD, previous cardiac surgery

Mortality, hospital

readmission

Mortality: NR

Readmission: p⫽.02

(⫹/⫺)

(table continues)

271

AFFECT, OVERLAP, AND HEART DISEASE

Table 1 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of depression, hopelessness, and vital exhaustion in samples with established CHD at baseline (continued)

Burg et al. (2003) 89 CABG patients 2.0 years BDI Risk Index Score (age, gender,

LV function, urgency of

CABG surgery, repeat

CABG), history of CHF,

history of COPD

Cardiac mortality p⬍.05

(⫹)

Note. The results of several large prospective investigations (e.g., Normative Aging Study [NAS], Western Collaborative Group Study [WCGS]) of initially healthy participants have been published in

separate studies. We include multiple reports using the same group of participants if there appeared to be important differences among them (e.g., different assessments, varying adjustments). If duration

of follow-up was the only appreciable difference among the studies, we report only the results from the longest follow-up period. Several groups of patients with CHD at baseline have also been followed

over a series of time points (e.g., Montreal Heart Attack Readjustment Trial [M-HART], Emotions and Prognosis Post-Infarct Project [EPPI]; Lane et al., 2000a, 2000b, 2001), which resulted in multiple

reports. Relatively brief follow-up periods are common in this literature (0 –18 months), and there is great variability among studies. Because length of follow-up may be more important in determining

the relation between negative emotions and CHD progression, we have included each relevant study and list those using identical samples in series. Several of the studies include multiple outcome measures

of CHD. When possible, we relied on hard (e.g., myocardial infarction [MI], CHD death) rather than soft (e.g., angina, hospital visits) CHD outcomes. Studies varied in the types of statistical adjustments

that were used. We report the most adjusted results from each study unless otherwise noted. Several studies by Denollet and colleagues (i.e., Denollet & Brutsaert, 1998; Denollet, Sys, & Brutsaert, 1995;

Denollet, Vaes, & Brutsaert, 2000) report crude results and results adjusted for a number of cardiovascular risk factors and Type D personality (Denollet, 2000). Because of the extensive overlap of this

construct with depression, anger, and anxiety, we report crude results before adjustment for these factors. In the Results column, a plus sign in parentheses indicates a positive relation, a minus sign indicates

a negative or null relation, and a plus-or-minus sign indicates equivocal results or findings of marginal statistical significance. DSM–III ⫽Diagnostic and Statistical Manual of Mental Disorders (3rd ed.);

ECG ⫽electrocardiogram; RR ⫽relative risk ratio; BP ⫽blood pressure; TSC ⫽total serum cholesterol; HR ⫽heart rate; mo. ⫽months; NHEFS ⫽National Health Examination Follow-Up Study;

SBP ⫽systolic blood pressure; BMI ⫽body mass index; IHD ⫽ischemic heart disease; PSE ⫽Present State Exam; GHQ ⫽General Health Questionnaire; NR ⫽no relation; HMO ⫽health maintenance

organization; SES ⫽socioeconomic status; HTN ⫽hypertension; EPESE ⫽Established Populations for the Epidemiological Studies of the Elderly; CES-D ⫽Center for Epidemiological

Studies–Depression Scale; CV ⫽cardiovascular; MMPI ⫽Minnesota Multiphasic Personality Inventory; OBD ⫽obvious depression; DIS ⫽Diagnostic Interview Schedule; KIHD ⫽Kuopio Ischaemic

Heart Disease Risk Factor Study; RH ⫽relative hazard; ECA ⫽Epidemiological Catchment Area study; OR ⫽odds ratio; GDS ⫽Geriatric Depression Scale; COPD ⫽chronic obstructive pulmonary

disease; NHANES I ⫽National Health and Nutrition Examination Study; WHR ⫽waist-to-hip ratio; HDL ⫽high-density lipoprotein cholesterol; BHAT ⫽beta-blocker heart attack trial; CHF ⫽

congestive heart failure; LVEF ⫽left ventricular ejection fraction; CABG ⫽coronary artery bypass graft surgery; RDC ⫽research diagnostic criteria; CAPS ⫽Cardiac Arrhythmia Pilot Study; BDI ⫽

Beck Depression Inventory; DSM–III–R ⫽Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.); ACE ⫽ACE inhibitors; PTCA ⫽percutaneous transluminal coronary angioplasty;

SCL-90 –R ⫽Symptom Checklist–90—Revised; EF ⫽ejection fraction; MBHI ⫽Millon Behavioral Health Inventory; HAM-D ⫽Hamilton Rating Scale for Depression; CAST ⫽Cardiac Arrhythmia

Suppression Trial; LDL ⫽low-density lipoprotein cholesterol; CAMIAT ⫽Canadian Amiodarone Myocardial Infarction Arrhythmia Trial; ICU ⫽intensive care unit; GMS ⫽Global Mood Scale;

HADS ⫽Hospital Anxiety and Depression Scale; FemCorRisk ⫽Stockholm Female Coronary Risk study; MONICA ⫽Monitoring Trends and Determinants in Cardiovascular Disease study; LV ⫽left

ventricular; NIMH ⫽National Institute of Mental Health; DSM–IV ⫽Diagnostic and Statistical Manual of Mental Disorders (4th ed.); NYHA ⫽New York Heart Association; QMMI ⫽Quality

Measurement and Management Initiative coronary revascularization project; SF-36 ⫽Medical Outcomes Study 36-item short form health survey.

272 SULS AND BUNDE

Table 2

Anxiety and Coronary Heart Disease (CHD)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of anxiety, worry, and panic in samples free of CHD at baseline

Coryell et al. (1982) 113 psychiatric inpatients 35.0 years Panic disorder Compared with age- and

gender-matched Iowa

population

Mortality due to circulatory

system disease

SMR:

Men ⫽2.00, p⬍.01;

women ⫽NR

(⫹/⫺)

Martin et al. (1985) 60 psychiatric outpatients 7.0 years Anxiety neurosis Compared with age-, sex-,

and race-specific 1970

mortality rates in

Missouri

Fatal CHD NR

(⫺)

Coryell et al. (1986) 155 psychiatric outpatients 12.0 years Anxiety neurosis per

records

Compared with age- and

gender-matched surgical

controls (herniorrhaphies,

appendectomies,

cholocystectomies)

CVD mortality p⫽.07

(⫹/⫺)

Haines et al. (1987) 1,457 men, Northwick Park

Heart Study

10.0 years Self-reported phobic anxiety

(Crown–Crisp Index;

Crown & Crisp, 1966)

Age, smoking, social class,

work shift (day or night),

SBP, Factor VII activity,

fibrinogen concentration,

TSC

Fatal IHD RR ⫽3.77 (1.64–8.64)

(⫹)

Weissman et al. (1990) 3,838 adults, ECA DIS panic disorder

(DSM–III)

Age, sex, SES, race, marital

status

Self-reported history of MI OR ⫽4.50 (1.70–12.30)

(⫹)

Allgulander & Lavori

(1991)

3,302 psychiatric inpatients 14.0 years Pure anxiety neurosis Compared with age- and

sex-specific mortality

rates of surrounding

county

Fatal CHD NR

(⫺)

Rosengren et al. (1991) 1,758 men 11.8 years Tension/Irritability/Anxiety/

Sleep Difficulties Scale

Age, SBP, TSC, smoking,

BMI, diabetes, family

history of MI,

occupational class,

marital status, leisure

time physical activity,

registration for alcohol

abuse

CHD or CVD mortality OR:

CHD ⫽1.50 (1.20–

1.90), CVD

mortality ⫽1.70

(1.20–2.40)

(⫹)

Eaker et al. (1992) 749 women Framingham

Heart Study

20.0 years Self-reported anxiety

symptoms (Framingham

Social Strain Scale)

Age, SBP, lipid ratio (TSC/

HDL), diabetes, smoking,

BMI

MI and CHD mortality RR ⫽7.80 (1.90–32.30)

(⫹)

Kawachi, Colditz, et

al. (1994)

33,999 male health

professionals, Health

Professional Follow-Up

Study

2.0 years Self-reported phobic anxiety

(Crown–Crisp Index)

Age, smoking, alcohol,

BMI, physical activity,

history of HTN, diabetes,

hypercholesterolemia,

family history of

premature MI

Fatal CHD RR ⫽2.45 (1.00–5.96),

p⫽.04

(⫹)

Kawachi, Sparrow, et

al. (1994)

2,271 men, NAS 32.0 years Self-reported anxiety

symptoms (Cornell

Medical Index)

Age, BMI, smoking, BP,

TSC, family history of

CHD, alcohol

Sudden cardiac death and

fatal CHD

OR:

Sudden cardiac death

⫽4.46 (0.92–

21.60), fatal CHD

⫽1.94 (0.70–5.41)

(⫹/⫺)

(table continues)

273

AFFECT, OVERLAP, AND HEART DISEASE

Table 2 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of anxiety, worry, and panic in samples free of CHD at baseline (continued)

Kubzansky et al.

(1997)

1,759 men, NAS 20.0 years Self-reported worry (from

NAS Worry Domains

Questionnaire)

Age, BMI, smoking, BP,

TSC, family history of

CHD, alcohol

All incident CHD events RR:

Nonfatal MI ⫽2.41

(1.40–4.13), p⬍

.01; CHD ⫽1.48

(0.99–2.20), p⫽

.04

(⫹)

Hippisley-Cox et al.

(1998)

327 adults with IHD and

897 age- and sex-matched

controls

Documented anxiety prior

to IHD diagnosis

Smoking, HTN, diabetes,

deprivation score

IHD NR

(⫺)

Prospective studies of anxiety, worry, and panic in samples with established CHD at baseline

Ahern et al. (1990) 502 patients post-MI and

arrhythmia, CAPS

1.0 year STAI Prior MI, LVEF, beta

blocker and/or digitalis

use, transmurality in

qualify MI, runs of

ventricular premature

complexes on the 24-hr

ECG at baseline

Death or cardiac arrest NR

(⫺)

Legault et al. (1992) 92 post-MI patients 12.0 mo. STAI Age, sex, SES Cardiac morbidity

(incidence of cardiac

events, anginal

symptoms, prescription

of anginal medications)

(⫺)

Frasure-Smith et al.

(1995b)

222 post-MI patients, EPPI 12.0 mo. STAI Previous MI, depression,

prescription of ACE

inhibitors, previous

depression

Cardiac events (fatal and

nonfatal MI, admission

for unstable angina,

arrhythmic deaths,

survived cardiac arrests)

OR ⫽2.52 (1.15–5.55),

p⫽.02

(⫹)

Frasure-Smith et al.

(1999)

896 post-MI patients, M-

HART, EPPI

12.0 mo. STAI Age, Killip class, Gender ⫻

LVEF, Gender ⫻Non-Q-

wave MI, Gender ⫻

Smoking

Cardiac mortality NR

(⫺)

Frasure-Smith &

Lespe´rance (2003a)

896 post-MI patients, M-

HART, EPPI

5.0 years STAI Age, sex, education,

smoking, prior MI,

thrombolytic treatment,

Q-wave MI, Killip class,

revascularization, LVEF,

prescription of hypo-

glycemic agents (for

nondiabetics) and beta

blockers

Cardiac-related mortality HR ⫽1.14 (0.93–1.38),

(⫺)

Moser & Dracup

(1996)

86 post-MI patients,

GUSTO trial

Period preceding

hospital discharge

Anxiety subscale of the

Brief Symptom Inventory

Age, sex, Killip class,

thrombolytic therapy

regiment, worst chest

pain score

MI complications

(reinfarction, new onset

ischemia, ventricular

fibrillation, sustained

ventricular tachycardia,

in-hospital death)

OR ⫽4.90 (2.10–

12.20), p⬍.01)

(⫹)

274 SULS AND BUNDE

Table 2 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of anxiety, worry, and panic in samples with established CHD at baseline (continued)

Carinci et al. (1997) 2,449 post-MI patients 6.0 mo. Cognitive–Behavioral

Assessment Hospital

Form questionnaire

None Mortality HR ⫽1.30 (0.80–2.10),

(⫺)

S. A. Thomas et al.

(1997)

308 post-MI patients with

asymptomatic ventricular

arrhythmia taking

nonactive medication,

CAST

1.6 years STAI LVEF, diabetes, anger out,

life events

Mortality OR ⫽1.06, p⬍.01

(⫹/⫺); nonsignificant if

participants taking

active medication

were included

Denollet & Brutsaert

(1998)

87 post-MI patients 7.9 years Dutch adaptation of

Spielberger’s State

Anxiety Scale

None Cardiac mortality OR ⫽3.40 (1.20–9.60),

p⫽.02

(⫹); nonsignificant in

model with type D

personality, LVEF,

three vessel disease,

poor exercise

tolerance, history of

previous MI,

smoking after MI

Ketterer et al. (1998) 144 men with diagnostic

coronary angiograms

59.7 mo. Ketterer Stress Symptoms

Frequency Checklist—

Anxiety/Worry

None Mortality, new MI,

revascularization

Inverse relationship, p⬍

.01

(⫺)

