Characterize populations and health outcomes Project 4 Multi-City Morbidity Study Characterize daily pollutant mixtures Source apportionment Source apportionment

Characterize populations and health outcomes

Project 4Multi-City Morbidity Study

Characterize daily pollutant mixtures

Sourceapportionment

Sourceapportionment

Examine impacts of air pollution mixtures on

acute severe morbidity

Examine impacts of air pollution mixtures on

acute severe morbidity

Examine and explain heterogeneity of

associations across cities

Examine and explain heterogeneity of

associations across cities

ED visitsED visits

Hospital admissionsHospital admissions

Sociodemographic and housing factorsSociodemographic

and housing factors

Conduct multi-city epidemiologic analysis

How and why do associations between air pollution mixtures and acute morbidity vary across cities?

SO4

NO3

NH4ECOC

metals

otherAtlanta

SO4

NO3

NH4EC

OC

metals

otherSt. Louis

Atmosphericmodeling (e.g., ROS)

Atmosphericmodeling (e.g., ROS)

Direct measurementsDirect measurements

Project 4 Study Team

• Stefanie Sarnat• Andrea Winquist• Mitch Klein• Lyndsey Darrow• Howard Chang• Jim Mulholland• Ted Russell• Paige Tolbert

• Joe Abrams• Mariel Friberg• Katie Gass• Brook Hixson• Jenna Krall• John Pearce• Cassie O’Lenick• Dongni Ye

Outline

• Study status• 2013 SAC comments and response• Data and analysis progress

• Selected results presentations• SCAPE exposure metrics comparison• Effect modification of air pollution-asthma associations• Effect modification of air pollution-CVD associations

(Andrea Winquist)• PM2.5 sources and cardiovascular ED visits (Jenna Krall)

• Project 4 plans over coming year

Population vulnerability (age, race, comorbidity, SES)

Temperature, seasonality

Measurement error (ambient pollutant spatial variability)

CR Functions

Personal exposures (to ambient)

True modifiers

Apparent modifiers

Cities:ATL

BHMDFWPGHSTL

Joint effects of single pollutants

Air Pollution Mixtures

ED Outcomes

Single Pollutants (O3, NO2, PM2.5)

Mixtures based on chemical properties (ROS)

Data-based exposures (SOM, C&RT)

Respiratory (All, ASW, COPD)

Cardiovascular (All, IHD, CHF)

PM Sources (CMB)

Project 4 Overview

2013 SAC Comments & Response• Suggestion to focus on cardiovascular endpoints, not only asthma

• Several analyses of CVD endpoints in progress

• Interest in the alternative methods of specifying exposure• Comparison of SCAPE exposure metrics in epidemiologic analyses underway

• With only 5 cities, limited ability to examine reasons for heterogeneity in air pollution-health associations

• Focus on consistencies observed across cities, including patterns of within-city effect modification

• Recommendation to refine data on potential effect modifiers• In-depth examination of neighborhood socioeconomic conditions• Extended air exchange rates estimation approach to multiple cities

• Consider how to formally incorporate uncertainty in source apportionment estimates in health analyses

• Multi-city source apportionment epi analyses in progress• Plans to incorporate Bayesian ensemble source apportioned estimates, as done for

Project 3

Data Collection and Processing• ED visit data

• Final for Atlanta, Birmingham, Dallas, St. Louis• Note - no Children’s data for Birmingham

• Pittsburgh data processed by UPitt; working on data use agreements

• AQ data• Various metrics providing data on 12 pollutants (gases,

PM2.5, major PM2.5 components) in 4 cities• Including CMAQ-OBS fused data, finalized over past year

• PM2.5 source apportionment in 4 cities• Receptor-based ensemble CMB at selected monitoring sites

Analysis Summary

1. Application of spatially-refined modeled estimates of ambient concentrations in epidemiologic analyses (Dionisio et al., 2014)

2. Examination of health effects of PM2.5 components (ions, carbon, speciated organics, elements) (Winquist et al., in press; Sarnat et al., provisional acceptance)

3. Methods for classifying and analyzing air pollution mixtures using multi-pollutant monitoring data, including self-organizing maps (Pearce et al., 2014), classification and regression trees (Gass et al., 2014), assessing multi-pollutant joint effects (Winquist et al., 2014), estimating health effects of organic chemical groupings, PM2.5 sources, and retrospectively-predicted reactive oxygen species

