Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA.
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
Environ Res. 2022 Sep;212(Pt D):113587. doi: 10.1016/j.envres.2022.113587. Epub 2022 May 30.
Implementing effective policy to protect human health from the adverse effects of air pollution, such as premature mortality, requires reducing the uncertainty in health outcomes models. Here we present a novel method to reduce mortality uncertainty by increasing the amount of input data of air pollution and health outcomes, and then quantifying tradeoffs associated with the different data gained. We first present a study of long-term mortality from fine particulate matter (PM) based on simulated data, followed by a real-world application of short-term PM-related mortality in an urban area. We employ information yield curves to identify which variables more effectively reduce mortality uncertainty when increasing information. Our methodology can be used to explore how specific pollution scenarios will impact mortality and thus improve decision-making. The proposed framework is general and can be applied to any real case-scenario where knowledge in pollution, demographics, or health outcomes can be augmented through data acquisition or model improvements to generate more robust risk assessments.
实施有效的政策以保护人类健康免受空气污染的不利影响，如过早死亡，需要降低健康结果模型的不确定性。在这里，我们提出了一种通过增加空气污染和健康结果的输入数据量，并量化与不同数据相关的权衡来降低死亡率不确定性的新方法。我们首先根据模拟数据展示了一项关于细颗粒物 (PM) 长期死亡率的研究，然后在城市地区进行了短期 PM 相关死亡率的实际应用。我们采用信息收益曲线来确定哪些变量在增加信息时更有效地降低死亡率的不确定性。我们的方法可用于探索特定的污染情况将如何影响死亡率，从而改善决策制定。所提出的框架是通用的，可以应用于任何真实案例场景，在这些场景中，可以通过数据获取或模型改进来增加对污染、人口统计学或健康结果的了解，从而生成更稳健的风险评估。
