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

全球疾病负担估计，环境空气污染导致每年超过400万人死亡 然而，美国、欧盟、印度和中国的监管机构一直不愿收紧标准，这可能是 代价不菲。这些成本和流行病学研究的观察性表明， 现有的标准会保护公众的健康是这种不情愿的一个主要原因。到目前为止， 没有为颗粒物成分设定单独的标准，健康影响评估也很少 考察环境公平性，因为缺乏特定于亚组的浓度-反应函数。 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疾病相关的风险上 短期暴露，而不是可能更大的长期影响。我们建议通过以下方式解决这些差距 使用国家数据(美国联邦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 地理编码到人口普查区组)关于死亡率和住院人数；使用因果建模技术 对忽略的混杂因素设计稳健；将环境混合的方法扩展到大数据设置 并使用它们来评估曝光的非线性和交互影响；使用最先进的模型 估计连续18年1公里网格上美国的每日空气污染和温度暴露； 使用最先进的方法估计相邻US的曝光误差，使用限制和样条法 解决低剂量影响的方法，并开发和使用最新的测量误差校正 在估计这些风险时考虑暴露误差的方法。 具体地说，我们将使用准实验设计(差异差异和自身对照)来控制 对于许多未测量的混杂因素，通过对对象分层(控制个人水平固定或 缓慢变化的协变量)或根据邻域分层(控制固定的和缓慢变化的 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， 对于暴露，我们将使用工具变量来调整未测量的混杂。我们将访问大型、 我们已经汇编了现成的数据集，包括全国医疗保险和医疗补助死亡率和入院人数， 和州级地理编码的死亡证明数据。我们将使用我们拥有的高度精确的国家模型 在1公里的网格上为日常污染而开发，并将分辨率提高到500米。我们将使用新的混合物 模型，快速贝叶斯核机回归(BKMR)，以解决污染和温度混合， 确定交互和非线性，并确定哪些曝光是最重要的(包括哪些 粒子组分)用于给定健康终点。我们将使用最先进的测量误差校正 方法(SIMEX)，以确定由于曝光误差导致的浓度-反应关系中的偏差。我们 将对BKMR方法进行补充，分析限于低于当前标准的观察结果，以及 用带有倾向分数的样条法来确定因果效应是否持续低于当前标准。

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.