4. Assessment of potential modifiers of the effects of ambient air pollution, including age, sex, race, neighborhood socioeconomic conditions, season, temperature, and air exchange rates

5. Statistical modeling approach to quantify projection uncertainties in future ambient ozone levels and their health impact due to climate change (Chang et al., 2014) and assessment of heat-related morbidity

Analysis Summary

1. Application of spatially-refined modeled estimates of ambient concentrations in epidemiologic analyses (Dionisio et al., 2014)

2. Examination of health effects of PM2.5 components (ions, carbon, speciated organics, elements) (Winquist et al., in press; Sarnat et al., provisional acceptance)

3. Methods for classifying and analyzing air pollution mixtures using multi-pollutant monitoring data, including self-organizing maps (Pearce et al., 2014), classification and regression trees (Gass et al., 2014), assessing multi-pollutant joint effects (Winquist et al., 2014), estimating health effects of organic chemical groupings, PM2.5 sources, and retrospectively-predicted reactive oxygen species

4. Assessment of potential modifiers of the effects of ambient air pollution, including age, sex, race, neighborhood socioeconomic conditions, season, temperature, and air exchange rates

5. Statistical modeling approach to quantify projection uncertainties in future ambient ozone levels and their health impact due to climate change (Chang et al., 2014) and assessment of heat-related morbidity




CR Functions


True modifiers

Apparent modifiers

Cities:ATL

BHMDFWPGHSTL



ED Outcomes






PM Sources (CMB)

SCAPE Exposure Metrics Comparison

Cities

St. Louis, MO-IL(15 counties + city; 8,623 mi2;

2,812,896 people; 326 people/mi2)

Franklin

Lincoln

Jefferson

Washington

Warren

St. Louis

St. Charles

Macoupin

Madison

St. Clair

Bond

Clinton

Jersey

Monroe

Calhoun

airport central site

Dallas, TX(12 counties; 8,928 mi2; 6,371,773 people; 714

people/mi2)Ellis

HuntWise Collin

Dallas

Parker

Denton

Tarrant

Kaufman

Johnson

Delta

Rockwall

Birmingham, AL(7 counties; 5,280 mi2; 1,128,047 people; 214

people/mi2)Bibb

Jefferson

Shelby

Walker

Blount

Chilton

St. Clair

Atlanta, GA(20 counties; 6,096 mi2; 5,110,183 people; 838

people/mi2)

FultonCarroll

Bartow

Cobb

CowetaJasper

Henry

Pike

Heard

Gwinnett

Walton

Cherokee

Meriwether

DeKalb

Newton

Paulding

Butts

Forsyth

Haralson

Pickens

Lamar

Fayette

Dawson

Douglas

Barrow

Spalding

ClaytonRockdale

Background

• Lack of spatial representativeness of available monitoring data for large metropolitan areas considered here

• Monitored ambient concentrations not fully comparable across different metro areas

• Different numbers and placement of monitors

• AQ core developed a data fusion method to combine monitored observations and modeled CMAQ outputs

• Compare use of CMAQ-OBS with other SCAPE exposure assignment approaches in epidemiologic analyses

SCAPE Exposure Metrics• Four metrics provide data on 12 pollutants in 4 cities

• Central monitoring site (CMS)• Population-weighted average (PWA) of observations from

available monitors• CMAQ-OBS PWA (CF PWA)• CMAQ-OBS at ZIP code tabulation areas, ZCTAs (CF ZIP)

• Each ZCTA assigned pollutant values from each exposure metric on each study day

• Analysis restricted to periods with daily data available for each exposure metric and ED data# ZCTAs 2002 2003 2004 2005 2006 2007 2008Atlanta 191Birmingham* 125Dallas 252St. Louis 256

*Birmingham NO2 analysis restricted to 2004-2008 due to lack of CMS data in earlier years

Modeling Approach

• Examined associations between each pollutant-exposure metric and ED visits

• Poisson regression models allowing for overdispersion• Same-day (lag 0) and 3-day (lag 0-2) moving average pollutant

concentrations • Control for ZCTA (maintains temporal analysis)• Control for time: Time splines with monthly knots, day of week

and holidays, season and interaction between day of week and holidays and season, hospital

