Social Relationships and Mortality Risk: A Meta-analytic Review

Statistically nonredundant effect sizes were extracted from 148 studies (–; see Table 1). Data were reported from 308,849 participants, with 51% from North America, 37% from Europe, 11% from Asia, and 1% from Australia. Across all studies, the average age of participants at initial evaluation was 63.9 years, and participants were evenly represented across sex (49% female, 51% male). Of the studies examined, 60% involved community samples, but 24% examined individuals receiving outpatient medical treatment, and 16% utilized patients in inpatient medical settings. Of studies involving patients with a pre-existing diagnosis, 44% were specific to cardiovascular disease (CVD), 36% to cancer, 9% to renal disease, and the remaining 11% had a variety of conditions including neurological disease. Research reports most often (81%) considered all-cause mortality, but some restricted evaluations to mortality associated with cancer (9%), CVD (8%), or other causes (2%). Participants were followed for an average of 7.5 years (SD = 7.1, range = 3 months to 58 years), with an average of 29% of the participants dying within each study's follow-up period.

Omnibus Analysis

Across 148 studies, the random effects weighted average effect size was OR = 1.50 (95% confidence interval [CI] = 1.42 to 1.59), which indicated a 50% increased likelihood of survival as a function of stronger social relations. Odds ratios ranged from 0.77 to 6.50, with substantial heterogeneity across studies (I² = 81% [95% CI = 78% to 84%]; Q (147) = 790, p<0.001; τ² = 0.07), suggesting that systematic effect size variability was unaccounted for. Thus factors associated with the studies themselves (e.g., publication status), participant characteristics (e.g., age, health status), and the type of evaluation of social relationships (e.g., structural social networks versus perceptions of functional social support) may have moderated the overall results. We therefore conducted additional analyses to determine the extent to which these variables moderated the overall results.

To assess the possibility of publication bias, we conducted several analyses. First, we calculated the fail-safe N to be 4,274, which is the theoretical number of unpublished studies with effect sizes averaging zero (no effect) that would be needed to render negligible the omnibus results. Second, we employed the “trim and fill” methodology described by Duval and Tweedie, to estimate the number of studies missing due to publication bias, but this analysis failed to reveal any studies that would need to be created on the opposite side of the distribution, meaning that adjustment to the omnibus effect size was unnecessary. Third, we calculated both Egger's regression test and the alternative to that test recommended by Peters and colleagues that is better suited to data in lnOR format. The results of both analyses failed to reach statistical significance (p>0.05). Finally, we plotted a contour-enhanced funnel plot (Figure 2). The data obtained from this meta-analysis were fairly symmetrical with respect to their own mean; fewer than ten studies were “missing” on the left side of the distribution that would have made the plot symmetrical. Based on these several analyses, publication bias is unlikely to threaten the results.

Moderation by Social Relationship Assessment, and by Participant and Study Characteristics

Given that structural versus functional components of social relationships may influence health in different ways,, the high degree of heterogeneity observed in the omnibus results may have been due in part to differences between the components of social relationships evaluated within and across studies. Hence the remaining analyses separately evaluate effect sizes obtained from structural, functional, and combined (structural and functional) measures of social relationships. Table 2 provides definitions of the types and subtypes of social relationships evaluated.

Structural aspects of social relationships

Sixty-three studies had data exclusive to structural measures of social relationships (see Figure 3). Across these studies, the random effects weighted average effect size was OR = 1.57 (95% CI = 1.46 to 1.70), which value fell within the CI of the omnibus results reported previously. The heterogeneity across studies was still quite large (I² = 84% [95% CI = 80% to 87%]; Q (62) = 390, p<0.001; τ² = 0.07), so we undertook metaregression with prespecified participant and study characteristics.

Metaregression is an analogue to multiple regression analysis for effect sizes. Its primary purpose is to ascertain which continuous and categorical (dummy coded) variables predict variation in effect size estimates. Using random effects weighted metaregression, we examined the simultaneous association (with all variables entered into the model) between effect sizes and prespecified participant and study characteristics (Table 3). To examine the most precise effect size estimates available and to increase the statistical power associated with this analysis, we shifted the unit of analysis and extracted effect sizes within studies that were specific to measures of structural aspects of social relationships. That is, if a study contained effect sizes from both structural and functional types of social relationships, we extracted the structural types for this analysis (with identical subtypes aggregated), which resulted in a total of 230 unique effect sizes across 116 studies. A total of 18% of the variance in these effect sizes was explained in the metaregression (p<0.001). As can be seen in Table 3, effect sizes based on data controlling for other variables were lower in magnitude than those based on raw data. Moreover, effect sizes differed in magnitude across the subtype of structural social relationships measured. Complex measures of social integration were associated with larger effect size values than measures of social participation. Binary measures of whether participants lived alone (yes/no) were associated with smaller effect size values. Average random effects weighted odds ratios for the various subtypes of social relationships are reported in Table 4.

Functional aspects of social relationships

Twenty-four studies had data exclusive to functional measures of social relationships (see Figure 4). Across these studies, the random effects weighted average effect size was OR = 1.46 (95% CI = 1.28 to 1.66), which value fell within the CI of the omnibus results reported previously. There was moderate heterogeneity across studies (I² = 47% [95% CI = 16% to 68%]; Q (23) = 44, p<0.01; τ² = 0.04), so we conducted a random effects metaregression using the same variables and analytic procedures described previously. We extracted 87 unique effect sizes that were specific to measures of functional social relationships within 72 studies. A total of 16.5% of the variance in these effect sizes was explained in the metaregression, but the model did not reach statistical significance (p = 0.46). The results were not moderated by any of the specified participant characteristics (age, sex, initial health status, cause of mortality) or study characteristics (length of follow-up, geographic region, statistical controls).

Combined assessments of social relationships

Sixty-one studies had combined data of both structural and functional measures of social relationships (see Figure 5). Across these studies, the random effects weighted average effect size was OR = 1.44 (95% CI = 1.32 to 1.58). A large degree of heterogeneity characterized studies (I² = 82% [95% CI = 78% to 86%]; Q (60) = 337, p<0.001; τ² = 0.09), and we conducted a random effects metaregression using the same variables and analytic procedures described previously. We extracted 64 unique effect sizes that evaluated combined structural and functional measures of social relationships within 61 studies. The metaregression explained only 6.8% of the variance in these effect sizes, and the model failed to reach statistical significance (p = 0.95). None of the variables in the metaregression moderated the results.