Research on aging has consistently demonstrated an increased chance of survival for older adults who are integrated into rich networks of social relationships. Theoretical explanations state that personal networks offer indirect psychosocial and direct physiological pathways. We investigate whether effects on and pathways to mortality risk differ between functional and structural characteristics of the personal network. The objective is to inquire which personal network characteristics are the best predictors of mortality risk after adjustment for mental, cognitive and physical health.
Methods and Findings
Empirical tests were carried out by combining official register information on mortality with data from the Longitudinal Aging Study Amsterdam (LASA). The sample included 2,911 Dutch respondents aged 54 to 85 at baseline in 1992 and six follow-ups covering a time span of twenty years. Four functional characteristics (emotional and social loneliness, emotional and instrumental support) and four structural characteristics (living arrangement, contact frequency, number of contacts, number of social roles) of the personal network as well as mental, cognitive and physical health were assessed at all LASA follow-ups. Statistical analyses comprised of Cox proportional hazard regression models. Findings suggest differential effects of personal network characteristics on survival, with only small gender differences. Mortality risk was initially reduced by functional characteristics, but disappeared after full adjustment for the various health variables. Mortality risk was lowest for older adults embedded in large (HR = 0.986, 95% CI 0.979-0.994) and diverse networks (HR = 0.948, 95% CI 0.917-0.981), and this effect continued to show in the fully adjusted models.
Functional characteristics (i.e. emotional and social loneliness) are indirectly associated with a reduction in mortality risk, while structural characteristics (i.e. number of contacts and number of social roles) have direct protective effects. More research is needed to understand the causal mechanisms underlying these relations.