WHEEZY STREET
Using Partnerships to Explore
How Traffic Drives Asthma
MN Environmental Public Health Tracking
April 10, 2012
Road Map
• Overview
• Rochester Epidemiology Project
• REP and Traffic data analysis
• Questions
Our partners:
EPA STAR Project
R8336270z0
• Goal: Develop outcome-based indicators for
measuring the impacts of fine particles on public
health in Minnesota
• Measuring the Impact of Fine Particles:
• http://www.health.state.mn.us/divs/hpcd/cdee/airquality.html
• Intended to evaluate progress resulting from PM
reduction policies, 2003-2009
MN air pollution reduction initiatives
• Minnesota Emissions Reduction Project (MERP)
• Diesel retrofits
• School buses; heavy duty public vehicles
• Project Green Fleet
• Other local initiatives
• Anti-idling ordinances
• Go Greener Initiative (Met Council)
• National initiatives
• Ultra Low Sulfur Diesel Fuel Rule
• Heavy Duty Diesel Regulations
• Transport Rule
Measuring impacts
Regulatory
or Policy
Changes
Reductions
in
Population
Exposures
• PM2.5
Reductions in
Adverse
Health
Outcomes
• Respiratory disease
outcomes
• Cardiovascular
disease
• Hospitalizations and
Deaths
Related National Activities
• Congress first appropriated funding to the
CDC to establish the National Tracking
Network in 2002
• The Network was charged to…
• Provide data that are nationally consistent for
tracking hazards, exposures, and health
• Make information available through a web-based
network (data portals)
Tracking Network (2012)
Network includes 23 states, NYC, and 5 academic partners
An Update on the
Rochester Epidemiology Project
(REP)
Barbara P. Yawn, MD MSc
Director of Research
Olmsted Medical Center
Co-Principal Investigator of the REP
What is the REP?
• Links together medical records of Olmsted
County, MN residents from the primary
sources of health care
• Facilitates access to medical records from
multiple institutions
• Archive historical medical records
• Geographic population based (not health plan)
REP Data
• 1.8 million patient records
• 50+ sources of health care
• Covering 1935-present
• 926,069 unique people
• Average of 2 medical records per person
• Range: 1-24 records
http://www.rochesterproject.org/
Why is the REP important?
• The REP provides a complete picture of all health
care delivered to a single individual
• John Smith, Olmsted County resident
• 1994:
• January: Treated at Olmsted Medical Center for hypertension
• May: Suffered a coronary event; seen in Mayo Emergency Room
• June: Seen for follow-up care at Olmsted Medical Center
Types of studies
Identification of cases
and controls
Identification of exposed
and non-exposed
Cumulative number of publications
Incidence and prevalence
Time trends
Risk and protective factors
Effectiveness of treatments
Natural history and outcomes
Genetic factors
2000
Quality of care
Cost of care
1500
1000
500
0
1965
1970
1975
1980
1985
1990
Year
1995
2000
2005
2010
Creation of the REP Census
• All Olmsted County residents with a
diagnosis between 1966 – present
• 496,356 People
• Goal: Establish a “clean” set of Olmsted
residents for a given point in time.
• No extra clean-up steps needed by individual
studies.
Person model
PERSON
OMC
OMCH
Mayo Clinic
Other
Sources
RFMC
Age
30
Medical visit dates
35
40
45
50
55
60
Medical visits
Utilization of medical care
Medical visit dates
495,000 unique persons
1.2 million medical records
15 million encounters
Medical visits are discrete
More details
1966
1970
1975
1980
1985
1990
1995
56
61
Unknown
32
36
41
46
51
Confirmed residency:
• 1 year on each side of visit if age > 3 y
• 6 months on each side if age < 3 y
Implied residency between visits if they are:
• within 3 years for women
• within 4 years for men
When residency changes between visits, the confirmed
and implied bars split the difference between visits.
Olmsted confirmed
Olmsted implied
Non-Olmsted confirmed
Non-Olmsted implied
Movement of Olmsted Population
Residents on Jan 1, 2000 followed through July 1, 2009
Died
Moved out of county (but still seen)
Lost to follow-up (Unknown)
Still Olmsted resident
10%
10%
10%
0%
0%
0%
Jan-09
20%
Jan-08
20%
Jan-07
20%
Jan-06
30%
Jan-05
30%
Jan-04
30%
Jan-03
40%
Jan-09
40%
Jan-08
40%
Jan-07
50%
Jan-06
50%
Jan-05
50%
Jan-04
60%
Jan-03
60%
Jan-02
60%
Jan-01
70%
Jan-00
70%
Jan-09
70%
Jan-08
80%
Jan-07
80%
Jan-06
80%
Jan-05
90%
Jan-04
90%
Jan-03
90%
Jan-02
100%
Jan-01
100%
Jan-00
100%
Jan-02
20-29 years old
Jan-01
10-19 years old
Jan-00
0-9 years old
Movement of Olmsted Population
Residents on Jan 1, 2000 followed through July 1, 2009
Died
Moved out of county (but still seen)
Lost to follow-up (Unknown)
Still Olmsted resident
10%
10%
0%
0%
Jan-09
20%
Jan-08
20%
Jan-07
30%
Jan-06
30%
Jan-05
40%
Jan-04
40%
Jan-03
50%
Jan-02
50%
Jan-09
60%
Jan-08
60%
Jan-07
70%
Jan-06
70%
Jan-05
80%
Jan-04
80%
Jan-03
90%
Jan-02
90%
Jan-01
100%
Jan-00
100%
Jan-01
All Olmsted Residents
Jan-00
90+ years old
Prevalence of selected conditions
compared to US estimates
Condition
Hypertension
Ages affected
≥65 years
U.S.