• Control for meteorology: Cubic terms for lag 0 max temp, and interaction of max temp with season; lag 1-2 moving average min temp (when modeling 3-day moving average exposure); lag 0-2 moving average mean dew point

Respiratory & Cardiovascular ED Visits*

*Identified using primary ICD-9 codes [indicated in brackets]:• Respiratory disease (RD): includes visits for pneumonia [480-486], chronic

obstructive pulmonary disease [491, 492, 496], asthma/wheeze [493, 786.07], and other RD [460-466, 477]

• Cardiovascular disease (CVD): includes visits for ischemic heart disease [410-414], cardiac dysrhythmia [427], congestive heart failure [428], and other CVD [433-437, 440, 443-445, 451-453]

Atlanta (2002-2008, 2557 days)

Birmingham(2002-2008, 2557 days)

Dallas (2006-2008, 1096 days)

St. Louis (2002-2007, 2004 days)

Total # Visits

Mean Visits / Day

Total # Visits

Mean Visits / Day

Total # Visits

Mean Visits / Day

Total # Visits

Mean Visits / Day

RD 1,022,735 400.0 173,317 67.8 490,826 447.8 572,494 285.7CVD 263,944 103.2 79,057 30.9 138,946 126.8 200,787 100.2

Pollutant Summary

StandardMedian IQR Median IQR Median IQR Median IQR Unit

CMS 0.7 0.7 0.9 0.8 0.5 0.4 0.7 0.5PWA 0.4 0.3 0.8 0.7 0.4 0.3 0.7 0.5CF PWA 0.6 0.3 0.4 0.3 0.3 0.2 0.4 0.2CF ZIP 0.5 0.4 0.3 0.2 0.3 0.3 0.3 0.2CMS 38.4 19.5 36.0 18.7 29.0 21.0 29.0 12.0PWA 20.8 10.5 30.7 15.5 22.8 16.7 27.0 11.8CF PWA 21.2 9.5 13.0 5.8 18.5 12.4 21.1 8.8CF ZIP 17.5 19.1 7.2 9.3 15.4 16.1 11.6 15.2CMS 39.3 29.4 40.2 26.0 35.2 20.3 33.5 27.1PWA 41.4 26.8 32.7 23.3 39.1 21.2 35.1 28.2CF PWA 40.5 27.0 40.1 22.9 39.7 20.0 36.5 25.3CF ZIP 40.4 26.0 40.2 21.3 40.1 19.7 38.4 24.8CMS 14.1 9.5 15.8 11.6 10.0 6.0 12.0 8.2PWA 12.9 8.3 14.3 9.7 10.3 5.9 12.4 8.1CF PWA 14.3 9.0 12.9 8.5 10.0 5.9 12.5 8.1CF ZIP 14.0 9.2 12.3 8.4 9.7 5.9 11.8 8.0

10

25

10

Atlanta Birmingham Dallas St. Louis

0.25

24-hr avg PM2.5 (ug/m3)

2002-2007517,032 ZCTA-days

8-hr max O3 (ppb)

1-hr max CO (ppm)

1-hr max NO2 (ppb)

488,387 ZCTA-days2002-2008 2002-2008

319,625 ZCTA-days2006-2008

277,288 ZCTA-days

CMS = central monitoring site; PWA = population-weighted average of observations from available monitors; CF PWA = CMAQ-OBS data fusion, PWA; CF ZIP = CMAQ-OBS at ZCTAs

Criteria Pollutants & RD ED Visits

Rate

Rati

o (9

5% C

I) pe

r Sta

ndar

d U

nit

ATLANTA2002-2008

ST. LOUIS2002-2007

DALLAS2006-2008

BIRMINGHAM2004-2008

3-day (lag 0-2) moving average pollution and respiratory disease (RD) ED visits

ATLANTA2002-2008

ST. LOUIS2002-2007

DALLAS2006-2008

BIRMINGHAM2004-2008

CO NO2

O3 PM2.5

Criteria Pollutants & CVD ED VisitsSame-day (lag 0) pollution and cardiovascular disease (CVD) ED visits

