Identifying low dose measurement error corrected effects of multiple pollutants using causal modeling

使用因果模型识别多种污染物的低剂量测量误差校正效应

• 批准号: 10332715
• 负责人: Joel D Schwartz
• 金额: $ 34.3万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10332715
• 关键词: Acute; Address; Admission activity; Advisory Committees; Age; Air; Air Pollutants; Air Pollution; Atrial Fibrillation; Attenuated; Censuses; Cessation of life; China; Chronic; Climate; Code; Critiques; Data; Data Set; Death Certificates; Death Rate; Detection; Dose; Event; Exposure to; Health; Hospitalization; India; Individual; Joints; Measurement; Measures; Medicaid; Medicare; Medicare/Medicaid; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; Nature; Neighborhoods; Nitrates; Nitrogen Dioxide; Observational Study; Outcome; Ozone; Participant; Pollution; Population; Public Health; Quasi-experiment; Records; Resolution; Risk; Risk Estimate; Science; Scoring Method; Stroke; Subgroup; Sulfate; Techniques; Temperature; Time; ambient air pollution; analytical method; base; burden of illness; causal model; cohort; cost; design; epidemiology study; experience; fine particles; health disparity; hospitalization rates; mortality; novel; particle; pollutant; prevent; response; simulation

The Global Burden of Disease estimates that ambient air pollution is responsible for over 4 million deaths per year, yet regulators in the US, EU, India, and China have been reluctant to tighten standards, which can be costly. Those costs and the observational nature of the epidemiology studies suggesting a tightening of existing standards would be protective of the public’s health is a major reason for this reluctance. To date, separate standards have not been set for particle components, and health impact assessments rarely examine environmental equity because of the paucity of subgroup-specific concentration-response functions. Further, studies on the effects of temperature on mortality and morbidity have focused on risk associated with short-term exposure, and not longer-term effects which may be larger. We propose to address these gaps by using national data (US Medicare and Medicaid data, and all age Death Certificate data from multiple states geocoded to a census block group) on mortality and hospital admissions; to use causal modeling techniques robust to omitted confounders by design; to extend methods for environmental mixtures to large data settings and use them to assess nonlinear and interactive effects of exposures; to use state of the art models estimating daily air pollution and temperature exposure for the contiguous US on a 1km grid for 18 years; to use state of the art methods to estimate exposure error in the contiguous US, to use restriction and spline methods to address low dose effects, and to develop and use state of the art measurement error correction methods to account for exposure error when estimating these risks. Specifically, we will use quasi-experimental designs (difference in differences and self-controlled) that control for many unmeasured confounders, either by stratifying on subject (controlling for individual level fixed or slowly varying covariates) or by stratifying on neighborhood (controlling for fixed and slowly varying neighborhood level covariates), while continuing to control for measured covariates. For acute effects of exposures, we will use instrumental variables to adjust for unmeasured confounding. We will access large, ready-to-use datasets we have compiled, including national Medicare and Medicaid mortality and admissions, and state-level geocoded death certificate data. We will use highly accurate national models we have developed for daily pollution on a 1km grid, and increase resolution to 500 m. We will use a new mixture model, fast Bayesian Kernel Machine Regression (BKMR), to address pollution and temperature mixtures, identify interactions and nonlinearities, and identify which exposures are most important (including which particle components) for a given health endpoint. We will use state of the art measurement error correction approaches (SIMEX) to identify biases in the concentration-response relationship due to exposure error. We will supplement the BKMR approach with analyses restricted to observations below current standards, and spline methods with propensity scores to determine whether causal effects continue below current standards.

全球疾病负担估计，环境空气污染每年导致超过 400 万人死亡。 但美国、欧盟、印度和中国的监管机构一直不愿收紧标准，这可能会导致 这些成本和流行病学研究的观察性表明需要收紧。 迄今为止，现有标准将保护公众的健康是这种不愿意的主要原因。 尚未针对颗粒成分制定单独的标准，并且很少进行健康影响评估 由于缺乏特定于亚组的集中反应函数，因此需要检查环境公平性。 此外，关于温度对死亡率和发病率影响的研究重点关注与 我们建议通过以下方式解决这些差距：短期影响，而不是可能更大的长期影响。 使用国家数据（美国医疗保险和医疗补助数据，以及来自多个州的所有年龄死亡证明数据 对死亡率和入院率进行地理编码以使用因果建模技术； 通过设计对忽略的混杂因素具有鲁棒性；将环境混合物的方法扩展到大数据设置 并用它们来评估暴露的非线性和交互效应，以使用最先进的模型； 估算 18 年来美国本土 1 公里网格内的每日空气污染和温度暴露情况； 使用最先进的方法来估计美国本土的曝光误差，使用限制和样条 解决低剂量效应的方法，以及开发和使用最先进的测量误差校正 在估计这些风险时考虑暴露误差的方法。 具体来说，我们将使用准实验设计（差异中的差异和自我控制）来控制 对于许多无法测量的混杂因素，可以通过对主题进行分层（控制固定的个人水平或 缓慢变化的协变量）或通过对邻域进行分层（控制固定和缓慢变化 邻域级协变量），同时继续控制测量的协变量。 暴露，我们将使用工具变量来调整无法测量的混杂因素。 我们编制的现成数据集，包括国家医疗保险和医疗补助死亡率和入院率， 我们将使用我们拥有的高度准确的国家模型。 为 1km 网格上的日常污染而开发，并将分辨率提高到 500 m 我们将使用新的混合物。 模型，快速贝叶斯核机器回归（BKMR），以解决污染和温度混合物， 识别相互作用和非线性，并确定哪些暴露是最重要的（包括哪些 对于给定的健康终点，我们将使用最先进的测量误差校正。 方法（SIMEX）来识别由于暴露误差而导致的浓度-反应关系中的偏差。 将通过仅限于低于当前标准的观察结果的分析来补充 BKMR 方法，并且 具有倾向得分的样条方法可以确定因果效应是否继续低于当前标准。