分布式滞后非线性模型在公共卫生中的广泛应用:全面审查
Ambreen Shafqat1, Eunsik Park1
1Department of Mathematics and Statistics Chonnam National University Gwangju South Korea.
GeoHealth
|December 11, 2025
概括
分布式滞后非线性模型 (DLNM) 有效地分析环境暴露和健康结果,揭示了复杂的时间关系. 这次审查强调了它在公共卫生研究中的实用性,尽管在标准化方面存在挑战.
科学领域:
- 环境健康 环境健康
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 分布滞后非线性模型 (DLNM) 越来越多地被用于公共卫生研究.
- 了解环境暴露和健康结果之间的复杂时间动态至关重要.
研究的目的:
- 审查DLNM在分析环境暴露和健康结果中的应用.
- 确定环境健康研究中DLNM的趋势,挑战和未来方向.
主要方法:
- 在Embase,PubMed,Web of Science和Scopus (2020年1月 - 2024年11月) 进行了系统的文献搜索.
- 使用DLNM评估环境因素 (温度,空气污染物) 和健康结果的研究被选和分析.
- 综合了来自36个国家的274项精选研究的数据.
主要成果:
- 发病率是最频繁报告的不良健康结果 (n=102),其次是住院治疗 (n=39) 和住院治疗 (n=40).
- 该审查确定了气候 (174) 和空气污染物 (131) 的各种数据来源,并指出缺乏标准化的热门值.
- DLNM在捕捉环境暴露对健康的滞后影响方面表现出有用性.
结论:
- DLNM是调查环境健康问题的宝贵工具,特别是了解延迟健康影响.
- 数据的标准化和计算效率仍然是挑战,但持续的发展正在改善DLNM的适用性.
- 未来的研究应该整合先进的统计方法,如机器学习,并将DLNM应用扩展到更广泛的环境健康场景.
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