Innovative Research for a Sustainable Future (+1) l Overview and Evaluation of Alternative Air Quality Exposure Metrics Used in Recent Air Pollution Epidemiological Studies Halûk Özkaynak 1, Kathie Dionisio 1, Lisa Baxter 1, Janet Burke 1, David Rich 2, Stefanie Sarnat 3, Jeremy Sarnat 3 and Rena Jones 4 1 US EPA, Office of Research and Development, National Exposure Research Laboratory, RTP, NC 2 University of Rochester, Rochester, NY ; 3 Emory University, Atlanta, GA; 4 National Cancer Institute, NIH, DHHS, Bethesda, MD Introduction Atlanta (ED) Study Figure 1. Tiers of exposure metrics relevant to air pollution epidemiology studies References Epidemiological studies of air pollution have traditionally relied upon surrogates of personal exposures, such as ambient concentration measurements from central-site monitoring stations. However, this approach may introduce exposure prediction errors and misclassification of exposures for a number of spatially heterogeneous pollutants, such as those associated with traffic emissions. We review a wide variety of alternative air quality and human exposure prediction approaches developed for subsequent applications to air pollution epidemiological studies that were conducted in the United States. The exposure metrics developed for particulate and gaseous pollutants are evaluated in the context of results from different epidemiological studies, performed in collaboration with EPA by Rutgers/Rochester/LBNL, Emory/Georgia Tech, New York State Department of Health and North Carolina State University. The full description of the study results from this broad research will appear in the October 2013 dedicated issue of the Journal of Exposure Science and Environmental Epidemiology. This poster presents selected results from a few of these studies conducted and provides a summary of key findings and lessons learned and recommendations, in order to improve the use of enhanced exposure metrics during future epidemiological studies of air pollution. Methods Alternative exposure metrics considered are: central site or interpolated monitoring data, regional pollution levels based on measurements or models (CMAQ) and local scale (AERMOD) air quality models, hybrid models, statistically blended modeling and measurement data, infiltration adjusted concentrations and population human exposure (SHEDS and APEX) model predictions (Fig.1). Health data sets used include: daily mortality and respiratory hospital admissions in New York City, daily hospital emergency department visits in Atlanta, daily myocardial infarctions across New Jersey and acute exacerbation of COPD in Cleveland. Exposure and epidemiological applications of alternative exposure metrics are compared across the different study areas. These metrics were then used to compare and contrast the estimated associations between short-term average ambient air pollution and acute morbidity or mortality. Figure 2. Atlanta multipollutant study area The alternative air pollution exposure metrics included central site measurements and estimated daily 24-hr average CO, NO x, PM 2.5, PM 2.5 SO 4, PM 2.5 EC, and 8-hr maximum O 3 ambient concentrations and population exposure distributions at each of the 187 ZIP code centroids analyzed (Figure 2). For predicting infiltration of ambient air indoors and modeling personal exposures to ambient pollution, air exchange rate (AER) estimates were derived by using a modified version of the LBL methodology, which is based on surveys assessing US building ventilation and leakage characteristics (J. Sanat et al. 2013). This method includes parameters that can either vary spatially or temporally across a geographic area. Spatially-varying AER predictors include the year a structure was built, as well as its size. AER components that exhibit variation over time include wind speed,opening and closing windows and ambient-indoor temperature gradients, which induce movement across building envelopes via the ‘stack effect’) Poisson generalized linear models were used to examine associations between daily measures of air pollution and daily counts of respiratory and cardiovascular ED visits. The basic form of the time-series model chosen was (S. Sarnat et al. 2013): log(E(Y kt )) = α + βpollution kt +  k λ k ZIP kt +  m λ m DOW kt +  n ν n hospital nt + g(γ 1,…,γ N ; time t ) +  o ξ o l0temp ot + η 1 dewpt t + η 2 dewpt t 2 + η 3 dewpt t 3 + δ 1 temp t + δ 2 temp t 2 + δ 3 temp t 3 Where, Y kt is the count of ED visits in ZIP code k on day t for the outcome of interest. Different lags of exposures and temperature variables were examined or specified. We compared associations for each pollutant-outcome combination among the five exposure metrics. For health effects analyses, data were restricted to the 169 ZIP codes that had APEX data available. In order to make valid comparisons, exposure metrics were also matched for missing values. Results: Atlanta Study Source: Özkaynak et al Figure 3 shows the modeled NO x concentrations (regional and regional + local ) for the study area. Strong impact of local or traffic sources are noted. EPA’s SHEDS population exposure model was used to predict PM 2.5, PM 2.5 EC and SO 4 exposures at each of the 187 zipcode centroids. For CO and NO x exposure predictions, EPA’s APEX exposure model (conceptually quite similar to the SHEDS model) was used instead, since it was already configured to run for