Social Networks, Social Cohesion, and Later-Life Health
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Our study contributes to the literature acknowledging the joint role of social networks and social cohesion in shaping individual’s health, focusing on the older population aged 50 and over. Exploiting rich ego-centered social network data from the Survey of Health, Ageing and Retirement in Europe and following the conceptual model of social integration and health proposed by Berkman et al. (Soc Sci Med 51:843–857. doi: 10.1016/S0277-9536(00)00065-4, 2000), we estimate multilevel models of self-reported and observer-measured later-life health outcomes. These models simultaneously account for (a) characteristics of 39,551 respondents’ personal social networks and (b) a measure of social cohesion—namely, participation in social organizations—across 57 Continental European regions, clustered in 14 countries. We find significant associations between individuals’ health and various social network characteristics (size, support, quality) as well as social cohesion. Moreover, cross-level interaction effects suggest that the social-network-health nexus is contextually bound. We conclude with a discussion of limitations and perspectives for future research.
KeywordsSocial networks Social cohesion Health Multilevel analysis SHARE
We are grateful for comments by three anonymous reviewers. This paper uses data from SHARE Wave 4 release 1.1.1, as of March 28th 2013 (DOI: 10.6103/SHARE.w4.111). The SHARE data collection has been primarily funded by the European Commission through the 5th Framework Programme (Project QLK6-CT-2001-00360 in the thematic programme Quality of Life), through the 6th Framework Programme (Projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th Framework Programme (SHARE-PREP, No. 211909, SHARE-LEAP, No. 227822 and SHARE M4, No. 261982). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and the German Ministry of Education and Research as well as from various national sources is gratefully acknowledged (see www.share-project.org for a full list of funding institutions).
- Aida, J., Kondo, K., Kawachi, I., Subramanian, S. V., Ichida, Y., Hirai, H., et al. (2013). Does social capital affect the incidence of functional disability in older Japanese? A prospective population-based cohort study. Journal of Epidemiology and Community Health, 67, 42–47. doi: 10.1136/jech-2011-200307.CrossRefGoogle Scholar
- Deindl, C., Hank, K., & Brandt, M. (2013). Social networks and self-rated health in later life. In A. Börsch-Supan, M. Brandt, H. Litwin, & G. Weber (Eds.), Active ageing and solidarity between generations in Europe (pp. 301–309). Berlin, Boston: De Gruyter.Google Scholar
- Elia, L., d’Hombres, B., Weber, A., & Saltelli, A. (2013). Income inequality and social outcomes: Bivariate correlations at NUTS1 level. Luxembourg: European Union.Google Scholar
- Islam, M. K., Merlo, J., Kawachi, I., Lindström, M., Burström, K., & Gerdtham, U.-G. (2006). Does it really matter where you live? A panel data multilevel analysis of swedish municipality-level social capital on individual health-related quality of life. Health Economics, Policy and Law, 1, 209–235. doi: 10.1017/S174413310600301X.CrossRefGoogle Scholar
- Kawachi, I., & Berkman, L. (2000). Social cohesion, social capital, and health. In L. Berkman & I. Kawachi (Eds.), Social epidemiology (pp. 174–190). New York: Oxford University Press.Google Scholar
- Kline, R. B. (2011). Principles and practice of structural equation modeling. New York, London: The Guilford Press.Google Scholar
- Litwin, H., Stoeckel, K., Roll, A., Shiovitz-Ezra, S., & Kotte, M. (2013). Social network measurement in SHARE wave four. In F. Malter & A. Börsch-Supan (Eds.), SHARE Wave 4, innovations and methodology (pp. 18–38). Munich: MEA—Max-Planck-Institute for Social Law and Social Policy.Google Scholar
- Meuleman, B., & Billiet, J. (2009). A Monte Carlo sample size study: How many countries are needed for accurate multilevel SEM? Survey Research Methods, 3, 45–58.Google Scholar
- Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata. College Station, TX: Stata Press.Google Scholar
- Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.Google Scholar
- Stephens, C., Alpass, F., Towers, A., & Stevenson, B. (2011). The effects of types of social networks, perceived social support, and loneliness on the health of older people: Accounting for the social context. Journal of Aging and Health, 23, 887–911. doi: 10.1177/0898264311400189.CrossRefGoogle Scholar