2006 Progress Report: Air Quality and Reported Asthma Incidence in IllinoisEPA Grant Number: R829402C003
Subproject: this is subproject number 003 , established and managed by the Center Director under grant R829402
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Center for Integrating Statistical and Environmental Science
Center Director: Stein, Michael
Title: Air Quality and Reported Asthma Incidence in Illinois
Investigators: Frederick, John , Dukic, Vanja , Naureckas, Edward , Rathouz, Paul , Zubrow, Alexis
Current Investigators: Frederick, John , Draghicescu, Dana , Dukic, Vanja , Eshel, Gidon , Im, Haekyung , Naureckas, Edward , Rathouz, Paul
Institution: University of Chicago
EPA Project Officer: Packard, Benjamin H
Project Period: March 12, 2002 through March 11, 2007
Project Period Covered by this Report: March 12, 2006 through March 11, 2007
RFA: Environmental Statistics Center (2001) RFA Text | Recipients Lists
Research Category: Environmental Statistics , Ecological Indicators/Assessment/Restoration , Health , Ecosystems , Air
This project is devoted to statistical analyses to define the relationship between air quality and respiratory health using data from the Chicago area. The goal of developing improved statistical models for linking acute asthma events to air quality indicators is a driving force behind most of the work, and development of these models has been ongoing throughout the 5-year duration of the project.
Results to date include a comparison of different measures of asthma incidence for use in population-based studies of the effects of air pollution on respiratory health and 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.
Interactions with Personnel from EPA
The Air Quality and Asthma Group has had ongoing interactions with Thomas Brody and Michael Compher (U.S. Environmental Protection Agency [EPA] Region 5, Air and Radiation Division) concerning studies that EPA is conducting with the Wisconsin and the Centers for Disease Control and Prevention (CDC) Public Health Tracking offices. The project is called the Public Health Air Surveillance Evaluation Project (PHASE). The effort seeks to develop, evaluate, and demonstrate the advantages and limitations of different methods for generating air quality surveillance data that could be routinely available to link with public health data as part of the CDC's Environmental Public Health Tracking Network.
Vanja Dukic and Edward Naureckas serve as members of the Chicago Asthma Consortium, together with Thomas Brody. This organization, composed of researchers from the health and air quality communities, addresses a variety of problems concerning causes, incidence, and treatment of asthma in the Chicago area. Dr. Dukic is a member of the Data Task Force within the consortium.
Research Activities and Results
The following subsections summarize research activities and results obtained to date.
A. Short-Term Respiratory Effects of Air Pollution in Metropolitan Chicago (V. Dukic). Vanja Dukic is continuing to work with Chava Zibman, a Ph.D. candidate in statistics. They are in the final stages of writing the paper entitled “Short-term Effects of Air Pollution on Respiratory Health in Chicago” and anticipate submitting it for publication to an applied statistics journal in the near future. The complementary review article aimed for the American Journal of Epidemiology is also in preparation, with Edward Naureckas and Paul Rathouz. Dr. Dukic and Ms. Zibman are also working with Michael Stein and Paul Rathouz on theory and methods for estimation of pollutant effects in the presence of unobserved confounders in linear and log-linear models using, among others, spectral domain techniques for multi-variate Gaussian time series simulation. This work is expected to be the second part of Chava Zibman’s Ph.D. thesis, anticipated to be completed by the end of 2007.
B. Air Quality Analyses Associated with the Dan Ryan Reconstruction Project (E. Naureckas and J. Frederick). In earlier years of the Center for Integrating Statistical and Environmental Science (CISES) project, we investigated the use of asthma medication usage, specifically prescription fill rates for short acting beta agonist rates, as an outcome variable to assess the effects of outdoor air quality on asthma morbidity. The power of our approach is that the relatively high numbers of short acting beta agonist prescriptions allowed analysis of small geographic areas using the existing EPA monitoring network. Our results demonstrated a link between excess fills for short acting beta agonists and elevations in outdoor ozone levels, even when below the current EPA standard. With the recent revision in EPA’s standard for airborne particulates in response to literature demonstrating an increase in all-cause mortality associated with particulates, there has been great interest in this pollutant as a possible exacerbant of asthma. Unfortunately, in the time period covered by the beta agonist dataset, the Chicago-area’s EPA particulate monitoring network did not provide the density of data necessary to perform the ZIP code level analysis we were able to perform using the ozone data.
The reconstruction of the Dan Ryan Expressway, a major highway running through the south side of Chicago, has provided an opportunity to further investigate this type of pollutant. As a major component of the project, the Illinois Department of Transportation (IDOT) has invested in an extensive network of monitors, both prior to and during construction, to assess the impact of the construction on communities surrounding the construction zone. The activity could create elevated loadings of atmospheric particulate matter arising from movement of dirt, demolition of old structures, use of construction materials, and burning of diesel fuel. IDOT began measurements to establish baseline levels of various pollutants at approximately 10 locations on the south side of Chicago in September 2004. These include course particulate matter (PM10) and fine particulate matter (PM2.5) as well as total dust loading, the abundances of diesel components, lead, and respirable dust.
