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).

项目摘要 我们擦除最大的冲突之一是空气污染，有充分的证明是暴露于细节（例如， PM2.5）增加了心血管疾病（CVD）和死亡的风险。但是，有两个批判知识 空白。首先，现有的研究主要将第一次不良健康事件的发生视为健康事件。 来因此，总体疾病伯恩和PM2.5暴露对疾病进展的影响仍然 未知。其次，据我们所知，没有研究评估暴露途径的因果途径 空气污染物会影响复发性心血管事件。没有更好地了解疾病进展的 和临床调解人，我们为监管政策和预防疾病提供信息的能力受到了严重阻碍。 对开发用于评估时间变化的因果效应的方法的注意力有限 姿势，尤其是在终止事件（例如死亡）的情况下。相关的方法论差距是能力 识别（时间变化）调解人或估计反复事件的时间变化的介导效应 结果。该提议解决了因果推断的这两个关键方法论差距，即总体 目标是阐明（i）PM2.5对CVD燃烧和进展的影响； （ii）关键因果 空气污染暴露会影响此类事件的途径。实现此类目标将由 对（a）已经统一和链接的A组成的前所未有的数据收集的新分析 Medicare数据（33多个受试者2000年至2019年）的个人（例如，年龄，性别，种族，医院日期 - 用于住院，死亡日期和邮政编码的任何可能原因的ization（例如，每日PM2.5，O3和 NO2水平；许多潜在的混杂因素）级别； （b）这些数据的增强版本，包括个人 - 盟友链接的B部分数据（医生访问和任何原因的门诊程序）的代表性样本 1500万Medicare注册（2012年至2019年）和（c）Medicare当前的收益调查（MCB）数据 同一研究期是由于无法衡量的混杂而导致可能偏见的次要分析。 用方法论方面，我们提出了强大的边际和结构嵌套模型，以估算 时间变化对PM2.5的瞬时和延迟影响对CVD事件的复发（AIM 1）。 我们进一步提出了一种半参数方法来识别临床上重要的途径 污染物增加了CVD住院和死亡复发的风险，并估计 介导的效果（AIM 2）。我们将实施我们的方法，并将其应用于上述丰富数据 来源，将特定的焦点集中在（AIM 3）：（i）估计PM2.5暴露对两个相关的因果影响 在死亡率存在下复发事件的因果估计； （ii）识别关键调解人并表征 暴露于空气污染的临床途径会增加CVD进展和死亡的风险；和， （iii）识别和研究生物学上合理的疾病组。最后，我们将创建和 传播由同行评审的统计软件传播，以确保易于使用和可访问性（AIM 4）。