Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #119379

Mona Choi, MN, RN, Family and Community Health, University of Maryland, School of Nursing, 655 West Lombard Street, Room 648, Baltimore, MD 21201-1579, 410-706-0312, mchoi@son.umaryland.edu, Frank C. Curriero, PhD, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe St, Baltimore, MD 21205, and Meg Johantgen, PhD, RN, School of Nursing, University of Maryland, 655 West Lombard Street, 475B, Baltimore, MD 21201.

Exposure assessments to determine the spatial extent and frequency of exceeded target values of air pollution and their potential health effects have been recognized as a priority of government agencies. Geographic Information Systems (GIS) facilitate these issues by providing a framework for the integration of spatial, health-related variables, environmental factors, and demographic and socioeconomic information. Previous time-series studies of air pollutants usually focused on a large geographical level, such as county and city. As a result, average values of air pollutants across the region were used. Most time series studies, thus, might have inaccurate estimation of population exposure because the same averaged level of exposure has been assigned to a large geographical area. Thus, geostatistics and GIS will be used to finer levels of geographic areas than in previous air pollution-health studies. The purpose of the study is to use traditional statistics and geostatistics in combination with GIS to examine the association between levels of ozone concentration and emergency department (ED) admissions for cardiovascular and respiratory conditions in the small local areas. This ecological cross-sectional study will demonstrate application of a geostatistical technique known as block kriging for estimating ozone exposures at the ZIP code level related to ED admissions in Maryland during summer of 2002. Other covariates included in multivariate analyses will be meteorological factors, demographic/socioeconomic variables, and lag effects of ozone. The visualized information from the process of GIS and inferential statistics can lead to identifying geographic areas that need more investigation of individual level or clinical research.

Learning Objectives:

• Understand the merits of applying geostatistics and GIS to analyze ozone exposures and health outcomes at the small area level (e.g., ZIP codes)
• Understand ozone exposures and other covariates related to ED admissions with cardiovascular and respiratory conditions as adverse health outcomes

Presenting author's disclosure statement:

I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.