Download Lack of Social Support in the Etiology and the Prognosis of Coronary

Lack of Social Support in the Etiology and the Prognosis of Coronary Heart
Disease: A Systematic Review and Meta-Analysis
JÜRGEN
BARTH, PHD, SARAH SCHNEIDER, BS,
AND
ROLAND
VON KÄNEL,
MD
Objective: To conduct a systematic review and meta-analysis on the relevance of low social support for the development and course
of coronary heart disease (CHD). Methods: Three electronic databases were searched (MEDLINE, PsycINFO/PSYNDEX, and
Web of Science 2007/03). More than 1700 papers were screened in a first step. We included prospective studies assessing the
impact of social support in either an initially healthy study population (etiologic studies) or in a study population with preexisting
CHD (prognostic studies). Outcomes: Myocardial infarction in etiologic studies; cardiovascular mortality and all-cause mortality
in prognostic studies. Effects were reported as relative risk (RR) or hazard ratio (HR). Results: There is some evidence for an
impact of low functional social support on the prevalence of CHD in etiologic studies (RR, range, 1.00 –2.23). In contrast, there
is no evidence of an impact of low structural social support on the prevalence of myocardial infarction in healthy populations (RR,
range, 1.01–1.2). In prognostic studies, results consistently show that low functional support negatively affects cardiac and all-cause
mortality (pooled RR, range, 1.59 –1.71). These results were also confirmed in analyses adjusted for other risk factors for disease
progression (pooled HR, 1.59). It remains unclear whether low structural social support increases mortality in patients with CHD
(pooled RR, between 1.56; pooled HR, 1.12, NS). Conclusions: Because the perception of social support seems important for CHD
prognosis, monitoring of functional social support is indicated in patients with CHD, and interventions to increase the perception
of positive social resources are warranted. Key words: social support, myocardial infarction, meta-analysis, coronary heart disease,
mortality.
AMI ⫽ acute myocardial infarction; BDI ⫽ Beck Depression Inventory; CABG ⫽ coronary artery bypass grafting; CHD ⫽ coronary heart disease; CI ⫽ confidence interval; CPK ⫽ creatinine
phosphokinase; DBP ⫽ diastolic blood pressure; f ⫽ females;
LVEF ⫽ left ventricular ejection fraction; m ⫽ males; MI ⫽
myocardial infarction; NYHA ⫽ New York Heart Association;
SBP ⫽ systolic blood pressure; SD ⫽ standard deviation; SES ⫽
socioeconomic status; yrs ⫽ years.
INTRODUCTION
bundant evidence suggests that psychosocial factors like
depression, anxiety, personality characteristics, chronic
life stress, and social isolation predict the development, the
course, and negative prognosis of coronary heart disease
(CHD) (1,2). Specifically, low social support is seen as a risk
factor for the development of CHD in previously healthy
individuals, and it also worsens the prognosis of patients with
established CHD (3–5).
Since the 1970s, theories of social support and its relationship to health and disease have seen considerable development
and refinement. In 1976, Cobb (6) defined social support as
“information leading the subject to believe that he is cared for
and loved, esteemed, and a member of a network of mutual
obligations.” In 1988, Seeman and Berkman (7) emphasized
the need to distinguish between dimensions of social networks
and the support they actually provide. Consistent with this
A
From the Institute of Social and Preventive Medicine (J.B., S.S.), Division
of Social and Behavioral Health Research, University of Bern, Bern, Switzerland; Department of General Internal Medicine, Division of Psychosomatic
Medicine, and Cardiovascular Prevention and Rehabilitation (S.S., R.v.K.), Bern
University Hospital, Inselspital, and University of Bern, Bern, Switzerland.
Disclosure: Dr. von Känel previously received funding from Pfizer AG.
and Wyeth Pharmaceuticals AG, both in Switzerland. The remaining authors
have no potential conflict of interest.
Address correspondence and reprint requests to Jürgen Barth, PhD, Institute of Social and Preventive Medicine (ISPM), University of Bern, Niesenweg 6, CH-3012 Bern, Switzerland. E-mail: [email protected]
Received for publication July 7, 2008, revision received May 18, 2009.
Supplemental digital content is available for this article. Direct URL citations
appear in the printed text, and links to the digital files are provided in the HTML
text of this article on the journal’s Web site www.psychosomaticmedicine.org.
DOI: 10.1097/PSY.0b013e3181d01611
Psychosomatic Medicine 72:229 –238 (2010)
0033-3174/10/7203-0229
Copyright © 2010 by the American Psychosomatic Society
view, there is now fair agreement among researchers and
clinicians (1,5) that social support can be conceptualized in
terms of two broad domains, namely, functional and structural
support.
