Integrating Air Pollution Prediction Models: Uncertainty Quantification and Propagation in Health Studies

整合空气污染预测模型：健康研究中的不确定性量化和传播

• 批准号: 10330579
• 负责人: Francesca Dominici
• 金额: $ 63.07万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-16 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10330579
• 关键词: Accounting; Address; Air Pollution; American; Area; Caliber; Cessation of life; Code; Communication; Communication Tools; Data; Data Scientist; Data Set; Disabled Persons; Elderly; Engineering; Environmental Exposure; Epidemiologist; Exposure to; Future; Goals; Health; Health Insurance; Heterogeneity; Hospitalization; Joints; Lead; Location; Low income; Medicare; Medicare/Medicaid; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; New York; Nitrogen Dioxide; Outcome; Ozone; Participant; Pattern; Peer Review; Performance; Policies; Policy Maker; Population; Provider; Regulation; Reporting; Research; Research Priority; Resolution; Resource Sharing; Risk Factors; Rural; Seasons; Shapes; Site; Software Tools; Statistical Methods; System; Time; Uncertainty; United States; United States National Institutes of Health; Visualization software; Work; ambient air pollution; base; cohort; data sharing; disability-adjusted life years; fine particles; global environment; improved; innovation; mortality; mortality risk; novel; open data; open source; particle; pollutant; predictive modeling; response; spatiotemporal; tool

Project Summary Ambient air pollution is a global environmental threat, contributing to millions of deaths and hundreds of millions of disability-adjusted-life-years (DALYs) annually. However, the major limitation of air pollution health studies remains exposure assessment. Although there have been great advances in air pollution assessment and several sophisticated spatio-temporal models have been developed to predict daily air pollution levels at the residential addresses of study participants, the performance of these models varies in space and time. Even the best on average performing prediction model, however, will have limited predictive ability in certain space and time points. Furthermore, the uncertainty associated with use of a single prediction model has been consistently ignored in health studies, which could lead to invalid inferences of the health effect estimates, and inconsistent findings across studies. We propose to address this critical gap by developing a novel ensemble model framework for exposure assessment in air pollution health studies, integrating information across multiple existing prediction models. With this approach, we will for the first time be able to comprehensively quantify any inter- and intra-model uncertainty associated with ambient air pollution exposures. We will develop ensemble methods both for single- and multi-pollutant settings. We propose to apply the developed methods and fully propagate exposure uncertainty in health effect estimation using two nationwide open cohorts, mainly Medicare and Medicaid, as well as an open cohort of hospital admissions in New York State (Statewide Planning and Research Cooperative System, SPARCS). These datasets provide information on approximately all elderly, low-income and disabled Americans across the United States (Medicare and Medicaid, respectively), with residential information the zip-code level, as well as 98% of all hospitalizations in NY State, with information available at the residential address. Specifically we will assess the long- and short-term impact of air pollution exposure on mortality (Medicare and Medicaid), and cardiorespiratory morbidity (all three cohorts).We communicate the air pollution predictions, the spatio-temporal uncertainty of air pollution exposure assessment and related health effect estimates to the public and regulatory agencies. The proposed novel paradigm to assess air pollution exposures in health studies will greatly improve communication of exposure uncertainty in the health effect estimates both to policy makers and the public, exactly responding to one of NIH's priority research areas. Our tools can be easily extended and will benefit integration of information and uncertainty characterization at different locations and at a global scale, as well as for other environmental exposures.

项目概要 环境空气污染是一个全球性的环境威胁，导致数百万人死亡和数百万人死亡。 每年数百万伤残调整生命年 (DALY)。然而，空气污染对健康的主要限制 研究仍然是暴露评估。尽管空气污染评估取得了巨大进步 并开发了几种复杂的时空模型来预测每日空气污染水平 由于研究参与者的居住地址不同，这些模型的性能随空间和时间的不同而变化。 然而，即使是平均性能最好的预测模型，在某些情况下的预测能力也有限。 空间和时间点。此外，与使用单一预测模型相关的不确定性已经被 在健康研究中一贯被忽视，这可能导致对健康影响估计的无效推断，以及 各项研究的结果不一致。我们建议通过开发一种新颖的集成来解决这一关键差距 空气污染健康研究中暴露评估的模型框架，整合了各个领域的信息 多个现有的预测模型。通过这种方法，我们将第一次能够全面地 量化与环境空气污染暴露相关的任何模型间和模型内的不确定性。我们将开发 适用于单污染物和多污染物环境的集成方法。我们建议应用开发的方法 并使用两个全国性开放队列充分传播健康影响估计中的暴露不确定性，主要是 医疗保险和医疗补助，以及纽约州住院入院的开放队列（全州范围） 规划与研究合作系统，SPARCS）。这些数据集提供了大约 美国各地的所有老年人、低收入和残疾人（医疗保险和医疗补助， 分别），以及邮政编码级别的住宅信息，以及纽约州 98% 的住院信息， 并可在居住地址获取信息。具体来说，我们将评估长期和短期影响 空气污染暴露对死亡率（医疗保险和医疗补助）和心肺疾病（所有三项）的影响 我们传达空气污染预测、空气污染暴露的时空不确定性 向公众和监管机构进行评估和相关健康影响估计。拟议的小说 在健康研究中评估空气污染暴露的范式将极大地改善暴露的交流 对政策制定者和公众的健康影响估计的不确定性，恰好响应了其中之一 NIH 的优先研究领域。我们的工具可以轻松扩展，有利于信息和技术的集成 不同地点和全球范围以及其他环境的不确定性特征 曝光。