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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, 229 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) 230 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 Psychosomatic Medicine 72:229 –238 (2010) 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. Psychosomatic Medicine 72:229 –238 (2010) 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 Psychosomatic Medicine 72:229 –238 (2010) 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 Psychosomatic Medicine 72:229 –238 (2010) 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. 233 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. REFERENCES 1. Rozanski A, Blumenthal JA, Davidson KW, Saab PG, Kubzansky L. The epidemiology, pathophysiology, and management of psychosocial risk factors in cardiac practice. The emerging field of behavioral cardiology. J Am Coll Cardiol 2005;45:637–51. 2. Rozanski A, Blumenthal JA, Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation 1999;99:2192–217. 3. Hemingway H, Marmot M. Evidence based cardiology: psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ 1999;318:1460 –7. 4. Kuper H, Marmot M, Hemingway H. Systematic review of prospective cohort studies of psychosocial factors in the etiology and prognosis of coronary heart disease. Semin Vasc Med 2002;2:267–314. 5. Lett HS, Blumenthal JA, Babyak MA, Strauman TJ, Robins C, Sherwood A. Social support and coronary heart disease: epidemiologic evidence and implications for treatment. Psychosom Med 2005;67:869 –78. 6. Cobb S. Social support as a moderator of life stress. Psychosom Med 1976;38:300 –13. 7. Seeman TE, Berkman LF. Structural characteristics of social networks and their relationship with social support in the elderly: who provides support? Soc Sci Med 1988;26:737– 49. 8. Cohen S, Wills TA: Stress, social support, and the buffering hypothesis. Psychol Bull 1985;98:310 –57. 9. Väänänen A, Kouvonen A, Kivimäki M, Pentti J, Vahtera J. Social support, network heterogeneity, and smoking behavior in women: the 10-town study. Am J Health Promot 2008;22:246 –55. 10. Jönsson D, Rosengren A, Dotevall A, Lappas G, Wilhelmsen L. Job control, job demands and social support at work in relation to cardiovascular risk factors in MONICA 1995, Göteborg. J Cardiovasc Risk 1999; 6:379 – 85. 11. Doherty WJ, Schrott HG, L M, Iasiello-Vailas L. Effect of spouse support and health beliefs on medication adherence. J Fam Pract 1983;17: 387– 441. 12. Knox SS, Uvnas-Moberg K. Social isolation and cardiovascular disease: an atherosclerotic pathway? Psychoneuroendocrinology 1998;23:877–90. 13. Reblin M, Uchino B. Social and emotional support and its implication for health. Curr Opin Psychiatry 2008;21:201–5. 14. Uchino B, Cacioppo J, Kiecolt-Glaser J. The relationship between social support and physiological processes: a review with emphasis on underlying mechanisms and implications for health. Psychol Bull 1996;119: 488 –531. 15. Wirtz PH, Redwine LS, Ehlert U, von Känel R. Independent association between lower level of social support and higher coagulation activity before and after acute psychosocial stress. Psychosom Med 2009;71: 30 –7. Psychosomatic Medicine 72:229 –238 (2010) SOCIAL SUPPORT AND CORONARY HEART DISEASE 16. George LK, Blazer DG, Hughes DC, N F. Social support and the outcome of major depression. Br J Psychiatry 1989;154:478 – 85. 17. Oxman TE, Berkman LF, Kasl S, Freeman DHJ, Barrett J. Social support and depressive symptoms in the elderly. Am J Epidemiol 1992;135. 18. Lett HS, Blumenthal JA, Babyak MA, Sherwood A, Strauman TJ, Robins C. Depression as a risk factor for coronary artery disease: evidence, mechanisms, and treatment. Psychosom Med 2004;66:305–15. 19. Frasure-Smith N, Lespérance F, Gravel G, Masson A, Juneau M, Talajic M, Bourassa M. Social support, depression, and mortality during the first year after myocardial infarction. Circulation 2000;101:1919 –24. 