these two pollutants from an earlier analysis conducted in Atlanta. Figure 4 shows results from the application of pollutant-specific alternative exposure metrics in the examination of association for asthma/wheeze ED visits in the Atlanta metro area during 1999 and 2002 (S. Sarnat et al. 2013). Finally, we also found positive associations for the interaction between AER and pollution on asthma ED visits for both CO and NO x indicating significant or near significant effect modification by AER on the pollutant risk ratio estimates (J. Sarnat et al. 2013). Figure 3. Modeled NO x concentrations Figure 4. Associations of different air pollution metrics with asthma/wheeze ED visits in Atlanta New Jersey (MI) Study The New Jersey (NJ) Triggering of Myocardial Infarction (MI) study used a time-stratified case- crossover design, with case periods defined as the 24 hour period before emergency room (ER) admission for MI. Subject-specific information was obtained from the statewide MIDAS database on individuals with primary diagnosis of Acute MI (ICD 410.x1) who were admitted from 1/2004 to 12/2006. Residents of NJ who live ≤10km from a monitoring station were chosen for the original (Rich et al. 2010) epidemiologic analysis. Four different exposure surrogates were used in the epidemiological analyses by Hodas et al. 2013: - Tier 1 - central site PM Tier 2a - exposures estimated using the Stochastic Human Exposure and Dose Simulation (SHEDS) model - Tier 2b - indoor concentrations of ambient PM 2.5 predicted using a deterministic mass balance model (Aerosol Penetration and Persistence, APP, model) and an air exchange rate model (Lawrence Berkeley National Laboratory Infiltration Model) -Tier 3 - a hybrid approach (SHEDS with modeled AER). Study Design: For each ambient PM 2.5 exposure surrogate (tier), we used the same time-stratified case-crossover design as in the initial analysis (Rich et al., 2010) to estimate the relative odds of a transmural infarction associated with increased exposure in the previous 24 hours. In this design, each patient contributed information both as a case during the period immediately before the MI, and as a matched control during times when a MI did not occur. Since each subject serves as their own control, factors that differ only across subjects are controlled by design. Case periods were defined as the 24 hour period before emergency department admission for MI. Control periods (3-4 per case depending on the number of days in the calendar month), defined as 24 hour periods in which no MI occurred, were matched to the case period by day of the week, time of day, year, and calendar month. Central-site PM 2.5 concentrations (Tier 1) and modeled ambient PM 2.5 exposures (Tier 2a, 2b, 3) corresponding to these case and control periods (cf. Baxter et al. 2013) were then contrasted in the statistical analyses. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM 2.5 is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM 2.5 that penetrates and persists indoors. Results: New Jersey Study Use of refined exposure surrogates did not result in larger health effect estimates, narrower CIs, or better model fits. Case-crossover conditional logistic regression results for MI with PM 2.5 were OR: (1.01, 1.20). Effect modification by residential AER was observed in both warm and cool seasons (Hodas et al. 2013). Each unit IQR increase in central site PM 2.5 concentrations was associated with increased relative odds of transmural MI in the high and middle AER tertiles (individually and combined), but not in the lowest AER tertile (Figure 5). Figure 5. Relative odds of MI associated with each IQR increase in ambient PM 2.5 by AER tertile New York City Studies Conclusions Figure 7. HRs for NYC respiratory hospitalizations for O 3 and PM 2.5 by Tertiles of Infiltration Ratio (E/C) Figure 6. PM 2.5 concentrations (C), exposures (E) and infiltration ratios (E/C) by census tract in NYC Figure 8. PM 2.5 concentration and exposure associations for emergency respiratory disease hospital admissions Acknowledgements Jones et al. (2013) used a case-crossover approach to evaluate the association between summertime ozone and PM 2.5 levels and daily respiratory hospitalizations in New York City during Comparisons were made between associations estimated using two pollutant exposure metrics: observed concentrations and predicted exposures estimated using the EPA’s SHEDS model. Effect modification of risk estimates calculated per IQR of each pollutant was also estimated by tertiles of predicted (E/C) or infiltration ratios. Mannshardt et al. (2013) investigated in NYC the sensitivity of the estimated health effect of three exposure metrics for PM 2.5 : ambient monitor data (AQS); estimated air quality concentrations from a deterministic atmospheric chemistry (CMAQ) model,and ; simulated individual daily average exposures based on the (SHEDS-PM) model. Using Hierarchical Bayesian modeling techniques, emergency respiratory disease hospital admissions for Medicare patients ages 65 and older for the period were then analyzed. Results: New York City Studies Results from two different types of epidemiological study designs both using respiratory hospitalization data in NYC: case crossover study with effect modification (Jones et al. 2013) and Hierarchical Bayesian modeling (Mannshardt et al. 2013) are shown in Figures 6 and 7 by Jones et al and in Figure 8 by Mannshardt et al Evaluation of Exposure Metrics 1)Modeled exposures are different than ambient air pollution concentrations due to either the influence of infiltration factors or varying times spent in non-ambient microenvironments: a) PM 2.5 exposures are lower than ambient levels, b) NO x and CO exposures are greater than ambient values. 