“Functional support” describes the aid and encouragement
that is provided to the individual by the social network.
Functional support has been further categorized into different
forms of support: instrumental (e.g., help getting tasks done),
financial, informational (e.g., providing information), appraisal (e.g., help evaluating a situation), and emotional (e.g.,
feelings of being loved). “Structural support” refers to the
characteristics of the network of people surrounding an individual and his/her interaction with this network. Important
issues are number of contacts (network size), frequency of
contact, membership of community groups, and marital status.
A variety of mechanisms have been suggested in which
social support might contribute to health and disease. In 1985,
Cohen and Wills (8) introduced two different although not
mutually exclusive models. The so-called buffering model
posits that social support protects persons from the potentially
pathogenic influence of stressful events. The alternative
model, the so-called main-effect model, proposes that social
resources have a beneficial effect by providing positive experiences and stability in life situation, irrespective of whether
persons are under stress or not. Evidence was found for both
models: structural social support was found to work mainly in
a direct way (main effect), whereas functional social support
was especially helpful in stressful situations (buffering model). In
healthy persons, structural social support would be associated
with better health. After critical life events (e.g., illness), however, functional social support would be expected to be especially
effective for an adaptive psychological coping response.
But how does social support affect CHD? Areas under
discussion are relationship to 1) health behaviors; 2) physiological mechanisms; 3) psychosocial factors, and 4) underlying third factors. 1) Health behaviors: There is evidence that
improved social support is associated with health-promoting
behaviors, which diminish the risk of CHD. For instance,
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J. BARTH et al.
individuals with higher social support were less likely to
smoke (9), more likely to perform physical activity during
leisure time (10), and had better adherence to medical recommendations (11). 2) Physiological mechanisms: Previous reviews (12–14) examined potential mechanisms linking social
support to physiological processes. Social support was associated with better regulation of blood pressure and reduced
cardiovascular reactivity to acute stress (14). There were also
associations between social support and perturbed endocrine
and autonomic nervous system function as demonstrated by
hyperactivity of the hypothalamic pituitary adrenal axis (12)
and elevated catecholamine levels (14). In addition, chronic
low-grade inflammation and coagulation activity both involved in CHD and its thrombotic complications were also
found to be elevated with low social support (13,15). 3)
Psychosocial factors: The relationship between social support
and CHD could also be mediated by factors like self-efficacy,
coping ability, and negative affect (5). A lack of social support
is known to lead to negative psychological states like anxiety
or depression, which, in turn, may influence health either
through a direct effect on physiological processes or through
adverse health behavior (2). The association between social
support and depression is particularly important because low
social support influences development and outcome of depression (16,17). In turn, depression is an important predictor of
CHD (2,18) and having social support reduces the CHD risk
in depressed individuals (19). 4) Underlying third factors: At
least part of the association between social support, depression, and CHD might be affected by a third factor like personality traits or shared genes (20,21).
Previous narrative reviews (3–5) on the impact of social
support on CHD included prospective cohort studies, which
assessed social support at study entry and diverse cardiac
outcomes at follow-up. Primary studies were divided between
etiologic studies, which investigated the role of social support
in an initially healthy study population, and prognostic studies, which included participants with preexisting CHD. The
various authors consistently concluded that there is evidence
for an association between social support and CHD from an
etiologic and prognostic perspective. This general conclusion
might, however, be premature, owing to the narrative reporting of results and the small overlap of included studies.
Specifically, three reviews (3–5) included 27 studies overall
published up to 1998, but only ten were cited in all three
reports. Of the more recent ten studies, published between
1999 and 2001, only five were included in the two reviews
(4,5) published after 1999.
To fill these gaps, we performed a systematic review and
meta-analysis on the role of social support in the development
and course of CHD and myocardial infarction (MI), respectively, addressing the following three questions:
1. Does having low social support increase the risk for the
development of MI in previously healthy individuals?
(etiology)
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2. Does lack of social support influence all-cause and
cardiac mortality in patients with preexisting CHD?
(prognosis)
3. Is the relationship between lack of social support and
these outcomes (reinfarction, all-cause and cardiac mortality) maintained after controlling for biological and
social variables?