Denollet et al. (2000) 319 patients with CHD 5.0 years GMS anxiety None Cardiac events and

revascularization

OR ⫽1.90 (1.00–3.60),

p⫽.04

(⫹); nonsignificant in

model with Type D

personality, LVEF,

and age

Lane et al. (2000a) 288 post-MI patients 1.0 year STAI None Recurrent cardiac events NR

(⫺)

Lane et al. (2000b) 288 post-MI patients 4.0 mo. STAI None Cardiac mortality NR

(⫺)

Lane et al. (2001) 288 post-MI patients 1.0 year STAI None Cardiac mortality NR

(⫺)

Hermann et al. (2000) 5,057 patients referred for

exercise testing (48.5%

with documented CHD)

5.7 years HADS anxiety Sex, age, exercise

endurance, exercise work

load, recent coronary

angiography, maximum

SBP during exercise,

history of CABG, history

of MI, positive exercise

ECG, HADS depression

Mortality OR:

Documented CHD ⫽

0.75 (0.64–0.88), p

⫽.01; CHD not

documented ⫽0.66

(0.52–0.83), p⬍

.01

(⫺)

Mayou et al. (2000) 347 post-MI patients,

MONICA

1.0 year HADS anxiety Significant baseline

predictors of mortality

Cardiac mortality NR

(⫺)

(table continues)

275

AFFECT, OVERLAP, AND HEART DISEASE

Table 2 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of anxiety, worry, and panic in samples with established CHD at baseline (continued)

Welin et al. (2000) 275 post-MI patients 10.0 years STAI Sex, LV failure, ventricular

dysrhythmia, social

support

Cardiac mortality NR

(⫺)

Note. The results of several large prospective investigations (e.g., Normative Aging Study [NAS], Western Collaborative Group Study) of initially healthy participants have been published in separate

studies. We include multiple reports using the same group of participants if there appeared to be important differences among them (e.g., different assessments, varying adjustments). If duration of follow-up

was the only appreciable difference among the studies, we report only the results from the longest follow-up period. Several groups of patients with CHD at baseline have also been followed over a series

of time points (e.g., Montreal Heart Attack Readjustment Trial [M-HART], Emotions and Prognosis Post-Infarct Project [EPPI]; Lane et al., 2000a, 2000b, 2001), which resulted in multiple reports.

Relatively brief follow-up periods are common in this literature (0-18 months), and there is great variability among studies. Because length of follow-up may be more important in determining the relation

between negative emotions and CHD progression, we have included each relevant study and list those using identical samples in series. Several of the studies include multiple outcome measures of CHD.

When possible, we relied on hard (e.g., myocardial infarction [MI], CHD death) rather than soft (e.g., angina, hospital visits) CHD outcomes. Most studies provide results at varying levels of adjustment.

We report the most adjusted results from each study unless otherwise noted. Several studies by Denollet and colleagues (i.e., Denollet & Brutsaert, 1998; Denollet, Sys, & Brutsaert, 1995; Denollet, Vaes,

& Brutsaert, 2000) report crude results and results adjusted for a number of cardiovascular risk factors and Type D personality (Denollet, 2000). Because of the extensive overlap of this construct with

depression, anger, and anxiety, we report crude results before adjustment for these factors. In the Results column, a plus sign in parentheses indicates a positive relation, a minus sign indicates a negative

or null relation, and a plus-or-minus sign indicates equivocal results or findings of marginal statistical significance. SMR ⫽standardized mortality ratio; NR ⫽no relation; CVD ⫽cardiovascular disease;

SBP ⫽systolic blood pressure; TSC ⫽total serum cholesterol; IHD ⫽ischemic heart disease; RR ⫽relative risk ratio; ECA ⫽Epidemiological Catchment Area study; DIS ⫽Diagnostic Interview

Schedule; DSM–III ⫽Diagnostic and Statistical Manual of Mental Disorders (3rd ed.); SES ⫽socioeconomic status; OR ⫽odds ratio; BMI ⫽body mass index; HDL ⫽high-density lipoprotein

cholesterol; HTN ⫽hypertension; CAPS ⫽Cardiac Arrhythmia Pilot Study; STAI ⫽Spielberger State–Trait Anxiety Inventory; LVEF ⫽left ventricular ejection fraction; mo. ⫽months; ECG ⫽

electrocardiogram; ACE ⫽ACE inhibitors; GUSTO ⫽Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries Trial; HR ⫽heart rate; CAST ⫽Cardiac

Arrhythmia Suppression Trial; GMS ⫽Global Mood Scale; HADS ⫽Hospital Anxiety and Depression Scale; CABG ⫽coronary artery bypass graft surgery; MONICA ⫽Monitoring Trends and

Determinants in Cardiovascular Disease study; LV ⫽left ventricular.

276 SULS AND BUNDE

Table 3

Anger and Coronary Heart Disease (CHD)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of hostility, anger, and anger expression in samples free of CHD at baseline

Hostility

Barefoot et al. (1983) 255 male medical

students

25.0 years Ho Scale Smoking, age, family history of

CHD, HTN

CHD incidence z⫽2.7, p⬍.001 (crude association),

still significant after

adjustment

(⫹)

Shekelle et al. (1983) 1,877 men, WES 20.0 years Ho Scale Age, SBP, TSC, smoking,

alcohol

CHD mortality p⫽.09

(⫹/⫺)

McCranie et al. (1986) 478 physicians 25.0 years Ho Scale None MI, CHD mortality, angina NR

(⫺)

Leon et al. (1988) 280 men 30.0 years Ho Scale TSC, DBP, SBP, smoking, age,

obesity

CHD, MI NR

(⫺)

Hearn et al. (1989) 1,399 men 33.0 years Ho Scale Smoking, HTN, family history

of CHD

CHD incidence and

mortality

(⫺)

Almada et al. (1991) 1,871 men, WES 25.0 years MMPI Cynicism scale Age, neuroticism, SBP, TSC,

smoking, alcohol

CHD mortality RR ⫽1.50 (1.10–2.20)

(⫹)

Maruta et al. (1993) 620 general medical

care patients

20.0 years Ho Scale Age, sex, HTN, relative weight CHD, CHD mortality NR

(⫺)

Barefoot et al. (1995) 730 adults 27.0 years Abbreviated Ho Scale Age, sex, BP, TSC,

triglycerides, insulin,

smoking, physical activity,

obesity

MI RR ⫽1.56 (1.05–2.32)

(⫹)

Everson et al. (1997) 1,599 adults, KIHD 9.0 years Cynical Distrust Scale

(derived from Ho

Scale)

Age, SBP, LDL, HDL, SES,

smoking, alcohol physical

activity, BMI, social support

factors, HTN, diabetes,

cancer

MI RH ⫽1.43 (0.63–3.26)

(⫹/⫺)

Iribarren et al. (2000) 374 adults CARDIA

study

10.0 years Ho Scale and CynDis Age, sex, race, field center,

education, alcohol, smoking,

SBP, BMI, LDL, 5-year

change in SBP

Coronary artery

calcification

OR:

Any calcification

Ho Scale ⫽2.38 (1.12–5.20),

CynDis ⫽1.54 (0.74–3.25)

Calcium score ⬎19 (only

controlled for age, sex, race,

and field center)

Ho Scale ⫽9.56 (2.29–65.90),

CynDis ⫽3.86 (1.05–18.60)

(⫹)

Niaura et al. (2002) 774 men, NAS 3.0 years Ho Scale Age, HDL, LDL, insulin, SBP,

BMI, WHR, alcohol,

smoking, caloric intake,

education, glucose

Incident CHD OR ⫽1.06 (1.01–1.11)

(⫹)

Anger

Kawachi et al. (1996) 1,305 men, NAS 7.0 years MMPI–2 Anger scale Age, smoking, DBP, SBP,

TSC, BMI, family history of

CHD, alcohol

Nonfatal MI: total CHD,

angina

RR:

Nonfatal MI ⫽1.60 (0.45–5.68), p

⫽.30; total CHD ⫽3.15

(0.94–10.50), p⫽.07; total

CHD ⫹angina ⫽2.66 (1.26–

5.61), p⫽.01

(⫹/⫺)

(table continues)

277

AFFECT, OVERLAP, AND HEART DISEASE

Table 3 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of hostility, anger, and anger expression in samples free of CHD at baseline (continued)

Anger (continued)

J. E. Williams et al.

(2000)

12,986 adults,

ARIC

4.4 years Spielberger Trait Anger

Scale

Age, race, education, sex,

WHR, alcohol, smoking,

LDL, HDL, diabetes

Hard CHD events HR ⫽1.63 (1.07–2.48), 2.69 (1.48–4.90),

p⫽.02, for normotensives,

1.00 for hypertensives

(⫹/⫺)

Anger expression

Ha¨llstro¨m et al. (1986) 795 women 12.0 years Cesarec–Marke

Personality Schedule

Aggression scale

Age Angina, coronary ECG, MI Coronary ECG, inverse association ( p

⬍.05); all other relationships,

(⫺)

Haynes et al. (1980) 1,674 adults,

Framingham

Heart Study

8.0 years Suppressed anger from

Framingham Type A

Scale

Age, SBP, TSC, smoking,

TABP

Incident CHD Women, p⬍.05; men, ns

(⫹/⫺)

Hecker et al. (1988) 250 CHD cases,

500 controls,

WCGS

8.5 years SI—Potential for

Hostility

TSC, DBP, smoking CHD incidence RR ⫽1.84, p⬍.01

(⫹)

Dembroski et al. (1989) Case control; 192

cases, 384

controls, MRFIT

7.1 years SI—Potential for

Hostility

Age, DBP, TSC, smoking Incident CHD RR ⫽1.46 (1.04–2.18), p⫽.03

(⫹)

Carmelli et al. (1991) 3154 men, WCGS 27.0 years SI—Potential for

Hostility

Age, SBP, TSC, smoking,

TABP, education, BMI

CHD mortality Age 39–48: NR, age 48–59; p⬍.01

(one-tailed)

(⫹/⫺)

Eaker et al. (1992) 749 women,

Framingham

Heart Study

20.0 years Framingham Reaction

to Anger Scales

Age MI and CHD mortality NR

(⫺)

Gallacher et al. (1999) 2,890 men,

Caerphilly study

9.0 years 11 items from

Framingham Study—

expression of anger

Preexisting IHD, social class,

employment status, SBP,

TSC, HDL, fibrinogen, white

cell count, GHQ-30 score,

social support, alcohol,

smoking, BMI, leisure

exercise, total caloric

consumption

Incident IHD RO:

Anger-out ⫽0, RO ⫽1.70 (1.26–

2.29), p⬍.01; Anger in ⫽

1.14 (0.89–1.47), p⫽.29

(⫹)

Chang et al. (2002) 1,055 men,

Precursors Study

36.0 years Three items (expressed

or concealed anger,

irritability, gripe

sessions)

TSC, BMI, parental history of

premature CHD, smoking,

HTN, diabetes, clinical

depression, alcohol

Premature MI and CHD RR:

MI ⫽6.40 (1.80–22.30), p⬍.01,

CHD ⫽3.50 (1.10–11.80), p.05

(⫹)

Mixture of anger-related

constructs

Koskenvuo et al. (1988) 2,885 men 3.0 years Three items (ease of

arousal, irritability,

argumentativeness)

Age, smoking, obesity, snoring,

alcohol

Incident IHD (death and

hospital admission)

(⫺)

Matthews et al. (1998) 200 postmenopausal

women

10 years Spielberger Trait Anger

Scale, AX (Anger-

In), Ho Scale

Triglycerides, pulse pressure,

smoking

IMT Trait anger, p⫽.26

Anger in, p⫽.02

Hostility, p⫽.03

(⫹)

278 SULS AND BUNDE

Table 3 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of hostility, anger, and anger expression in samples with established CHD at baseline

Hostility

Irvine et al. (1999) 671 post-MI

patients,

CAMIAT

2.0 years Ho Scale Previous MI, CHF, social

participation, social network

contacts, dyspnea/fatigue

SCD, cardiac mortality NR

(⫺)

M. W. Kaufmann et al.

(1999)

331 post-MI

patients

1.0 year Ho Scale LVEF, prior MI, CHF, CABG,

previous stroke, diabetes,

age, HTN, family history of

CHD

Mortality NR

(⫺)

Chaput et al. (2002) 792 women with

documented

CHD, HERS

4.1 years Ho Scale Age, BMI, LDL, HDL,

triglycerides, lipoprotein,

self-rated health, education,

smoking, alcohol, physical

activity, diabetes, HTN, prior

MI, creatinine clearance,

race, marital status, use of

medications

CHD events, nonfatal MI,

CHD mortality

RH:

CHD events ⫽1.88 (1.01–3.53),

nonfatal MI ⫽2.03 (1.02–

4.01), CHD mortality ⫽1.34

(0.28–6.47)

(⫹/⫺)

Anger

Mendes de Leon et al.

