增强改善空气质量的健康益处:在各种场景中进行的一项比较研究
Chuang Han1, Chuanlong Cheng1, Ying Liu1
1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44# Wenhuaxi Road, Lixia District, Jinan, 250012, Shandong, China.
概括
非线性模型揭示了复杂的空气污染与疾病的联系. 使用实际预测的入院率,而不是平均值,为改善空气质量提供了更准确的健康益处计算.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 传统的线性模型可能不准确地评估改善空气质量的健康益处.
- 空气污染物和疾病之间的关系往往复杂且非线性.
- 之前的研究可能通过使用平均疾病数字作为参考来高估健康益处.
研究的目的:
- 探索空气污染物 (O3,PM10,PM2.5,SO2) 和冠状动脉心脏病 (CHD) 的住院患者之间的非线性关联.
- 评估不同参考号码设置对健康益处计算的影响.
- 确定精确的方法来量化改善空气质量的健康益处.
主要方法:
- 利用了CHD的住院数据,气象数据和齐博市 (2015-2019) 的空气污染物数据.
- 采用了通用添加模型 (GAM) 来分析暴露-反应关系.
- 使用预测住院人数与平均住院人数作为参考计算的健康福利比较.
主要成果:
- 在臭氧 (O3) 和心血管疾病住院病例 (RR 1.0143) 之间观察到日志线性关系.
- 对于PM10,PM2.5和SO2,发现了日志非线性关系,风险增加然后减少.
- 使用GAM预测的入院人数计算的健康益处低于使用平均入院人数计算的健康益处.
- 根据世卫组织的指导方针,O3,PM10和PM2.5的可归因分数分别为1.97%,11.82%和11.82%.
结论:
- 非线性建模对于准确评估空气污染对健康的影响至关重要.
- 使用预测入院率提供了比平均入院率更可靠的健康益处估计.
- 准确的计算方法,基于已确定的暴露-反应关系,对于政策决策至关重要.
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