ATLANTA2002-2008

ST. LOUIS2002-2007

DALLAS2006-2008

BIRMINGHAM2004-2008

ATLANTA2002-2008

ST. LOUIS2002-2007

DALLAS2006-2008

BIRMINGHAM2004-2008

CO NO2

O3 PM2.5

Rate

Rati

o (9

5% C

I) pe

r Sta

ndar

d U

nit

PM Components & CVD ED VisitsSame-day (lag 0) pollution and cardiovascular disease (CVD) ED visits

Rate

Rati

o (9

5% C

I) pe

r Sta

ndar

d U

nit

PM2.5 SO42- NO3

- OC EC

ATLANTA, 2002-2008

Summary

• Differences between CMS and CF metrics larger for CO and NO2 than O3 and PM2.5

• Often stronger associations with CF metrics than with CMS suggestive of reduced exposure measurement error

• Some unanticipated weaker associations using CF ZIP vs. CF PWA

• Due to greater error in ZIP estimates? ZIP-level estimates driven by CMAQ, with error estimates greater for primary pollutants when away from monitors

• Due to exposure measurement error from mobility patterns that take patients away from home ZIP codes?

• Future work to assess whether error estimates are related to observed pattern of RRs across pollutant metrics




CR Functions


True modifiers

Apparent modifiers

Cities:ATL

BHMDFWPGHSTL



ED Outcomes






PM Sources (CMB)

Effect Modification of Air Pollution-Asthma Associations

Objectives• Strong associations of ozone and traffic-related

pollutants on asthma ED visits in Atlanta and other cities

• Examine potential within-city modification of air pollution-asthma associations

• Understanding of susceptible subpopulations• Are results any more/less consistent across cities within

defined strata?

• Modifiers considered• Age, sex, race (Brooke Hixson)• Neighborhood socioeconomic conditions (Cassie O’Lenick)

Methods

• Daily ED visits for children (5-18 yrs) with a primary or secondary diagnosis of asthma or wheeze in 20-county Atlanta, 2002-2008

• 128,758 visits overall• Distributed across 191 ZCTAs

• Daily ZCTA-level pollutant concentrations from CMAQ-OBS fusion

• Paired with data on ZCTA-level socioeconomic conditions

Counts of asthma ED visits by ZCTA in Atlanta

Socioeconomic Variables

• Acquired data from two sources to account for potential changes to neighborhood socioeconomic conditions over time

• Census 2000 data: assigned to 2002-2004 ED data• 2007-2011 ACS data: assigned to 2005-2008 ED data• Data from Geolytics, standardized to 2010 borders

• Selected variables describing different ZCTA level socioeconomic conditions:

• Education, income, poverty, wealth, working class, unemployment• Composite metrics (e.g., Townsend index of deprivation, Carstairs index,

neighborhood deprivation index, others)

• ZCTAs grouped into strata according to various definitions:• Deprivation area (yes/no), e.g.

• Undereducated area (≤25% of population with high school diploma)• Poverty area (≥20% of population living below the federal poverty line)

• Above/below median, 75th percentile, 90th percentile, or in quartiles of continuous SES variables

Modeling Approach

• Conditional logistic regression• Matching on day of week, month, and year of ED visit,

and ZCTA of patient residence• Additional control for time (spline with 2 knots per

year), meteorology, season, and hospital entry/exit• 3-day (lag 0-2) moving average pollutant concentrations

• Effect modification by ZCTA-level socioeconomic factors examined via stratification

• Here, focus on analyses considering poverty• Mean % below poverty in Atlanta ZCTAs = 13.2%, range

<5% to >45%

Modification by Poverty via Poverty Area*

O3 CO NO2 PM2.5 EC

*ZCTA labeled as an ‘poverty area’ if ≥20% of population living below the federal poverty line

Odd

s Ra

tio (9

5% C

I) pe

r Sta

ndar

d U

nit

Census 2000

2007-2011 ACS

# Asthma ED Visits

# ZCTAs # ZCTAs Not Poverty Area (PA) 174 156 100,194Poverty Area 17 35 28,564

Census 2000

2007-2011 ACS

ATLANTA, 2002-2008, 5-18 yr olds

Modification by Poverty via Quartiles of % Below Poverty

O3 CO NO2 PM2.5 EC

Census 2000

2007-2011 ACS

Odd

s Ra

tio (9

5% C

I) pe

r Sta

ndar

d U

nit

Census 2000 2007-2011 ACS # Asthma ED Visits

Quartiles # ZCTAs Quartiles #ZCTAsQ1 <5.5 47 <8.5 47 28,623Q2 >5.5 ≤ 8.5 49 >8.5 ≤ 13.1 49 29,151Q3 >8.5 ≤ 11.9 47 >13.1 ≤ 17.8 48 29,884Q4 >11.9 48 >17.8 47 41,100

