title: Social network types and the health of older adults: exploring reciprocal associations.
abstract: Social network types have been proved to have significant impacts on older population's health outcomes. However, the existing discoveries are still inconsistent, which may be attributed largely to the heterogeneous measures and methods scholars used and to the unidirectional causalities presumed in most research. This study addresses these gaps by using more-refined measures to explore whether the network types have differential impacts on older adults' health outcomes, and whether a reverse causal relationship exists between older adults' health conditions and the network types they adopted. Using data from three recent waves (2005, 2008, and 2012) of the Chinese Longitudinal Healthy Longevity Survey (n = 4190), we constructed four network types using the K-means clustering method (i.e., diverse, friend, family, and restricted), and examined their impacts on a variety of health outcomes (i.e., physical, cognitive, psychological, and overall well-being). Our results demonstrate that there are strong reciprocal associations between these two factors. On the one hand, a diverse network type yielded the most beneficial health outcomes as measured by multiple health indicators, and the friend-focused network type is more beneficial than the family-focused network type in physical outcomes but not in psychological outcomes. On the other hand, we found that a decrease in all health indicators leads to withdrawal from more-beneficial network types such as a diversified network type, and a shift to less-beneficial network types such as family-focused or restricted networks. The understanding of this reciprocal association could encourage programs designed to enhance healthy aging to focus on improving the bridging social capital of older adults so that they can break the vicious cycle between network isolation and poor health conditions.
authors: Center for Population and Development Studies, Renmin University of China, RM 1001, Academic Research Building A, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, China. Electronic address: li.ting@ruc.edu.cn.
authors: Ting
authors: T
authors: Li
authors: Guanghua School of Management, Peking University, RM 313, BLDG 2, Beijing 100871, China. Electronic address: yanlong.zhang@gsm.pku.edu.cn.
authors: Yanlong
authors: Y
authors: Zhang
mesh_terms: D000203:Activities of Daily Living; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D002681:China; D005190:Family; D005260:Female; D033062:Friends; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D007398:Interpersonal Relations; D008137:Longitudinal Studies; D008297:Male; D008603:Mental Health; D008875:Middle Aged; D012111:Residence Characteristics; D012737:Sex Factors; D012934:Social Isolation; D012944:Social Support; D012959:Socioeconomic Factors
keywords: Autoregressive cross-lagged model; China; K-means clustering; Older population's health; Social networks