Abstract: Effects of Personal, Social, and Environmental Characteristics on Negative Health Outcomes Among Older Adults in China (Society for Social Work and Research 23rd Annual Conference - Ending Gender Based, Family and Community Violence)

Schedule:

Saturday, January 19, 2019: 9:00 AM

Golden Gate 5, Lobby Level (Hilton San Francisco)

* noted as presenting author

Andrew Scharlach, PhD, Eugene and Rose Kleiner Professor of Aging, University of California, Berkeley

Xue Bai, Assistant Professor, The Hong Kong Polytechnic University

Marla Stuart, PhD, Fellow, Guizhou Berkeley Big Data Innovation Research Center (GBIC) Moore/Sloan Data Science Fellow, Berkeley Institute for Data Science (BIDS), University of California, Berkeley, CA

Cheng Ren, MSSA, Graduate Student Researcher, University of California, Berkeley, CA

Yingyang Zhang, BA, Graduate Student Researcher, Huazhong University of Science and Technology

Background and Purpose: Chronic health conditions account for 86% of all deaths and more than 70% of the total burden of disease among older adults in China. Chronic health conditions are affected by personal characteristics such as age, gender, education level, household income, occupation, and ethnic group; social resources such as marital status, location of adult children, and government assistance; and environmental conditions such as household composition and service availability. In response to the China State Council’s call for improvements in health and social services for older adults, this study aims to (1) create profiles of older adults with shared personal, social, and environmental characteristics, and (2) assess the extent to which these profiles are associated with negative health outcomes.

Methods: To develop a whole-person profile of older adults, this study merged data from three government Bureaus: Health, Civil Affairs, and Human Resources. The study sample was comprised of 67,556 residents ages 60 and older from a southern district in China. The analyses included geo-coding and unsupervised machine learning to identify groups of older adults that appear to have greater vulnerability for negative health outcomes based on personal, social, and environmental characteristics.

Results: This study found that older adults in the district tend to be divided roughly into three clusters of vulnerability, based on the type of work they did, where they live, their level of education, and their ethnicity. One group is more likely to have worked in professional positions, live in urban settings, be more highly educated, and be of Han ethnicity. This group appears to have low levels of vulnerability for negative health outcomes. A second group tends to be composed of individuals who have worked in agricultural jobs, live in rural settings, have little or no formal education, and are ethnic minorities. A third group is primarily widows over the age of 80 with mixed experiences related to occupation, household location, education, and ethnicity. The second and third groups appear to have higher levels of vulnerability for negative health outcomes. Moreover, these three groups are differentially located, suggesting a spatial dimension to vulnerability.

Conclusions and Implications: A primary goal of this study was to test the feasibility of merging data from different government Bureaus and to use this merged data to develop practice and policy recommendations. This was highly successful in that the approached Bureaus all generously shared their data and, due to the commonly used national identification number, merging data across Bureaus was easy and accurate. However, it was less successful due to missing information in key domains. Nevertheless, the findings can serve as the basis for policy recommendations regarding what services are appropriate, where they should be located, and who should deliver them. Specifically, the local government may consider ensuring that health and social services are integrated, include a focus on prevention, and are scaled to the level of need.