ATLANTA, 2002-2008, 5-18 yr olds

Summary

• Strong air pollution-asthma associations among 5-18 yr olds in Atlanta

• Associations generally stronger among patients living in commonly-designated ‘deprived’ areas based on poverty and education (not shown)

• Consistent u-shaped pattern of effect mod for % BP and most other SES metrics: strongest associations among patients living in Q1 (high SES) and Q4 (low SES) ZCTAs

• Observed for most pollutants, for different asthma outcome definitions, for both CF PWA and CF ZIP

• Examination of factors that may differ by SES may facilitate results interpretation, e.g., patient characteristics, ambient concentrations, etc.

Modification by Patient-Level Sex and Race in Atlanta, 5-18 yr olds

• Atlanta findings from Hixson et al. analysis

• Slightly stronger association among males

• Significantly stronger association among non-white than white patients

• Same findings in St. Louis• Corresponds with Project 3

findings

Overall Female

Rate

Rati

o (9

5% C

I) pe

r Sta

ndar

d U

nit O3

NO2

Male White Non-White




CR Functions


True modifiers

Apparent modifiers

Cities:ATL

BHMDFWPGHSTL



ED Outcomes






PM Sources (CMB)

Effect Modification of Air Pollution-CVD Associations (Andrea Winquist)

Air Pollution and Cardiovascular Disease• Daily ambient air pollutant concentrations have been found to be

associated with the daily number of hospitalizations and emergency department visits for cardiovascular diseases

• The strength of association has varied across geographic locations• Reasons for heterogeneity in the observed strength of association

are not clear; some potential reasons include:• Differences in population characteristics that may influence

susceptibility to cardiovascular effects of air pollution

• Differences in seasonality and meteorology

• Differences in pollutant mixtures

• Differences in factors influencing personal exposure to ambient air pollution

• Differences in measurement error

• Non-linear dose-response curves

Meteorologic Characteristics by City, 2006 Mean SD Min 25% 50% 75% MaxMax Temp (⁰F)

Atlanta 73.1 14.2 38 62 73 85 98Birmingham 75.7 14.9 40 65 76 89 100

Dallas 80.5 16.0 31 70 83 94 107St. Louis 68.3 18.6 16 53 69 85 101

Min Temp (⁰F)Atlanta 53.8 14.3 19 42 54 67 77

Birmingham 54.0 15.4 17 41 54 68 79Dallas 58.2 15.8 20 44 61 72 83

St. Louis 48.9 17.3 5 35 47 65 82

Dewpt Temp (⁰F)Atlanta 49.1 15.3 4.6 37.5 52.1 61.7 72.1

Birmingham 50.3 15.1 6.3 39.4 52.5 63.5 71.9Dallas 50.2 15.2 12 38.8 54.3 63.3 73.2

St. Louis 44.9 16.8 -4.4 32.3 46.6 59 74.4

• Dallas is overall the warmest and St. Louis is overall the coldest

• St. Louis is the least humid

American Community Survey Data, 2010

• St. Louis and Birmingham have a higher percentage of the population at older ages than Atlanta and Dallas

• Dallas has a higher percentage of the population at younger ages

< 5 5-14 15-24 25-34 35- 44 45-54 55-59 60-64 65-74 75-84 ≥850.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

Atlanta Dallas St. Louis Birmingham

Age Category (years)

Perc

enta

ge o

f Pop

ulati

on

Age

Atlanta Birmingham Dallas St. LouisNumber of ZCTAs with ED visits and census data 191 125 252 256

Overall % below poverty level* 13.2% 14.3% 14.0% 12.4%Range of ZCTA-specific % below poverty level

At study period start 2.9-45.9 2.8-52.4 1.3-46.0 1.5-50.1At study period end 1.7-45.5 0-54.2 0-48.1 0-70.7

Number (%) of ZCTAs with ≥20% below poverty level (“poverty area”)

