母亲的情况和健康的代际传播:基于机器学习的基于模型的递归分区方法
Dweepobotee Brahma1, Souvik Ghosh2
1Indian Institute of Technology, Jodhpur, India.
Economics and human biology
|November 24, 2025
概括
母亲的健康状况显著影响到婴儿的健康传播. 更糟糕的条件加剧了这一代际健康联系,突出了针对性社会政策的必要性,以打破不良健康的循环.
科学领域:
- 公共卫生 公共卫生
- 卫生经济学 卫生经济学
- 机器学习 机器学习
背景情况:
- 代际健康传播是复杂的,受到母亲因素的影响.
- 以前的模型往往忽视了不同社会经济环境中健康传播的异质性.
研究的目的:
- 调查不同母亲的情况如何对健康的代际传播产生不同影响.
- 确定与不同从母亲传给婴儿的疾病传播率相关的特定情况概况.
主要方法:
- 利用来自机器学习的基于新型模型的递归分区算法.
- 包含参数不稳定的计量经济学测试,以分析健康传播系数.
- 检查了母亲健康的短期和长期标志物.
主要成果:
- 鉴定了不同孕产妇病情概况的代际健康传播率的显著异质性.
- 证明一个适合所有人的全球模型不足以研究这种现象.
- 在不太有利的情况下,从母亲到新生儿之间发现了更强大的代际健康传播.
结论:
- 母亲的环境在塑造代际健康方面发挥着至关重要的作用.
- 需要有针对性的社会安全网政策来解决健康传播中的差异.
- 了解这些差异性途径是打破世代间不良健康循环的关键.
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