Air Pollution and
Built Environment:
How Where You Live Affects Your Health
Francine Laden, ScD
Mark and Catherine Winkler Associate Professor of Environmental
Epidemiology
Harvard School of Public Health
Boston MA USA
Overview
• The Nurses’ Health Study
• Air pollution
– Exposure modeling
– Associations with health
• The Built Environment
– Conceptual model
– The county sprawl index
– Individual level measures
• Summary
The Nurses’ Health Studies
• Prospective cohort studies of US women
– NHS: 121,700 nurses enrolled in 1976, aged
30-55
– NHSII: 118,000 nurses enrolled in 1989,
aged 25-45
• Followed every 2 years by mailed
questionnaire
– Disease follow-up
– Risk factors and exposures
At Baseline…
NHS 1976
NHSII 1989
And Now…
NHS 1986-2010
NHSII 1989-2009
AIR POLLUTION
EXPOSURES
Spatio-temporal Models
• GIS techniques
– Complex model including existing
monitoring networks, weather, and
– GIS covariates including distance to
road, elevation, land-use, county level
emissions, population density, point
source emissions
• Monthly average models PM10, PM2.5,
PM10-2.5
Average Monthly PM2.5
Distance to Major Road
US Census Road
Classifications
 A1 (primary roads,
typically interstates,
with limited access)
 A2 (primary major,
non-interstate roads)
 A3 (smaller, secondary
roads, usually with
more than two lanes)
Hazardous Air Pollutants
(HAPs)
• EPA National Air Toxics Assessments
– 1990, 1996, 1999, 2002, 2006
– Includes metals, diesel particulate,
methylene chloride, quinoline, styrene,
trichlorethylene, vinyl chloride
• Census tract level estimated
concentrations of pollutants from
outdoor sources based on dispersion
models
ASSOCIATIONS WITH HEALTH
All-cause Mortality and PM10
Northeastern Region 1992-2004
1.30
1.20
Hazard Ratio
16% increase
per 10 μg/m3
↑ in 12-month
avg PM10
1.10
1.00
0.90
1 month avg
3 month avg
12 month avg
24 month avg
36 month avg
48 month avg
Adjusted for age, year, season and state of residence
Puett et al. AJE 2008: 168:1161–68
Mortality and Coronary Heart
Disease – 10 μg/m3 ↑ Fine and
Coarse PM
HR (95% CI)
Outcome
PM2.5
PM10-2.5
All-cause mortality
1.29
(1.03,1.62)
0.96
(0.82,1.12)
First CHD
1.10
(0.76,1.60)
1.01
(0.78,1.31)
Fatal CHD
2.13
(1.07,4.26)
0.91
(0.56,1.48)
Non-fatal MI
0.71
(0.44,1.13)
1.06
(0.77,1.47)
Adjusted for the other size fraction, age, state, year, season,
smoking , BMI, risk factors for CHD, physical activity,
neighborhood SES.
Puett et al. EHP 2009: 117:1697–1701
Effect Modification BMI and Smoking
Fatal CHD and PM10
2.82
3.3
HR per
10 μg/m3
Δ
2.8
2.3
1.8
1.64
1.41
1.3
1.03
0.98
0.85
0.8
Never
Smoker
Former
Smoker
BMI≥30
BMI<30
Current
Smoker
Puett et al. AJE 2008: 168:1161–68
Cognitive Decline
• PM can access the brain via
– Circulation
– Intranasal route → direct translocation
through olfactory bulb
• … where it may precipitate
inflammatory response, injure BBB,
increase amyloid beta
• Associations with CVD, stroke, and
vascular risk factors
Cognitive Decline
• NHS participants ≥ 70 yrs n= ~17,000
• Cognitive assessment by telephone
– Tests of working memory attention,
global cognition, verbal
memory/learning and verbal fluency
– Baseline administered 1995-2001
– 2nd and 3rd approx 2 and 4 yrs later
• PM10, PM2.5, PM10-2.5
Long-term exposure to PM10-2.5 in relation
to decline in standardized cognitive score
0.02
Ptrend = 0.01
0.01
Difference in global
cognitive score change per
2 years, by increasing
quintile of PM10-2.5
0
5
-0.01
-0.02
ref
6
7
8
9
10
11
12
13
14
15
1 year of age
(ref: lowest quintile)
-0.03
-0.04
-0.05
Median of PM10-2.5 quintile, μg/m3
Adjusted for age, education, husband's education, smoking history, physical activity, and
alcohol consumption.