At study period start 20 (10%) 23 (18%) 54 (21%) 32 (13%)At study period end 35 (18%) 37 (30%) 58 (23%) 48 (19%)

Overall % less than a high school education (among people aged ≥ 25 years)* 12.6% 14.8% 17.1% 11.3%

Range of ZCTA-specific % with < high school educationAt study period start 2.5-39.1 2.2-49.4 1.4-57.8 2.2-47.2At study period end 0.8-34.1 0.5-54.6 0-56.8 0-49.1

Number (%) of ZCTAs with ≥25% with < HS education (“low education area”)

At study period start 44 (23%) 65 (52%) 54 (21%) 49 (19%)At study period end 22 (12%) 31 (25%) 54 (21%) 21 (8%)

Note: There was one ZCTA in St. Louis and one ZCTA in Dallas for which census information was not available.

Socioeconomic Characteristics by City, 2006

*2008-2010 American Community Survey

Model Specification• Poisson Regression Models allowing for overdispersion

• Outcome: All cardiovascular disease (CVD; ICD9 codes 410 – 414, 427, 428, 433 – 437, 440, 443 – 445, 451 – 453)

• Air quality estimates at lag 0• PM2.5 and EC• CMAQ fused estimates previously described

• Control for ZCTA (within ZCTA analysis)• Time control

• Time splines with monthly knots• Day of week and holidays• Season and interaction between day of week and holidays and season • Time period of participation for each hospital

• Meteorology control• Cubic terms for maximum temperature (lag 0)• Interaction between cubic terms for maximum temperature and season• Cubic terms for dew point (moving average of lags 0-2)

• Estimates scaled to common unit (close to IQR; PM2.5 10 μg/mᵌ, EC 0.5 μg/mᵌ)

Overall Model Results

PM2.5 and EC

• Largely null results for PM2.5

• For EC, suggestion of stronger associations in Atlanta and St. Louis than in Birmingham and Dallas


PM2.5


EC

• No consistent pattern of effect modification by season

• Significant associations in both Atlanta and St. Louis observed in both seasons

• Associations not more similar across cities within season

By Season

EC(ZCTA-specific estimates)

Warm Season: May-Oct Cold Season: Nov-Apr

Atlanta Birmingham Dallas St. LouisBoth Warm Cold Both Warm Cold Both Warm Cold Both Warm Cold

EC

By Age and Gender

EC (ZCTA-specific estimates)• Not much variation

across age groups, except for Dallas

• Associations slightly stronger for women than for men (except Atlanta)

• Associations not more similar across cities within age and gender categories


EC

By ZCTA Socioeconomic Characteristics

EC(ZCTA-specific estimates)


• Lower SES groups had stronger associations in Dallas and St. Louis, opposite pattern in Birmingham

• Associations not more similar across cities within these SES categories

EC

Preliminary Conclusions• There are differences between the cities in age distribution,

SES, meteorology and pollutant levels

• For CVD, there is not a lot of variation across the cities in the strength of associations with PM2.5 and EC (generally weak associations overall), but statistically significant associations observed for EC in Atlanta and St. Louis

• There was no consistent evidence of effect modification of the association between CVD and EC

• These stratifications for this outcome and pollutant did not explain between-city heterogeneity (but there was not a lot of heterogeneity)

• Future analyses will consider specific types of cardiovascular outcomes and additional pollutants




CR Functions


True modifiers

Apparent modifiers

Cities:ATL

BHMDFWPGHSTL



ED Outcomes






PM Sources (CMB)

PM2.5 Sources and Cardiovascular Disease ED Visits (Jenna Krall)

Aims

1. Provide a framework for conducting multi-city analyses of the

associations between PM2.5 sources and emergency department

(ED) visits

2. Estimate city-specific associations between short-term exposure to

sources of PM2.5 and ED visits for cardiovascular disease (CVD)

• Current analyses show no significant associations between

central monitor measurements of total PM2.5 or major PM2.5

chemical constituents and ED visits for CVD causes

For Atlanta, Birmingham, Dallas, and St. Louis:

• ED visits for CVD causes including including ischemic heart disease

[410-414], cardiac dysrhythmia [427], congestive heart failure [428], and

other CVD [433-437, 440, 443-445, 451-453]

