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首页> 外文会议>Joint annual meeting of the International Society of Exposure Science and the International Society for Environmental Epidemiology >Estimating Asthma, Myocardial Infarction, and Heart Failure Hospitalizations and Emergency Room Visits in New York City from PM2.5 Exposure Using a Bayesian Modeling Approach
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Estimating Asthma, Myocardial Infarction, and Heart Failure Hospitalizations and Emergency Room Visits in New York City from PM2.5 Exposure Using a Bayesian Modeling Approach

机译：使用贝叶斯建模方法从PM2.5暴露估算纽约市的哮喘，心肌梗塞和心力衰竭住院和急诊室就诊情况

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Fine particulate matter has been shown to influence the frequency and severity of respiratory and cardiovascular diseases, and also increases inflammatory proteins and heart rate variability (HRV). The typical air pollution-focused health study uses concentration data from the nearest ground-based air quality monitor(s), which have missing data on the temporal scale due to filter collection schedules (once every 3 days or once every 6 days), and on the spatial scale due to monitor placement. To overcome these data gaps, this project used a Bayesian model to generate estimates of PM2.5 in areas with and without air quality monitors. This was achieved by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Air Quality model predictions of PM2.5 concentrations, into ambient concentration surfaces covering selected geographic areas. The objectives of this study were to: 1) demonstrate that the inputs to the model could include AOD data in addition to measurement data from monitors and modeling estimates from air quality models, and; 2) determine if inclusion of AOD surfaces in Bayesian model algorithms resulted in air pollutant concentration surfaces which accurately predicted hospital admittance and emergency room visits for Ml, asthma, and HF. The focus was the New York City, NY metropolitan and surrounding areas from 2004-2006. The results showed PM2.5 exposures above the National Ambient Air Quality Standard (NAAQS) value (12 mg/m3) were associated with increased risk of asthma, Ml and HF. Estimates derived from concentration surfaces incorporating AOD had a similar estimate of risk as compared to those derived from combining monitor and CMAQ data alone. This study demonstrated that PM2.5 concentrations from satellite data can be used to supplement PM2.5 monitor data and air quality model estimates of PM2.5 in assessing risk associated with three common health outcomes.
机译：细颗粒物已被证明会影响呼吸系统疾病和心血管疾病的发生频率和严重程度，还会增加炎症蛋白和心率变异性（HRV）。以空气污染为重点的典型健康研究使用了最近的地面空气质量监测器的浓度数据，这些数据由于过滤器的收集时间表（每3天一次或每6天一次）而在时间尺度上缺少数据，并且由于显示器的位置，在空间尺度上。为了克服这些数据差距，该项目使用贝叶斯模型来生成带有或不带有空气质量监测器的区域中PM2.5的估算值。这是通过将监视器测量的PM2.5浓度，从卫星气溶胶光学深度（AOD）数据得出的PM2.5浓度估算值以及PM2.5浓度的空气质量模型预测值合并到覆盖选定地理区域的环境浓度表面中来实现的。这项研究的目的是：1）证明模型的输入除了监视器的测量数据和空气质量模型的建模估计值外，还可以包括AOD数据;以及2）确定在贝叶斯模型算法中包括AOD表面是否导致空气污染物浓度表面准确预测了M1，哮喘和HF的住院率和急诊就诊率。重点是2004年至2006年的纽约市，纽约市及周边地区。结果显示，高于国家环境空气质量标准（NAAQS）值（12 mg / m3）的PM2.5暴露与哮喘，MI和HF风险增加相关。与仅结合监测器和CMAQ数据得出的估计值相比，结合了AOD的浓缩表面得出的估计值具有相似的风险估计。这项研究表明，卫星数据中的PM2.5浓度可用于补充PM2.5监测数据和PM2.5的空气质量模型估计，以评估与三种常见健康结果相关的风险。

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