摘要
[目的]了解我国老年人异地养老意愿,分析其影响因素,为完善异地养老服务体系,优化养老资源配置提供参考。[方法]基于CHARLS数据,以安德森卫生服务利用行为模型为框架,采用逐步回归法,对7535名老年人异地养老意愿及影响因素进行实证研究。[结果]7535名老人中,有异地养老意愿的995人(13.2%)。单因素分析显示,不同教育程度、居住地、居住模式、生活来源、本地医疗服务满意度的老人异地养老意愿差异有统计学意义(P<0.05);Logistic回归分析显示,教育水平、居住地、居住模式、本地医疗服务满意度均为老年人异地养老意愿的影响因素(P<0.05)。[结论]老年人异地养老意愿较低。应充分考虑老年人异地养老意愿的影响因素,细分老年人群,引导多样化异地养老形式;确保医疗资源供给,简化异地就医手续;完善保障政策,提高异地养老老人经济收入,从而提高老年人异地养老意愿。
Objective To know pension willingness in other place of the elderly and analyze its influence factors so as to provide reference for perfecting service system of pension in other place and optimizing pension resource allocation.Methods Based on CHARLS data,it adopted Anderson’s health service utilization behavioral model as the frame,used the method of stepwise logistic regression to empirically analyze pension willingness in other place and its influence factors in 7535 elders.Results Among 7535 elderly people,995(13.2%)had pension willingness in other place.Single factor analysis result showed that there was statistically significant in the difference of pension willingness in other place whom with different education level,place of residence,residential pattern and local medical service satisfaction(P<0.05).Conclusions The pension willingness in other place of the elderly is low.We should take full consideration to the influence factors of the pension willingness in other place.Subdivide elderly group,guide the diversified forms of old-age care.Insure the supply of medical resources,simplify the procedures of medical treatment in different places,improve the protection policy and the economic income of the elderly.Thus,improve pension willingness in other place of the elderly.
作者
连慧莹
曹阳
LIAN Hui-ying;CAO Yang(School of International Pharmaceutical Business,China Pharmaceutical University,Nanjing Jiangsu 211198,China)
出处
《卫生软科学》
2020年第3期38-44,53,共8页
Soft Science of Health
基金
江苏省研究生科研与实践创新计划项目:长三角地区异地养老发展路径研究(KYCX18_0834)
中国药科大学“双一流”学科创新团队建设项目(CPU2018GY39)
关键词
老年人
异地养老
安德森行为模型
影响因素
elderly
pension in other place
Andersen’s behavioral model
influencing factor