2)The results provide an indication that accounting for daily variability in AER within a single-city time series model may explain heterogeneity in longitudinal risk for MI, respiratory hospitalizations and asthma ED visits associated with several common urban and regional (PM 2.5 and O 3 ) pollutants (Hodas et al. 2013, Jones et al. 2013, and J Sarnat et al. 2013). Influence of Study Design 1)In studies of the health effects of exposure to air pollution, the type of study design has important implications for the study results and their interpretation (S. Sarnat et al. 2013, Hodas et al. 2013, Jones et al. 2012, Mannshardt et al. 2013). Case-crossover and time-series studies are designed to account for temporal variability, while cohort studies capture both temporal and spatial variability. 2)The use of refined exposure estimation approaches may have minimal effects on case-crossover and times-series studies, especially for regional pollutants such as PM 2.5 (S. Sarnat et al. 2013, Hodas et al and Jones et al. 2013). However, S. Sarnat et al observed stronger associations with more refined exposure estimates at the zipcode level for local pollutants in a time- series study design in Atlanta. 3)Epidemiological study designs and methodological considerations (i.e., case-crossover, time series or Hierarchical Bayesian techniques) for short-term effects studies can make a difference in our ability to estimate the role of better exposure surrogates on health effects ( S. Sarnat et al. 2013, Jones et al. 2013, Hodas et al. 2013, Mannshardt et al. 2013). Recommendations 1)We suggest combining existing and new techniques for estimating ambient concentrations (e.g., merging CMAQ and the AERMOD atmospheric dispersion models) or incorporating highly resolved satellite data (Kumar et al. 2013) using a variety of Hierarchical Bayesian models to blend different levels of ambient concentration, housing and exposure-related information (e.g., Mannshardt et al. 2013), and ultimately linking ambient concentration predictions with population exposure models (i.e., by using SHEDS or APEX). 2)We encourage the broader use of alternative exposure metrics in future epidemiological studies of both short and long-term effects of individual pollution. More detailed discussion of the key research findings, lessons learned and recommendations for future work can be found in Baxter et al Vlad Isakov(EPA)Stephen Graham (EPA) Barbara Turpin (Rutgers)Natasha Hodas (Rutgers) Melissa Lunden (LBNL)Jim Mulholland (Georgia Tech) Elizabeth Mannshardt (NCSU)Lianne Sheppard (Univ. of Washington) Baxter, L. K., Burke, J., Lunden, M. M., Turpin, B., Rich, D. Q., Thevent-Morrison, K., Hodas, N., and Özkaynak. Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM 2.5 exposure compared to central-site monitoring data. Journal of Exposure Science and Environmental Epidemiology, 23: 241–247, Baxter, L.K., Dionisio, K. L., Burke, J., Sarnat, S.E., Sarnat, J.A., Hodas, N., Rich, D.Q., Turpin, B.J., Jones, R.R., Mannshardt, E., Kumar, N., Beevers, S.D and Özkaynak, H. (2013). Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations. Journal of Exposure Science and Environmental Epidemiology (under review). Hodas, N., B. Turpin, et al. (2013). Comparison of refined exposure surrogates when estimating the risk of myocardial infarction associated with acute increase in PM2.5 concentrations. Journal of Exposure Science and Environmental Epidemiology (accepted; on-line) Jones R.R., Özkaynak H., Nayak S.G., Garcia V.C., Hwang S.A., Lin S. (2013). Associations between summertime ambient pollutants and respiratory morbidity in NYC: comparison of results for ozone and PM using ambient concentrations and predicted exposures. Journal of Exposure Science and Environmental Epidemiology, (Accepted). Kumar, N., D. Liang, et al. (2013). A hybrid methodology for developing ambient PM2.5 exposure for epidemiological studies. Journal of Exposure Science and Environmental Epidemiology (accepted). Mannshardt, E., K. Sucic, et al. (2013). Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions. Journal of Exposure Science and Environmental Epidemiology (accepted) Özkaynak, H., Baxter, L., Dionisio, K., Burke, J. (2013). Air pollution exposure prediction approaches used in air pollution epidemiology studies. Journal of Exposure Science and Environmental Epidemiology (accepted; on-line). Rich, D.Q., Kipen, H.M., Zhang, J., Kamat, L., Wilson, A.C., Kostis, J.B. (2010). Triggering of transmural infarctions, but not non-transmural infarctions, by ambient fine particles, Environmental Health Perspectives, 118: Sarnat, J. A., S. E. Sarnat, et al. (2013). Spatiotemporally-resolved air exchange rate as a modifier of acute air pollution related morbidity. Journal of Exposure Science and Environmental Epidemiology (accepted; on-line). Sarnat, S. E., J. A. Sarnat, et al. (2013). Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta. Journal of Exposure Science and Environmental Epidemiology (accepted). This poster does not necessarily reflect EPA policy