Measurement Errors in Environmental Epidemiology

描述（由申请人提供）：在几乎所有的环境和流行病学研究中，暴露测量误差是一个可能的偏倚来源，通常会导致对相对风险的低估，并失去检测影响的统计能力。在本项目的前一个周期中，我们扩展了回归校准方法，用于调整多变量回归模型（包括Cox模型和logistic模型）中的测量误差，以适应环境流行病学中遇到的研究设计和数据结构。我们在许多关于环境暴露于内毒素、甲基叔丁基醚、铅和室内二氧化氮对健康影响的出版物中介绍了这些新方法，并开发了公开可用的软件。在这个项目的下一个周期中，我们将重点关注空气污染流行病学方面的问题，特别是接触颗粒和元素碳对全因死亡率、心血管死亡率和肺癌死亡率的慢性影响。我们组建了一个由世界各地领先的统计学家、环境科学家和环境流行病学家组成的跨学科团队，我们将开发方法来调整Cox回归模型中的测量误差，该模型适用于六个城市研究、护士健康研究、荷兰队列研究和MESA-Air的前瞻性队列设计，涉及累积和12个月的平均暴露指标。将仔细关注曝光时间和曝光度量的剂量-响应曲线的潜在非线性的重要问题，特别是，消除由于曝光测量误差而导致的这些特征的量化偏差。NHS和MESA-AIR中可用的生物标志物数据将用于改进暴露验证。方便使用者使用的软件会在网上发布，方便新方法的广泛应用。公共卫生相关性：最近研究的推断表明，美国每年约有10万人过早死亡可能与接触空气中的颗粒有关。对接触空气污染成分的有限数据的解释表明，长期接触的影响似乎比急性接触的影响大得多。替代暴露度量的测量误差已被发现是相当大的，但统计工具无法在分析中明确地调整这种偏差来源。我们建议为此开发方法，并将其应用于有关空气污染与死亡率关系的四项主要研究：六个城市研究、护士健康研究、MESA-Air和荷兰队列研究。这些分析将大大提高我们对暴露于空气污染的健康影响的理解，包括确定暴露的关键时间和暴露-反应关系中的潜在非线性，并将有助于未来的风险评估和政策制定。

DESCRIPTION (provided by applicant): Exposure measurement error is a likely source of bias in nearly all environmental and epidemiological studies, typically leading to under-estimation of relative risks and loss of statistical power to detect effects. In the previous cycle of this project, we extended the regression calibration method for adjustment for measurement error in multivariate regression models, including Cox models and logistic models, to accommodate the study designs and data structures encountered in environmental epidemiology. We featured these new methods in a number of publications on the health effects of environmental exposure to endotoxin, methyl tert-butyl ether, lead, and indoor NO2, and developed publicly available software. In the next cycle of this project, we will focus on issues in air pollution epidemiology - in particular, the chronic effects of particulate exposure and elemental carbon on all-cause mortality, cardio-vascular mortality and lung cancer mortality. Having assembled an inter-disciplinary team of leading statisticians, environmental scientists and environmental epidemiologists from around the world, we will develop methods to adjust for measurement error in Cox regression models suitable for the prospective cohort designs of the Six Cities Study, Nurses' Health Study, Netherlands Cohort Study, and MESA-Air in relation to cumulative and 12 month running average exposure metrics. Careful attention will be paid to the important issues surrounding timing of exposure and potential non-linearity of the dose-response curves of the exposure metrics, in particular, removing bias in the quantification of these features due to exposure measurement error. The biomarker data available in NHS and MESA-AIR will be used to improve the exposure validation. User- friendly software will be posted on the web, facilitating widescale application of the new methods. PUBLIC HEALTH RELEVANCE: Extrapolation from recent studies has suggested that approximately 100,000 premature deaths in the United States may be associated with exposure to airborne particles each year. Interpretation of the limited data on exposure to constituents of air pollution suggests that the effects of long-term exposure appear to be substantially greater than those of acute exposure. Measurement error in alternative exposure metrics has been found to be substantial, yet the statistical tools are not available to adjust for this source of bias explicitly in analysis. We propose to develop methods for doing this, and apply them to four major studies on air pollution in relation to mortality: the Six Cities Study, the Nurses' Health Study, MESA-Air, and the Netherlands Cohort Study. These analyses will substantially improve our understanding of the health effects of exposure to air pollution, including identification of the critical times of exposure and potential non-linearity in the exposure-response relationship, and will be useful in future risk assessment and policy development.