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

全球疾病负担估计，环境空气污染每年造成400多万人死亡。 然而，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 很贵。这些成本和流行病学研究的观察性质表明， 现行标准对公众健康的保护是这种不情愿的主要原因。到目前为止， 颗粒物成分没有单独的标准，健康影响评估也很少 研究环境公平性，因为缺乏亚组特定的浓度响应函数。 此外，关于温度对死亡率和发病率的影响的研究集中在与以下因素相关的风险上： 短期接触，而不是长期影响，可能更大。我们建议通过以下方式弥补这些差距： 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 地理编码为人口普查区块组）的死亡率和住院率;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 并使用它们来评估暴露的非线性和交互作用;使用最先进的模型 估计18年内美国本土1 km网格上的每日空气污染和温度暴露; 使用最先进的方法来估计邻近美国的暴露误差，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方法 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）， 对于许多未测量的混杂因素，通过对受试者分层（控制个体水平固定或 缓慢变化的协变量）或通过在邻域上分层（控制固定和缓慢变化的协变量 邻域水平协变量），同时继续控制测量的协变量。对于急性影响， 暴露，我们将使用工具变量来调整未测量的混杂因素。我们将获得大量， 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， 和州一级的地理编码死亡证明数据我们将使用高度精确的国家模型， 针对1 km网格上的每日污染而开发，并将分辨率提高到500 m。我们将使用一种新的混合物 模型，快速贝叶斯核机器回归（BKMR），以解决污染和温度的混合， 识别相互作用和非线性，并识别哪些风险最重要（包括哪些风险） 粒子组件）。我们将使用最先进的测量误差校正 方法（SIMEX），以确定由于暴露误差导致的浓度-反应关系偏倚。我们 将对BKMR方法进行补充，分析仅限于低于当前标准的观测， 样条方法与倾向分数，以确定因果关系是否继续低于目前的标准。

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.