MATERIALS AND METHODS
Inclusion Criteria
General Inclusion Criteria
1) Study Design: Prospective cohort studies were included because they
reflect the most accurate observational design to address questions of etiology
and prognosis (3). Cross-sectional and retrospective studies were excluded;
the latter are prone to recall bias. 2) Studies were included independent of
size, publication date, and language. 3) Only studies that assessed hard end
points were included, as they provide the most reliable results. Particularly,
studies on angina pectoris only (22), progression of atherosclerosis (23,24), or
combined end points like “coronary events” including angina pectoris or
revascularization (25) were excluded.
Additional Inclusion Criteria for Etiologic Studies
All study participants had 1) to be apparently healthy at study entry; and
2) to experience fatal or nonfatal MI at study end point.
Additional Inclusion Criteria for Prognostic Studies
1) All study participants had to have preexisting CHD, e.g., MI; and 2) MI
(nonfatal or fatal), cardiovascular death, or all-cause mortality to be the end
points of the study.
Participants
Study participants of etiologic studies were adult men and women who
were free of subjective symptoms of angina pectoris, had never had a
conspicuous test for CHD, and were not predominantly suffering from any
other disease. Participants of prognostic studies were adult men and women
with preexisting clinically verified CHD. We considered CHD to be present
in prognostic studies if a) it was clinically verified by coronary angiography;
b) MI had occurred and met at least two of three criteria (typical chest pain;
characteristic changes on electrocardiogram, such as new Q wave or ST
elevation; elevated cardiac enzymes); or c) coronary artery bypass grafting
(CABG) had been carried out (note: CABG surgery is only performed in the
presence of CHD). If the majority of patients of a particular study suffered a
cardiac disease other than CHD (e.g., congestive heart failure (26,27) or had
undergone cardiac surgery other than CABG (28), these studies were
excluded.
Predictor (Independent Variable)
As outlined in the introduction, social support is the total amount of aid
and care an individual actually receives from and perceives in social networks. Different methods were used to assess social support in terms of
structural characteristics and types of aid provided by a network. A common
method is the use of self-report questionnaires. Some studies used structured
or unstructured telephone or face-to-face interviews. Census registers were
often used to obtain information on marital status. For our systematic review,
we accepted all types and assessment methods of social support whenever the
authors demonstrated that they had assessed relevant construct aspects. We
analyzed the predictive value of a maximum of two social support measures
(i.e., one functional measure and one structural measure). For the metaanalysis, we included only one measure for social support, according to a
hierarchy of measures (see Data Analysis).
Outcome Measures (Dependent Variables)
In previous research, many different outcome measures were used, ranging from soft end points like angina pectoris to hard end points like death. To
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SOCIAL SUPPORT AND CORONARY HEART DISEASE
provide the most reliable results, we decided to include only studies reporting
on hard end points. Thoracic pain, in particular, is caused by many somatic
and psychological conditions (e.g., anxiety, somatization), of which critical
ischemia of the myocardium is only one possibility and only in this case
would qualify for a diagnosis of “angina pectoris” (29). We further decided
not to include results referring to a follow-up period of ⬍1 month (e.g.,
inhospital mortality) in our systematic review. For etiologic studies, we
considered only nonfatal and fatal MI as outcomes. In prognostic studies, we
analyzed three different outcomes: MI (nonfatal or fatal), cardiovascular
death, and all-cause mortality. In neither study type did we analyze data on
surgical procedures like angioplasty or bypass surgery, because the decision
for these procedures depends also on subjective clinical assessments.
Literature Search and Data Sources
The literature search included published and unpublished papers from
several databases in English and German. MEDLINE (U.S. National Library
of Medicine, 1950 –2007/03), PsycINFO (American Psychological Association, 1806 –2007/03), PSYNDEX (German database of the Center for Psychological Information and Documentation at the University of Trier,
Germany, 1806 –2007/03), and Web of Science (Social Sciences Citation
Index, 1900 –2007/03) were all searched for relevant studies. Different search
strategies were developed for each database. MEDLINE was searched
through the terms (“social* support*” or “social* network*” or “social*
isolation*”) and (coronar* or “myocard* infarct*”). Web of Science was
searched through the terms (“social* support*” or “social* network*” or
“social* isolation*”) and (coronar* or “myocard* infarct*”). PsycINFO and
PSYNDEX were searched through the terms (“social* support*” or “socials*
network*” or “social* isolation*” or “sozial* unterstütz*” or “sozial*
netzwerk*” or “sozial* isolation*”) and (coronar* or “myo?ard* infar?t*” or
koronar* or herzinfarkt*). The searches resulted in 642 references from
MEDLINE, 446 references from PsycINFO/PSYNDEX, and 1103 references
from Web of Science. All results were downloaded and stored in the reference
database program EndNote 10. From the total of 2191 references, 1739
references remained after deletion of duplicates.
