In addition, future research will benefit from methodological considerations, including a greater focus on prospective survey designs and corresponding longitudinal analyses, dyadic information about social relationships, and qualitative data. This approach fits with the life course notion that determinants of current health originate early in life and accumulate across the life span (Ben-Shlomo and Kuh 2002). Taking full advantage of prospective surveys through longitudinal data analysis and wider application of multilevel modeling could shed more light on the social processes involved in building, sustaining, and benefiting from social ties across the life course.
Most studies on social ties and health use individual-level data, as surveys typically collect information from one member per household. However, social ties, by definition, involve more than one person. Studies that include dyads show that individuals in the same relationship often experience and report on their relationship in quite different ways (Proulx and Helms 2008). Independent reports, as well as discrepancies between reports, may be linked to health outcomes. We should take advantage of existing longitudinal data sets that include more than one focal individual. New data collection efforts should go beyond the individual to include data from a range of linked social ties. As recent work shows, including reports from several network members may reveal important relationship/health linkages that go beyond one individual (Smith and Christakis 2008).
Finally, most research on social ties and health has relied on assessment of quantitative data sources. Quantitative data are essential for identifying patterns between variables in the general population and, particularly, for revealing how social location (e.g., as defined by life course stage, race, and gender) is associated with regularity in social experiences (e.g., relationships and health). However, population-level data are limited in their ability to reveal rich social contexts that allow us to analyze the meanings, dynamics, and processes that link social ties to health over time. Thus, blending qualitative and quantitative methods provides the opportunity to build on the strengths of both methodologies and to address how structure and meaning coalesce to shape health outcomes at the population level (Pearlin 1992). Information obtained from qualitative data may also suggest flirt promo code new explanations (e.g., new psychosocial mechanisms or connections between mechanisms) for relationship/health linkages, and for group differences in those linkages, and those explanations can be further explored using population-level data.
Solid scientific evidence shows that social relationships affect a range of health outcomes, including mental health, physical health, health habits, and mortality risk. Sociologists have played a major role in establishing these linkages, in identifying explanations for the impact of social relationships on health, and in discovering social variation (e.g., by age and gender) in these linkages at the population level. The unique perspective and research methods of sociology provide a scientific platform to suggest how policy makers might improve population health by promoting and protecting Americans’ social relationships. Recent and projected demographic trends should instill a sense of urgency in developing policy solutions. Specifically, the confluence of smaller families, high divorce rates, employment-related geographical mobility, and population aging means that adults of all ages, and in particular the elderly, will be at increasing risk of social isolation and shrinking family ties in the future (Cacioppo and Hawkley 2003).
An earlier version of this article was presented at the 2009 annual meeting of the American Sociological Association. This research was supported by National Institute on Aging grant RO1AG026613 (PI: Debra Umberson), National Institute of Child Health and Human Development grant 5 R24 HD042849 (PI: Mark D. Hayward) and 2 T32 HD007081 (PI: Robert A. Hummer) awarded to the Population Research Center at the University of Texas at Austin.