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  • Breann Coffaro

Lars David Perlmutt

Improved Risk Communication using a Multipollutant Air Quality Index

Background 



Exposure to air pollution has been shown to be associated with increased morbidity and mortality.  The major U.S cohort studies, the American Cancer Society Study and Harvard Six Cities studies, estimate that mortality from chronic exposure to air pollution is most strongly associated with fine particulates (PM2.5), leading to an increase in total mortality of 4% and 5%, respectively, per 10 ug/m3 increase in PM2.5. The National Morbidity, Mortality, and Air Pollution Study (NMMAPS) estimates an average .26% increased risk in total mortality one day later from acute exposure to PM10 across 90 cities in the United States. 3 Additionally, across 14 US cities, a 1% increase in cardiovascular disease hospital admissions, and a 2% increase in pneumonia and chronic obstructive pulmonary disease for each 10 ug/m3 increase in PM10 is estimated in individuals 65 years and older. The US EPA currently communicates daily health risks and air quality via a single pollutant air quality index (AQI).  As a risk communication tool, the AQI is intended to inform the public with facts leading to informed behavioral changes, based on individual health status. This method is limited, though, and fails to consider the cumulative effects of exposure to multiple pollutants. The main hypothesis is that the development of a multipollutant air quality index, based on morbidity, can improve the risk communication of health effects from endemic air pollution conditions.  



Methods 



The root of this hypothesis is based on a study by Stieb et al. (2008), but the current study will focus on developing an index based on morbidity data (respiratory hospital admissions), as opposed to mortality. This study will use respiratory hospital admissions data from New York City (NYC), existing respiratory hazard functions, and ambient pollutant concentrations from 2007 to 2010 to assess the associated burden of illness within each air quality classification of the US EPA AQI. Time-series analysis will be modeled in R, using combinations of 2-, 3-, 4-, and 5-pollutant values to model the number of daily respiratory hospitalizations, and a multipollutant index will be constructed using the hazard function of each pollutant derived from time-series analysis.  



Results 



This study seeks to evaluate the effects of air pollution using a multipollutant approach and will improve the understanding of respiratory effects due to exposure to chemical mixtures of air pollutants. The development of a risk communication tool based on respiratory hospitalizations and exposure to multiple pollutants has greater validity to populations over a wide range of ages, in addition to just susceptible populations.

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