• Concentrations of source apportioned PM2.5

• Estimated from PM2.5 chemical speciation data measured at a single

central monitoring site in each city

• Daily data for Atlanta (Jefferson St. SEARCH site)

• 1-in-3 day data for Birmingham, Dallas, St. Louis (CSN sites)

• Chemical mass balance (CMB) run with ensemble-based source

profiles (EBSP) derived using Atlanta data

• EBSPs generated for warm and cold seasons using CMB with

molecular markers, CMB with gas constraints, positive matrix

factorization (PMF), and chemical transport model using CMAQ

• Sources vary across cities (e.g. no PM2.5 from metals in Atlanta)

• Total PM2.5 mass from central site monitors

Data

City-specific associations between PM2.5 sources and ED visits for CVD causes

were estimated using Poisson time series regression models with

overdispersion

• Same-day (lag 0) exposure to PM2.5 and source apportioned PM2.5

• Single pollutant models (PM2.5 source or total PM2.5 and covariates)

• Multiple source models (all PM2.5 sources and covariates)

Covariates• Time splines with monthly knots• Temperature

• Cubic terms for lag 0 maximum temperature• Cubic terms for lag 1-2 moving average minimum temperature

• Cubic terms for lag 0-2 moving average dew point temperature• Indicator variables for season, day of week, holidays, hospitals• Interaction terms between season and maximum temperature and

season and day of week/holidays

Methods

Atlanta Birmingham Dallas St. LouisStart date 1999-01-03 2004-01-01 2006-01-05 2001-01-01

End date 2009-12-31 2010-12-31 2009-08-29 2007-06-26

Observation days 3,667 821 350 735

Number of hospitals 41 12 36 36

ED visits for CVD causes

N 356,075 27,564 44,499 73,107

Daily mean (standard deviation)

97 (28) 34 (7) 127 (18) 99 (14)

Results:

• Time frame and days of data for each city• ED visits for CVD causes for each city

Concentrations in μg/m3 IQR Atlanta Birmingham Dallas St. LouisTotal PM2.5 9.10 15.58 (7.84) 16.98 (9.28) 10.69 (4.64) 13.61 (7.10)

Source apportioned PM2.5

Mobile 1.47 2.16 (1.73) 1.71 (1.70) 0.81 (0.79) 1.80 (1.16)

Gas vehicles 0.78 1.01 (0.93) 0.69 (0.75) 0.46 (0.37) N/A

Diesel vehicles 1.17 1.15 (1.15) 1.03 (1.35) 0.35 (0.57) N/A

Burning 0.96 1.65 (1.26) 1.08 (1.14) 1.87 (3.22) 1.36 (1.15)

Coal-fired power plant 0.13 0.13 (0.12) 0.22 (0.29) 0.01 (0.02) N/A

Metals 0.43 N/A 0.63 (0.57) N/A 0.22 (0.24)

Dust 0.33 0.46 (0.51) 0.60 (0.73) 0.62 (1.04) 0.47 (0.69)

Secondary organic carbon 3.69 1.98 (1.57) 2.08 (1.93) 1.19 (0.89) 1.13 (1.14)

Ammonium sulfate 1.56 3.27 (3.32) 3.35 (3.43) 2.28 (2.55) 3.50 (3.60)

Ammonium bisulfate 0.94 2.20 (1.80) 1.69 (1.54) 1.17 (0.98) 1.68 (1.87)

Ammonium nitrate 1.58 1.10 (1.00) 1.08 (1.01) 1.04 (1.18) 3.05 (3.24)

Results: Concentrations of total PM2.5 and source apportioned PM2.5

• Median interquartile range (IQR, Q3 – Q1) across cities • Mean (standard deviation) concentration for each city

N/A indicates source was not measured for a particular city

Results: Associations of lag 0 pollution and ED visits for CVD causes

Single pollutant models (total PM2.5 or PM2.5 source and covariates only) Multiple source models (all PM2.5 sources and covariates simultaneously)

*statistically significant at α = 0.05

**

**

In general, associations between PM2.5 sources and ED visits for CVD causes

were null across cities• Results mostly consistent with observed null associations between