(1996)

149 patients after

successful PTCA

18.0 mo. State–Trait Anger Scale Age, smoking, HTN, prior MI,

history of

hypercholesterolemia,

duration of complaints,

residual stenosis after PTCA,

vital exhaustion

Recurrent events RR ⫽2.32 (0.82–6.54), p⫽.11

(⫹/⫺)

Denollet & Brutsaert

(1998)

87 post-MI patients 7.9 years Dutch adaptation of

Spielberger’s Trait

Anger Scale

None Cardiac mortality or

nonfatal MI

OR ⫽3.40 (1.20–9.60), p⫽.02

(⫹); nonsignificant in model with

Type D personality, LVEF,

three vessel disease, poor

exercise tolerance, history of

previous MI, smoking after MI

Anger expression

Ahern et al. (1990) 502 patients post-

MI and

arrhythmia,

CAPS

1.0 year Anger Expression from

Prior MI, LVEF, beta blocker

and/or digitalis use,

transmurality in qualify MI,

runs of ventricular premature

complexes on the 24-hr ECG

at baseline

Death or cardiac arrest NR

(⫺)

Frasure-Smith et al.

(1995b)

222 post-MI

patients, EPPI

12.0 mo. AX (Anger-in and

Anger-out)

Previous MI, prescription of

ACE inhibitors, previous

depression, anxiety,

depression

Cardiac events (fatal and

nonfatal MI, admission

for unstable angina,

arrhythmic deaths,

survived cardiac arrests)

(⫺)

(table continues)

279

AFFECT, OVERLAP, AND HEART DISEASE

Table 3 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of hostility, anger, and anger expression in samples with established CHD at baseline (continued)

Anger (continued)

Frasure-Smith et al.

(1999)

896 post-MI

patients, M-

HART, EPPI

12.0 mo. AX (Anger-In and

Anger-Out)

Age, Killip class, Gender ⫻

LVEF, Gender ⫻Non-Q-

wave MI, Gender ⫻

Smoking

Cardiac mortality NR

(⫺)

Frasure-Smith &

Lespe´rance (2003a)

896 post-MI

patients, M-

HART, EPPI

5.0 years AX (Anger-In and

Anger-Out); three

visual analog scales

(frequency, duration,

and intensity of

anger)

Age, sex, education, smoking,

prior MI, thrombolytic

treatment, Q-wave MI, Killip

class, revascularization,

LVEF, prescription of

hypoglycemic agents (for

nondiabetics) and beta

blockers

Cardiac mortality NR

(⫺)

Goodman et al. (1996) 41 PTCA patients 18.0 mo. SI Hostility Gender, race Restenosis p⬍.01

(⫹)

S. A. Thomas et al.

(1997)

308 post-MI

patients with

asymptomatic

ventricular

arrhythmia taking

nonactive

medication,

CAST

1.6 years AX (Anger-In and

Anger-Out)

LVEF, diabetes, STAI, life

events

Mortality For low anger-out, OR ⫽1.30, p⫽

.02 for men only

(⫹/⫺)

Mixture of anger-related

constructs

Koskenvuo et al. (1988) 104 men with HTN

and IHD

3.0 years Three items (ease of

anger arousal,

irritability,

argumentativeness)

Age, smoking, obesity, snoring,

alcohol, dyspnea

Incident IHD (deaths and

hospitalizations)

RR ⫽21.10 (1.59–2.82)

(⫹)

Ketterer et al. (1998) 144 men with

diagnostic

coronary

angiograms

59.7 mo. Ketterer Stress

Symptoms Frequency

Checklist—

Aggression,

irritability, anger,

impatience

None Mortality, new MI,

revascularization

Inverse relationship, p⫽.03

(⫺)

Angerer et al. (2000) 150 patients with

documented CHD

2.0 years German adaptations of

AX and Ho Scale

Age, marital status, no.

stenoses ⬎49%, HDL,

intake of lipid-lowering

drugs

CHD progression OR for anger-out ⫽1.08 (0.97–1.21)

High anger-out and low social

support, RR ⫽3.19, p⬍.05

All others, ns

(⫹/⫺)

280 SULS AND BUNDE

Table 3 (continued)

Study Sample Follow-up Assessment Adjustment Outcome Result

Prospective studies of hostility, anger, and anger expression in samples with established CHD at baseline (continued)

Mixture of anger-related

constructs (continued)

Welin et al. (2000) 275 post-MI

patients

10.0 years Anger-in (Karolinska

Scales of

Personality),

Irritability Scale of

Buss–Durkee

Hostility Inventory

Sex, LV failure, ventricular

dysrhythmia, social support

Cardiac mortality NR

(⫺)

Note. The results of several large prospective investigations (e.g., Normative Aging Study, Western Collaborative Group Study [WCGS]) of initially healthy participants have been published in separate

of time points (e.g., Montreal Heart Attack Readjustment Trial [M-HART], Emotions and Prognosis Post-Infarct Project [EPPI]; Lane et al., 2000a, 2000b, 2001), which resulted in multiple reports.

Relatively brief follow-up periods are common in this literature (0 –18 months), and there is great variability among studies. Because length of follow-up may be more important in determining the relation

between negative emotions and CHD progression, we have included each relevant study and list those using identical samples in series. Several of the studies include multiple outcome measures of CHD.

or null relation, and a plus-or-minus sign indicates equivocal results or findings of marginal statistical significance. Ho Scale ⫽Cook–Medley Hostility Scale; HTN ⫽hypertension; WES ⫽Western

Electric Study; SBP ⫽systolic blood pressure; TSC ⫽total serum cholesterol; NR ⫽no relation; DBP ⫽diastolic blood pressure; MMPI ⫽Minnesota Multiphasic Personality Inventory; RR ⫽relative

risk ratio; BP ⫽blood pressure; KIHD ⫽Kuopio Ischaemic Heart Disease Risk Factor Study; LDL ⫽low-density lipoprotein cholesterol; HDL ⫽high-density lipoprotein cholesterol; SES ⫽

socioeconomic status; BMI ⫽body mass index; RH ⫽relative hazard; CARDIA ⫽Coronary Artery Risk Development in Young Adults Study; CynDis ⫽Cynical Distrust Scale (derived from the Ho

Scale); OR ⫽odds ratio; WHR ⫽waist-to-hip ratio; ARIC ⫽Atherosclerosis Risk in Communities Study; ECG ⫽electrocardiogram; TABP ⫽Type A behavior pattern; SI ⫽Structured Interview for

Type A; MRFIT ⫽Multiple Risk Factor Intervention Trial; IHD ⫽ischemic heart disease; GHQ-30 ⫽General Health Questionnaire; RO ⫽relative odds; AX ⫽Spielberger Anger Expression Scale;

IMT ⫽intimamedia thickness; CAMIAT ⫽Canadian Amiodarone Myocardial Infarction Arrhythmia Trial; SCD ⫽sudden cardiac death; LVEF ⫽left ventricular ejection fraction; CABG ⫽coronary

artery bypass graft surgery; HERS ⫽Heart and Estrogen/Progestin Replacement Study; PTCA ⫽percutaneous transluminal coronary angioplasty; CAPS ⫽Cardiac Arrhythmia Pilot Study; ACE ⫽ACE

inhibitors; CAST ⫽Cardiac Arrhythmia Suppression Trial; STAI ⫽State–Trait Anxiety Inventory; LV ⫽left ventricular.

281

AFFECT, OVERLAP, AND HEART DISEASE

healthy at baseline. The evidence is mixed, however, with respect

to prognostic samples. The majority of evidence (66% of the

studies) from patient samples supports an association between

depression and disease progression or mortality, but there also are

several negative findings.

Some researchers have been critical of the positive results

reported in prognostic studies because the adjustments for con-

founding risk factors (e.g., disease severity) might not have been

sufficiently comprehensive (e.g., Lane, Carroll, Ring, Beevers, &

Lip, 2000a). Also, the diagnosis of depression could be con-

founded by the patient’s medical condition. For example, self-

reports of fatigue may be not a sign of depression but rather a

consequence of more severe but improperly measured cardiac

disease (placing the patient at greater risk of future mortality;

Carney & Freedland, 2003; Mendes de Leon, 1999). Of course,

subclinical disease (i.e., for which symptoms are not yet observ-

able) also may be present in some portion of nominally healthy

participants (Carney, Freedland, & Jaffe, 2001). There are, how-

ever, healthy population studies with positive results that had

follow-up periods of several decades (e.g., Ford et al., 1998). The

possibility that subclinical disease was manifesting itself as de-

pression at baseline seems unlikely to explain the depression–CHD

associations found in those studies.

Anxiety

Anxiety has been defined as an aversive state “resulting from

feelings of being unable to predict, control or obtain desired

outcomes” (Barlow, 2000, p. 1247). In the assessment of anxiety,

some researchers have used diagnostic interviews (American Psy-

chiatric Association, 1987, 1994) and diagnostic criteria, whereas

others have used self-report rating scales to measure trait anxiety

(e.g., Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983).

To identify relevant studies, we conducted MEDLINE and

PsycINFO searches along similar lines to the search for depres-

sion, except that we substituted anxiety,fear,phobic anxiety, and

worry for depression and dysphoria. As in the depression–CHD

search, we also made use of the reference lists of prior reviews

(e.g., Hayward, 1995) and followed the same strategies with re-

spect to study inclusion criteria, statistical reporting, and reliance

on hard cardiac endpoints in summarizing the results.

Healthy Populations at Baseline

The search found 12 relevant studies involving healthy partici-

pants, with samples ranging from 60 to approximately 34,000 and

follow-up periods from 2 years to 35 years (see Table 2). Each

reported hard endpoints (e.g., documented MI or cardiac mortal-

ity). Five studies using multiple adjustments for other CHD risk

factors reported positive, significant associations for anxiety. An-

other study (Kawachi, Sparrow, et al., 1994) had marginally sig-

nificant associations after adjustment. Two other studies found

mixed results—the effect either was marginally significant

(Coryell, Noyes, & House, 1986) or was obtained in only one

gender (specifically men; Coryell, Noyes, & Clancy, 1982). Three

studies (Allgulander & Lavori, 1991; Hippisley-Cox, Fielding, &

Pringle, 1998; Martin, Cloninger, Guze, & Clayton, 1985) found

no significant risk of CHD associated with anxiety; however, these

studies compared the number of cardiac deaths in a psychiatric

sample (e.g., anxiety patients) with age- and sex-specific CHD

mortality rates in the same geographic area or with mortality of

age- and sex-matched controls. These were not true prospective

designs and are suboptimal because traditional CHD factors were

not controlled. Coryell et al. (1982, 1986) also used this less-than-

optimal design. On the basis of the best prospective evidence from

samples without clinical disease at baseline, anxiety appears to be

related to risk of developing CHD. RRs from the studies with

positive results ranged from 2.40 to 7.80.

Populations With Known CHD

Seventeen published reports (14 independent samples) assessed

the association between anxiety and subsequent cardiac death or

documented CHD in prognostic samples (see Table 2). The sample

sizes ranged from 86 to 5,057, with follow-up from 4 months to 10

years. Only 4 studies found positive associations between anxiety

at baseline and subsequent cardiac morbidity or mortality. An

additional study found anxiety to be a significant risk factor in a

subsample (patients taking a nonactive medication; S. A. Thomas,

Friedmann, Wimbuh, & Schron, 1997). The remainder of the

studies found null results, and 1 reported an inverse relationship

(Ketterer et al., 1998). Thus, evidence for the role of anxiety in

patients with known CHD is meager.

Summary

As is the case for depression, the evidence supporting a role for

anxiety in cardiac disease risk is more consistent in initially

healthy samples than in patient populations. In fact, a significant

risk of CHD progression conferred by anxiety in persons with

known cardiac disease was obtained only in a few studies. This

may indicate that negative emotions play less of a role in CHD

progression than in development of CHD. Alternatively, the weak

results may stem from the difficulties of differentiating anxiety

from medical condition. In addition, trait anxiety may be difficult

to distinguish from the normal, appropriate, and short-term fears a

person might manifest after being hospitalized. For example, Lavie

and Milani (2004) found anxiety symptoms in 40% of cardiac

patients. After exercise rehabilitation, only 16% exhibited anxiety

symptoms. It is possible that the 24% who improved were only

manifesting short-term reactions to the stress of hospitalization.

The remainder may have been more chronically predisposed to

anxiety. Failure to differentiate state from trait anxiety in cardiac

patients might be responsible for the weak and inconsistent effects

in the literature.

Anger, Cynical Hostility, and Anger Expression

Contemporary interest in anger as a CHD risk factor was

prompted by M. Friedman and Rosenman’s (1959, 1974; Rosen-

man, Brand, Sholtz, & Friedman, 1976) research on the Type A

coronary-prone behavior pattern, which included impatience, irri-

tation, and anger as components scored from a measure to assess

Type A behavior called the Structured Interview (SI). Although

subsequent studies found Type A behavior to be a weak and

inconsistent predictor of CHD incidence (e.g., Ragland & Brand,

1988), both hostility and antagonistic behavioral tendencies

(scored as subcomponents of Type A from the SI; Matthews,

Glass, Rosenman, & Bortner, 1977) and anger– hostility question-

naire measures (T. W. Smith, 1992; R. B. Williams et al., 1980)

282 SULS AND BUNDE

were found to predict CHD in some studies. Three aspects of the

anger complex have received the majority of researchers’ atten-

tion— hostility, anger, and anger expression. As in the case of

anxiety and depression, the tendency has been to treat these anger

dimensions as distinct and independent in epidemiological

research.

