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: 9885918
• 负责人: Francesca Dominici
• 金额: $ 63.29万
• 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/9885918
• 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; Outcome; 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; 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.

项目摘要 环境空气污染是全球环境威胁，造成数百万死亡和数百人的死亡 每年数以百万计的残疾调整生活年度（达利人）。但是，空气污染健康的主要局限性 研究仍然是暴露评估。尽管空气污染评估取得了巨大进展 并且已经开发了几种复杂的时空模型，以预测每日空气污染水平 研究参与者的住宅讲话，这些模型的性能在时空和时间上有所不同。 但是，即使是最好的表现预测模型，在某些方面的预测能力也有限 空间和时间点。此外，使用单个预测模型相关的不确定性已经 在健康研究中始终被忽略，这可能导致对健康效应估计的无效推断，并且 整个研究的发现不一致。我们建议通过发展一个新颖的合奏来解决这一关键差距 在空气污染健康研究中进行暴露评估的模型框架，整合信息 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 量化与环境空气污染暴露相关的任何模型间和内部内部和内部的不确定性。我们将发展 单污染物和多污染物设置的集合方法。我们建议应用开发的方法 并完全使用两个全国性的公开群体来完全传播健康效应估计的暴露不确定性，主要是 Medicare和Medicaid，以及在纽约州的公开招生队列（全州 计划与研究合作系统，SPARCS）。这些数据集提供了有关的信息 全美的所有老年人，低收入和残疾美国人（Medicare和Medicaid， 分别），使用住宅信息，邮政编码水平以及纽约州所有住院的98％ 在住宅地址可用的信息。具体而言，我们将评估长期和短期影响 死亡率（Medicare和Medicaid）和心肺发病率的空气污染暴露 队列）。我们传达空气污染预测，空气污染暴露的时空不确定性 评估和相关的健康效应估计对公众和监管机构。提出的小说 评估健康研究中空气污染暴露的范式将大大改善暴露的沟通 健康效应的不确定性对政策制定者和公众都估计，完全回应 NIH的优先研究领域。我们的工具很容易扩展，并将受益于信息集成和 在不同位置和全球范围以及其他环境的不确定性表征 暴露。