Examining Short-Term Air Pollution Exposures and Health Effects: Atlanta as a Case Study

1. Examining Short-Term Air Pollution Exposures and Health Effects: Atlanta as a Case Study Jeremy Sarnat, Emory University Emory University: Sarnat SE, Darrow L, Flanders D, Kewada P, Klein M, Strickland M, Tolbert PE Georgia Tech: Mulholland J, Russell AG EPA: Isakov V, Crooks JL, Touma J, Özkaynak H CMAS Conference October 12, 2010

2. Outline of Talk • Study designs used to assess short-term exposures, acute health responses • Population-based timeseries analyses • Cohort and panel studies • Considerations/concerns related to exposure data • Examples: Study of Particles and Health in Atlanta (SOPHIA); Emory-Ga Tech EPA COOP • Considerations/concerns related to health data • Example: Atlanta Commuters Exposure Study

3. I. Study Designs 1. Population-based studies • Assess relationship between daily or multi-day ambient pollution concentrations and mortality, ED visits, hospitalization • Data analysis – regression • Timeseries, Poisson (using daily counts data) • Case-crossover, Logistic (using data on individual visits/deaths) • Relatively inexpensive • Can evaluate a single study area, or multiple cities • Large N, statistical power to detect subtle changes in endpoint • Atlanta SOPHIA study • Data on 10,206,389 ED visits from 41 of 42 hospitals in the 20-county study area for the period 1993-2004 • Objective to assess short-term associations between air pollution and cardiorespiratory ED visits, hospital admissions, adverse birth outcomes, implanted cardioverter defibrillator (ICD) events

4. SOPHIA Study of Particles and Health in Atlanta > 8,300 sq miles • = Acute care facility = Jefferson St. station Data include 10,206,389 ED visits from 41 of 42 hospitals in 20-county Atlanta, 1993-2004

5. PM2.5 Asthma Visits 24-hr standard Annual standard Data Analysis • Exposure = daily air pollution measurements • Outcome = daily cardiopulmonary emergency department visits • Poisson generalized linear models (GLM) • 3-day moving average (lags 0, 1, 2) for each pollutant • Control for time, day-of-week, holidays, hospital entry/exit, temperature, dew point

6. Pediatric ED Visits for Asthma Strickland et al., AJRCCM, 2010

7. I. Study Designs 2. Cohort or panel studies • Assess relationship between sub-daily, daily or multi-day ambient pollution concentrations and sub-clinical, clinical changes in health • Data analysis – regression • Linear mixed effect models common • Assume that pollution term(s) in model reflect mean personal exposure of population • Only time-varying factors can confound results • Good exposure/health for small N • Relatively expensive and cumbersome

8. II. Considerations Related to Exposure Data • Approach valid if exposure metric accurately captures patterns of pollutant spatiotemporal variability across modeling domain • Exposure error, exposure misclassification, measurement error • Varies by pollutant • Concentration ≠ Exposure • Lagged exposures and response • Time-varying factors can confound results • Confounding by co-pollutants • Disaggregating individual effects vs. effects from mixtures

9. II. Considerations Related to Exposure Data • Approach valid if exposure metric accurately captures patterns of pollutant spatiotemporal variability across modeling domain • Exposure error, exposure misclassification, measurement error • Varies by pollutant • Concentration ≠ Exposure • Lagged exposures and response • Time-varying factors can confound results • Confounding by co-pollutants • Disaggregating individual effects vs. effects from mixtures

10. Jerrett et al., 2005 • 22,905 subjects living in LA area between 1982 – 2000 • 5,856 deaths • 23 PM2.5 and 42 O3 monitors used to create a spatial grid of pollution concentrations • Examine association between long term exposure and excess mortality • Compare with Pope et al., 2002 (ACS)

11. Emory-Ga Tech EPA COOP • Objectives • Develop and evaluate five alternative exposure metrics for ambient traffic-related and regional pollutants • Apply metrics to two studies examining ambient air pollution and acute morbidity in Atlanta, GA • SOPHIA Atlanta ED & ICD studies • Hypotheses • Finer spatial resolution in ambient concentrations & inclusion of exposure factors in analyses  changes in estimated distribution of population exposures compared to ambient monitoring data • Use of refined estimates  reduced exposure error  greater power to detect epidemiologic associations of interest

12. Current Project • Develop 5 alternative metrics of exposure • Traffic: CO, NOX, EC • Regional: O3, SO42- • Mix: PM2.5 • Daily, ZIP code level • For sub-period, 1999-2002 • For current analysis: • Results using Metricsi, iii, iv, v (i) Ambient Monitoring Data Emissions Data (ii) Spatially- Interpolated Background Modeling: (iii) AERMOD (iv) Hybrid Spatially-Resolved Concentrations (v) Exposure Factors Spatially-Resolved Exposures

13. Time Series of Coefficients of Variability: Comparison of Background vs. Hybrid output NOx Modeling helps to resolve spatiotemporal variability in pollutant concentrations important for timeseries epi analysis Slide courtesy of V. Isakov

14. Preliminary Results of Epidemiologic Analysis of ED Visits in Atlanta CC CC CC CC CC CC

15. II. Considerations Related to Exposure Data • Approach valid if central site accurately reflects patterns of pollutant spatiotemporal variability across modeling domain • Exposure error, exposure misclassification, measurement error • Varies by pollutant • Concentration ≠ Exposure • Lagged exposures and response • Time-varying factors can confound results • Confounding by co-pollutants • Disaggregating individual effects vs. effects from mixtures