Cynical Hostility

Hostility is conceptualized as cynical attitudes about others

(e.g., Barefoot, Dodge, Peterson, Dahlstrom, & Williams, 1989;

T. W. Smith, 1992). One of the most popular measures of the

hostility dimension is the Cook–Medley Hostility (Ho) Scale

(Cook & Medley, 1954). It consists of items taken from the

Minnesota Multiphasic Personality Inventory and measures ten-

dencies to view the world in a negative, cynical fashion (Barefoot,

1992; T. W. Smith, 1992). Although this instrument is frequently

used, its internal reliability is low (Contrada & Jussim, 1992;

Martin, Watson, & Wan, 2000); its subscales have often been more

successful than the composite score in predicting CHD (Barefoot

et al., 1989).

Anger

Anger refers to feelings of being treated unjustly and is accom-

panied by subjective arousal. The Spielberger Trait Anger Scale

(Spielberger, Jacobs, Russell, & Crane, 1983) is a popular self-

report inventory that assesses the frequency and intensity of angry

affect.

Anger Expression

The third dimension, anger expression, refers to tendencies to be

verbally or physically antagonistic. It is frequently operationalized

as the interactional style component of the Potential for Hostility

Scale derived from the SI (Dembroski, MacDougall, Costa, &

Grandits, 1989; Hecker, Chesney, Black, & Frautsch, 1988). The

Interpersonal Hostility Assessment Technique (IHAT; Haney et

al., 1996) also assesses antagonistic behaviors in response to the

SI.

The Anger-Out scale of the Spielberger et al. (1985) Anger-

Expression Questionnaire evaluates tendencies to express aggres-

sion outwardly using a self-report questionnaire format, whereas

the Anger-In scale purportedly measures tendencies to suppress or

withhold anger.

As before, we conducted searches for studies that involved one

or more of these dimensions, using MEDLINE and PsycINFO with

terms such as trait anger,anger expression,Potential for Hostility,

and aggression and by examining the reference lists of prior

narrative and quantitative reviews (e.g., T. Q. Miller et al., 1996;

Myrtek, 2001). In contrast to depression and anxiety, decisions

about how to classify different measures into anger, hostility, and

anger expression were more problematic, because some invento-

ries are composed of multidimensional items. For example, the

Potential for Hostility subcomponent of the SI (Dembroski et al.,

1989) has some elements of cynically hostile attitudes and angry

affect, although it primarily assesses antagonistic expression. A

factor analytic investigation (Martin et al., 2000) of a broad array

of anger, hostility, and anger expression measures and the system

used by T. Q. Miller et al. (1996) aided us in the classification.

Populations Without Known CHD

Twenty-three studies with healthy samples at baseline were

identified (see Table 3). Sample sizes ranged from 200 to 12,986,

and follow-up ranged from 3 years to 36 years. Twenty-one studies

reported analyses of endpoints that can be considered hard (e.g.,

cardiac death or documented MI or disease), and these studies are

the focus of the discussion.

Cynical hostility. Eleven studies measured cardiac death

and/or documented CHD. Of the 11, 5 reports found that higher

levels of hostility at baseline were significantly ( p⬍.05) associ-

ated with subsequent manifestations of CHD. Two other reports

found marginally significant associations. The 4 remaining studies

reported null findings. In sum, 7 of 11 studies found that hostility

was statistically associated with the subsequent development of

documented CHD at either conventional or marginally significant

levels. RRs in the positive studies ranged from 1.40 to 9.60. A

meta-analysis (T. Q. Miller et al., 1996) reported an effect size of

.07 associated with cynical hostility and CHD risk.

Trait anger. Only three relevant studies were found. J. E.

Williams et al. (2000) reported a positive relation between anger

and fatal or nonfatal MI or SCD. Matthews, Owens, Kuller,

Sutton-Tyrrell, and Jansen-McWilliams (1998), who assessed the

thickness of the arterial lesions, a measure of disease progression,

found no relation with anger. Kawachi et al. (1996) found mixed

results.

Anger expression. This category included Potential for Hos-

tility scale from the SI (Dembroski et al., 1989), the Anger Ex-

pression scales (Spielberger et al., 1985), and Framingham Anger

Reaction or Expression Scales (Eaker, Pinsky, & Castelli, 1992;

Haynes & Feinleib, 1980). Of the nine relevant studies reporting

results for fatal MI and/or documented MI as endpoints, five found

significantly greater CHD risk associated with higher anger ex-

pression, and two had significant effects with respect to particular

subsamples (in Haynes, Feinleib, & Kannel, 1980, only women; in

Carmelli et al., 1991, only 48 –59-year-olds). The two remaining

studies had null results. RRs for the studies with positive results

ranged from 1.46 to 6.40. A meta-analysis (T. Q. Miller et al.,

Like anger, hostility, and anger expression, depression and anxiety can

be further differentiated (e.g., endogenous depression, exogenous depres-

sion; panic disorder, generalized anxiety disorder). Davidson, Rieckmann,

and Lespe´rance (2004) made a persuasive case for “disentanglement of

different types of depressive symptoms” (p. 166) to identify those most at

risk and in need of behavioral treatment. Unfortunately, these subtypes

have not been systematically studied with respect to the risk of CHD. That

is the reason we have treated anxiety and depression as generic constructs

in this review.

Although both SI-derived subcomponent scoring indices refer to hos-

tility, the correlations of the Potential for Hostility Scale and the IHAT with

the Ho Scale are quite modest. In a factor analytic study, Martin (1996;

Martin et al., 2000) found that IHAT scores were most strongly correlated

with a general anger expression factor.

The Anger-In scale was considered as a measure of anger expression

because it refers to suppression or withholding of anger. It could be argued

that anger-in is more appropriately classified as trait anger. Some research-

ers have found strong correlations between anger-in and neuroticism (rs⫽

.80), which suggests that anger-in may actually be measuring generalized

negative affectivity (NA; Martin & Watson, 1997). Whether study out-

comes with the Anger-In scale are considered as evidence for trait anger or

eliminated from the review, conclusions do not differ.

283

AFFECT, OVERLAP, AND HEART DISEASE

1996) reported an effect size of .18 for potential for hostility in

prospective studies of healthy samples. A recent meta-analysis

(Myrtek, 2001) with more rigorous criteria (e.g., counting only the

longest follow-up) found an effect size of .04. Myrtek (2001)

observed, in light of the magnitude of the association, that “the

practical meaning for prediction and prevention is questionable”

(p. 251).

Populations With Known CHD

Fifteen relevant reports were identified, of which 14 measured

fatal or nonfatal MI as an outcome. Sample sizes ranged from 41

to 896 patients, and follow-ups ranged from 1 year to 10 years.

Cynical hostility. There were six relevant studies; only a single

study reported a significant risk of CHD associated with higher

hostility (Chaput et al., 2002). The remaining studies reported null

effects.

Trait anger. Three studies measured trait anger in patients

with existing diseases; all involved fatal or nonfatal MI as an

outcome. One investigation (Denollet & Brutsaert, 1998) found

that higher trait anger significantly predicted occurrence of another

MI, a second study found a marginally significant positive asso-

ciation (Mendes de Leon, Kop, de Stuart, Bar, & Appels, 1996),

and the third found no relation between trait anger and CHD

(Welin, Lappas, & Wilhelmsen, 2000).

Anger expression. There were seven published reports, but

only six independent data sets that measured anger expression.

With respect to cardiac mortality or incidence, four studies re-

ported null results, and two others reported significant positive

associations between anger expression and CHD progression in

subsamples.

Summary

Krantz and McCeney (2002) observed that “the effects of hos-

tility may be difficult to identify and/or do not apply in all

populations” (p. 353). This appears to be the case with respect to

anger expression and trait anger also. However, as in the cases of

depression and anxiety, when positive results were found for

cynical hostility, they were more likely to be exhibited in samples

with initially healthy persons. Results with patient samples are

very weak and inconsistent with respect to anger, hostility, and

anger expression.

Overall Conclusions From the Literature Review

A large body of evidence relating negative emotions to CHD

risk has accumulated in the last 3 decades. Consistent with prior

reviews (Rozanski et al., 1999; Rugulies, 2002), there is supportive

evidence particularly for depression and anxiety from prospective

studies of initially healthy samples. Evidence is more mixed for

aspects of anger, with perhaps the strongest evidence associated

with cynical hostility and anger expression (see Krantz & Mc-

Ceney, 2002). Results from patient samples are weaker and more

mixed. The possibility should be considered that weak and nega-

tive results from patient samples may be a consequence of the

difficulties of distinguishing affective dispositions from patients’

medical conditions, especially shortly after hospitalization. In any

case, the preceding survey provides sufficient evidence to con-

clude that depression and anxiety are likely risk factors for the

development of cardiac disease. The role of these negative emo-

tions in disease progression (i.e., in patients with known disease)

is less clear.

Negative Affectivity

A broadband personality dimension referred to as negative

affectivity (NA) is conceptualized as a higher order construct that

subsumes all of the negative emotions (Watson & Clark, 1984).

NA is defined as a general disposition to chronically experience

anxiety, sadness, guilt, anger, irritability, and other negative emo-

tions. Indeed, factor analytic studies have found that anxiety,

depression, and anger are lower level constructs that all load with

the broad dimension of NA (rs⫽.71–.85; Costa & McCrae,

1992).

Associations between the general disposition to experience neg-

ative emotions and physical disease might be expected (H. S.

Friedman & Booth-Kewley, 1987a; Suls & Rittenhouse, 1991).

The wear and tear associated with chronic psychological distress

(i.e., NA) that is described by contemporary stress theories (e.g.,

Lazarus & Folkman, 1984) might lead to exaggerated physiolog-

ical arousal, eventuating in biological and physiologic processes

that increase the risk of stress-related diseases (Selye, 1976).

Costa and McCrae (1987) and Watson and Pennebaker (1989)

have proposed, however, that NA is associated with somatic self-

reports but not with actual physical illness (Costa & McCrae,

1987; Watson & Pennebaker, 1989). In this account, high-NA

individuals tend to be internally focused and highly attuned to

somatic sensations. Their NA leads them to interpret ambiguous

and benign bodily sensations as symptoms of physical illness

(Watson & Pennebaker, 1989). This can explain the inverse cor-

relations that are sometimes reported between anxiety and athero-

sclerosis (e.g., Costa, Fleg, McCrae, & Lakatta, 1982). The

high-NA individual’s oversensitivity to chest pain of muscular

origin may lead to somatic complaints, treatment seeking, and

invasive diagnostic procedures (see H. S. Friedman, 2000).

Costa and McCrae (1987) and Watson and Pennebaker’s (1989)

theories led to more critical examination of self-report data in

health psychology and also to greater reliance on objective indi-

cators of illness, such as angiography and immune activation.

However, as evidence on the association between NA and self-

reported illness has accumulated in recent years, the picture has

become more complex. In some studies, global illness self-reports

are positively correlated with NA (e.g., Watson & Pennebaker,

1989), but in other cases they are uncorrelated (e.g., Leventhal,

Hansell, Diefenbach, Leventhal, & Glass, 1996). For example, NA

was not associated with complaints of vague, flulike symptoms in

cross-sectional or longitudinal analyses among older adults who

received inoculations (Diefenbach, Leventhal, Leventhal, &

Patrick-Miller, 1996; see Martin, Rothrock, Leventhal, & Lev-

enthal, 2003, for a more detailed review).

Some recent prospective evidence indicates that NA may be

related to increased risk of actual physical illness (e.g., Neeleman,

Ormel, & Bijl, 2001). For example, Cohen et al. (1995) found that

high-NA participants were more likely to catch a clinically con-

firmed cold after the administration of an active rhino virus. To

date, few studies (Frasure-Smith & Lespe´rance, 2003b; Todoro,

Shen, Niaura, Spiro, & Ward, 2003) have used broad measures of

NA to predict objectively confirmed CHD; however, several of the

prospective studies reviewed earlier found positive associations

284 SULS AND BUNDE

among anxiety, depression, anger, and CHD. These negative emo-

tions are subsumed by the NA construct, which suggests that this

general disposition might be responsible for the measurement and

construct overlap among the specific emotions that have been

positively associated with development and progression of CHD.

Pathways Between Affective Dispositions and CHD

The summary of the empirical literature indicated that prospec-

tive cohort studies have found evidence, albeit mixed, that nega-

tive emotions appear to be related to the development of CHD. The

question considered in this section is how these negative disposi-

tions might increase the risk of CHD.

Anger, anxiety, and depression are thought to play direct or

indirect roles in the disease process by exacerbating the biological

and physiological processes underlying cardiopathogenesis that

were described earlier (e.g., T. W. Smith & Ruiz, 2002). One

general class of factors relates to adverse health behaviors (see

Table 4). Persons who are depressed, anxious, or angry may

engage in more adverse health behaviors, and these behaviors may

contribute to cardiopathogenesis (T. W. Smith & Anderson, 1986;

T. W. Smith & Gallo, 2001; Suls & Sanders, 1989). For example,

smoking tends to be more common among angry, anxious, and

depressed persons (e.g., Black, Zimmerman, & Coryell, 1999).

