Estimating Asthma, Myocardial Infarction, and Heart Failure Hospitalizations and Emergency Room Visits in New York City from Exposure to Ambient Fine Particulate Matter using a Hierarchical Bayesian Model Approach
Citation:
Hall, EricS. Estimating Asthma, Myocardial Infarction, and Heart Failure Hospitalizations and Emergency Room Visits in New York City from Exposure to Ambient Fine Particulate Matter using a Hierarchical Bayesian Model Approach. NEHA 2018 AEC and HUD Healthy Homes Conference, Anaheim, CA, June 25 - 28, 2018.
Impact/Purpose:
Presented at the informatics track in the 2018 National Environmental Health Association (NEHA) Annual Education Conference (AEC) in Anaheim California from 25 - 28 June 2018
Description:
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). A novel approach was developed to reduce the spatial and temporal gaps in the ambient measurement of fine particulate matter (PM2.5), and to generate more realistic and representative concentration values for use in epidemiological studies. 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 Hierarchical Bayesian Model (HBM) to generate estimates of PM2.5 in areas with and without air quality monitors. It achieved this by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5 concentrations into ambient concentration surfaces covering selected geographic areas. There major objectives of this study were: 1) to demonstrate that the inputs to the HBM could be expanded to include AOD data in addition to measurement data from PM2.5 monitors and modeling estimates from the CMAQ air quality model, and; 2) to determine if inclusion of AOD surfaces in HBM model algorithms resulted in PM2.5 air pollutant concentration surfaces which more accurately predicted hospital admittance and emergency room visits for MI, asthma, and HF. This study focused on the New York City, NY metropolitan and surrounding areas during the 2004–2006 time-period. The results showed PM2.5 exposures above the National Ambient Air Quality Standard (NAAQS) value (12 g/m3) were associated with increased risk of asthma, MI and HF. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and 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.