16. Modeling Exposure Factors in Epi Analyses of Short-term Exposures • Examine whether inclusion of pollutant infiltration (Finf) estimates affect epi results • Greater infiltration of ambient pollution  greater signal with ambient-based exposure metric • Consider air exchange rate (AER) surrogates • Require readily accessible data and easy to use in population-based studies • Temporal factors = meteorological • Stratified analysis by AER category • ED study • Zip-code resolved daily estimates of AER

17. Zip code resolved AERs in Atlanta

18. Estimating AER using LBNL Approach Temporally-varying Spatially-varying where NL = exp(b0 +b1yr built + b2floor area + e) H = height of home (m) fS = stack effect estimate fW = wind effect estimate T = temperature (K) V = wind speed (m/sec) AER  Chan, W. R.; Price, P. N.; Nazaroff, W. W.; Gadgil, A. J., Distribution of residential air leakage: Implications for health outcome of an outdoor toxic release. Indoor Air 2005: Proceedings of the 10th International Conference on Indoor Air Quality and Climate, Vols 1-5 2005, 1729-1733

19. PM2.5 – CVD and Resp ED Visits by AER Strata (+Hybrid Metric) AER Strata (hr-1) CVD Visits AER Strata (hr-1) Resp Visits

20. III. Considerations Related to Health Data • Administrative records (e.g., death certificates, medical billing records) • Lack of information about subject location in time and space • Residence only? Mobility pattern throughout domain? • Lack of information about sub-clinical steps in mechanistic pathway • Panel-based design used to address some of these issues

21. Atlanta Commuters Exposure (ACE) Study • Measure in-vehicle pollutant concentrations and corresponding acute health response for a cohort of health and asthmatic commuters • Scripted 2h commute during morning rush hour periods in Atlanta • Highly-speciated in-vehicle particulate exposure measurements • Detailed continuous and pre-post commute health measurements • Provide means of comparison with modeled estimates, roadside and central site monitoring  validation of traffic exposure models

23. Summary • Timeseries and cohort/panel studies constitute complementary approaches to address concerns in examinations of short-term exposures and acute effects • Modeled data may and can provide opportunities to reduce error in population-based timeseries analyses • Validity of approaches and interpretation of results still ongoing • Panel studies may serve to validate, highly spatially-resolved modeled estimates • How can models informs cohort and panel studies?

25. Health data analysis based on Poisson models to examine the association between ambient pollutant concentrations and counts of cardiovascular and respiratory emergency department visits Epidemiological statistical models: log(E(Ykt)) = α + β exposure metrickt + kγkakt+ …other covariates k: 225 Zip codes t: 365 days x 4 years b - risk ratio for increments of one interquartile range (IQR) in corresponding pollutant concentrations Modeling Approach • Where Ykt = daily deaths, ED visits or hospital admission counts in area k on day t for outcome chosen (e.g., respiratory or cardiovascular) • Exposure Metrics are Monitored or Modeled Ambient Pollution concentrations for area k on day t

26. CVD ED Visits & PM2.5by AER Strata AERMOD CS BG HYBRID AERMOD CS BG HYBRID 24-hr PM2.5

27. Validity of approach? • What if we have better means of assigning exposure? (e.g., spatiotemporal models) • Will this improve estimates of magnitude of effect, strength of effect? • Is there a way to compare whether a given assignment approach is ‘better’ than another?

28. Summary • Varying degrees of spatial and temporal variability observed for different exposure metrics • Variability more pronounced for traffic-related (CO, NO2) vs. regional (SO42-) pollutants • Similar magnitudes of association across metrics observed for CVD outcome • Robust results for spatially heterogeneous pollutants as well • Hybrid metric  strongest associations for respiratory outcome • Significant for CO, PM2.5; CS non-significant • Suggestive evidence of AER as a modifier of effect for models using hybrid metric

29. Challenges - Future Directions • Magnitude and strength of association affected by numerous factors • RRs from spatiotemporal ambient pollutant do not necessarily reflect exposure • Future work will incorporate both exposure factors and spatially-resolved ambient concentrations for epi models • Metric V, SHEDS

30. Temporal Associations • Exposure contrast in time-series studies • Temporal differences • One daily pollutant value  daily ED visits • With spatially-resolved daily data • Let variation over time within each ZIP code provide exposure contrast • Daily ZIP-specific pollutant values  daily ZIP-specific ED visits

31. Spatial and Temporal Characteristics of Ambient Monitoring Data in Atlanta • For PM2.5, temporal variability between the days dominates, while spatial patterns of concentrations between the monitoring sites vary only by 10-30% within a given day. This is as expected because PM2.5 is a regional pollutant and the day to day variability reflects the movement of various air masses and the influence of photochemical transformations

32. Spatial and Temporal Characteristics of Ambient Monitoring Data in Atlanta • For NOx, the pattern is different; both temporal and spatial variability exists. Unlike PM2.5, NOx concentrations can vary by a factor of 3 for any given day. This pollutant is highly influenced by local sources of emissions and thus the concentrations do not change unless there is a shift in meteorological conditions within the day

33. Time Series of Coefficients of Variability PM2.5 Modeling helps to resolve spatial scale and provide variability in pollutant concentrations that is important for the epi analysis

34. Defining terms We estimated several air tightness parameters: • Infiltration surrogates • Home age • Home size • # of rooms, home area, home value • Normalized Leakage (NL) = describes relative leakage for a range of building types • Unitless (leakage area per exposed envelope area) • Most single-family homes have NL values between 0.2 – 2 • Air Exchange Rate (AER) • Expressed in hr-1 • AER > 1  well-ventilated

35. SES surrogates – estimated ach ACH [hr-1] % of Low Income Households ACH [hr-1] Median Income