Depressed persons are also less compliant with medical regimens

(e.g., DiMatteo, Lepper, & Croghan, 2000) and more sedentary

(e.g., Kritz-Silverstein, Barrett-Connor, & Corbeau, 2001).

Some NA dispositions also are correlated with elevated status

on traditional cardiac risk factors (e.g., obesity, cholesterol) that

result from adverse health behaviors and/or reflect environmental

factors or genetic predisposition. For example, persons who score

highly in cynical hostility also tend to be obese and have elevated

low-density cholesterol (e.g., Weidner, Sexton, McLellarn, Con-

nor, & Matarazzo, 1987). Associations of depression with hyper-

cholesterolemia are more complex, with some studies showing a

positive association but other studies showing inverse correlations

(Steegmans, Hoes, Bak, van der Does, & Grobbee, 2000). There is

some evidence that depression, anxiety, and anger place people at

higher risk of developing hypertension, another CHD risk factor

(K. Davidson, Jonas, Dixon, & Markovitz, 2000; Jonas & Lando,

2000; Markovitz, Matthews, Kannel, Cobb, & D’Agostino, 1993).

Diabetes, which confers a three- to fourfold increase in CHD risk,

is twice as prevalent in depressed individuals (Anderson, Freed-

land, Clouse, & Lustman, 2001) and more common in hostile

persons (Niaura et al., 2000).

Stress exposure also has been implicated in the development

(e.g., Iso et al., 2002) and progression (Tennant, Palmer, Lange-

luddecke, Jones, & Nelson, 1994) of CHD. This is especially

relevant in the present context because persons who are more

disposed to chronic NA tend to experience more major and minor

life stressors (e.g., H. S. Friedman, 2000; Magnus, Diener, Fujita,

& Pavot, 1993; T. W. Smith & Frohm, 1985; Suls, Green, & Hillis,

1998). Exposure to life stressors should increase activation of the

sympathoadrenal (SNS) and hypothalamic–pituitary–adreno-

corticol (HPA) axes, which can promote cardiopathogenesis. In

addition, there is evidence that certain dispositions, such as hos-

tility, are associated with less social support and more social

isolation (e.g., T. W. Smith & Frohm, 1985). Depressed persons

also tend to create unsatisfying interactions that may undermine

social support behaviors from others (Coyne, 1985). As a result,

depressed or hostile persons may lack an important stress buffer

(Cohen & Wills, 1985).

Physiological reactivity to stress also may be associated with

dispositional affect (Krantz & Manuck, 1984). Anger, hostility,

and depression have been associated with elevated SNS and HPA

activation (T. W. Smith & Ruiz, 2002; Troxler, Sprague, Albanese,

Fuchs, & Thompson, 1977). Increases in plasma catecholamines

lead to vasoconstriction, platelet aggregation, and elevated heart

rate, all of which are damaging to the cardiovascular system

(Matthews, Manuck, & Saab, 1986; T. W. Smith & Gallo, 2001).

Elevated cortisol can promote the development of atherosclerosis

and accelerate injury of vascular endothelial cells. HPA dysregu-

lation in depression has been well documented (Gold, Gabry,

Yasuda, & Chrousos, 2000; Maas et al., 1994; Musselman et al.,

1998). Both SNS and HPA hyperreactivity may speed the devel-

opment of CHD and worsen the prognosis for patients with un-

derlying disease.

In addition, lower heart rate variability (HRV; reflecting sym-

pathovagal imbalance– heightened SNS arousal, decreased para-

sympathetic nervous system regulation, or both) is a risk factor for

cardiac arrhythmias and cardiac arrest (Curtis & O’Keefe, 2002).

Depressed patients tend to have significantly decreased HRV

(Carney et al., 1995). In this regard, in a high proportion of the

deaths among depressed cardiac patients reported by Frasure-

Smith et al. (1995b), cardiac arrhythmia was suggested as the

cause. Furthermore, some of the strongest epidemiologic results

(Kawachi, Colditz, et al., 1994) for anxiety are specifically with

SCD, which tends to be associated with cardiac arrhythmias.

Depression has also been linked to inflammation, which could

increase the potential of plaque rupture and thrombus formation

(Danner, Kasl, Abramson, & Vaccarino, 2003). In addition, proin-

flamatory cytokines are elevated in depression (e.g., Dentino et al.,

1999); these immune agents may promote endothelial damage and

platelet aggregation, which, in turn, increase the risk of cardiac

Table 4

Selected Mechanisms and Representative Studies for Relations

Between Negative Affective Dispositions and Coronary Heart

Disease

Mechanism Study

Adverse health behavior

Smoking Black et al. (1999)

Adherence DiMatteo et al. (2000)

Exercise Kritz-Silverstein et al. (2001)

Stress T. W. Smith & Frohm

(1985)

Tennant et al. (1994)

Iso et al. (2002)

Low heart rate variability Carney et al. (1995)

Coagulation Markovitz et al. (1996)

Cardiac risk factor

Low-density cholesterol Weidner et al. (1987)

Hypertension Jonas & Lando (2000)

Diabetes Anderson et al. (2001)

Physiological hyperreactivity Troxler et al. (1977)

T.W. Smith & Allred (1989)

Musselman et al. (1998)

Gold et al. (2000)

Inflammation Danner et al. (2003)

G. E. Miller et al. (2003)

Suarez (2003)

285

AFFECT, OVERLAP, AND HEART DISEASE

events (Kop, 2003; Musselman et al., 1998). Recent evidence also

has shown positive associations between hostility and inflam-

mation (G. E. Miller, Freedland, Carney, Stetler, & Banks, 2003;

Suarez, 2003).

A related mechanism concerns blood coagulation and platelet

activation (see Musselman et al., 1998). Blood platelets interact

with components of damaged vessel walls and coagulation factors,

which can increase the atherosclerotic process and formation of

thrombi. In addition, platelets stimulate uptake of lipoprotein,

further increasing vascular damage. Markovitz and Matthews

(1991) described how psychological stress might trigger platelet

activation and produce cardiac events. Since their proposal, several

studies have documented that depressed persons exhibit greater

platelet activation (e.g., Musselman et al., 1996). There also are

similar results for persons who are cynically hostile (Markovitz,

Matthews, & Smitherman, 1996).

It is important to note that several of the biological, physiolog-

ical, and behavioral processes described above are implicated in

the gradual progression of CHD and also in the short-term risk of

cardiac events (Kop, 1999). For example, acute increases in heart

rate, blood pressure, and coronary constriction may be mediated by

SNS and HPA activation prompted by an interpersonal conflict or

physical exertion. Thus, acute stressors may lead to electrical

instability and increased oxygen demand, which, in turn, increase

the risk of arrhythmia, ischemia, and plaque rupture in persons

with developing CHD.

Some notable gaps in the literature should be acknowledged. For

example, stressor hyperreactivity has been studied extensively

with respect to hostility (T. W. Smith, 1992; Suarez & Williams,

1990; Suls & Wan, 1993) but less so with respect to the other

aspects of the anger complex and with respect to depression and

anxiety. Similarly, the connections between inflammatory pro-

cesses and depression have received more empirical attention than

the connections between inflammatory processes and anger and

anxiety. Certain dispositions may be stronger instigators of spe-

cific kinds of biological, physiological, and behavioral disease

processes (see Kop, 1999, p. 483). Further, the affective disposi-

tions may have different roles in the development versus the

progression of CHD. Kop (1999) has theorized that anger has its

main impact via sympathetic activity and elevated lipids. How-

ever, depression, which he viewed as more episodic, may exert its

effects via sympathovagal imbalance and coagulation. In the ab-

sence of systematic investigations of relations among all three

negative affective dispositions and their possible pathways, this

proposal remains an intriguing empirical question. Despite such

gaps, existing knowledge documents several independent and in-

teractive pathways by which predispositions to experience fre-

quent and intense episodes of negative affect can increase damage

to the cardiovascular system. In the next section, we discuss the

traditional model that describes these pathways.

Conventional Model for Affective Disposition–CHD

Associations

Figure 1 depicts the conceptual model that underlies much of the

research in this area. Individual differences, assessed by interviews

or self-report questionnaires (measurement level), are assumed to

tap psychological differences in hostility, anger, and anger expres-

sion; depression; and anxiety (construct level). The three disposi-

tions are associated with increased exposure to other CHD risk

factors or set in motion biologic and psychophysiologic processes

and behaviors that promote cardiopathogenesis.

As noted above, an implicit assumption of researchers is that the

effects of the negative emotions are independent. At minimum,

researchers’ preferred measurement and analytic strategies seem to

imply that the negative affects have distinctive and independent

effects. This may be problematic, however, if measures of hostil-

ity, anger, and anger expression; depression; and anxiety are

correlated (a problem of measurement overlap). Furthermore, the

three affective dispositions may share critical psychological fea-

tures or symptoms (construct overlap) that make it difficult to

identify which negative emotions (or whether all) actually contrib-

ute to CHD risk. Because both measurement and construct overlap

are important for understanding the strengths and limitations of

empirical evidence and conceptualizations regarding affect–CHD

associations, we describe the nature of the overlap in the next

sections.

Overlap of Affective Dispositions

Hostility, Anger, and Anger Expression

Measurement overlap. Figure 2 depicts the conventional treat-

ment of the attitudinal, emotional, and behavioral dimensions of

hostility, anger, and anger expression as independent predictors of

CHD. As described earlier, cynical hostility, angry affect, and

anger expression refer to distinct individual-differences constructs,

each purportedly measured with its own inventories, subscales,

and interviews. Typically, however, the same set of pathophysio-

logical processes, events, and health behaviors in Table 4 have

been hypothesized as mediators of disease outcomes. At the level

of mediating mechanisms there has been little attention paid to

differentiation of cardiopathogenic mechanisms among different

affects (see Kop, 1999, for a notable exception).

The attitudinal, affective, and behavioral components of trait

anger (see Figure 2) can be empirically differentiated at the mea-

surement level, however. Martin et al. (2000) conducted a factor

analysis of 24 self-report anger-related measures, several of which

are used by health psychologists. A three-factor model, consistent

with the affective, behavioral, and cognitive components discussed

above, provided the best fitting statistical solution. This finding,

which builds on earlier factor analytic work of multiple anger

inventories (H. S. Friedman, Tucker, & Reise, 1995; Musante,

MacDougall, Dembroski, & Costa, 1989), provides empirical sup-

Figure 1. Conventional model of pathways between negative affects and

heart disease.

286 SULS AND BUNDE

port for the classification into cynical hostility, anger experience,

and anger expression (Martin et al., 2000; T. W. Smith, 1992; see

also Buss & Perry, 1992).

However, results from Martin et al. (2000) also suggest that

there is significant overlap among the dimensions of anger. Cor-

relations among the three factors ranged from .28 to .44, consistent

with findings that anger-related measures demonstrate significant

relatedness (possibly because of shared and multidimensional

items). The empirical overlap observed among anger, hostility, and

anger expression measures suggests that the model underlying the

relations among these negative emotions and CHD more closely

resembles that shown in Figure 3.

Construct overlap. The overlap across the three dimensions of

anger may also be the result of different dimensions being psy-

chologically related in meaningful ways (e.g., Averill, 1983).

Although angry affect is not necessary or sufficient for expression,

emotion is commonly the instigator of verbal or physical aggres-

sion. Similarly, the label anger expression suggests a behavioral

manifestation of the affective dimension. Hostility (an attitudinal

orientation) is conceptualized differently than anger, but intuition

suggests that chronic cynicism should play a (causal) role in angry

affect and aggression resulting from interpersonal conflict, at least

in some cases. Persons who are cynical and mistrusting of others

are apt to interpret others’ actions in negative ways, which leads to

conflict, provocation, angry affect, and anger expression (e.g.,

Dodge, Price, Bachorowski, & Newman, 1990). The conceptual

relatedness of these dimensions suggests that the model leading to

CHD may need further revision to account for possible overlap at

the construct level (see Figure 4).

In summary, conventional psychological measures of cynical

hostility, angry affect, and anger expression are moderately corre-

lated at minimum. The conceptual connections among dimensions

suggest that the overlap is unlikely to be wholly due to common-

ality of measurement. Cynical attitudes, angry affect, and anger

expression are logically and psychologically related.

Overlap of Depression and Anxiety

Perhaps the most recognized and well-studied area of overlap

within the spectrum of negative emotionality concerns the relation

between anxiety and depression. Self-report measures of the two

constructs generally correlate between .45 and .75 (Watson et al.,

1995). Furthermore, several factor analyses of depression and

anxiety assessments have yielded a single primary factor (Dobson,

1985; Gotlib, 1984). In addition to the strong relations among

self-report measures, high comorbidity rates between anxiety and

depressive disorders consistently have been observed (Mineka,

Watson, & Clark, 1998).