20. Lara ME, Leader J, Klein DN. The association between social support and course of depression: Is it confounded with personality? J Abnorm Psychol 1997;135:478 – 82. 21. Raynor DA, Pogue-Geile MF, Kamarck TW, McCaffery JM, Manuck SB. Covariation of psychosocial characteristics associated with cardiovascular disease: genetic and environmental influences. Psychosom Med 2002;64:191–203. 22. Medalie JH, Goldbourt U. Angina pectoris among 10,000 men. II. Psychosocial and other risk factors as evidenced by a multivariate analysis of a five year incidence study. Am J Med 1976;60:910 –21. 23. Angerer P, Siebert U, Kothny W, Muhlbauer D, Mudra H, von Schacky C. Impact of social support, cynical hostility and anger expression on progression of coronary atherosclerosis. J Am Coll Cardiol 2000;36: 1781– 8. 24. Wang HX, Mittleman MA, Leineweber C, Orth-Gomer K. Depressive symptoms, social isolation, and progression of coronary artery atherosclerosis: the Stockholm female coronary angiography study. Psychother Psychosom 2006;75:96 –102. 25. Orth-Gomer K, Wamala SP, Horsten M, Schenck-Gustafsson K, Schneiderman N, Mittleman MA. Marital stress worsens prognosis in women with coronary heart disease. JAMA 2000;284:3008 –13. 26. Coyne JC, Rohrbaugh MJ, Shoham V, Sonnega JS, Nicklas JM, Cranford JA. Prognostic importance of marital quality for survival of congestive heart failure. Am J Cardiol 2001;323. 27. Murberg TA, Bru E. Social relationships and mortality in patients with congestive heart failure. J Psychosom Res 2001;51:521–7. 28. Oxman TE, Freeman DHJ, Manheimer E. Lack of socialparticipation or religious strength and comfort as risk factors for death after cardiac surgery in the elderly. Psychosom Med 1995;57:5–15. 29. Sheps D, Creed F, Clouse R. Chest pain in patients with cardiac and noncardiac disease. Psychosom Med 2004;66:861–7. 30. Chandra V, Szklo M, Goldberg R, Tonascia J. The impact of marital status on survival after an acute myocardial infarction: a population based study. Am J Epidemiol 1983;117:320 –5. 31. Ruberman W, Weinblatt E, Goldberg J, Chaudhary B. Psychosocial influences on mortality after myocardial infarction. N Engl J Med 1984; 311:552–9. 32. Ahern DK, Gorkin L, Anderson JL, Tierney C, Hallstrom A, Ewart C, Capone RJ, Schron E, Kornfeld D, Herd JA, Richardson DW, Follick MJ. Biobehavioral variables and mortality or cardiac arrest in the Cardiac Arrhythmia Pilot Study (CAPS). Am J Cardiol 1990;66:59 – 62. 33. Follick MJ, Ahern DK, Gorkin L, Niaura RS, Herd JA, Ewart C, Schron E, Kornfeld D, Capone RJ. Relation of psychosocial and stress reactivity variables to ventricular arrhythmias in the Cardiac Arrhythmia Pilot Study (CAPS). Am J Cardiol 1990;66:63–7. 34. Mendes de Leon C. Risk of mortality and coronary heart disease by marital status in middle-aged men in the Netherlands. Int J Epidemiol 1992;21:460 – 6. 35. Denollet J, Sys SU, Stroobant N, Rombouts H, Gillebert TC, Brutsaert DL. Personality as independent predictor of long-term mortality in patients with coronary heart disease. Lancet 1996;347:417–21. 36. Herlitz J. The feeling of loneliness prior to coronary artery bypass grafting might be a predictor of short- and long-term postoperative mortality. Eur J Vasc Endovasc Surg 1998;16:120 –5. 37. Frasure-Smith N, Lespérence F, Juneau M, Talajic M, Bourassa MG. Gender, depression, and one-year prognosis after myocardial infarction. Psychosom Med 1999;61:26 –37. 38. Wiklund I, Oden A, Sanne H, Ulvenstam G, Wilhelmson C, Wilhelmsen L. Prognostic importance of somatic and psychosocial variables after a first myocardial infarction. Am J Epidemiol 1988;128:786 –795. 39. Sloan RP, Bigger JT Jr. Biobehavioral factors in Cardiac Arrhythmia Pilot Study (Caps). Review and examination. Circulation 1991;83(4 Suppl):II52–7. 