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WISCONSIN’S ENVIRONMENTAL PUBLIC HEALTH TRACKING PROGRAM
The Importance of Air Quality Data for Public Health Applications
Marni Y.V. Bekkedal, Ph.D.Bureau of Environmental & Occupational Health
Department of Health [email protected]
(608) 267-3811
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Pew Environmental Health Commission Environmental Health Review 2001 Report
America’s Environmental Health Gap: Lack of basic information linking environment and chronic disease that undermines
intervention and prevention.
Environmental health system is inadequate & fragmented Responsibilities are scattered among multiple agencies Unable to link environmental and health databases
Recommended a “Nationwide Health Tracking Network for diseases and exposures”
Environmental Public Health Tracking
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Environmental Public Health Tracking Grantees
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What is Environmental Public Health Tracking?
• Surveillance– Systematic, coordinated tracking of hazards,
exposures and health outcomes
• Data linkage– Examine potential relationships– Develop screening level tools to generate future
hypotheses
• Integrating environment and health– improve understanding of relationships between
environmental exposures and public health outcomes to guide action
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Making Data and Information Accessible
A. Find relevant data and assess utility for surveillance
B. Prioritize surveillance topics
C. Complete linkage projects
D. Create internet-based portals at national and state levels
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A. Find relevant data and assess utility for surveillance
• Wisconsin is rich with environmental and health data
• Wisconsin already has a strong environmental health partnership infrastructure
• The data have limitations for use in surveillance
• The databases are not designed for linkages
• Resources for modifying data infrastructure are limited
THE STRENGTHS OFTEN COUNTER THE LIMITATIONS
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B. Prioritize surveillance topics
• What data are available?
• Which contaminants and health effects are relevant?– Biological plausibility
• Is it an issue that is important in Wisconsin?
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C. Complete Linkage ProjectsCurrent Foci
asthma & heart attacks
ozone & PM2.5
childhoodcancer
Hazardousair pollutantsCollaborative project at the national
level:CDC, EPA Maine, New York,
Wisconsin
Collaborative project at the state levelDPH, DNR
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Linking Asthma Hospitalizations with Ozone and
PM2.5
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BURDEN OF ASTHMA (2001)Defined as ICD code 493 in primary or first other diagnosis fields for hospital discharge.
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LINKAGE TOOLS Limited number of monitors
http://maps.dnr.state.wi.us/imf/dnrimf.jsp?site=wisards
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LINKAGE TOOLSData from single monitor applied to geopolitical boundary
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LINKAGE TOOLSTemporal matching is complex
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Expanding Work to a National Level
• Public Health Air Surveillance Evaluation (PHASE) project
• Multi-state (ME-NY-WI)• Multi-agency (CDC, EPA)
– Air Quality characterization evaluation• How to apply regulatory data to public health
– Methods development• How to link health and hazard data for analysis
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PHASE Project Tasks
1. Selecting and Defining health outcomes2. Estimating and Assigning exposures3. Calculating the relationship between the
health outcome and estimated exposure4. Evaluating air quality characterization
methods for their utility in public health
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1. Selecting & Defining Health OutcomesW i s c o n s i n R e s i d e n t A s t h m a H o s p i t a l i z a t i o n s 2 0 0 1
1 9 7 01 8 3 6
1 7 1 61 6 0 4 1 5 5 6
1 2 1 5 1 2 0 7
0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0F
req
ue
nc
y
Mo
nd
ay
Tu
esd
ay
Wed
nes
day
Th
urs
day
Fri
day
Sat
urd
ay
Su
nd
ay
0
10
20
30
40
50
60
70
11/20/2000 1/9/2001 2/28/2001 4/19/2001 6/8/2001 7/28/2001 9/16/2001 11/5/2001 12/25/2001 2/13/2002
ozoneAsthma hospital
admissions
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2. Estimating and Assigning exposures• Raw monitor data
• Ozone collected daily from April15-October 15
• PM collected every 3 days (all year round)
http://maps.dnr.state.wi.us/wisards/
• EPA Interpolated data (4km, 12km, 36km)
• Daily Ozone & PM2.5
• CMAQ & Hierarchical Bayesian (36km only)
• Jan 1 - Dec 29 Ozone and PM 2.5
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2. Estimating and Assigning exposures• Zip code of residence at time of event
– Geographic Centroid of Zip Code (closest value & average of nearest values )– Population Weighted Centroid of Zip Code (closest value & average of nearest values )– Average of air quality within zip code polygon– Use raw monitor data (assign closest monitor data)
Correlations >.97. Used geographic centroid and closest value
Geographic Centroid
Population-Weighted Centroid
Closest Monitor
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3. Calculating the relationship between the health outcome and estimated exposure
• Assign “exposure” based on air quality estimate
• Account for possible delay between exposure and when person arrives at hospital
• Run statistics – Case cross-over vs. Time Series
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4. Evaluate air quality characterization methods for public health utility
• Correlate results to see how different they are from one another
• Rate the ease of use and other practical aspects
• Make recommendation– Hierarchical Bayesian methodology
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Linking Cancer with Hazardous Air
Pollutants
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Steps in the Project
1. Selecting and Defining health outcomes2. Estimating and Assigning exposures3. Calculating the relationship between the
health outcome and estimated exposure4. Evaluating air quality characterization