L. A. Clark’s (1989) review reported that 57% of depressed

patients met criteria for an anxiety disorder; Kessler et al. (1996)

reported a rate of 58%. Although all types of anxiety disorders do

not show the same level of comorbidity with depression, research-

ers have observed considerable overlap of panic disorder with

agoraphobia, generalized anxiety disorder, social phobia, simple

phobia, and obsessive– compulsive disorder (Kessler et al., 1996;

Moras et al., 1996). Some studies have found lifetime comorbidity

rates as high as 73% for generalized anxiety disorder and depres-

sion (L. A. Clark, 1989) and 67% for panic–agoraphobia and

depression (Moras et al., 1996). In fact, Kessler (1997) found that

the comorbidity of anxiety disorders with depression was compa-

rable in size to the comorbidity among the anxiety disorders. This

relation is not just confined to the co-occurrence of diagnostic

entities, as considerable overlap has been observed at the symptom

level as well (Fawcett & Kravitz, 1983).

Genetic research also has supported a strong relation between

anxiety and depression. A twin study conducted by Kendler,

Neale, Kessler, Heath, and Eaves (1992) revealed an identical

diathesis for generalized anxiety disorder and depression; later

evidence suggests there is moderate genetic overlap between de-

pression and panic disorder and that there are variable associations

with the phobias (Kendler, Neale, Kessler, Heath, & Eaves, 1993a;

Kendler et al., 1995). The shared genetic factor appears to be

closely related to NA (Kendler, Neale, Kessler, Heath, & Eaves,

Figure 2. Conventional model of pathways between anger cluster and

heart disease.

Figure 3. Model of pathways between anger cluster and heart disease

(with measurement overlap).

Figure 4. Model of pathways between anger cluster and heart disease

(with measurement and construct overlap).

287

AFFECT, OVERLAP, AND HEART DISEASE

1993b), which suggests that the comorbidity between anxiety and

depression can be largely attributed to a shared disposition to

experience a range of negative emotions.

The empirical overlap seems to be due in part to a lack of

discriminant validity at the measurement level (depicted in Figure

5). Many self-report and clinician-based measures of anxiety and

depression contain a mixture of depressive and anxious items (e.g.,

D. A. Clark, Beck, & Stewart, 1990).

Overlap Among Anger, Depression, and Anxiety

The relation of anger to the other negative emotions has been

less extensively studied, but here, too, there is evidence of signif-

icant overlap. Anxiety, angry hostility, and depression are the three

facets with the highest loadings on the Neuroticism factor of the

NEO Personality Inventory (Costa & McCrae, 1992). Intercorre-

lations among the facets range from .47 to .85. Angry hostility also

is significantly correlated with the depression (r⫽.52) and anxiety

(r⫽.47) facets. Moreno, Fuhrman, and Selby (1993) found low

discriminant validity among measures of anger, hostility, and

depression, and the presence of anger in depressive disorders also

has been documented (Koh, Kim, & Park, 2002; Robbins & Tanck,

1997). Depression and hostility consistently are associated at the

self-report (Biaggio & Godwin, 1987; Felsten, 1996) and symptom

(B. L. Kennedy, Morris, Pedley, & Schwab, 2001) levels; mea-

sures of hostility and anger have demonstrated positive and sig-

nificant relations with neuroticism and anxiety as well (Carmody,

Crossen, & Wiens, 1989; Fava et al., 1993; Swan, Carmelli, &

Rosenman, 1989).

A highly relevant study by H. S. Friedman and Booth-Kewley

(1987b) computed correlations among dimensions of SI for assess-

ment of Type A behavior and questionnaire measures of depres-

sion, anxiety, and the Ho Scale in a sample of middle-aged men

with a history of MI and in 50 healthy controls. Depression,

anxiety, and Ho Scale scores were significantly correlated, but the

association between depression and anxiety was stronger (r⫽.68)

than the correlations with the Ho Scale (rs between .30 and .40).

The depression–anxiety cluster also was correlated significantly

with health status (post-MI vs. healthy), independently of SI Type

A. (In this sample, hostility was unrelated to illness status).

Construct Overlap Among Anger, Cynical Hostility, and

Anger Expression; Depression; and Anxiety

Besides measurement overlap, which may represent poor dis-

criminant validity among some instruments, mood disorders also

have shared symptoms, which suggests that overlap also exists at

the construct level (e.g., Mineka et al., 1998). Figure 6 depicts this

additional complication.

Moderate to strong correlations among measures of different

NA dispositions and evidence of comorbidity (e.g., between de-

pression and anxiety) have led some researchers to propose that

depression and anxiety may be variants of a unitary construct

(Lipman, 1982; Stravrakaki & Vargo, 1986). However, these af-

fects do have some distinguishing features that can be discrimi-

nated at the self-report (Watson et al., 1995), neuropsychological

(Keller et al., 2000), and neurobiological (R. J. Davidson, 2000)

levels. Obviously, evidence of overlap does not imply that these

emotions lack distinctive qualities (D. A. Clark et al., 1990). This

idea is consistent with hierarchical models of affect and personal-

ity that distinguish between general, common factors and specific

factors. The lower level of the hierarchical model reflects the

specific content, that is, the distinctive qualities of the individual,

discrete affects. The upper level reflects the affect’s valence, that

is, whether it represents a negative or a positive state.

L. A. Clark and Watson (1991) proposed a hierarchical model in

which two broad, higher order factors (Negative Affectivity and

Positive Affectivity) are each composed of several correlated yet

distinct emotional states (fear, anger, joy, etc.). This model in-

cludes a common factor reflecting general distress or NA, an

anhedonia factor specific to depression, and symptoms of somatic

arousal specific to anxiety. Barlow, Chorpita, and Turovsky (1996)

have proposed a similar approach, arguing that general distress is

a manifestation of anxiety, that somatic arousal is closely associ-

ated with fear, and that anhedonia is unique to depression. Con-

siderable evidence supports such divisions (Brown, Chorpita, &

Barlow, 1998; Watson et al., 1995), but the shared factor is of most

interest for the current discussion. These results suggest that the

chronic experience of general distress is central to both anxiety and

depression and that NA is responsible for the overlap of anxiety

and depression at the measurement and construct levels of

analysis.

The degree of conceptual overlap between anger and the other

negative emotions may vary according to which aspect of the trait

anger dimension (cynical hostility, anger, or anger expression) is

being considered. However, the experience of NA seems to be

important for all aspects of the construct. Thus, there is meaningful

overlap among depression; anxiety; and anger, hostility, and anger

expression not only at the empirical level but at the construct level

as well. Figure 6 depicts the model that incorporates both levels of

overlap.

Evidence of Overlap From Pharmacologic and

Psychotherapy Treatment Studies

A review of pharmacological treatments for clinical depression,

anxiety, and anger provides further evidence for their relatedness

at the construct level. Pharmacological evidence for overlap is

perhaps most evident in the widespread anxiolytic effects of the

antidepressant medications. Selective serotonin reuptake inhibitors

(SSRIs) have demonstrated efficacy in the treatment of various

anxiety disorders, including panic disorder, obsessive– compulsive

disorder, social anxiety disorder, posttraumatic stress disorder, and

generalized anxiety disorder, whereas tricyclic antidepressants and

monoamine oxidase inhibitors have had more limited effectiveness

(for a review, see Dunner, 2001). Although antidepressants appear

Figure 5. Model of pathways between negative affects and heart disease

(with measurement overlap).

288 SULS AND BUNDE

to have a wide range of efficacy for the treatment of anxiety

disorders, the effects of anxiolytic medications on depressive

symptoms have been more equivocal. Benzodiazapines generally

are not viewed as efficacious treatments for depression, but several

anxiolytics (e.g., alprazolam, buspirone) have demonstrated anti-

depressant effects (for a review, see Levine, Cole, Chengappa, &

Gershon, 2001). An overview of effective pharmacological treat-

ments for anger and aggression suggests considerable overlap with

anxiety and depression as well, although the relations appear to be

unidirectional. Anxiolytics and SSRIs have demonstrated efficacy

in the treatment of aggressive and violent behaviors (for a review,

see Lavine, 1997), and antidepressants are effective in reducing

anger attacks in individuals with accompanying depression (Fava

et al., 1993; Fava & Rosenbaum, 1999). Medications specifically

targeting aggression or anger, however, have generally not been

used to treat depression or anxiety. Although there is imperfect

reciprocity among effective pharmacological treatments for clini-

cal depression, anxiety, and anger, the relatedness of these treat-

ments is consistent with the data presented thus far, suggesting

some specificity among clinical entities but marked overlap as

well.

Further evidence comes from the psychotherapy literature. Psy-

chological treatments targeting one group of symptoms (depres-

sion, anxiety, or anger) often have beneficial effects on the others

(Barrowclough et al., 2001; Borkovec & Ruscio, 2001; Brown,

Antony, & Barlow, 1995; Dahlen & Deffenbacher, 2000; Kolko,

Brent, Baugher, Bridge, & Birmaher, 2000; March, Amaya-

Jackson, Murray, & Schulte, 1998). In a sample of patients with

panic disorder and comorbid depression, Laberge, Gauthier, Coˆte´,

Plamondon, and Cormier (1993) found that successful treatment of

panic led to the remission of both disorders. Persons, Roberts, and

Zalecki (2003) observed that self-ratings of depression and anxiety

were highly predictive of each other over the course of effective

cognitive– behavioral treatment, even after they controlled for

overlapping symptoms and the general distress common to both

syndromes. Moras, Tefler, and Barlow (1993) also found that

depression and anxiety changed together during psychotherapy. In

a cognitive intervention focused on anger reduction, Deffenbacher,

Dahlen, Lynch, Morris, and Gowensmith (2000) observed reduc-

tions in anxiety and depression as well as improvements in anger.

Although psychotherapies specific to each cluster of symptoms

have been developed and implemented, overlap among their treat-

ment effects provides further evidence for the relatedness of de-

pression, anxiety, and anger at the construct level. Of course, many

psychotherapy treatments have nonspecific effects, so this evi-

dence for construct overlap is not as persuasive as that shown by

pharmacologic treatment.

Implications

The relationships depicted in Figure 6 are considerably more

complex than those in Figure 1, although the latter model contin-

ues to be the explicit or implicit model adopted in most behavioral

epidemiological research. Figure 6 assumes that the negative af-

fects are correlated both because of measurement overlap and

because of shared symptoms or features. This recognition has

several implications for interpreting the cumulative evidence link-

ing affect to CHD risk.

Overlap and Inconsistent Epidemiological Results

The degree of overlap among the different affective dispositions

may provide a partial explanation for the inconsistencies seen in

the epidemiological literature. If a study reports a positive associ-

ation between an affective disposition and CHD, it may result from

the action of that disposition or through its correlation with one of

the other affects. Conversely, a weak or null effect may be a

consequence of minimal overlap with the cardiotoxic affect. This

is especially likely because the intercorrelations among the three

dispositions tend to vary as a function of the sample and the

measure of affective disposition. With this realization, the incon-

sistent results in the epidemiological literature are scarcely sur-

prising. (We hasten to add, however, that this is unlikely to be the

sole reason for the inconsistency.)

Implications for Evidence Concerning Negative Emotions

and Risk of Initial CHD Events

Although depression; hostility, anger, and anger expression; and

anxiety have been identified as risk factors in separate investiga-

tions, there are virtually no studies that have measured all three

emotion constructs and assessed their simultaneous effects in

multivariate analyses. Thus, despite the passage of 16 years, H. S.

Friedman and Booth-Kewley’s (1987a) question, which began this

article, still remains largely unanswered, at least with respect to

CHD.

The clearest implication of the model in Figure 6 is that

researchers should assess all relevant psychosocial constructs and

statistically test their simultaneous effects on CHD outcomes.

The oversight on the part of previous researchers may be due to

several factors. In describing the need for more studies that include

multiple measures, Frasure-Smith and Lespe´rance (2003b) pointed

to researchers’ “concerns about Type II errors with multiple mea-

sures in small samples, coupled with the need for clinically feasi-

ble approaches to screening” (p. 634). Beyond these reasons, we

think that Kaplan (1995) correctly characterized past behavioral

epidemiology and health psychology– behavioral medicine as

tending to operate with a “risk factor of the month” (p. 208)

strategy. Further, even when a data set contains multiple emotion

or trait measures, competitive pressures of funding and publication

H. S. Friedman, Tucker, Schwartz, et al. (1995) have examined the

simultaneous effects of different psychological variables in the classic

Termite genius sample. However, because the sample is only of moderate

size, H. S. Friedman et al. have not examined the effects of personality on

specific types of physical disease.

Figure 6. Model of pathways between negative affects and heart disease

(with measurement and construct overlap).

289

AFFECT, OVERLAP, AND HEART DISEASE

make piecemeal reporting of risk factors an attractive strategy,

despite its conceptual failings.