Weuve et al. Arch Intern Med 2012: 172:219-27
Stronger association with measures of
long-term exposure to PM10-2.5
0.005
0.000
Past 5 yrs
-0.005
Difference in global
cognitive score
change per 2 years,
per 10 μg/m3
increase in PM10-2.5
-0.010
Since 1989
1 year of age
-0.015
-0.020
Past
month
-0.025
Past yr
-0.030
Past 2 yrs
-0.035
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol consumption.
Long-term exposure to PM2.5 in relation to
decline in standardized cognitive score
0.02
Ptrend = 0.11
0.01
Difference in global
cognitive score change per
2 years, by increasing
quintile of PM2.5
(ref: lowest quintile)
0
9
10
11
12
13
14
15
16
17
-0.01
18
19
20
1 year of age
-0.02
-0.03
-0.04
-0.05
Median of PM2.5 quintile, μg/m3
Adjusted for age, education, husband's education, smoking history, physical activity,
and alcohol consumption.
Stronger association with measures of
long-term exposure to PM2.5
0.015
0.010
Difference in global
cognitive score
change per 2 years,
per 10 μg/m3
increase in PM2.5
Past yr
0.005
0.000
-0.005
Past 2
yrs
Past 5
yrs
Since
1989
1 year of age
-0.010
-0.015
-0.020
Past
month
-0.025
-0.030
-0.035
-0.040
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol
consumption.
Parkinson’s Disease
PM10
PM2.5
Quartiles
(g/m3)
cases
Quartiles
(g/m3)
cases
4.3-18.8
117
Ref
0-11.4
120
Ref
18.8-21.6
135
1.27
(0.98, 1.64)
11.4-13.3
124
1.08
(0.83, 1.40)
21.6-24.9
138
1.33
(1.02, 1.72)
13.3-`15.4
136
1.17
(0.90-1.52)
24.9-68.9
125
1.28
(0.96-1.70)
15.4-49.8
135
1.19
(0.90,1.56)
0.08
P for trend
P for trend
Per 10 g/m3
515
RR
(95% CI)
1.16
(0.96-1.40)
Per 10 g/m3
RR
(95% CI)
0.18
515
1.34
(0.95, 1.89)
Adjusted for age, smoking, region population density, caffeine intake and ibuprofen use
Palacios et al. in preparation
Diabetes
Particulate Matter
1 IQR ↑
HR (95% CI)
Distance to Road
meters
HR (95% CI)
PM2.5
0.99 (0.92,1.08)
<50
1.14 (1.03,1.27)
PM10-2.5
1.04 (0.98,1.11)
50-99
1.16(0.99,1.35)
100-199
0.97(0.88,1.08)
200+
1 (reference)
Adjusted for age, season, year, state, smoking , BMI,
hypertension, alcohol intake, physical activity, and diet.
