评估长期暴露于低水平环境空气污染对健康的不良影响:因果推理方法的实施。

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.

机构信息

Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.

出版信息

Res Rep Health Eff Inst. 2022 Jan;2022(211):1-56.

DOI:
Abstract

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM), nitrogen dioxide (NO), and ozone (O) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project. Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses. Our work provides comprehensive evidence of associations between exposures to PM NO, and O and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM and mortality, even at PM levels below or equal to 12 μg/m, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure. For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m decrease in PM led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM levels lower than 12 μg/m (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]). Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM and slightly elevated the causal effects of NO exposure on all-cause mortality. The results for O remained almost unchanged. We found evidence of a harmful causal relationship between mortality and long-term PM exposures adjusted for NO and O across the range of annual averages between 2.77 and 17.16 μg/m (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards. There is, in general, a harmful causal impact of long-term NO exposures to mortality adjusted for PM and O across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO exposures on all-cause mortality are nonlinear with statistical uncertainty. The ERCs of long-term O exposures on all-cause mortality adjusted for PM and NO are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O exposures of 50 ppb versus those with 30 ppb. institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred. A list of abbreviations and other terms appears at the end of this volume.

摘要

本报告提供了由 HEI 资助的一项研究的主要发现和关键结论的最终总结,该研究旨在“评估长期暴露于低水平环境空气污染对健康的不良影响”。这是该主题的第二份也是最后一份报告。该研究旨在推进四个关键领域的调查和方法开发。首先,它专注于预测 2000-2016 年美国大陆高空间分辨率(1 公里×1 公里)的短期和长期细颗粒物(PM)、二氧化氮(NO)和臭氧(O)暴露,并将这些预测与健康数据联系起来。其次,它开发了新的因果推理方法,用于估计暴露-反应(ER)曲线(ERC)并调整测量混杂因素。第三,它将这些方法应用于医疗保险和医疗补助受益人的索赔数据,以估计与低水平环境空气污染的短期和长期暴露相关的健康影响。最后,它开发了可重复研究的管道,包括数据共享、记录链接和统计软件的方法。我们的 HEI 资助工作支持了广泛的分析组合和统计方法的开发,这些方法可用于稳健地理解短期和长期暴露于低水平环境空气污染的健康影响。我们的第一阶段报告(Dominici 等人,2019 年)提供了我们的统计方法、数据分析和关键发现的高级概述,分为以下五个领域:(1)暴露预测,(2)低水平环境空气污染物暴露的流行病学研究,(3)敏感性分析,(4)因果推理中的方法贡献,以及(5)开放获取的研究数据平台。本报告包括对整个研究项目的全面概述。考虑到我们的(1)庞大的研究人群,(2)大量的敏感性分析,以及(3)对协变量平衡的透明评估,表明模拟随机试验的因果推理质量,我们得出结论,在因果推理所需假设的条件下,我们的结果总体上表明,长期 PM 暴露可能与死亡率有关。这个结论假设因果推理假设成立,更具体地说,我们充分考虑了混杂偏差。我们探索了各种建模方法,进行了广泛的敏感性分析,发现我们的结果在方法和模型上都是稳健的。这项工作依赖于公开可用的数据,我们提供了允许我们分析可重复的代码。我们的工作提供了环境 PM、NO 和 O 暴露与各种健康结果之间关联的综合证据。在本报告中,我们报告了长期暴露于 PM 与死亡率之间因果关系的更具体结果,即使在 PM 水平低于或等于 12μg/m3 且 Medicare 受益人(65 岁及以上)的死亡率也如此。这项工作依赖于新开发的用于连续暴露的因果推理方法。对于 2000-2016 年期间,我们发现所有统计方法都得出了一致的结果:PM 降低 10μg/m3 导致死亡率统计学显著降低,范围在 6%至 7%之间(=1-1/风险比[HR])(HR 估计值为 1.06[95%CI,1.05 至 1.08]至 1.08[95%CI,1.07 至 1.09])。当研究始终暴露于 PM 水平低于 12μg/m3 的 Medicare 受益人群时,估计的 HR 更大(1.23[95%CI,1.18 至 1.28]至 1.37[95%CI,1.34 至 1.40])。将多污染物模型和单污染物模型的结果进行比较,我们发现调整其他两种污染物略微减弱了 PM 的因果效应,并略微提高了 NO 暴露对全因死亡率的因果效应。O 的结果几乎没有变化。我们发现,在整个 Medicare 受益人群中,从 2000 年到 2016 年,在大陆美国,在整个范围内(包括>98%的观测值),长期 PM 暴露与 NO 和 O 调整后的死亡率之间存在有害的因果关系,年平均值在 2.77 到 17.16μg/m3 之间。我们的结果与最近报告长期暴露于 PM 与低暴露水平下不良健康结果之间存在强烈关联的流行病学研究一致。重要的是,在暴露水平低于当前国家标准的情况下,曲线几乎呈线性,表明即使在这些标准以下的暴露水平下,有害影响也会加剧。在 PM 和 O 调整后的长期 NO 暴露与死亡率之间,存在一个有害的因果影响,其范围在年平均值 3.4 到 80ppb 之间(包括>98%的观测值)。然而,在低于当前国家标准的低水平(年平均值≤53ppb)下,NO 暴露对全因死亡率的因果影响是非线性的,存在统计不确定性。PM 和 NO 调整后的长期 O 暴露与全因死亡率的 ERC 几乎在 45ppb 以下呈平坦状,没有统计学上显著的影响。然而,当 O 暴露高于 45ppb 时,我们观察到危险增加,当比较 O 暴露的 Medicare 受益人的年平均值为 50ppb 与 30ppb 时,HR 约为 1.10。

请注意:这份报告是由多个美国联邦政府机构、学术机构、行业组织、非政府组织和国际

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