With respect to the cynical hostility, anger, and anger expression

domain, researchers have made some significant advances in dis-

tinguishing among dimensions. Reanalyses involving subcompo-

nent scoring of the SI data from the Western Collaborative Group

Study (Rosenman et al., 1976) have identified several different

behavioral dimensions and assessed their simultaneous impact on

CHD risk (Matthews et al., 1977). Although these subcomponents

do not correspond to the hostility, anger, and anger expression

structure described above (the SI was not designed to elicit the full

spectrum of negative emotions), Hecker et al. (1988) found the

component dimensions of potential for hostility, competitiveness,

and Type A content to be significantly related to CHD incidence in

univariate analyses. With simultaneous statistical entry, however,

only potential for hostility remained a significant risk factor, which

led the researchers to conclude that it is the toxic component. Such

results reinforce the need to measure and simultaneously test the

effects of putative risk factors (see also Houston, Chesney, Black,

Cates & Hecker, 1992; Powell & Thoresen, 1985).

The absence of a systematic set of studies that followed a

nominally healthy sample (at baseline) and tested the simultaneous

effects of hostility, anger, and anger expression; depression; and

anxiety on CHD risk represents a serious gap in the literature. A

research report from Chang, Ford, Meoni, Wang, and Klag (2002)

is worthy of mention in this context because it acknowledged the

possibility of overlap. Approximately 1,000 men enrolled in med-

ical school completed a questionnaire about how they typically

react to stress. Three items were defined as indicating anger—

“expressed or concealed anger,” “irritability,” and “gripe ses-

sions.” These men were followed up for 32– 48 years. Prior to 55

years of age, those participants who scored at the highest level of

anger had a threefold risk of CHD compared with men with lower

levels of anger. Other items from the questionnaire tapping de-

pressive and anxiety symptoms also were entered into the analyses,

but anger remained a significant predictor of MI. The authors,

unfortunately, did not report the results for anxiety and depression.

It is noteworthy that anger had a significant effect, independently

of the other affective dispositions; however, the anger measure was

a composite that did not differentiate between anger and anger

expression. In addition, cynical hostility was not measured at all.

The construct and measurement overlap described earlier

prompts us to ask whether the increased risk of developing CHD

associated with anger is in fact due to cynical attitudes, angry

affect, the correlations with other negative emotions, or the corre-

lations with NA. Next, we consider the same issue with respect to

disease progression.

Implications for Evidence Concerning Negative Emotions

and Risk in Persons With Existing Heart Disease

In samples with existing disease, the situation is somewhat

different, because the correlations among psychological constructs

have been recognized by researchers, and simultaneous assessment

of different emotion constructs has been conducted. Ahern et al.

(1990) tested predictive associations among anger expression,

depression, and anxiety and cardiac events in the Cardiac Arrhyth-

mia Pilot Study involving several hundred post-MI patients. Of the

three constructs, only depression emerged as a significant predictor

of cardiac morbidity or mortality. Frasure-Smith et al. (1995b)

examined how well depression, anxiety, anger-in, and anger-out

(measured in the hospital after MI) predicted cardiac events in the

subsequent 12 months. Their results first confirmed the intercor-

relations among negative affects, which ranged from .10 to .43.

Analysis of prognostic risk showed that increases in all recurrent

cardiac events, including reinfarction, admission for unstable an-

gina, and arrhythmic events, were associated with depression and

anxiety in the hospital, independently of each other and CHD

severity.

A recent report from the same research team (Frasure-Smith &

Lespe´rance, 2003a) with a larger sample of MI patients (combin-

ing a prospective cohort with the standard care arm of an inter-

vention study) and a 5-year follow-up reported similar results. The

larger sample also allowed the researchers to factor analyze the

results from the psychological measures. NA (including depres-

sion), overt anger, and social support emerged as distinct factors;

however, only depression and other aspects of NA (notably, anx-

iety and distress) predicted cardiac events independently of each

other and cardiac disease severity.

Mendes de Leon et al. (1996) assessed the role of state–trait

anger and vital exhaustion for risk of new cardiac events in

patients undergoing angioplasty. Both trait anger and vital exhaus-

tion were independently associated with recurrent events after

standard risk factors were controlled. A study by Angerer et al.

(2000) assessed the impact of social support, anger expression, and

cynical hostility on progression of atherosclerosis documented by

angiography in a cohort of patients with CHD. Only low social

support and high anger-out scores predicted progression of cardiac

disease within 2 years. When the results were reanalyzed with

controls for potential confounding variables (e.g., age, serum cho-

lesterol), only low social support was significantly related to

atherosclerosis. (The interaction between support and anger out

also was significant; this finding is discussed in a later section.)

In summary, unlike the case for healthy samples, a small set of

studies has tested the simultaneous influence of the negative af-

fects in patients with extant disease. However, the results are

inconsistent, and not all relevant constructs have been assessed in

the same study. Some evidence indicates that depression and

perhaps other elements of NA may have independent roles in the

risk of recurrent cardiac events. Further, hostility and anger ex-

pression seem to play inconsistent roles once CHD develops (but

see the next section). This reinforces a point advanced by Scheier

and Bridges (1995) that the “potency and importance of person

variables (and other psychological factors) may vary depending on

where the patient is in the disease course” (p. 264).

Acute Emotional States, Affective Dispositions, and

Cardiac Events

Prior discussion has focused on the role of chronic affect in

coronary risk; however, acute emotional outbursts also have been

implicated in cardiac events (Kamarck & Jennings, 1991; Kop,

1999). For example, the occurrence of catastrophic environmental

events, such as earthquakes (Lear & Kloner, 1996), war (Meisel et

al., 1991), and even observing competitive sporting events (Carroll

et al., 2002) are associated with an increase in cardiac deaths.

Emotional stress has been reported to occur immediately prior to

the onset of cardiac symptoms in 4% to 18% of cases (Behar et al.,

290 SULS AND BUNDE

1993; Tofler et al., 1990). Mittleman et al. (1995) used a recall

interview and a case cross-over design with 1,623 MI survivors to

compare the occurrence of angry outbursts in the 2-hr period

immediately preceding the onset of the MI with its expected

frequency on the basis of control data obtained from the same

patients. The risk of MI was doubled for individuals who had an

episode of anger in the preceding 2 hr. Results also indicated that

MI victims reported higher levels of anxiety prior to the onset of

symptoms than during other times.

These findings are complemented by studies of the effects of

mental stress on silent myocardial ischemia (i.e., episodes when

the heart does not receive sufficient blood flow but no pain is

experienced by the individual). Gullette et al. (1997) had CHD

patients undergo 48 hr of ambulatory electrocardiogram monitor-

ing along with concurrent self-report measures of activities and

emotions. Occurrence of negative emotions in the hour before

onset of myocardial ischemia was compared with the usual fre-

quency during hours in which ischemia did not occur. After

physical activity and time of day were controlled, reports of both

tension and frustration were associated with an RR of 2.20 for

myocardial ischemia. (The risk associated with sadness was also

2.20, but it was not statistically significant after adjustment for

confounding variables.) Gullette et al.’s study is noteworthy be-

cause they obtained emotional ratings prior to the clinical events,

so retrospective bias is not a plausible explanation for their results.

In summary, available evidence suggests that negative emotions

can serve as triggers of cardiac events.

Acute emotions may act as triggers because emotional outbursts

are associated with SNS activation, which increases blood pres-

sure, heart rate, and myocardial oxygen demand, leading to dis-

ruption of atherosclerotic plaque (e.g., Muller, Tofler, & Stone,

1989). Mental stress also may increase platelet aggregability and

coagulation (e.g., Jern et al., 1989). Depending on other conditions

created by hemostatic and vasoactive forces, a thrombus may

become occlusive, precipitating myocardial ischemia and/or ne-

crosis (e.g., Muller, Abela, Nesto, & Tofler, 1994).

Earlier discussion of overlap and correlation among the negative

emotions has relevance for evidence concerning acute emotions as

triggers of MI. Specifically, the particular negative emotion—

anger, anxiety, or sadness—that serves as a trigger may not nec-

essarily be the affect the individual experiences most of the time.

Because of the overlap and correlations among the three negative

affects, an angry outburst may be the precipitating event for a

depressed or anxious person, whereas sadness might be the trigger

for a person who is typically angry.

Individuals who are primarily depressed, angry, or anxious may

frequently experience other negative emotions (as would be ex-

pected from research on trait NA). For example, Mammen et al.

(1999) found that women who experienced anger attacks were

more likely to report higher state and trait anger and aggression

and were also more likely to be depressed. In a related study, Fava

et al. (1993) reported higher anxiety scores among depressed

women who experienced anger attacks. Results from the same

study showed that after Fluoxetine treatment for depression, anger

attacks disappeared in 71% of the participants who had previously

reported them. These findings reinforce the argument about neg-

ative emotion overlap and also suggest that correlated emotions

may just as likely be the MI triggers as the emotions that are more

closely tied to the affective disposition.

Acute Versus Chronic Emotions and Processes Instigating

Cardiac Events

Differentiating among types of negative emotions and between

states and traits may be important for understanding mechanisms

that mediate the relations among negative emotions and cardio-

pathogenesis. One scenario is that predominate (dispositional)

emotions may be associated with processes that instigate the

atherosclerotic process, which requires a considerable length of

time to develop from fatty streaks to atherosclerotic plaques.

However, a different acute emotional episode may be the trigger of

a cardiac event via plaque disruption or the instigation of cardiac

arrhythmias. This recognition may lead to insights about the spe-

cific roles that different negative emotions play in cardiopathogen-

esis. Kop (1999, 2003), for example, has proposed that transient

(i.e., nonchronic) risk factors may be mainly involved in the

transition from stable to unstable atherosclerotic plaques. In con-

trast, chronic risk factors may contribute to processes that are

involved in atherosclerosis. Although depressive symptoms are

chronic in many individuals, symptom intensity is episodic. This

may explain why severity of atherosclerosis is not associated with

severity of depression (e.g., Frasure-Smith et al., 1993; Kop,

2003). Conversely, hostility shows more consistent associations

with risk of developing CHD, but its association with increased

risk of reinfarction is weak and inconsistent (cf. Mendes de Leon

et al., 1996). Hostility (and perhaps anxiety) may be more involved

in physiologic processes that promote atherosclerosis (e.g., Kop,

1999).

Independent and Interactive Effects of the Three Affects

Although the three negative dispositions are moderately to

strongly correlated, they have distinctive features, as noted earlier.

Simultaneous statistical assessment of the effects of correlated

variables, however, tends to reduce the apparent contribution of

individual factors. This is another reason why researchers have

tended not to conduct studies involving simultaneous evaluation of

all relevant negative affects.

If testing with statistical simultaneous entry of the different

affects has been rare, then one would not expect that interactions

among the three affects have been assessed. The few studies that

tested interactions produced null effects (Frasure-Smith & Lespe´r-

ance, 2003a). Because very large samples are needed to detect

interaction effects, this outcome is not surprising.

The possibility of synergistic effects should not be discounted,

however. In the mental health domain, D. A. Clark et al. (1990)

identified purely depressed, purely anxious, and mixed anxious–

depressed subgroups in a psychiatric outpatient sample. The com-

bined subgroup was characterized as having a depth of symptom-

atology comparable to that of the pure groups but greater general

maladjustment. The greater distress and mixture of depressive and

anxious thinking may create higher stress responses in the mixed

group. These may translate into exaggerated SNS and HPA acti-

vation, which promote disease processes.

A study described earlier by Angerer et al. (2000) found evi-

dence of an interaction among psychological risk factors: the

high–anger-out and low-support patient group had the highest

relative risk of progression (3.2). To the extent that low social

support is dispositional, these results are interesting. The implica-

tion is that studies with larger samples that systematically assess

291

AFFECT, OVERLAP, AND HEART DISEASE

additive and interactive effects of psychological risk factors should

find that certain combinations of negative affective dispositions

emerge as synergistic.

Implications for Behavioral Treatment

A final implication was discussed by Mendes de Leon (1999),

who commented on the development of effective behavioral inter-

ventions to alleviate some of the adverse psychosocial character-

istics in MI patients. Acknowledging the potential role of several

negative emotions for CHD risk and their coexistence, he observed

that psychosocial interventions might best “adopt an inclusive

approach . . . targeting negative emotions and psychosocial condi-

tions generally, rather than trying to isolate or focus on a single

‘risk’ or condition” (Mendes de Leon, 1999, p. 739). Our review

and analysis concur with this perspective; depression, cynical

hostility, anger, anger expression, and anxiety share features and

are intercorrelated. For an intervention to be effective, it may need

to address the negative affects collectively rather than in piecemeal

fashion (also see H. S. Friedman, 1991). Perhaps the focus in

ENRICHD (Writing Committee for the ENRICHD Investigators,

2003) on treating depression (and low social support) was not

effective in reducing cardiac events because related negative af-

fects were not treated.

Conclusions

In the last 4 decades, much evidence based on prospective

designs has accumulated on the role of psychosocial factors and

risk of CHD. Despite the advances by psychosomatic researchers,

however, several issues are unresolved. Negative emotions, espe-

cially depression and anxiety, appear to be related to increased

CHD risk in healthy samples, but it is unclear whether these

emotions have independent and/or additive effects or whether

some affects may only appear to incur risk because they are

correlated with the others. Whether the overlap is a consequence of

a lack of discriminant validity of psychological measures and/or

represents overlapping features among the negative affects them-

selves is unclear. The answer probably is that both are responsible.

