RECIPROCAL RELATIONSHIP BETWEEN SOCIAL NETWORK CHARACTERISTICS AND MENTAL HEALTH AMONG OLDER ADULTS IN THE UNITED STATES: DIFFERENTIATING BETWEEN NETWORK STRUCTURE AND NETWORK FUNCTION

BONNIE BUI

POSTDOC FELLOW, TULANE UNIVERSITY

CENTER FOR STUDIES OF DISPLACED POPULATIONS

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DNAC 2019 SOCIAL NETWORKS & HEALTH WORKSHOP

INTRODUCTION TO THE PROBLEM

• Depression among older adults is often related to other physical health outcomes.
• Because of increased risk for chronic conditions and functional limitations among older adults, older adults are at increased risk for chronic depression.
• The maintenance of social relationships is an important component of older adult mental health.

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INTRODUCTION TO THE PROBLEM

• Prior research have found:
• The characteristics of a network (e.g., number of ties and how supportive those ties are) have impacts on mental health, both perceived and actual.
• Social support can buffer individuals from impacts of stressful life events (stress buffer hypothesis).
• However, poor mental health may make maintenance of an individual’s network difficult, compromising the ability to derive any potential benefits from social support.

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LITERATURE OVERVIEW

• Social Relationships and Health are linked (Berkman et al. 2000; Umberson and Montez 2010)
• Increased risk of mortality for persons with low quantity, and sometimes low quality, of social relationships (House, Landis, and Umberson 1988)
• Socially isolated individuals are less able to buffer the impact of health stressors, putting them at greater risk for negative health outcomes (House et al. 1988).
• Some of the ways social relationships can potentially influence health are through the following:
• Social support (Thoits 1995)
• Social influence (Friedkin 2001)
• Social engagement (Glass et al. 2006)
• Person-to-person contact (Laumann et al. 1989)
• Access to material resources (Granovetter 1973)

RATIONALE FOR THE CURRENT STUDY

• In the literature, there is more attention paid to how changes in social networks and support affect health.
• Less attention to how changes in baseline health impact changes in social networks.
• Findings in the literature are inconsistent, which is largely due to different ways of operationalizing networks, support, and health.
• Much of the research in the literature also sometimes conflate network structure with social support.

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RESEARCH GOALS

• The purpose of this research is to investigate whether there is reciprocal (bidirectional) associations between personal/local network characteristics and health outcomes among older adults, using various measures for different dimensions on egocentric network structure to find which specific network characteristics matter, and in what way.
• My focus is on conceptually distinguishing network structure and social support by using derived egocentric network measures from roster data to represent network structure.

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DISTINCTION BETWEEN NUMBER AND QUALITY OF TIES

• Social support is not an inherent part of social ties. Social ties can also be sources of social stress that manifests itself in worsening health (Walen and Lachman 2000).
• Social integration may be a necessary but not sufficient condition for good health. Beyond merely being socially integrated, having supportive ties is important for health.
• A distinction needs to be made between social integration and having supportive ties (Berkman and Glass 2000).

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SOCIAL NETWORKS AND SUPPORT INFLUENCES MENTAL HEALTH.

HEALTH ALSO INFLUENCES INDIVIDUALS’ ABILITY TO FORM OR MAINTAIN NETWORKS.

RESEARCH QUESTIONS

• Does local network structure affect depressive symptoms for older adults?
• Does social support affect depressive symptoms for older adults?
• Do older adults’ depressive symptoms affect local network structure?
• Do older adults’ depressive symptoms affect social support?

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DATA

• National Social Life, Health, and Aging Project (NSHAP)
• Survey uses a national area probability sample of adults born between 1920 and 1947, or ages 57 to 85 at the time of the Wave 1.
• Two waves:
• Wave 1, 3,005 interviews, 2005-2006
• Wave 2, 3,377 interviews, 2010-2011
• I only use data from adults who were re-interviewed at Wave 2 (N=2.261).

