Health investments and well-being of middle-aged and elderly populations: a panel data analysis based on China.

IF 2.5 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Di Wang, Weihua Luo, Liangru Zhou, Gan Xu, XinYang Lv
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引用次数: 0

Abstract

Background: As the global population ages and life expectancies rise, improving the health and equity of middle-aged and older individuals has become a universal goal, especially with the economic benefits of the demographic dividend decreasing. Health investments (HI), which are crucial for improving health outcomes (HO) and protecting human capital, play a key role in achieving these objectives. This study aims to examine the impact of HI on the health status of middle-aged and elderly individuals, analyze issues of health equity among this population, and enhance their overall health level while fostering economic growth.

Methods: This study, based on Grossman's health demand theory and China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020 (n = 11,138), examines middle-aged and elderly individuals (aged 45 years and above) across 28 Chinese provinces. A panel data model is used to assess HI and HO, with composite indices created using the entropy method. HI includes leisure, healthcare, and living environments, whereas HO covers self-reported short- and long-term health. A high-dimensional fixed-effects model is used to analyze the impact of HI on HO. Health equity is explored using the income Gini coefficient, health investment concentration index (I-CI), and health outcome concentration index (H-CI), with decomposition performed using the Shapley method.

Results: HI positively affects HO in middle-aged and elderly individuals in China. The key factors that influence HO are gender, age, household registration (HR), and income. Income inequality is significant, with an average Gini coefficient of 0.492. The I-CI averages 0.081, indicating higher investment concentration among wealthier groups. The major factors that influence the I-CI are household registration (34.9%), income (33.1%), employment (18.8%), and education (11.7%). The H-CI averages 0.033, with better outcomes associated with higher education. The key factors influencing H-CI are age (46.7%), gender (16.7%), income (15.2%), and education (10.7%).

Conclusion: HI significantly improves the HO and enhances the health human capital of middle-aged and elderly individuals. However, these investments tend to favor wealthier groups, whereas HO are more favorable among those with higher education. Income and education levels are the key drivers of inequity in both HI and HO.

Abstract Image

Abstract Image

健康投资与中老年人口福祉:基于中国的面板数据分析。
背景:随着全球人口老龄化和预期寿命的增加,改善中老年个体的健康和公平已成为一个普遍的目标,特别是随着人口红利的经济效益的减少。卫生投资对于改善卫生成果和保护人力资本至关重要,在实现这些目标方面发挥着关键作用。本研究旨在探讨健保对中老年人健康状况的影响,分析中老年人的健康公平问题,在促进经济增长的同时提高中老年人的整体健康水平。方法:本研究基于Grossman的健康需求理论和中国健康与退休纵向研究(CHARLS) 2011 - 2020年的数据(n = 11,138),对中国28个省份的45岁及以上的中老年个体进行了调查。使用面板数据模型评估HI和HO,并使用熵法创建复合指数。健康指数包括休闲、医疗保健和生活环境,而健康指数涵盖自我报告的短期和长期健康状况。采用高维固定效应模型分析了高生产率对高生产率的影响。采用收入基尼系数、健康投资集中度指数(I-CI)和健康结果集中度指数(H-CI)对健康公平性进行探讨,并采用Shapley方法进行分解。结果:在中国中老年人群中,HI正影响HO。影响HO的主要因素是性别、年龄、户籍、收入。收入不平等显著,平均基尼系数为0.492。I-CI平均为0.081,表明较富裕群体的投资集中度较高。影响I-CI的主要因素依次是户籍(34.9%)、收入(33.1%)、就业(18.8%)、教育(11.7%)。H-CI平均为0.033,与高等教育相关的结果更好。影响H-CI的主要因素为年龄(46.7%)、性别(16.7%)、收入(15.2%)、教育程度(10.7%)。结论:高健康指数显著提高了中老年人的健康人力资本。然而,这些投资倾向于更富有的群体,而HO在受过高等教育的人群中更受欢迎。收入和教育水平是高卫生指数和高卫生指数不平等的主要驱动因素。
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来源期刊
Population Health Metrics
Population Health Metrics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
0.00%
发文量
21
审稿时长
29 weeks
期刊介绍: Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.
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