Study Deletion
Of the 1739 references, 1632 papers were excluded by title and abstract
(Fig. 1). The resulting 107 papers were retrieved for a detailed evaluation.
Another 79 papers were excluded after consultation of full text. Nine papers
(30 –38) were additionally identified from previous nonsystematic reviews
(3–5) by hand searching. Finally, 37 papers (30 – 66) were included in our
systematic review and 32 provided sufficient statistical information for metaanalysis (30 –37,39 – 62). This procedure resulted in a final number of 32
papers, of which 26 were prognostic and six were etiologic. For each of the
1711 excluded papers, the reasons for exclusion were coded as follows: 812
papers used different outcome criteria, 526 papers were nonempirical, 258
papers investigated different populations, 73 papers used different predictors,
40 papers were excluded because of the study design, and two papers were
conducted with a nonhuman sample. During data extraction, we further
excluded five papers owing to insufficient information (38,63– 66).
For a reliability analysis of the selection process, a sample of 103 papers
of the 1739 citations initially retrieved were independently reviewed by two
authors (either S.S. and J.B. or S.S. and R.v.K.). Percentage agreement and
interrater variability (␬) for different categories were as follows: paper
included ⫽ 98% (0.66), paper excluded because of inappropriate population ⫽ 89% (0.74), inappropriate outcome ⫽ 89% (0.76), inappropriate
predictor ⫽ 97% (0.65), inappropriate study design ⫽ 99% (0.66), study
participants being nonhuman ⫽ 100% (1), study being not empirical ⫽ 98%
(0.94).
Data Extraction
For data extraction, a paper pencil extraction sheet was used. This consisted of sections on setting, type and measurement of predictors (e.g.,
structural social support), sample characteristics, and outcome data on cardiac
events, cardiac death, and all-cause mortality. Data from all included papers
were independently rated by two of three authors (either S.S. and J.B. or S.S.
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Figure 1.
Flow chart for the selection of the studies after initial search.
and R.v.K.). After the initial coding, differences were resolved by discussion
with the third author (either J.B. or R.v.K.) and ended in one final coding
based on mutual consent.
Data Analysis
The effect of social support on the development or prognosis of CHD was
included in the meta-analysis as crude relative risk (RR) without adjustment
for baseline variables. If adjustments were done, hazard ratios (HRs) were
used which took into account baseline differences in relevant prognostic
variables. These HR more precisely represent the pure effect of social support
on morbidity or mortality. For both measures, corresponding confidence
intervals (CIs) were used to estimate the precision of the effect. If results were
presented by a two by two table, we separately calculated RR values. More231
J. BARTH et al.
over, all odds ratios were converted into RRs with the approach of Zhang and
Yu (67). All statistical analyses were done with STATA 9 by the command
metan (68). The reported summary statistics were calculated as random
effects models based on the idea of heterogeneity between studies. Pooling
was done according to the DerSimonian and Laird method, using inverse
variance of the primary studies as implemented in the command metan in
STATA. The CI represents 95% ranges, which indicate a statistically significant effect when values of 1.0 are excluded from the RR or HR values.
Values of RR or HR ⬎1.0 indicate an effect of social isolation or low social
support on the development or prognosis of CHD.
Heterogeneity between studies was assessed by examining forest plots of
studies, by calculating a ␹2 heterogeneity test, and through I2 statistics. The ␹2
value tests for statistically significant heterogeneity between studies and
indicates heterogeneity if statistical significance is found. In addition, higher
I2 values indicate greater variability between studies than would be expected,
owing to chance alone (range, 0% to 100%). Higgins and colleagues (69)
proposed as indicators of low, moderate, or high heterogeneity I2 values of
25%, 50%, and 75%.
We limited our analysis to a maximum of two social support measures
(i.e., one structural measure and one functional measure). As many studies
provided results for several structural support measures, we defined a hierarchy (i.e., 1. living alone; 2. marital status; 3. few contacts; 4. structural
support not otherwise specified or combined measures of structural support),
taking the highest available hierarchy level for data entry in our meta-analysis.
Studies that did not differentiate between structural and functional social
support were considered to be functional support studies. This resulted in two
main analyses for social support: first, structural social support; and second,
functional social support.
We investigated the effect of social support on cardiac mortality and
all-cause mortality. If a study provided results on both outcome measures, we
decided to use the results for cardiac mortality in the combined analyses. As
the effect of social support on recurrent MI was rarely reported, no analyses
were performed with that outcome.
