解读健康水平与人类定居点环境之间的关联:中国的机器学习驱动的省级分析
1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Frontiers in public health
|September 19, 2025
概括
中国的快速城市化对公共卫生产生影响,最佳健康与基础设施,数字接入和服务的协同发展有关. 协调的城市规划对于以健康为导向的政策至关重要.
科学领域:
- 环境健康 环境健康
- 城市规划 城市规划
- 公共卫生政策 公共卫生政策
背景情况:
- 中国的快速城市化大大改变了人类居住环境 (HSE),给公共卫生带来了机遇和挑战.
- 现有的研究往往缺乏对HSE-健康联系的全面,宏观层面的理解,专注于微观层面的背景,单个维度或特定疾病.
研究的目的:
- 在中国省级评估人口健康与多维HSE特征之间的关联.
- 发现HSE和人口健康水平之间的非线性关系和相互作用效应.
主要方法:
- 使用Entropy-TOPSIS从2012年至2022年的31个中国省份的面板数据构建了一个复合的健康水平指数 (HLI).
- 采用XGBoost机器学习来建模HSE-HLI关系,使用SHAP值和部分依赖图 (PDP) 进行解释.
主要成果:
- XGBoost表现出强大的预测能力,表现优于基准模型.
- 确定了关键的影响力HSE特征:医疗机构床位数量 (NMIB),城市化率 (UR),移动电话透率 (MPPR),人均道路面积 (RAPC),人口密度 (PD) 和城市气体透率 (UGPR).
- 观察到非线性关系和值效应;最佳健康与高UR,MPPR,RAPC和中度NMIB相关,强调协同发展.
结论:
- 卫生健康关系是非线性的,多维的,互动的.
- 有效的城市卫生治理需要城市化,数字基础设施,公共服务和合理的医疗保健资源配置的协调发展.
- 研究结果为快速城市化地区的以健康为导向的城市规划和政策提供了可操作的见解.
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