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: 10634894
• 负责人: Joel D Schwartz
• 金额: $ 13.57万
• 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/10634894
• 关键词: Acceleration; Address; Air Pollutants; Air Pollution; Area; Big Data; Bromine; Calcium; Cardiovascular system; Cause of Death; Cessation of life; Chronic; Copper; Data; Data Set; Dose; Environmental Epidemiology; Environmental Health; Epidemiologic Methods; Epidemiologist; Exposure to; Goals; Heart Arrest; Heart failure; Hybrids; Individual; Iron; Joints; Lead; Link; Longitudinal cohort; Measurement; Measures; Medicare; Metals; Methods; Modeling; Morality; Myocardial Infarction; Myocardial Ischemia; Neighborhoods; Nickel; Nitrates; Nitrogen Dioxide; Outcome; Ozone; Pollution; Population; Potassium; Preparation; Probability; Quasi-experiment; Regression Analysis; Regulation; Research Personnel; Resolution; Risk; Silicon; Stroke; Sulfate; Techniques; Temperature; Time; Toxic effect; Training; United States Centers for Medicare and Medicaid Services; Update; Vanadium; Weight; Work; Zinc; ambient air pollution; attributable mortality; cardiovascular disorder epidemiology; cardiovascular health; career; causal model; cohort; design; experience; fine particles; indexing; mortality; novel; parent grant; pollutant; processing speed; response; skills

Project Summary The Candidate will contribute to the goals of the parent grant by using updated hybrid air pollution exposure models and Medicare administrative data and link them with the newly acquired National Death Index (NDI) data. The NDI data identifies the specific cause of death allowing the Candidate to establish a complementary project that addresses the casual effect of air pollutants with cause-specific cardiovascular mortality both individually and jointly within the Medicare population. Specifically, the study will employ a quasi-experimental design (difference-in- differences) that controls for both measured and many unmeasured confounders by stratifying on either the subject (controlling for time invariant or slowly varying individual covariates) or the neighborhood (controlling for time invariant or slowly varying neighborhood level covariates). This is a major advantage of DID since many unmeasured and measured confounders are controlled for by designed. We will employ updated exposure models for the years 2000-2020 with an increased spatial resolution of 500m. By using a novel mixture technique, fast Bayesian Kernel Machine Regression (BKMR), that accelerates the processing speed of the traditional BKMR, the study can analyze big datasets such as Medicare. Fast BKMR identifies interactions between individual pollutants, nonlinearities at low concentrations, and the overall effect of the mixture. We will also produce estimates on a restricted subset of the Medicare population that lived in areas that never exceeded the current EPA standards. This will help draw conclusions on whether the current regulations are sufficient to protect cardiovascular health. With the support of this supplement, we will incorporate the Candidate’s background in cardiovascular epidemiology and mixture methods into our current Medicare study. Her proposed work for this supplement not only aligns well with the objectives of the primary R01 but has the potential to contribute to the ongoing efforts to tighten ambient air pollution regulations

项目摘要 候选人将通过使用最新的混合空气污染来促进母基金的目标 暴露模型和医疗保险管理数据，并将它们与新获得的 国家死亡指数（NDI）数据。NDI数据确定了具体的死亡原因， 候选人建立一个补充项目，解决空气污染物的偶然影响 在医疗保险范围内， 人口具体而言，该研究将采用准实验设计（差异- 差异），通过分层控制测量和许多未测量的混杂因素， 受试者（控制时不变或缓慢变化的个体协变量）或 邻域（控制时不变或缓慢变化的邻域水平协变量）。这 是DID的主要优势，因为许多未测量和测量的混杂因素得到控制 为设计。我们将采用2000-2020年更新的暴露模型， 空间分辨率提高到500米。通过使用一种新的混合技术，快速贝叶斯核， 机器回归（BKMR），它加快了传统BKMR的处理速度， 研究可以分析大型数据集，如医疗保险。快速BKMR识别 单个污染物，低浓度下的非线性，以及混合物的总体影响。我们 还将对居住在以下地区的医疗保险人口的有限子集进行估计： 从未超过现行的环保署标准这将有助于得出结论， 目前的法规足以保护心血管健康。有了这个支持 补充，我们将纳入候选人的心血管流行病学背景， 混合方法到我们目前的医疗保险研究中。她为这一补充提出的工作不仅 与主要R 01的目标一致，但有潜力为正在进行的 政府致力收紧环境空气污染规例