For proving the effect of adjustments, we grouped the studies according to
the dimensions of adjustments in a subgroup analysis. This procedure was
used to achieve more homogeneous results because the same mediating
variables were included in the primary study. Five different groups of variables were defined from a theoretical perspective and their relevance for
prognosis was taken into account. As there was considerable variability
between studies in terms of the type of adjusted variables, we conducted
sensitivity analyses only for variables if four or more studies remained after
exclusion of nonadjusted studies. The relevant factors, which led to an
inclusion of the studies in this sensitivity analysis, were: a) age: studies
remained if an adjustment for age was done; b) smoking status: smoking had
to be controlled for in the adjusted model; c) socioeconomic status, meaning
that education or income was controlled for; d) metabolic factors (i.e.,
hypertension, blood lipids, body mass index, diabetes mellitus): at least one
item had to be controlled in the adjusted model; e) myocardial function factor
(i.e., left ventricular ejection fraction [LVEF], New York Heart Association
[NYHA] class, congestive heart failure, Killip class, prior MI, arrhythmia):
studies had to be adjusted for two variables, one being LVEF, NYHA,
congestive heart failure or Killip class and the other being prior MI or
arrhythmia.
RESULTS
Description of Etiologic Studies
Six papers were included in our systematic review of
etiologic studies (34,46,50,55,59,62). Two of these six papers
used the same participant sample and were therefore merged
in Supplemental Table 1 (see Supplemental Digital Content 1,
which displays descriptive information regarding prospective
studies with apparently healthy persons on the incidence of
nonfatal or fatal MI, http://links.lww.com/PSYMED/A8)
(46,55). This resulted in a total number of five etiologic
studies. Three studies were carried out in Sweden, one in the
232
United States, and one was from the Netherlands. All studies
were published in English. Two studies analyzed a randomly
selected population and one study each was restricted to health
professionals, railway engine drivers, and persons who had
been employed at their worksite for at least 4 years. Length of
follow-up ranged between 4 years and 10.3 years. No specific
intervention to enhance social support was carried out in any
study. Sample sizes at the time of study enrollment ranged
from 500 to 45,414 participants. Three studies were restricted
to men, one study included men and women, and one study
was unclear in terms of the proportion of men and women
included. Age of study participants ranged from 25 years to 77
years. All studies assessed socioeconomic status (SES) by
occupational level and two studies additionally on educational
level. The independent variable (social support level) was
assessed by self-report questionnaires in four studies. One
study obtained information on social support from census
data. Three studies used perceived functional support as predictor, two of them focusing on social support at work. Two
studies used structural support as predictor. Main outcome measure was nonfatal MI in one study and combined fatal and
nonfatal MI in four studies. Four of the five studies statistically
controlled for biological and social variables. One study additionally controlled for the time period since enrolment. None of
the studies controlled for psychological variables.
Description of Prognostic Studies
Twenty-six papers were included in our systematic review of
prognostic studies. Several papers reported on the same sample
of participants and were therefore merged in Supplemental Table
2 (see Supplemental Digital Content 2, which displays descriptive information of prognostic studies with patients suffering
from clinically verified LHD, http://links.lww.com/PSYMED/A9).
This resulted in a total number of 20 prognostic studies.
Twelve of these 20 studies were carried out in the United
States, three studies were from Canada, two from Sweden, one
from the United Kingdom, one from Belgium, and one was
international. All studies were published in English. Fifteen
studies included patients with MI, three studies analyzed patients with angiographically verified CHD, and one study
investigated patients with CHD who had undergone CABG.
One study included patients with MI, CABG, or angioplasty.
In three studies, patients additionally suffered from cardiac
arrhythmia and two studies included patients with depression.
Three studies carried out an intervention— two of which were
psychosocial interventions, and one was a nursing intervention. Periods of follow-up ranged between 6 months and 14.5
years. Sample sizes at the time of study enrollment ranged
from 194 to 13,240 participants. Nineteen studies included
men and women and one was restricted to men. Age of study
participants ranged from 25 years to 85 years. Thirteen studies
assessed SES by education. Four studies additionally registered data on income. In nine studies, assessment of SES was
unclear. Eighteen of the 20 studies provided information on
clinical characteristics of patients at baseline. The independent
variable (social support) was assessed by self-report in 13
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SOCIAL SUPPORT AND CORONARY HEART DISEASE
Figure 2. Forest plot of prognostic studies on assessing the effect of functional social support on all-cause mortality in patients with coronary heart disease.