CVD ED visits and PM2.5 from central monitoring sites in Atlanta

• We observed positive, statistically significant associations of CVD ED

visits with gas vehicles and mobile sources in Atlanta• We did not observe these associations in other cities, which had

shorter time series and temporally sparser pollution data

compared with Atlanta

Future work:

1. Associations between PM2.5 sources and ED visit subcategories

• CVD: ischemic heart disease and congestive heart failure• Respiratory: asthma and wheeze

2. Incorporate uncertainty from estimating PM2.5 sources into health effect

regression models

3. Develop multi-city model

4. Long-term goal to incorporate CMAQ-CMB outputs

Conclusions and future work

Project 4 Plans Over Coming Year• Submit drafted manuscripts:

• Hixson et al., Modification of air pollution-asthma by age• Gass et al., 3-city C&RT

• Finalize analyses, draft and submit manuscripts:• Sarnat et al., SCAPE exposure metrics comparison• O’Lenick et al., Modification of air pollution-asthma by SES (poster)• Winquist et al., Modification of air pollution-CVD associations• Krall et al., Multi-city PM2.5 sources and CVD (poster)• Pearce et al., Atlanta SOM epi application

• Make progress on additional analyses• ROS-DTT and ED visits in Atlanta• Organic chemical groups and ED visits in Atlanta• Assessment of effect modification by air exchange rates• Assessment of effect modification by temperature• Quintiles analysis

Accepted/Published in Past Year• Sarnat SE, Winquist A, Schauer JJ, Turner J, Sarnat JA. Fine particulate matter components and

emergency department visits for respiratory and cardiovascular diseases in St. Louis. Environmental Health Perspectives, provisional acceptance.

• Winquist A, Schauer JJ, Turner J, Klein M, Sarnat SE. Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity. Journal of Exposure Science and Environmental Epidemiology, in press.

• Dionisio KL, Baxter LK, Chang HH. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environmental Health Perspectives 122:1216-1224, 2014.

• Winquist A, Kirrane E, Klein M, Strickland M, Darrow LA, Sarnat SE, Gass K, Mulholland JA, Russell AG, Tolbert PE. Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004. Epidemiology 25:666-673, 2014.

• Pearce JL, Waller LA, Chang H, Klein M, Mulholland J, Sarnat J, Sarnat S, Strickland M, Tolbert P. Using self-organizing maps to classify days by air quality for air pollution epidemiological mixtures research. Environmental Health 13:56, 2014.

• Chang HH, Hao H, Sarnat SE. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmospheric Environment 89:290-297, 2014.

• Gass K, Klein M, Chang HH, Flanders WD, Strickland MJ. Classification and regression trees for epidemiologic research. Environmental Health 13:17, 2014.

Pollutant Spearman Correlations

CMS PWA CF PWA CMS PWA CF PWA CMS PWA CF PWA CMS PWA CF PWACO PWA 0.90 0.88 0.89 0.75

CF PWA 0.84 0.89 0.77 0.89 0.82 0.94 0.76 0.95CF ZIP 0.70 0.75 0.86 0.62 0.71 0.81 0.76 0.88 0.94 0.62 0.73 0.78

NO2 PWA 0.88 0.98 0.94 0.78CF PWA 0.75 0.87 0.78 0.81 0.88 0.96 0.73 0.94CF ZIP 0.53 0.62 0.74 0.50 0.52 0.70 0.79 0.87 0.91 0.45 0.58 0.66

O3 PWA 0.99 0.98 0.97 0.99CF PWA 0.99 0.99 0.98 0.98 0.97 0.99 0.99 0.99CF ZIP 0.96 0.97 0.98 0.95 0.96 0.98 0.95 0.97 0.98 0.97 0.98 0.99

PM2.5 PWA 0.97 0.96 0.97 0.96CF PWA 0.95 0.98 0.94 0.98 0.95 0.99 0.96 0.98CF ZIP 0.92 0.94 0.97 0.91 0.96 0.98 0.93 0.97 0.99 0.94 0.95 0.97


*Average of ZCTA-specific correlations

CMS = central monitoring site; PWA = population-weighted average of observations from available monitors; CF PWA = CMAQ-OBS data fusion PWA; CF ZIP = CMAQ-OBS at ZCTAs

Documents

Characterize populations and health outcomes Project 4 Multi-City Morbidity Study Characterize daily pollutant mixtures Source apportionment Source apportionment