Statistical methods to characterize causal mechanisms by which air pollution affects the recurrence of cardiovascular events

描述空气污染影响心血管事件复发因果机制的统计方法

• 批准号: 10660281
• 负责人: Francesca Dominici
• 金额: $ 179.01万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-23 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660281
• 关键词: Acute myocardial infarction; Address; Adoption; Affect; Age; Air Pollutants; Air Pollution; Attention; Behavior; Biological; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cessation of life; Clinical; Clinical Pathways; Collection; Computer software; Data; Data Collection; Data Sources; Disease; Disease Progression; Enrollment; Ensure; Environmental Health; Epidemiology; Event; Exposure to; Fee-for-Service Plans; Gender; Goals; Health; Health Hazards; Heart Diseases; Hospitalization; Hyperlipidemia; Hypertension; Individual; Inpatients; Knowledge; Link; Mediating; Mediation; Mediator; Medicare; Medicare Part A; Medicare claim; Methodology; Methods; Modeling; Modernization; Nature; Nitrogen Dioxide; Outcome; Outpatients; Ozone; Parameter Estimation; Pathway interactions; Peer Review; Policies; Procedures; Process; Race; Recurrence; Risk; Sampling; Scourge; Statistical Methods; Structural Models; Surveys; Syndrome; Techniques; Time; Visit; Work; ambient air pollution; beneficiary; burden of illness; cardiovascular disorder risk; computational platform; fine particles; human old age (65+); improved; innovation; mortality; novel; open source; pollutant; prevent; primary outcome; secondary analysis; semiparametric; simulation; statistical and machine learning

Project Summary One of our era's greatest scourges is air pollution, and it is well documented that exposure to fine particles (e.g., PM2.5) increases the risk of cardiovascular disease (CVD) and death. However, there are two critical knowledge gaps. First, existing studies have mainly considered the occurrence of the first adverse health event as health out- comes; hence, the impact of overall disease burden and of PM2.5 exposure on disease progression both remain unknown. Second, to our knowledge, there are no studies assessing the causal pathways by which exposure to air pollutants impacts recurrent cardiovascular events. Without a better understanding of disease progression and clinical mediators, our ability to inform regulatory policy and prevent disease is severely hampered. Limited attention has been given to developing methods for assessing the causal effect of time varying ex- posures, especially in the presence of a terminating event like death. A related methodological gap is the ability to identify (time varying) mediators or estimating mediated effects of time varying exposure for a recurrent event outcome. This proposal addresses these two critical methodological gaps in causal inference, the overarching goals being to elucidate (i) the impact of PM2.5 on the burden and progression of CVD; and, (ii) the key causal pathways by which air pollution exposure impacts such events. Accomplishing such goals will be facilitated by new analyses of an unprecedented data collection consisting of (a) an already harmonized and linked Part A Medicare data (33+ million subjects 2000 to 2019) at both the individual (e.g., age, gender, race, date of hospital- ization for any of the possible causes for hospitalizations, date of death) and zip code (e.g., daily PM2.5, O3 and NO2 levels; many potential confounders) levels; and, (b) an augmented version of these data including individu- ally linked Part B data (doctor visits, and outpatient procedures for any cause) for a representative sample of over 15 million Medicare enrollees (2012 to 2019) and, (c) the Medicare Current Beneficiary Survey (MCBS) data for the same study period, as a secondary analysis to account for possible bias due to unmeasured confounding. In methodological terms, we propose robust marginal and structural nested models that allow estimation of both instantaneous and delayed effects of time varying exposure to PM2.5 on recurrence of CVD events (Aim 1). We further propose a semiparametric approach to identify clinically important pathways by which exposure to air pollutants increases the risk of recurrence of CVD hospitalizations and death, and estimate the corresponding mediated effects (Aim 2). We will implement our methods and apply them to the above-described rich data source, focusing specifically on (Aim 3): (i) estimating the causal effects of PM2.5 exposure on two relevant causal estimands for recurrent events in the presence of mortality; (ii) identifying key mediators and characterize clinical pathways through which exposure to air pollution increases risks of CVD progression and death; and, (iii) identifying and investigating the disease groups that are biologically plausible. Finally, we will create and disseminate open-source, peer-reviewed statistical software to ensure ease-of-use and accessibility (Aim 4).

项目概要 我们这个时代最大的冲刷之一是空气污染，有充分的证据表明，接触细颗粒物（例如， PM2.5）会增加心血管疾病（CVD）和死亡的风险但是，有两个关键知识。 首先，现有研究主要将首次不良健康事件的发生视为健康状况。 因此，总体疾病负担和 PM2.5 暴露对疾病进展的影响仍然存在。 其次，据我们所知，没有研究评估暴露的因果途径。 在没有更好地了解疾病进展的情况下，空气污染物会影响复发性心血管事件。 和临床调节剂，我们告知监管政策和预防疾病的能力受到严重损害。 人们对开发评估随时间变化的因果效应的方法给予了有限的关注。 姿势，特别是在出现死亡等终止事件的情况下，一个相关的方法论差距是能力。 识别（随时间变化的）中介因素或估计随时间变化的暴露对重复事件的中介效应 该提案解决了因果推理中的两个关键方法论差距，即首要问题。 目标是阐明 (i) PM2.5 对 CVD 负担和进展的影响；以及 (ii) 关键因果关系； 空气污染暴露影响此类事件的途径将有助于实现这些目标。 对前所未有的数据收集的新分析，包括 (a) 已经协调和链接的 A 部分 个人的医疗保险数据（2000 年至 2019 年 33+ 百万受试者）（例如年龄、性别、种族、住院日期） 任何可能的住院原因、死亡日期）和邮政编码（例如每日 PM2.5、O3 和 NO2 水平；许多潜在的混杂因素）水平；以及，（b）这些数据的增强版本，包括个体 与 B 部分数据（就诊和出于任何原因的门诊程序）相关联的代表性样本超过 1500 万医疗保险参保者（2012 年至 2019 年），以及 (c) 医疗保险当前受益调查 (MCBS) 数据 同一研究时期，作为二次分析，以解释由于未测量的混杂因素可能导致的偏差。 在方法论方面，我们提出了稳健的边际和结构嵌套模型，可以估计 随时间变化的 PM2.5 暴露对 CVD 事件复发的瞬时和延迟影响（目标 1）。 我们进一步提出了一种半参数方法来确定暴露于空气中的临床重要途径 污染物增加CVD住院复发和死亡的风险，并估计相应的 我们将实施我们的方法并将其应用于上述丰富的数据。 源，特别关注（目标 3）：(i) 估计 PM2.5 暴露对两个相关的因果影响 存在死亡的情况下反复发生事件的因果估计；(ii) 确定关键中介因素并描述特征； 暴露于空气污染会增加 CVD 进展和死亡风险的临床途径； (iii) 识别并研究生物学上合理的疾病组。最后，我们将创建并研究这些疾病。 传播开源、经过同行评审的统计软件，以确保易用性和可访问性（目标 4）。