methods for their utility in public health
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1. Selecting and Defining health outcomes
• Childhood cancer• Potentially shorter latency• Potentially less variability in residential history• Priority for our staff
• Approximately 270 cases diagnosed annually in WI• Significant concern from public
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Birth Diagnosis
Maternal Exposure Exposure(s)
2. Estimating and Assigning Exposures
Data Source: Vital Records Data Source: Cancer Registry
Data Source: DNR
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2. Estimating and Assigning Exposures
• Estimate changes in exposure assignments over the exposure period
– How far do mothers move between time of conception to birth?• Do not currently have a way to estimate
– How far do children move during the time between birth and diagnosis?• Linked birth records with cancer records
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Distance Moved from Birth Address to Diagnosis Address
Distance Moved(Miles)
% of Cases (N=351)
0 56.4
0.1 -.5 2.9
.6– 1.0 3.7
1.1 – 2.0 8.3
2.1 – 3.0 4.0
3.1 -4.0 2.6
> 4.0 22.2
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2. Estimating and Assigning Exposures
1. 1-to-1 Case to Facility/Emissions source
2. Buffer Zone Analysis
3. RAIMI Model
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1-to-1 Case to Facility/Emissions Source
Environmental Monitoring Data (WI DNR)
• BRRTS- remediation sites/land fills/underground storage
tanks
• AEMS- air emissions
• SWAP- sources of drinking water contamination
Childhood Cancer Data
• Case birth address
• Case diagnosis address
• Control birth address
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1-to-1 Case to Facility/Emissions Source
Linked three files based on closest facility only
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Mean Distance (miles) from Closest
Contamination Source by Source Type Environmenta
lHazard/
Source
Cases by Birth Address
(min-max)N = 357
Cases by
Diagnosis
Address(min-max)
N=357
Controls by Birth Address
(min-max)N=3591
BRRTS Sites 0.51 (0-5.13)
.49 (0-19.13)
0.49 (0-19.60)
AEMS Sites 1.32 (0-13.4)
1.34 (.01-10.11)
1.39 (0-21.40)
SWAP Sites (Area)
0.39 (0-4.02)
0.39 (0-3.44)
0.38 (0-19.38)
SWAP Sites (Lines)
3.02 (0-40.96)
3.24 (0-43.49)
3.31 (0-43.40)
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1-to-1 Case to Facility/Emissions Source
• Do-able• Practical • Value of this linkage method uncertain
– Helps with understanding how far cases and controls are from facilities
– Exposure assessment - may not make sense scientifically
• 1-to-1 linkage does not take into account multiple facilities • 1-to-1 linkage does not take into account other sources of
exposure• Spatial and temporal gaps are an issue
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Buffer Zone Analysis
• Case Files – Diagnosis Address (n=421)– Birth Address (n=421)
• Control File– Birth Address (1253)
• 3 buffers – 0-.5 miles– .5-1.0 miles– 1.0-5.0 miles
• Link with emissions data
1-5 miles.5-1.0
0-.5
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Buffer Zone Analysis
• Resulting Files:– For each case/control file – emissions data
for all facilities falling within each of the 3 different buffers
• Case File – 1.4 million records (combines birth address and diagnosis address data), 1.3 GB
• Control file – 1.2 million records, 1.4 GB• Computing Time (not including time required to
set up) – 12 hours
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Buffer Zone Analysis
• Do-able?– Yes, BUT - Computing time very intensive
• Practical and Feasible?– size of dataset is an obstacle
• Value of Linkage Data?– Still difficult to determine
• Approach does take into account emissions from multiple facilities
• Spatial and temporal gaps remain a major issue
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RAIMI Model
• Regional Air Impact Modeling Initiative• Accounts for multiple contaminants and potency• Has better geographic resolution than other
methods• Temporal resolution is still poor
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Modeling Hazards:Cumulative Exposure to
Hazardous Air Pollutants (HAPS)
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Modeling Hazards:Hazardous Air Pollutants (HAPS)
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A E M S D A T A
-
5 0 , 0 0 0
1 0 0 , 0 0 0
1 5 0 , 0 0 0
2 0 0 , 0 0 0
2 5 0 , 0 0 0
3 0 0 , 0 0 0
3 5 0 , 0 0 0
4 0 0 , 0 0 0
4 5 0 , 0 0 019
89
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Y E A R
AM
OU
NT
(in
lbs.
)
Application of Hazard Data
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RAIMI Model EstimatesAir Emissions Facilities
Cancer Control
Exposure Assignment – closest point? Mean? Smoothed?
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RAIMI Model
• Is possible and feasible• Scientifically based• Exposure Assignment will still be
challenging• Additional sources beyond ambient air
emissions are needed- still may underestimate risk
• Validation needed– Is hypothesis generation, NOT hypothesis
testing
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Summary of Linkage Projects
Linkage
Method
Feasible Practical Value added
Scientific
Overall Grade
1) 1-to-1 Linkage A B C D C+
2) Buffer Analysis B D C C C
3) RAIMI Model C C A A B
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Future Linkage Work for Wisconsin Environmental Public Health Tracking
• Finalize the analyses for asthma links to ozone and PM2.5
• Repeat asthma project with Emergency Room visits
• Repeat asthma project to include more years• Incorporate data for cancer cases with RAIMI
model information– Select cancers and compounds with
biologically plausible linkages
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Future Directions for Wisconsin Environmental Public Health Tracking• Repeat asthma project to include more
states
• Support inclusion of mobile sources of emissions into RAIMI
• Work with partners to get better exposure estimates for emissions
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Making Data and Information Accessible
A. Find relevant data and assess utility for surveillance
B. Prioritize surveillance topics
C. Complete linkage projects
D. Create internet-based portals at national and state levels
• Secure portal• Public portal