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 我们这个时代最大的祸害之一是空气污染，有充分的证据表明，接触fiNe颗粒(例如， PM2.5)会增加心血管疾病(CVD)和死亡的风险。然而，有两个关键知识 差距。首先，现有的研究主要认为fi第一次不良健康事件的发生是健康输出- 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 未知。第二，据我们所知，目前还没有研究评估暴露于 空气污染物会影响反复发生的心血管事件。没有更好地了解疾病的发展 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严重阻碍。 人们对开发评估时变因素的因果效应的方法给予了有限的关注。 伪装，尤其是在发生像死亡这样的终结事件的时候。一个相关的方法论差距是能力 识别(时变)介体或估计时变暴露对复发事件的介体效应 结果。这一建议解决了因果推理中的这两个关键的方法论差距，最重要的是 目的是阐明(I)PM2.5对心血管疾病负担和进展的影响；以及(Ii)主要原因 暴露于空气污染影响这类事件的途径。实现这些目标将通过以下方式促进 对史无前例的数据收集的新分析，包括：(A)已经协调一致并相互关联的A部分 2000年至2019年的医疗保险数据(3300万受试者)在个人(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、臭氧和 NO2水平；许多潜在混杂因素)水平；以及，(B)这些数据的增强版本，包括个人- 具有代表性的样本B部分数据(因任何原因就诊和门诊手术) 1,500万联邦医疗保险参保人(2012年至2019年)和，(C)#年联邦医疗保险现行Benefi年度调查(MCBS)数据 在同一研究期间，作为二次分析，以说明由于未测量的混杂可能造成的偏差。 在方法论方面，我们提出了稳健的边际和结构嵌套模型，允许估计 时变暴露于PM2.5对心血管疾病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。 污染物增加了心血管疾病住院和死亡的复发风险，并估计相应的 中介效应(目标2)。我们将实现我们的方法，并将它们应用于上述丰富的数据 来源，重点是fi(目标3)：(1)估计PM2.5暴露对两个相关的 在存在死亡率的情况下对复发事件的因果估计；(2)确定关键调节因素并确定其特征 暴露于空气污染增加心血管疾病进展和死亡风险的临床途径；以及， (3)确定和调查生物学上可信的疾病组。最后，我们将创建和 传播开放源码、经同行审查的统计软件，以确保易用性和可及性(目标4)。