Relative risk unadjusted for other risk factors. n ⫽ 7. ES ⫽ effect size; CI ⫽ confidence interval. Additional funnel plots can be retrieved from the first author.
studies, by interview in three studies, by self-report plus
interview in one study, and by medical records in one study.
The method of assessment was unclear in two studies. Six
studies used structural support as predictor, five used functional support, and nine used both structural and functional
support as predictor. Outcome measure was all-cause mortality in 12 studies and cardiovascular mortality in four studies.
Three studies assessed both, cardiovascular mortality and allcause mortality. One study assessed MI and all-cause mortality. Fourteen studies controlled for biological and/or psychological and/or social and/or other variables.
Effects of Social Support in Etiologic Studies
Because of the small number of etiologic studies, we did
not carry out a meta-analysis. Instead, we provide a quantitative summary of studies included.
Effects of Functional Social Support
Three etiologic studies analyzed the impact of functional
support on MI risk. One study found a significant effect of low
functional support on MI prevalence (unadjusted HR, 1.53;
95% CI, 1.02–2.28). One study found a significant effect only
for women (unadjusted HR, 2.23; 95% CI, 1.17– 4.25) but not
for men (unadjusted HR, 1.00; 95% CI, 0.69 –1.45). One study
reported an effect size of 0.20 from a time-dependent Cox
regression analysis not reaching statistical significance. Taken
together, these results suggest some evidence for the negative
impact of low functional social support on the incidence of CHD.
Effects of Structural Social Support
Two of the five etiologic studies analyzed the effect of
structural support on the prevalence of MI but none of them
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found evidence for a higher risk of socially isolated persons. One study reported a nonsignificant adjusted RR for
MI of 1.1 (95% CI, 0.7–1.9l). The other study found an
inverse effect of structural social support with a slightly
higher 4-year occurrence rate of MI in persons with high
social support (adjusted RR, 1.32; 95% CI, 0.98 –1.79) than
in persons with medium support (adjusted RR, 1.04; 95% CI,
0.76 –1.40) or low support (adjusted RR, 1.00; 95% CI, 0.58 –
1.71). It is important to note that none of these results reached
statistical significance. Taken as a whole, the evidence for a
negative impact of low structural social support on the prevalence
of MI is, so far, low.
Effects of Social Support in Prognostic Studies
Effects of Functional Social Support
The effect of low functional support on either cardiac
mortality or all-cause mortality (combined outcomes) was
significant with an unadjusted RR of 1.71 (95% CI, 1.26 –
2.31, n ⫽ 9). Heterogeneity was large (␹2 ⫽ 37.75; df ⫽ 8;
p ⬍ .001; I2 ⫽ 78.8%). Lack of functional social support also
had a significant effect on all-cause mortality with an RR of 1.59
(95% CI, 1.17–2.16; n ⫽ 7) (Fig. 2). Heterogeneity between
studies was large (␹2 ⫽ 21.23; df ⫽ 6; p ⫽ .002; I2 ⫽ 71.7%).
Even after we controlled for other risk factors, the effect of
functional social support on all-cause mortality remained
significant with an adjusted HR of 1.59 (95% CI, 1.21–2.08;
n ⫽ 6) with large heterogeneity between studies (␹2 ⫽ 22.66;
df ⫽ 5; p ⬍ .001; I2 ⫽ 77.9%) (Fig. 3). These results
consistently suggest an impact of low functional social support on mortality in patients with CHD.
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J. BARTH et al.
Figure 3. Forest plot of prognostic studies on assessing the effect of functional social support on all-cause mortality in patients with coronary heart disease.
Hazard ratio adjusted for other risk factors. n ⫽ 6. ES ⫽ effect size. Additional funnel plots can be retrieved from the first author.
Effects of Structural Social Support
Missing structural social support had a significant effect on
all-cause mortality with an RR of 1.41 (95% CI, 1.17–1.70; n ⫽
10). There was, however, marked heterogeneity between studies
(␹2 ⫽ 51.64; df ⫽ 9; p ⬍ .001; I2 ⫽ 82.6%). Lack of structural
social support had no significant effect on cardiac mortality with
an RR of 1.56 (95% CI, 0.94 –2.58; n ⫽ 5). There was also
considerable heterogeneity between studies (␹2 ⫽ 24.22; df ⫽ 4;
p ⬍ .001; I2 ⫽ 83.5%). In studies assessing the effect of structural support on either cardiac mortality or all-cause mortality
(combined outcome) with adjustment for other risk factors, there
was a nonsignificant adjusted HR of 1.12 (95% CI, 0.98 –1.29)
(Fig. 4). Heterogeneity between studies was considerable (␹2 ⫽
24.89; df ⫽ 7; p ⫽ .001; I2 ⫽ 71.9%; n ⫽ 9).
