A measurement study of the environmental quality and medical expenditures of elderly individuals: causal inference based on machine learning.

IF 3.2 3区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Yu Zhang, Sheng Chen, Dewen Liu
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引用次数: 0

Abstract

Background: The global surge of environmental pollution exacerbates health issues, disease incidence, and economic strain. In China, the increasing healthcare costs of the elderly population necessitate addressing this challenge as part of the "Healthy China" strategy. We explore the impact of environmental quality on elderly healthcare expenses.

Methods: This study devised a comprehensive environmental quality index for 30 Chinese provinces, excluding Tibet, which was correlated with medical expenses for individuals older than 60 years, using China Family Panel Studies (CFPS) data. Because the traditional econometric model cannot solve the endogeneity problem and the selection of instrumental variables is subjective, a new machine learning algorithm is adopted based on the traditional ordinary least squares (OLS) model and the fixed effect model to conduct causal analysis to ensure the reliability of the results. Finally, heterogeneity analysis was conducted based on the generalized random forest algorithm.

Results: Southern provinces such as Jiangxi and Guangxi exhibited superior environmental qualities. A regional analysis revealed a gradient where environmental quality decreased from west to east and from south to north. Both conventional and machine learning methodologies underscored a pivotal finding: enhanced environmental qualities significantly curtail elderly healthcare expenses. A heterogeneity assessment revealed that such improvements predominantly benefit elderly people in the eastern and central regions, with marginal impacts in the west. For different groups, the improvement of environmental quality can significantly reduce the medical expenditure of people aged 60 to 75, with bedtime hours between 9 and 11 PM and a lower household income.

Conclusions: This study, employing machine learning and traditional models, demonstrates that enhancements in environmental quality significantly reduce medical costs for the elderly in China, especially in the eastern and central regions, and among demographics such as individuals aged 60-75 and low-income households. These findings underscore the potential of environmental policies to lower medical costs within the "Healthy China" initiative framework. However, the study's scope is limited by the environmental quality index and the extent of data coverage, indicating a need for further research expansion.

环境质量与老年人医疗支出的测量研究:基于机器学习的因果推断。
背景:全球环境污染激增,加剧了健康问题、疾病发病率和经济压力。在中国,老年人口的医疗费用不断增加,因此有必要将应对这一挑战作为 "健康中国 "战略的一部分。我们探讨了环境质量对老年人医疗费用的影响:本研究利用中国家庭面板研究(CFPS)数据,为除西藏以外的中国 30 个省份设计了综合环境质量指数,并将其与 60 岁以上老年人的医疗费用相关联。由于传统计量经济学模型无法解决内生性问题,且工具变量的选择具有主观性,因此在传统普通最小二乘法(OLS)模型和固定效应模型的基础上,采用了一种新的机器学习算法进行因果分析,以确保结果的可靠性。最后,基于广义随机森林算法进行异质性分析:江西和广西等南方省份的环境质量较好。区域分析显示,环境质量从西到东,从南到北呈梯度下降。传统方法和机器学习方法都强调了一个重要发现:环境质量的提高显著降低了老年人的医疗费用。异质性评估显示,环境质量的改善主要惠及东部和中部地区的老年人,对西部地区的影响微乎其微。对不同群体而言,环境质量的改善可显著降低 60 至 75 岁、就寝时间在晚上 9 点至 11 点之间且家庭收入较低的人群的医疗支出:本研究利用机器学习和传统模型证明,环境质量的改善能显著降低中国老年人的医疗费用,尤其是东部和中部地区的老年人,以及 60-75 岁人群和低收入家庭的医疗费用。这些发现强调了环境政策在 "健康中国 "倡议框架内降低医疗费用的潜力。然而,研究范围受到环境质量指标和数据覆盖范围的限制,表明需要进一步扩大研究范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Public Health
Archives of Public Health Medicine-Public Health, Environmental and Occupational Health
CiteScore
4.80
自引率
3.00%
发文量
244
审稿时长
16 weeks
期刊介绍: rchives of Public Health is a broad scope public health journal, dedicated to publishing all sound science in the field of public health. The journal aims to better the understanding of the health of populations. The journal contributes to public health knowledge, enhances the interaction between research, policy and practice and stimulates public health monitoring and indicator development. The journal considers submissions on health outcomes and their determinants, with clear statements about the public health and policy implications. Archives of Public Health welcomes methodological papers (e.g., on study design and bias), papers on health services research, health economics, community interventions, and epidemiological studies dealing with international comparisons, the determinants of inequality in health, and the environmental, behavioural, social, demographic and occupational correlates of health and diseases.
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