40. Berkman LF, Leosummers L, Horwitz RI. Emotional support and surPsychosomatic Medicine 72:229 –238 (2010) 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. vival after myocardial-infarction—a prospective, population-based study of the elderly. Ann Intern Med 1992;117:1003–9. Williams RB, Barefoot JC, Califf RM, Haney TL, Saunders WB, Pryor DB, Hlatky MA, Siegler IC, Mark DB. Prognostic importance of social and economic resources among medically treated patients with angiographically documented coronary artery disease. JAMA 1992;267: 520 – 4. Case RB, Moss AJ, Case N, McDermott M, Eberly S. Living alone after myocardial infarction. Impact on prognosis. JAMA 1992;267:515–9. Gorkin L, Schron EB, Brooks MM, Wiklund I, Kellen J, Verter J, Schoenberger JA, Pawitan Y, Morris M, Shumaker S. Psychosocial predictors of mortality in the Cardiac Arrhythmia Suppression Trial-1 (CAST-1). Am J Cardiol 1993;71:263–7. Greenwood D, Packham C, Muir K, Madeley R. How do economic status and social support influence survival after initial recovery from acute myocardial infarction? Soc Sci Med 1995;40:639 – 47. Farmer IP, Meyer PS, Ramsey DJ, Goff DC, Wear ML, Labarthe DR, Nichaman MZ. Higher levels of social support predict greater survival following acute myocardial infarction: the Corpus Christi Heart Project. Behav Med 1996;22:59 – 66. Kawachi I, Colditz GA, Ascherio A, Rimm EB, Giovannucci E, Stampfer MJ, Willett WC. A prospective study of social networks in relation to total mortality and cardiovascular disease in men in the USA. J Epidemiol Community Health 1996;50:245–51. Rankin-Esquer LA, Miller NH, Myers D, Taylor CB. Marital status and outcome in patients with coronary heart disease. J Clin Psychol Med Settings 1997;4:417–35. Woloshin S, Schwartz LM, Tosteson ANA, Chang CH, Wright B, Plohman J, Fisher ES. Perceived adequacy of tangible social support and health outcomes in patients with coronary artery disease. J Gen Intern Med 1997;12:613– 8. Irvine J, Basinski A, Baker B, Jandciu S, Paquette M, Cairns J, Connolly S, Roberts R, Gent M, Dorian P. Depression and risk of sudden cardiac death after acute myocardial infarction: Testing for the confounding effects of fatigue. Psychosom Med 1999;61:729 –37. Piros S, Karlehagen S, Lappas G, Wilhelmsen L. Risk factors for myocardial infarction among Swedish railway engine drivers during 10 years follow-up. J Cardiovasc Risk 2000;7:395– 400. Welin C, Lappas G, Wilhelmsen L. Independent importance of psychosocial factors for prognosis after myocardial infarction. J Intern Med 2000;247:629 –39. Frasure-Smith N, Lesperance F, Gravel G, Masson A, Juneau M, Talajic M, Bourassa MG. Social support, depression, and mortality during the first year after myocardial infarction. Circulation 2000;101:1919 –24. Brummett BH, Barefoot JC, Siegler IC, Clapp Channing NE, Lytle BL, Bosworth HB, Williams RB, Mark DB. Characteristics of socially isolated patients with coronary artery disease who are at elevated risk for mortality. Psychosom Med 2001;63:267–72. O’Shea JC, Wilcox RG, Skene AM, Stebbins AL, Granger CB, Armstrong PW, Bode C, Ardissino D, Emanuelsson H, Aylward PHE, White HD, Sadowski Z, Topol EJ, Califf RM, Ohman EM. Comparison of outcomes of patients with myocardial infarction when living alone versus those not living alone. Am J Cardiol 2002;90:1374 –7. Eng PM, Rimm EB, Fitzmaurice G, Kawachi I. Social ties and change in social ties in relation to subsequent total and cause-specific mortality and coronary heart disease incidence in men. Am J Epidemiol 2002;155: 700 –9. Frasure-Smith N, Lesperance F. Depression and other psychological risks following myocardial infarction. Arch Gen Psychiatry 2003;60:627–36. Burg MM, Barefoot J, Berkman L, Catellier DJ, Czajkowski S, Saab P, Huber M, DeLillo V, Mitchell P, Skala J, Taylor CB. Low perceived social support and post-myocardial infarction prognosis in the enhancing recovery in coronary heart disease clinical trial: the effects of treatment. Psychosom Med 2005;67:879 – 88. Brummett BH, Mark DB, Siegler IC, Williams RB, Babyak MA, Clapp Channing NE, Barefoot JC. Perceived social support as a predictor of mortality in coronary patients: effects of smoking, sedentary behavior, and depressive symptoms. Psychosom Med 2005;67:40 –5. Andre-Petersson L, Hedblad B, Janzon L, Ostergren PO. Social support and behavior in a stressful situation in relation to myocardial infarction and mortality: who is at risk? Results from prospective cohort study “men born in 1914,” Malmo, Sweden. Int J Behav Med 2006;13:340 –7. Jaffe AS, Krumholz HM, Catellier DJ, Freedland KE, Bittner V, Blumenthal JA, Calvin JE, Norman J, Sequeira R, O’Connor C, Rich MW, 237 J. BARTH et al. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 238 Sheps D, Wu C. Prediction of medical morbidity and mortality after acute myocardial infarction in patients at increased psychosocial risk in the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) study. Am Heart J 2006;152:126 –35. Lett HS. Dimensions of social support in the prognosis of patients recovering from acute myocardial infarction. Dissertation Abstracts DA 2005;3182023. Andre-Petersson L, Engstrom G, Hedblad B, Janzon L, Rosvall M. Social support at work and the risk of myocardial infarction and stroke in women and men. Soc Sci Med 2007;64:830 – 41. Reed D, McGee D, Yano K, Feinleib M. Social networks and coronary heart disease among Japanese men in Hawaii. Am J Epidemiol 1983;117: 384 –96. Johnson JV. The impact of workplace social support, job demands and work control upon cardiovascular disease in Sweden. Dissertation Abstracts International 1986;47:591. Badura B, Schott T. Zur Bedeutung psychosozialer Faktoren bei der Bewaeltigung einer chronischen Krankheit. The significance of psychosocial factors for coping with chronic illness. Zeitschrift fuer Gerontopsychologie und psychiatrie 1989;2:149 –54. Frasure Smith N, Lesperance F, Talajic M. The impact of negative emotions on prognosis following myocardial infarction: is it more than depression? Health Psychol 1995;14:388 –98. Zhang J, Yu K. What’s the relative risk? A method of correcting the odds ratios in cohort studies of common outcomes. JAMA 1998;280:1690 –1. Harris R, Bradburn M, Deeks J, Harbord R, Altman D, Steichen T, Sterne J: METAN: Stata module for fixed and random effects meta-analysis. Boston: Boston College Department of Economics; 2009. Higgins J, Thompson S, Deeks J, Altman D. Measuring inconsistency in meta-analyses. BMJ 2003;327:557– 60. Lett H, Blumenthal J, Babyak M, Catellier D, Carney R, LF B, Burg M, 71. 72. 73. 74. 75. 76. 77. 78. 79. Mitchell P, Jaffe A, Schneiderman N. Social support and prognosis in patients at increased psychosocial risk recovering from myocardial infarction. Health Psychol 2007;26:418 –27. Cohen S. Psychosocial models of the role of social support in the etiology of physical disease. Health Psychol 1988;7:269 –97. Orth-Gomer K, Rosengren A, Wilhelmsen L. Lack of social support and incidence of coronary heart disease in middle-aged Swedish men. Psychosom Med 1993;55:37– 43. Barth J, Schumacher M, Herrmann-Lingen C. Depression as a risk factor for mortality in patients with coronary heart disease: a meta-analysis. Psychosom Med 2004;66:802–13. Wulsin LR, Bonita S. Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychosom Med 2003;65:201–10. Reifman A. Social relationships, recovery from illness, and survival: a literature review. Ann Behav Med 1995;17:124 –31. Mant J, Wilson S, Parry J, Bridge P, Wilson R, Murdoch W, Quirke T, Davies M, Gammage M, Harrison R, Warfield A. Clinicians didn’t reliably distinguish between different causes of cardiac death using case histories. J Clin Epidemiol 2006;59:862–7. Hogan B, Linden W, Najarian B. Social support interventions: do they work? Clin Psychol Rev 2002;22:381– 440. Barrera M, Glasgow RE, McKay HG, Boles SM, Feil EG. Do internetbased support interventions change perceptions of social support? An experimental trial of approaches for supporting diabetes selfmanagement. Am J Community Psychol 2002;30:637–54. van Dam HA, van der Horst FG, Knoops L, Ryckman RM, Crebolder HFJM, van den Borne BHW. Social support in diabetes: a systematic review of controlled intervention studies. Patient Educ Couns 2005;59: 1–12. Psychosomatic Medicine 72:229 –238 (2010)