From these mixed results, it remains unclear whether low
structural social support increases mortality among patients
with preexisting CHD.
Sensitivity Analysis for Adjusted Analyses
In a second step, we conducted a sensitivity analysis with
two goals. First, we wanted to reduce heterogeneity between
studies to provide more statistically meaningful results. Second, we aimed for a critical examination of the adjustment
procedures in the primary studies. Results for the effects of
low structural social support on cardiac mortality or all-cause
mortality did not considerably differ from the results of the
analysis of all studies. A nonsignificant adjusted HR of 1.16
(95% CI, 0.97–1.39; n ⫽ 6) was calculated after exclusion of
studies not adjusting for age. A nonsignificant HR of 1.15
(95% CI, 0.93–1.42; n ⫽ 4) was calculated after exclusion of
studies not adjusting for metabolic factors. Exclusion of stud234
ies without an adjustment for myocardial function yielded an
HR of 1.11 (95% CI, 0.87–1.41; n ⫽ 5); exclusion of studies
not adjusting for SES yielded an HR of 1.25 (95% CI, 0.87–
1.80; n ⫽ 4). Heterogeneity between studies was somewhat
lower than in the original analysis with an I2 range from
58.0% to 69.8%. As the pooled estimate did not reach the level
of statistical significance in all the subgroup analyses, it seems
questionable if structural social support is an important predictor for prognosis after CHD.
The effect of low functional support on all-cause mortality
remained stable as all studies were adjusted for age. After
exclusion of studies without adjusting for metabolic factors,
the effect of functional support was comparable to the primary
analysis (HR, 1.70; 95% CI, 1.19 –2.41; n ⫽ 4) and heterogeneity between studies was slightly reduced (␹2 ⫽ 9.27; df ⫽
3; p ⫽ .026; I2 ⫽ 67.6%). All other subgroups of adjusted
statistical models were performed fewer than four times and
therefore not pooled in a meta-analysis.
DISCUSSION
Within this comprehensive systematic review, we found
evidence for a beneficial effect of perceived social support in
CHD development and prognosis. Owing to the low number
of etiologic studies, we did not aggregate these by metaanalytic procedures. On a primary study level, we found some
evidence for a higher cardiac risk if persons felt less socially
supported. None of the included studies, however, supported
the idea that structural social conditions (e.g., living alone)
influence the development of CHD. Contrary to these weak
results on the relevance of social support as risk factor for
CHD development, we found stronger results on social supPsychosomatic Medicine 72:229 –238 (2010)
SOCIAL SUPPORT AND CORONARY HEART DISEASE
Figure 4. Forest plot of prognostic studies on assessing the effect of structural social support on cardiac or all-cause mortality (combined outcome) in patients
with coronary heart disease. Hazard ratio adjusted for other risk factors. n ⫽ 8. ES ⫽ effect size; CI ⫽ confidence interval. Additional funnel plots can be retrieved
from the first author.
port playing a role for CHD prognosis. Within our sample of
20 prognostic studies, the impact of low functional support on
mortality was quite high and also present in studies with an
adjustment for other risk factors of CHD. Moreover, low
structural social support was found to increase mortality in
patients with CHD. In the statistical model adjusted for other
risk factors, this result failed to achieve a level of significance.
Because social support is related to several other risk
factors of CHD, it is essential for the interpretation of the
results to conduct adjusted statistical analyses with potential
confounders and mediators. Therefore, five categories were
built from the publications to give a description of the type of
adjustment. Nevertheless, we found an effect of functional
social support in the adjusted analysis, even after exclusion of
not comprehensively adjusted models. But this subgroup analysis has the limitation that only one study (70) was included in
all statistical models, as this study alone adjusted for all five
dimensions sufficiently.
The risk of developing CHD is so far not clearly related to
the amount of social support available. This is unexpected
because, from a theoretical perspective, there are some strong
pathways relating low social support to cardiac risk behavior,
changes in physiology, and underuse of medical treatment.
The number of etiological studies is still quite low, which is
remarkable because this issue is a very important aspect in
social sciences (71). Given the scant findings in the published
etiologic literature, unpublished null findings would further
diminish support for an effect of social support (functional
and/or structural) on MI. Earlier reviews came to a more
optimistic evaluation of the evidence of social support on the
development of CHD. As our review excluded studies on soft
Psychosomatic Medicine 72:229 –238 (2010)
signs of CHD (like angina), some well-received studies were
excluded (25,72), but this limitation to studies with clear
outcome criteria increases internal validity of our findings.