During year 5 of CISES, we began analyzing the available PM10 and PM2.5 data collected along the construction zone, with emphasis on statistical comparisons to data from EPA-sponsored sites on the south side of Chicago. The time period considered was January through June 2005, coincident with the early phase of the construction activity. The IDOT-EPA comparisons are complicated by the fact that the IDOT measurements for any given species do not occur on a daily basis, and the exact location of any measurement may lie at various points along the expressway. Therefore, our analyses were restricted to the limited number of coincidences.
The comparison of PM10 measurements from the IDOT network with those from four EPA monitors in the same part of the city showed significant positive correlations (P = 0.01), and no persistent effect of the construction activity was evident. In three of the four cases, the regression line relating the IDOT and EPA datasets differed only slightly from a straight line with a slope of unity and an intercept of zero. However, in all cases, the largest individual PM10 values, near 70 μg m-3, appear in the IDOT data. A comparison of IDOT and EPA data for PM2.5 was possible for only one EPA location. In this case, the EPA measurements explained 41 percent of the variance in the IDOT data, while the means of coincident measurements were virtually identical, being 16.7 μg m-3 and 16.5 μg m-3 for EPA and IDOT, respectively.
Based on the early work completed as of this writing, the effect of the construction activity lies in the sporadic appearance of elevated PM10 abundances, confined to a single day per occurrence. These spikes are imposed on a PM10 background typical of that for Chicago’s south side. Additional analysis is ongoing for the remainder of year 5 of CISES.
C. Space-Time Modeling of 20 Years of Daily Air Temperature in the Chicago Metropolitan Region (H. K. Im, P. Rathouz and J. Frederick). This study arose out of the need, in the asthma-ozone project, to have spatially resolved temperature measurements as a key exposure variable parallel to our measures of ozone, which are also spatially and temporally resolved. Dr. Im took on this project at the beginning of her appointment as Research Associate, under the direction of Dr. Rathouz. Initially, the goal of this paper was to construct a space-time interpolation model for maximum daily temperature. The project rapidly evolved into a more elaborate analysis owing to several facets of the analysis:
- It quickly became clear that, in order to obtain sufficient information on both the space and the time structure of the data, we would require many more than the 4 years of data with which we had been working. We therefore are now working with 20 years of data from 1981 to 2000, inclusive.
- We learned that, while the so-called “lake effect” on temperature (that is, the tendency for temperatures to be warmer in the winter and colder in the summer near the lake) is widely claimed to exist, there was little in the way of formal empirical analysis or description of this effect in the meteorological literature.
- The analysis is considerably complicated by the seasonal variation not only in the mean model, but also in the variance-covariance structure. Temperature is less variable in the summer, and auto-regressive time parameters and spatial covariance parameters vary strongly by season.
The goal of our analysis is currently to develop a space-time model of both the mean and variance-covariance structures for approximately 12 stations in the Chicago area using 20 years of data, accounting for the strong seasonal variation in these structures. Our analysis aims both to inform the meteorological literature, by providing a parsimonious description of these processes, and to provide a model useful for daily spatial interpolation across the region. Our model incorporates a description of the lake effect on temperature by allowing mean temperature to vary as a function of distance from the lake and winds from the east or west. We also model effects of latitude and of winds from the north and south. Throughout, we allow for differential effects as a function of season of the year. We approach the problem by first developing time-series models for temperature at each station. In so doing, we investigate variability in time-series variance-covariance structure as a function of station (spatial non-homogeneity) and of season of the year (non-stationarity). Building on the time-series models, we develop a spatial model for the time-series innovation terms, again examining how the spatial parameters vary as a function of season. The resulting parsimonious space-time model permits spatial interpolation of air temperature on any given day using data from the current day and days nearby in time.
D. Disease Mapping and Aggregation-Consistent Modeling (P. Rathouz). Work done by Paul Rathouz during the past year has continued to advance in two areas: (1) development of a statistical model and computational method for disease mapping; and (2) development of a new model for longitudinal and spatial environmental epidemiology data. These efforts are summarized below.
Study D1. Mapping asthma outcomes aggregated by ZIP code:
The main ideas and goals of this project have been described in previous annual reports. Efforts in the past year have focused primarily on wrapping up the loose ends and bringing the project to a close. The thrust of the project is to develop a statistical model and a new computational technique for mapping smoothed disease incidence rates over space when the data consist of event counts aggregated over geographical areas such as ZIP codes, census tracts or counties. The method works by: (1) tiling the entire region with cells; (2) using the tiling to approximate the integrals required to compute the variance-covariance matrix of area-level average disease risk; and (3) basing spatial interpolation on best-linear unbiased prediction of disease risk given the area-aggregated data disease counts.
