Last update: 3/2/04

PIs: Vanja Dukic, John Frederick, Edward T. Naureckas, Paul Rathouz

This project focuses on statistical investigations into the relationship between air quality and respiratory health in the Chicago area. Efforts to date have centered on (A) the identification of atmospheric states most likely to be accompanied by elevated ground-level ozone amounts in Illinois, (B) the elaboration and development of statistical models to describe the spatio-temporal structure in ground-level ozone, (C) the examination and comparison of different measures of asthma incidence for use in population-based studies of the effects of air pollution on respiratory health., and (D) the development of statistical models to link acute asthma occurrence in Chicago's Medicaid population to levels of ozone, particulate matter, pollen and meteorological conditions in both urban-aggregated and spatially-resolved ways.

Problems encountered are mostly centered on the spatial, and in some cases the temporal, coverage of specific datasets related to atmospheric conditions, to air quality and to health outcomes. The health outcome data are organized spatially at the level of ZIP code. At any given day, ozone is measured hourly at a minimum of 11 sites in Cook County, but this still does not provide sufficiently dense coverage for all of Cook County’s 56 ZIP codes. Pollen is a significant factor in asthma occurrence, but the available data include only one daily value for the entire Chicago area. The dataset for large particles, PM10, has sparse spatial coverage and contains information at irregular time intervals, where the period between data points at a specific site can be up to six days. Data on PM2.5 are not available for the same time frame for much of our health outcome data. Therefore, we are faced with significant problems of incompatible space-time scales across the multiple data sources to be used in our project. In response to the issues posed by such problems of scale, we are investigating a variety of statistical spatio-temporal modeling techniques, the use of independent data sets with higher spatial resolution, and physical models (MM5, CMAQ) for use in generating spatiotemporal structure in localized factors that may influence asthma incidence. The problems identified are significant, and they have served as a part of the wider impetus for developing important new statistical methodology that will be applicable in our study as well as in other studies with similar study designs.

Upcoming work in this project will make extensive use of new spatio-temporal statistical methods (developed in another CISES-supported effort) as well as the capability in physical modeling of meteorology and air quality at high spatial resolution. In addition, the longer-term goal of developing improved statistical models for linking acute asthma events to air quality indicators is a driving force behind most of our efforts and work will continue on these models for the duration of the project.