This study does not support the idea that low social support is
equally related to the development and the prognosis of CHD.
This notion is contrary to other studies on psychosocial risk
factors of CHD. The most often studied risk factor depression
was found to be of equally strong relevance before and after a
cardiac event (73,74).
We were able to detect some more studies that were not
included in previous reviews (3). Therefore, we were able to
do separate analyses for functional and structural support in
prognostic studies. Our findings go beyond earlier reviews,
which report on mixed findings if both aspects of social
support were studied separately (75). We found clear evidence
for the negative prognostic impact of functional social support
in crude and adjusted models, but did not prove the same for
structural social support after adjustment for other prognostic
risk factors.
Our study has several limitations, which are quite common
in systematic reviews. Publication bias might be present,
which means that only significant results were published. As
the included studies were only observational, this might be
more often the case than in clinical trials. Funnel plots of the
presented results showed a hint for a publication bias because
smaller studies with negative results are missing, especially in the
adjusted analysis. However, the larger studies report on consistent results. We found much heterogeneity, which means that the
pooled estimate must be interpreted with caution. Unfortunately,
we have not been able to reduce heterogeneity by limiting the
study pool to studies with the same adjustment of other risk factor
235
J. BARTH et al.
domains. This heterogeneity might be caused by differences in
measuring social support between studies. We separated studies
according to functional and structural support; however, within
these constructs, there is huge variability that could not be addressed by our meta-analysis.
Implications for Research
We found good evidence for a differential effect of functional social support (e.g., perceived social support) and structural social support (e.g., living alone) on the prognosis of
CHD. Therefore, it is strongly recommended that both aspects
of social support in upcoming studies should be measured to
gain more insight into this differential effect. A limitation of
the included studies, however, was the broad assessment of
functional social support by scales. Three studies focused on
emotional support only (36,40,50), whereas comparative studies on different aspects of functional social support are missing. Comparative studies could, therefore, try to narrow the
predictor on a specific aspect to obtain more conclusive information about the specificity of social support, instead of
comparing different broad scales on functional social support
(70). Most of the prognostic studies report either on all-cause
mortality or on cardiac mortality. Despite the limited reliability of clinical judgments of the cause of death (76), a more
differentiated assessment would be of interest for the generation of disease models. It would be especially useful to have
both predictors in one study, which was only done by four
prognostic studies so far (42,51,58,60). Such results would
help to identify the specificity of social support for cardiac
prognosis beyond higher mortality of persons with low social
support. Another important point is the set of other cardiovascular risk factors to be used in the adjusted statistical models. We
formed five categories within our review to cluster possible other
risk factors for the prognosis of CHD. As a minimum, future
studies may want to include one variable per category. As age,
SES, health risk behaviors (e.g., smoking), metabolic factors, and
cardiac functioning are highly relevant for prognosis, these risk
factors should always be considered in adjusted statistical models. These variables can conceptually be divided into confounding factors (i.e., age, SES, cardiac functioning) and mediators
(i.e., health behavior, metabolic factors), which are related to
social support and CHD equally. Assuming that in statistical
model with mediators, social support fails to reach the level of
significance as a predicting variable, the relevance of social
support might still be given because it might act via the mediator
on the development of prognosis of CHD. However, if possible
confounding factors are not considered in the analysis, some
doubt about the impact of social support on prognosis of CHD
will remain.
Clinical Implications
As low functional social support is strongly associated with
higher mortality in patients with CHD, a main clinical goal
should focus on the development of efficacious intervention
strategies to alter the perception of social support. So far, the
evidence for specific intervention strategies is limited (77).
236
There are, however, some intervention strategies in other
diseases, which are built on the idea of using other patients
with the same medical condition as a personal resource (via
Internet or in group meetings) (78,79). Until specific intervention strategies for patients with CHD are devised, a more
detailed monitoring of patients with low social support might
be an important first step in increasing the survival of patients
after a cardiac event. Such interventions could be of particular
help in increasing compliance with medication and adherence
to life-style interventions. Identification of CHD patients with
low functional social support by screening measures might be
of use, and patients with a limited social network can also be
identified. A larger clinical emphasis on the comprehensive
treatment of their medical condition seems indicated, owing to
their higher mortality risk.
We thank Irène Bächler for publication retrieval and Thomas
Munder and Karin Aebersold for helpful comments on the manuscript. We also thank Sven Trelle and Simon Wandel for helpful
feedback during all stages of this review.
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