These interpretational problems have been better appreciated by

researchers examining the role of psychological constructs in

persons with existing cardiac disease, but even in this literature all

of the relevant negative affects have not been evaluated in a single

study. Unfortunately, our review of the literature also shows, with

the possible exception of depression, that studies of populations

with known diseases do not present as strong or consistent a role

for negative emotions in CHD progression.

Studies conducted with larger samples provide greater statistical

power to make a multiple predictor approach more feasible and

may resolve some of these questions. Careful reexamination of

existing data sets and pooling of data may also permit the appli-

cation of statistical strategies to assess multiple additive and in-

teractive influences. We hope that this article will encourage such

efforts.

Adoption of more sophisticated statistical approaches is unlikely

to be sufficient to make appreciable progress, however. There has

been a tendency for behavioral epidemiologists to either create

their own measures or borrow individual-differences measures

from the mainstream personality and clinical psychopathology

literatures without fully considering issues of construct and dis-

criminant validity or the psychological or psychiatric models that

inspired the development of these instruments. For example, em-

pirical reports continue to be published that tend to treat all kinds

of anger as the same or conflate anxiety and depression.

There also has been too little effort in making explicit the

theories and models that underlie behavioral epidemiology. In this

article, we hope that the presentation of more complex conceptual

models that recognize the overlap and incorporate findings from

the study of individual differences and psychopathology will fa-

cilitate advancements in this area. At minimum, the present article

argues strongly for more intensive collaboration of epidemiolo-

gists, health psychologists, personalogists, psychiatrists, and clin-

ical psychopathologists to study the relation between affect and

CHD.

There also needs to be more appreciation that the clustering and

overlap of negative affective dispositions may make specificity of

emotion less critical for CHD risk. That is, anger, anxiety, and

depression may not have distinctive, independent effects. They

may all increase risk because they share a general disposition to

experience chronic and intense negative emotions. H. S. Friedman

and Booth-Kewley (1987a) argued that affect-specific connections

to diseases (e.g., the cancer-prone personality) were not supported

by the empirical evidence. Similarly, the specificity of the negative

affect may be less important than the presence of chronic NA for

promoting risk of CHD. Finally, although the present focus has

been on CHD, psychosomatic research on the role of negative

emotions in other physical disorders, such as arthritis and gastro-

intestinal disorders, has also been active. Hence, the conceptual

models and analysis presented in this article also might be usefully

applied to these other conditions.

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Received October 22, 2003

Revision received September 28, 2004

Accepted October 12, 2004 䡲

300 SULS AND BUNDE

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Dec 2023

Aim: The aim of this research was to develop a model exploring the structural relationship between personality dimensions and attachment styles, communication skills, and marital conflicts, mediated by early maladaptive schemas in couples facing divorce. Method: This was a descriptive-correlational study. The population included all couples facing divorce who visited counseling centers in Takestan city during the second half of 2020 and 2021. A convenience sampling method was used, and the sample size was determined as 373 individuals based on Sample Power software. The research instruments included the Big Five Personality Traits Questionnaire, Communication Skills (Queendom, 2004), Marital Conflicts (Jones, 1997), Young’s Early Maladaptive Schemas (2007), and Hazan and Shaver’s Attachment Styles (1987). Data analysis was performed using SPSS and Amos software. Results: The results showed that personality dimensions do not significantly relate to marital conflicts among couples facing divorce (p<0.05). Similarly, attachment styles do not significantly relate to marital conflicts (p<0.05). However, early maladaptive schemas played a mediating role in the relationship between personality dimensions and marital conflicts, with a significant t-value (17.97) and significance level (p<0.05). Additionally, early maladaptive schemas had a mediating role in the relationship between personality dimensions and communication skills, indicated by a significant t-value (17.93) and significance level (p<0.05). Furthermore, early maladaptive schemas mediated the relationship between attachment styles and marital conflicts, with a t-value (12.87) and significance level (p<0.05). Conclusion: Considering that early maladaptive schemas can play a mediating role in the relationships of couples facing divorce, they are a significant factor in shaping personality, attachment styles, and marital conflicts.

EFFICACY OF HOMOEOPATHIC CONSTITUTIONAL MEDICINES IN THE MANAGEMENT OF HYPOTHYROIDISM

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Aug 2020

To study about the prevalence of hypothyroidism in the age of 15- 55 years. To study the efficacy of homoeopathic constitutional treatment in Hypothyroidism

A Randomized Trial of the Effectiveness of Cognitive−Behavioral Therapy and Supportive Counseling for Anxiety Symptoms in Older Adults

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Oct 2001

The authors used a randomized trial to compare cognitive−behavioral therapy (CBT) and supportive counseling (SC) in the treatment of anxiety symptoms in older adults who met Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) criteria for anxiety disorders. Both conditions had a 6-week baseline no-treatment phase. Treatment was delivered primarily in patients' own homes and in an individual format. Outcomes were assessed at posttreatment and at 3-, 6-, and 12-month follow-ups. There was no spontaneous improvement during the baseline phase. Both groups showed improvement on anxiety measures following treatment, with a better outcome for the CBT group on self-rating of anxiety and depression. Over the follow-up period, the CBT group maintained improvement and had significantly greater improvement than the SC group on anxiety and 1 depression measure. Treatment response for anxiety was also superior for the CBT group, although there was no difference between groups in endstate functioning.

Negative Affect Relates to Cross-Sectional but Not Longitudinal Symptom Reporting: Data From Elderly Adults

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Jul 1996

To test hypotheses about the relationship between negative affect and symptom reports, symptom reports of 4 groups of elderly participants (N = 76; mean age = 73.5 years) were compared: those high on measures of both depression and anxiety, those high on one measure and low on the other, and those low on both measures. Symptom reports were obtained before and after 3 simultaneously given active inoculations (influenza; tetanus toxoid; and keyhole limpet hemocyanin, a neoantigen) and 3 similarly given placebo injections. Cross-sectional analyses replicated associations between negative affect and reports of elevated systemic (flulike) symptoms. Local symptoms (sore arm and redness at injection site) increased significantly from before to after active inoculations. Reports of systemic symptoms declined from before to after for both active and placebo inoculations regardless of affect groups. The results add to previous research showing that negative affect is related to cross-sectional symptom reporting but not to increases in symptom reporting from before to after a symptom-producing inoculation procedure.

The Impact of Negative Emotions on Prognosis Following Myocardial Infarction: Is It More Than Depression?

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Sep 1995

This study examined the importance of major depression, depressive symptoms, history of major depression, anxiety, anger-in, anger-out, and perceived social support, measured in the hospital after a myocardial infarction (MI), in predicting cardiac events over the subsequent 12 months in a sample of 222 patients. Cardiac events included both recurrences of acute coronary syndromes (unstable angina admissions and survived and nonsurvived MI recurrences) and probable arrhythmic events (survived cardiac arrests and arrhythmic deaths). Major depression, depressive symptoms, anxiety, and history of major depression all significantly predicted cardiac events. Multivariate analyses showed that depressive symptoms, anxiety, and history of major depression each had an impact independent of each other, as well as of measures of cardiac disease severity.

Cognitive Specificity and Positive-Negative Affectivity: Complementary or Contradictory Views on Anxiety and Depression?

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May 1990

A principal factor analysis, conducted on a mixed psychiatric outpatient sample (N = 470) , identified both common and specific dimensions underlying anxiety and depression. Although an initial single-factor extraction accounted for a significant proportion of variance in cognitive and symptom measures of anxiety and depression, a two-factor solution, in which anxiety and depression formed separate dimensions, proved to be the better solution. MANOVAS performed on pure depressed, pure anxious, and mixed anxious/depressed subgroups provided evidence of a specific cognitive profile for anxiety and depression. The mixed subsample evidenced greater severity, a mixed cognitive and symptom profile, and character traits that may indicate increased vulnerability to psychological disturbance. Results are discussed in terms of Beck's (1976) cognitive content-specificity hypothesis and the positive-negative affect model (Watson & Tellegen, 1985).

Health Complaints, Stress, and Distress: Exploring the Central Role of Negative Affectivity

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Apr 1989

Most current models in health psychology assume that stress adversely affects physical health. We re-examined this assumption by reviewing extensive data from the literature and from six samples of our own, in which we collected measures of personality, health and fitness, stress, and current emotional functioning. Results indicate that self-report health measures reflect a pervasive mood disposition of negative affectivity (NA);self-report stress scales also contain a substantial NA component. However, although NA is correlated with health complaint scales, it is not strongly or consistently related to actual, long-term health status, and thus will act as a general nuisance factor in health research. Because self-report measures of stress and health both contain a significant NA component, correlations between such measures likely overestimate the true association between stress and health. Results demonstrate the importance of including different types of health measures in health psychology research.

Coronary Heart Disease and Type A Behaviors: Update on and Alternative to the Quantitative Review

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Nov 1988

Karen A. Matthews

This article reports a meta-analysis of the association of Type A behaviors and coronary heart disease (CHD). It differs from a previous meta-analysis reported in Booth-Kewley and Friedman (1987): it uses different decision rules and updates that analysis by using the most recently published papers from ongoing investigations and by including published reports from four new prospective investigations. The results of the present meta-analysis show that Type A behavior across all measures and prospective study designs is not a reliable predictor of CHD incidence, when the number of independent studies and number of participants in those studies are weighted. However, Type A—CHD associations are significant in population-based studies. It is suggested that Type A may be an important influence on the precipitating factors accounting for initial CHD events.

Psychological Predictors of Heart Disease: A Quantitative Review

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May 1987

After 3 decades of intensive research, there is still confusion about the nature and reliability of relations between psychological factors and coronary heart disease (CHD). A meta-analysis, or quantitative review, was performed to integrate and organize the results of studies that investigated certain personality variables in relation to CHD. The personality variables included were anger, hostility, aggression, depression, extroversion, anxiety, Type A, and the major components of Type A. The meta-analytic framework helps focus attention on issues needing clarification. The results indicate that modest but reliable associations exist between some of the personality variables and CHD. The strongest associations were found for Type A and, surprisingly, for depression, but anger/hostility/aggression and anxiety also related reliably to CHD. The Structured Interview diagnosis of Type A was shown to be clearly superior to the Jenkins Activity Survey as a predictor of CHD. The Type A-CHD relation was smaller in prospective than in cross-sectional studies and smaller in recent than in less recent studies. This review also revealed that information about the interrelations of personality predictors of CHD is sorely needed. The picture of coronary-proneness revealed by this review is not one of a hurried, impatient workaholic but instead is one of a person with one or more negative emotions. We suggest that the concept of the coronary-prone personality and its associated research be broadened to encompass psychological attributes in addition to those associated with Type A behavior and narrowed to eliminate those components that the accumulated evidence shows to be unimportant.

Studying Depressed Persons' Interactions With Strangers and Spouses

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May 1985

James C. Coyne

Doerfler and Chaplin (1985) argued that studies of interactions between depressed persons and strangers are irrelevant to the evaluation of an interactional model of depression. Yet, such studies of the acquaintance process have a unique contribution to make in that they allow for the emergence of depressive interactions in the absence of participants having had previous negative experiences together. They are also important for the interpretation of studies of the marital interactions of depressed persons. Although stimulated by the emergence of an interactional model of depression, development of interactional treatment strategies need not await the full development of the model and must ultimately be evaluated independently.

Understanding the Association Between Socioeconomic Status and Physical Health: Do Negative Emotions Play a Role?

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Jan 2003

In this article, the authors evaluate the possible roles of negative emotions and cognitions in the association between socioeconomic status (SES) and physical health, focusing on the outcomes of cardiovascular diseases and all-cause mortality. After reviewing the limited direct evidence, the authors examine indirect evidence showing that (a) SES relates to the targeted health outcomes, (b) SES relates to negative emotions and cognitions, and (c) negative emotions and cognitions relate to the targeted health outcomes. The authors present a general framework for understanding the roles of cognitiveemotional factors, suggesting that low-SES environments are stressful and reduce individuals' reserve capacity to manage stress, thereby increasing vulnerability to negative emotions and cognitions. The article concludes with suggestions for future research to better evaluate the proposed model.

Efficacy and Specific Effects Data on New Treatments: A Case Study Strategy With Mixed Anxiety-Depression

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Jun 1993

The case study research strategy presented here can be used to develop new psychotherapeutic treatments, test theorized mechanisms of action, and obtain initial outcome data of the type needed to support treatment outcome grant applications. The strategy is illustrated by 2 case studies of a new psychotherapeutic intervention for patients with coexisting generalized anxiety disorder and major depression as described in the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association, 1987). The treatment is a modification and integration of existing treatments for panic disorder (Barlow & Craske, 1989) and for major depression (Klerman, Weissman, Rounsaville, & Chevron, 1984).

Anger, Anxiety, and Depression as Risk Factors for Cardiovascular Disease: The Problems and Implications of Overlapping Affective Dispositions

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