Prevalence/100
70.3
Mood disorders
18-29 years
21.4
23.2
≥60 years
11.9
23.9
≥20 years
11.3
9.0
≥65 years
26.9
23.9
Osteoarthritis
≥65 years
33.6
44.4
Asthma
0-17 years
9.6
10.6
≥18 years
7.7
8.4
≥65 years
29.7
21.4
Prostate cancer
All ages
1.6
2.1
Breast cancer
All ages
1.7
2.1
Colon cancer
All ages
0.4
0.4
HIV infection
All ages
0.13
0.13
Diabetes
Osteoporosis
Olmsted County
Prevalence/100
70.7
Top 15 conditions by age
Skin disorders
80
Vision disorders
Osteoarthritis and joint disorders
70
Spondylosis, intervertebral disc
disorders, and other back problems
60
Disorders of lipid metabolism
Other upper respiratory disease
Prevalence (per 100)
50
Anxiety, depression, and bipolar
disorders
40
Chronic neurologic disorders
Hypertension
30
Headaches; including migraine
20
Diabetes
Arrhythmias
10
Esophageal disorders
0
Asthma
0-18
>18-29
>29-49
>49-64
>64
Thyroid disorders
Age (years)
Expansion progress
Olmsted County
N = 145,000
8-county region of
South Eastern
Minnesota
N = 360,000
Methodological publications
• St. Sauver et al., Use of a medical records linkage system to
enumerate a dynamic population over time: The Rochester
Epidemiology Project. Am J Epidemiol, 2011
• St. Sauver et al., Generalizability of epidemiologic findings
and public health decisions: An illustration from the Rochester
Epidemiology Project. Mayo Clinic Proceedings (in press)
REP Contacts
CO PIs
Walter Rocca, MD MPH
[email protected]
Mayo Clinic, Health Sciences Research
Barbara P. Yawn, MD MSc
[email protected]
507 287 2758
Olmsted Medical Center, Research
http://www.rochesterproject.org/
EXHAUST, EXPOSURE
AND EXACERBATIONS
Asthma Exacerbations and Traffic exposure in Olmsted County
MDH, MPCA, Rochester Epidemiology Project Partnership
EPA STAR Grant
• Explore air quality and health
• Hospital discharge data
• Zip code level data
• PCA air quality monitors
Twin Cities 7 county metro
Olmsted County
REP Asthma Exacerbation Data
• All asthma encounters during 2000-2010
• Asthma exacerbations were defined three ways:
1) An inpatient hospitalization for asthma
2) An emergency department visit for asthma
3) Three or more outpatient visits for asthma
within a two-week time period
REP Asthma Exacerbation
• Asthma exacerbations were totaled for each
person
• Years in REP for the 11 year study period
• Asthma outcome of interest 
Individual Data
• Exacerbations/Year
• Address – Geocoded to exact location
• Age
• Sex
REP Asthma Exacerbations
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REP - 19,915 People
Sex
Male
Female
Missing
N
%
Adult / Pediatric
7,932
39.8
Adult
Exacerbations
10,100
50.7
Pediatric (0-18)
per Year
%
N
1,883None
9.5
16,218
81.44
1-3
3,664
18.40
4-6
28
0.14
7-9
3
0.02
10-12
1
0.01
> 12
1
0.01
N
11,413
%
57.3
8,502
42.7
Traffic data – MNDOT
• Annual Average Daily Traffic for road
segments
• Commercial/Diesel and Light duty/Cars
Traffic data exposure measures – MPCA
• Three measures of exposure for each
person at their address
• Vehicle Kilometers Traveled
• 250 meter buffer
• 500 meter buffer
• Traffic density
VKT Traffic calculations
250 meter buffer
VKT Traffic calculations
500 meter buffer
Traffic Density calculation
Traffic Density
Data for each person
• Asthma Exacerbations/Year
• Age
• Sex
• VKT 250 meter buffer
• VKT 500 meter buffer
• Traffic density
First look at association
Average Asthma Exacerbations per Year by
Traffic Density quintiles
Exacerbations/Year
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1
2
3
Lowest Traffic
4
5
Highest Traffic
Traffic Density Quintile
SES/Poverty measure
• Block group for each geocoded address
• Income to Poverty ratio for each block group
• Percent below 1.0 within block group assigned
to each person
Multivariate Analysis
• Outcomes
1. Any exacerbation vs. no exacerbation
• Logistic regression
2. Number of exacerbations
• Poisson regression
Multivariate Analysis
Predictors
• Age
• Sex
• Poverty indicator
• VKT 250 meter buffer
• VKT 500 meter buffer
• Traffic density
Results – Traffic density and Asthma
Variable
Age
Sex
Poverty
Traffic
Odds Ratio
0.999
1.101
6.328
1.082
95% CI
(0.997, 1.000)
(1.020, 1.189)
(3.820, 10.48)
(1.060, 1.104)
Odds of Any Exacerbation increased 8.2%
for every unit increase in Traffic Density
Similar results for two other traffic measures
VKT 250 – 12.5% increase
VKT 500 – 5.9% increase
Future Directions
• Better SES indicator
• Statewide analysis
• More methods of analysis
• Other health outcomes
• Age specific analysis
Acknowledgements
Minnesota Department of Health
Minnesota Pollution Control Agency
Rochester Epi Project and
Jean Johnson, PhD
Greg Pratt , PhD
Olmsted Medical Center
Chuck Stroebel, MPH
Kari Palmer, MS
Barbara Yawn, MD
Allan Williams , PhD
Margaret McCourtney
Peter Wollan , PhD
Naomi Shinoda, MSPH
Cassie McMahon
Wendy Brunner, MS
Lisa Herschberger, MS, MPH
Paula Lindgren, MS
Minnesota Department of Transportation
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