Innovations over the past year on this project include: (1) completing a review of the statistical literature on this problem, which demonstrated that our approach is decidedly an innovative one; (2) developing measures of numerical precision of variance-covariance values amongst the areas; and (3) further improving the computational algorithm so that the model can be fitted and the smoothed plot generated, using up to 6000 cells over the Chicago region, in approximately 10 minutes on the CISES computers. This relatively low level of computational burden suggests that the method is practical to use in applied disease mapping work.
In the remaining project time, we will complete this work, and we anticipate submitting a manuscript to Environmetrics.
Study D2. Longitudinal and spatial analysis of short-term respiratory health effects of air pollution: aggregation-consistent modeling:
The goal of this project is to develop a statistical model and a method of estimation for data in air pollution epidemiology studies that have both a longitudinal and a spatial component. For example, in our study, we have data on each individual at each point in time (each day), while individuals are also arranged by ZIP code. We would like to exploit the longitudinal structure of the data and develop a model that allows for individual differences in respiratory health outcomes. We would also like to describe the spatial structure of asthma event incidence. Finally, we would like a model that is “aggregation-consistent.” By this, we mean that the model can incorporate data from a variety of levels of aggregation. For example, outcomes are essentially aggregated over ZIP codes at a given point in time, because we do not have any person data at finer spatial resolutions than ZIP code. Many models currently in use for air pollution epidemiology are not aggregation consistent. They yield inferences on the scale of the data which arise in a given study, but these inferences are not comparable to those in other studies with data on a different scale and are awkward to use when exposure, outcome, and other data are on differing scales.
The advantage of the longitudinal component of the data is that it allows us to increase statistical efficiency and to control confounding in the detection of effects of daily fluctuations in air pollution and weather on acute asthma outcomes. These benefits accrue because the analysis essentially hinges on the degree to which health outcomes and air pollution covary within a person over a given time window. Effects estimated in such an analysis are automatically adjusted for all factors that are constant within a person, within a time-window or within a person-time window.
In addition, a spatial modeling component is also important because it permits the display and study of spatial variability in health outcomes that is not explained by person-time-specific, person-specific, or time-specific covariates that are in the model. Such studies are useful for hypothesis generation, for examining changes in explained variability as covariates are added to the model, etc.
Our modeling approach to this problem has been described in the previous annual report. It was ongoing work on this problem that sparked the investigation of the space-time structure of temperature being spearheaded by Dr. Im. Dr. Rathouz delayed further work on this project this year in order to pursue the temperature project, but he will revisit this project and complete a paper during the final months of CISES.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other subproject views:||All 21 publications||4 publications in selected types||All 3 journal articles|
|Other center views:||All 115 publications||69 publications in selected types||All 47 journal articles|
||Naureckas ET, Dukic V, Bao X, Rathouz P. Short-acting β-agonist prescription fills as a marker for asthma morbidity. Chest 2005;128(2):602-608.||
Supplemental Keywords:RFA, Economic, Social, & Behavioral Science Research Program, Health, Scientific Discipline, PHYSICAL ASPECTS, Air, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, HUMAN HEALTH, particulate matter, Applied Math & Statistics, Ecosystem/Assessment/Indicators, Ecosystem Protection, Health Risk Assessment, Risk Assessments, Monitoring/Modeling, Ecological Effects - Environmental Exposure & Risk, Ecological Effects - Human Health, Environmental Monitoring, Health Effects, Physical Processes, Environmental Statistics, Ecological Risk Assessment, Engineering, Chemistry, & Physics, Environmental Engineering, EPA Region, Ecological Indicators, particulates, risk assessment, ecological effects, monitoring, asthma, atmospheric particulate matter, health risk analysis, watersheds, emissions monitoring, ecological health, ozone , particulate, stratospheric ozone, human health effects, ozone, sediment transport, airborne particulate matter, computer models, exposure, air pollution, chemical transport modeling, chemical transport, trend monitoring, environmental health effects, air pollutant-induced pulmonary inflammation, statistical models, human exposure, ecological risk, water, ecosystem health, environmental indicators, PM, ecological models, aersol particles, chemical transport models, Region 5, data models, modeling studies, air quality, statistical methodology, human health risk, stochastic models
Progress and Final Reports:Original Abstract
Main Center Abstract and Reports:R829402 Center for Integrating Statistical and Environmental Science
Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829402C001 Detection of a Recovery in Stratospheric and Total Ozone
R829402C002 Integrating Numerical Models and Monitoring Data
R829402C003 Air Quality and Reported Asthma Incidence in Illinois
R829402C004 Quasi-Experimental Evidence on How Airborne Particulates Affect Human Health
R829402C005 Model Choice Stochasticity, and Ecological Complexity
R829402C006 Statistical Approaches to Detection and Downscaling of Climate Variability and Change