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APPROPRIATENESS OF DATA

• Dataset is appropriate because two time points allow analysis of change in 5 years, although it is unknown whether this lag is too long when looking at the impacts of networks on health.
• Includes detailed social network data

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SOCIAL NETWORK ROSTER DATA

• Social Network Roster
• Respondents were asked to identify some of the people they interact with on a regular basis and are asked the following:
• What their relationship is to each person
• Whether each person named resides in the same household
• Their gender and whether older or younger than the respondent
• Frequency of contact
• Closeness of the relationship
• Questions on instrumental support, emotional openness, and level of demands person made on respondent
• How frequently each person named talked to others who were named

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MEASURES

• Mental Health
• Depressive Symptoms (11-item CES-D scale)
• Network Structure
• Network size (sum of alters)
• Number living with ego
• Proportion female
• Number of close ties
• Density (ratio of actual ties to total possible ties)
• Frequency of contact with alters (summed across all alters)

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MEASURES

• Social Support
• Perceived social support from family and friends (4-item scale)
• Demographic and Control Variables
• Age
• Indicator for female
• Indicator for white
• Indicator for college
• Cohabitation Status

RESEARCH METHOD

• Longitudinal analyses: conditional change method(refer to Paul Allison)
• Used to examine change in dependent variables between waves.
• Examine the relationship between an independent variable and a dependent variable at time 2 while controlling for the effects of that dependent variable at time 1 by including a lagged dependent variable in the model.
• Allows us to account for baseline differences between respondents.
• OLS regression to examine whether depressive symptoms, support, and network characteristics predict changes for future depressive symptoms, support, and networks, five years from the baseline survey.

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IMPUTATION AND WEIGHTING

• Imputation:
• Multiple imputation with chained equations (MICE) to address missing data
• Weighting:
• “svyset” commands in Stata/SE 15.1 to adjust for nonresponse and correct for sampling design

SAMPLE DESCRIPTIVES

LOCAL NETWORK & SUPPORT 🡪 CES-D

• Social support positively associated with later depressive symptoms.
• Density negatively associated with later depressive symptoms.

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LOCAL NETWORK & CES-D 🡪 SUPPORT

• Close ties and frequency of tie activation are positively associated with social support.
• Depressive symptoms at time 1 negatively associated with perceived social support 5 years later.

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CES-D & SUPPORT 🡪 LOCAL NETWORK

• Support at time 1 positively associated with the network size, number of close ties, and contact with alters at time 2.

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SUMMARY OF FINDINGS

• Reciprocal associations between social support and CES-D, as expected.
• Support is protective of depression, but depression can undermine support.
• Reciprocal associations between social support and select local network measures (number of close ties and frequency of contact with alters).
• No reciprocal associations between CES-D and local network measures.
• Influence of local network on CES-D is largely indirect through social support.

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• Of local network measures, only density associated with later depressive symptoms.
• Association is positive, which is counter to the expectation that more connected networks would be protective of mental health because of higher levels of social support in denser networks.
• Possible explanation: Depressed individuals have fewer friends, resulting in smaller and denser networks.

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• Strongest link between local network and CES-D is social support.
• Future research should focus on social support as an important pathway in which personal networks can impact mental health.

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REFERENCES

• Berkman, Lisa F. and Thomas Glass. 2000. “Social Integration, Social Networks, Social Support, and Health.” In Social Epidemiology. New York: Oxford Univ. Press.
• Berkman, Lisa F., Thomas Glass, Ian Brissette, and Teresa E. Seeman. 2000. “From Social Integration to Health: Durkheim in the New Milennium.” Social Science & Medicine 51(6):843-857.
• Friedkin, Noah E. 2001. “Norm Formation in Social Influence Networks.” Social Networks 23(3):167-189.
• Glass, Thomas A., Carlos F. Mendes De Leon, Shari S. Bassuk and Lisa F. Berkman. 2006. “Social Engagement and Depressive Symptoms in Late Life: Longitudinal Findings.” Journal of Aging and Health 18(4):604-628.
• Granovetter, Mark S. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78(6):1360-1380.
• House, James S., Karl R. Landis and Debra J. Umberson. 1988. “Social Relationships and Health.” Science 241(4865):540-545.
• Laumann, Edward O., John H. Gagnon, Stuart Michaels, Robert T. Michael and James S. Coleman. 1989. “Monitoring the AIDS Epidemic in the United States: A Network Approach.” Science 244(4909):1186-1189.
• Thoits, Peggy A. 1995. “Stress, Coping, and Social Support Processes: Where Are We? What Next?” Journal of Health & Social Behavior Special(1995):53-79.
• Umberson, Debra, and Jennifer Karas Montez. 2010. “Social Relationships and Health: A Flashpoint for Health Policy.” Journal of Health and Social Behavior 51(S):S54-S66.
• Walen, Heather R. and Margie E. Lachman. 2000. “Social Support and Strain from Partner, Family, and Friends: Costs and Benefits for Men and Women in Adulthood. Journal of Social and Personal Relationships 17:5-30.

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Thank you! Any additional comments and suggestions, please send to bbui@tulane.edu