Zhang Yu, Chen Sheng, Liu Dewen
School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, 569 Yuelu Ave., Yuelu District, Changsha, 410205, China.
School of Science, Hunan University of Technology and Business, 569 Yuelu Ave., Yuelu District, Changsha, 410205, China.
Arch Public Health. 2024 Oct 30;82(1):195. doi: 10.1186/s13690-024-01386-2.
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.
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.
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.
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.
全球环境污染的激增加剧了健康问题、疾病发病率和经济压力。在中国，老年人口不断增加的医疗保健成本使得应对这一挑战成为“健康中国”战略的一部分。我们探讨环境质量对老年人医疗费用的影响。
本研究利用中国家庭追踪调查（CFPS）数据，为中国除西藏外的30个省份设计了一个综合环境质量指数，并将其与60岁以上人群的医疗费用相关联。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环境质量较好。区域分析显示出一种梯度，即环境质量从西向东、从南向北下降。传统方法和机器学习方法均突出了一个关键发现：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，这种改善主要惠及东部和中部地区的老年人，对西部地区影响较小。对于不同群体，环境质量的改善可显著降低60至75岁、晚上9点至11点睡觉且家庭收入较低人群的医疗支出。
本研究采用机器学习和传统模型表明，环境质量的提高显著降低了中国老年人的医疗成本，尤其是在东部和中部地区，以及60 - 75岁人群和低收入家庭等人口群体中。这些发现凸显了环境政策在“健康中国”倡议框架内降低医疗成本的潜力。然而，该研究的范围受到环境质量指数和数据覆盖范围的限制，表明需要进一步扩大研究。