Puett et al. 2011 EHP 119: 384-389
Uterine Fibroids
Risk for each 10 μg/m3 increase in PM2.5 among 67,487 women
in NHSII, 1993-2007; 5,814 cases
Exposure
HR (95% CI)
2 year avg
1.08 (0.98-1.18)
4 year avg
1.09 (0.98-1.20)
Cumulative avg
1.12 (1.03-1.22)
Adjusted for age, calendar time, race, current BMI,
smoking status, parity, OC use, age at menarche, age
at first and last birth, time since last birth, total months
of exclusive breastfeeding, antihypertensive
medication use and blood pressure, and Census tract
level median income and median home value
Mahalingaiah et al. in preparation
Rheumatoid Arthritis
Distance to A1-A3
(meters)
Cases
Person yrs
HR (95% CI)
0 to < 50
52
136,205 1.31 (0.98-1.74)
≥50 to < 200
67
271,200 0.84 (0.65-1.08)
≥200
568
1,976,600 1 (reference)
Hart et al. EHP 2009;117: 1065-1069
Autism and HAPS
Roberts et al, submitted
THE BUILT ENVIRONMENT
The Built Environment:
IOM Definition
• Land-Use Patterns
– Spatial distribution of human activities
• Transportation Systems
– Physical infrastructure and services that
provide the spatial links or connectivity
among activities
• Design Features
– Aesthetic, physical, and functional qualities
of the built environment, such as the
design of buildings and streetscapes, and
relates to both land use patterns and the
transportation system
Street
connectivity
Physical activity
environment
Residential or
population
density
Access to physical
activity resources
Access, density,
and diversity of
destinations
Access/
density
food
retail
Food
environment
Access/
density
food
service
Conceptual model:
Effects of the built environment on
physical activity and obesity
Physical
activity
Obesity
Morbidity
/
Mortality
Supermarkets
and grocery
stores
Convenience
stores
Dietary
intake
Sit-down
restaurants
Fast-food
restaurants
* Food retail and food service facilities could also
be physical activity destinations.
Sprawl
• Development outpaces population growth
• Low density
• Rigidly separated homes, shops, and
workplaces
• Roads marked by large blocks and poor
access
• Lack of well-defined activity centers, such
as downtowns
• Lack of transportation choices
• Relative uniformity of housing options
The County Sprawl Index
• Developed by the National Center for
Smart Growth
• Incorporates 6 Census based measures
of
– Residential density
– Street accessibility
• Calculated for the year 2000
• Higher sprawl index = higher density
– New York County, NY = 352.1
– Jackson County, GA = 62.6
:
Sprawl Index and BMI/Physical
Activity: Cross sectional analyses (2000)
β (95% CI)
1 SD (25.7) ↑ in Density
Outcome
Weight
BMI (kg/m2)
Physical Activity
Total METS
0.30 (0.04, 0.57)
Walking METS
0.23 (0.14, 0.33)
Outdoor METS
0.34 (0.20, 0.47)
-0.08 (-0.14, -0.02)
Adjusted for age, smoking, race, and husband's education
James et al. AJPH in press
Weight Gain by Quintiles of Sprawl
Difference in Rate of Weight Gain
(lbs. per year)
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.1
Difference in Change in Walking
(METs per year)
Change in Walking METs
5
4
3
2
1
0
-1
-2
-3
-4
Personal Level Built Environment
Objective Measures
• By creating buffers around
an address we can
measure
– Residential density
• # housing units/area
– Land use mix
• Density of walking
destinations
• Diversity
– Street connectivity
• Intersection density
• Pedestrian route directness
Land Use Mix
Walking destinations:
Counts of businesses
within the buffers
based on stores,
facilities, and services
from 2006 InfoUSA
spatial database on
businesses, which
include grocery stores,
restaurants, banks, etc.
Street Connectivity
Intersection Count:
Number of
intersections within
each buffer
Nuances of How Exposure
is Defined
• Definition of neighborhood is complex
– Appropriate buffer size?
– Types of buffers?
• Are people actually “using” their
neighborhood?
• How are people actually “using”
businesses
SUMMARY
Location, Location, Location
• Knowing a person’s address, or better
yet residential history, gives us the
opportunity to estimate a multitude of
environmental exposures
• Residential address allows relatively
inexpensive assessment of exposures
unknown to the participant
Location, Location, Location
• Meaningful environmental assessments
can be made at the area and personal level
– There are limitations and sources of error
not discussed here
• GIS is a powerful tool for inexpensively
incorporating assessment of
environmental exposures into large
cohorts
– Bounds only defined by what has been
georeferenced in the appropriate space
and time
Acknowledgments
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Jaime Hart
Philip Troped
Peter James
Jeff Yanosky
Steve Melly
Christopher Paciorek
Biling Hong
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Robin Puett
Jennifer Weuve
Donna Spiegelman
Marc Weisskopf
Natalia Palacios
Andrea Roberts
Andrew Kinlock