Empirical analysis of health-related behaviors among older Hakka adults: a latent class analysis

Author:

Cai Longhua,Zhang Lingling,Liu Xiaojun

Abstract

BackgroundLittle is known about health-related behaviors of the older Hakka population in China. We aimed to explore the characteristics and correlates of health-related behaviors among older Hakka adults.MethodsWe used data from the China’s Health-Related Quality of Life Survey for Older Adults 2018. Latent class analysis (LCA) defined latent classes of health-related behaviors for 1,262 older Hakka adults aged 60 and above. Generalized linear regression and multinomial logistic regression analysis were used to identify factors influencing the number and the latent classes of health-related behaviors, respectively.ResultsThe LCA showed that the latent classes could be stratified as the risk group (14.82%), healthy group (55.71%), and inactive group (29.48%). Sex, age, years of education, current residence, living arrangement, average annual household income, and currently employed were associated with the number of healthy behaviors. Compared with the participants in the healthy group, widowed/others (OR = 5.85, 95% CI = 3.27, 10.48), had 15,001–30,000 (OR = 2.05, 95% CI = 1.21, 3.47) and 60,001 or higher (OR = 3.78, 95% CI = 1.26, 11.36) average annual household income, and currently employed (OR = 3.40, 95% CI = 1.99, 5.81) were highly associated with risk group. Additionally, the participants who are widowed/others (OR = 4.30, 95% CI = 2.70, 6.85) and currently employed (OR = 1.95, 95% CI = 1.27, 2.98) were highly associated with the inactive group.ConclusionThis study identified factors specifically associated with older Hakka adults’ health-related behaviors from an LCA perspective. The findings indicate that policymakers should give more attention to older adults living alone and implement practical interventions to promote health-related behaviors among them.

Funder

National Natural Science Foundation of China

Publisher

Frontiers Media SA

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3