air pollution and built environment: how where you live affects your health
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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 HealthBoston 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
0.90
1.00
1.10
1.20
1.30
Haz
ard
Rat
io
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
16% increase per 10 μg/m3 ↑ in 12-month
avg PM10
Puett et al. AJE 2008: 168:1161–68
All-cause Mortality and PM10
Northeastern Region 1992-2004
OutcomeHR (95% CI)
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
Mortality and Coronary Heart Disease – 10 μg/m3 ↑ Fine and
Coarse PM
Effect Modification BMI and SmokingFatal CHD and PM10
BMI<30BMI≥30
0.8
1.3
1.8
2.3
2.8
3.3
Never Smoker
Former Smoker Current
Smoker
1.41
0.980.85
2.82
1.64
1.03HR p
er10
μg/
m3
Δ
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
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol consumption.
Median of PM10-2.5 quintile, μg/m3
ref
5 6 7 8 9 10 11 12 13 14 15
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
Ptrend = 0.01
Difference in global cognitive score change per
2 years, by increasing quintile of PM10-2.5
(ref: lowest quintile)
1 year of age
Weuve et al. Arch Intern Med 2012: 172:219-27
Stronger association with measures of long-term exposure to PM10-2.5
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol consumption.
-0.035
-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
Past month
Past 2 yrs
Past 5 yrsSince 1989
Past yr
Difference in global cognitive score
change per 2 years,per 10 μg/m3
increase in PM10-2.5
1 year of age
Long-term exposure to PM2.5 in relation to decline in standardized cognitive score
9 10 11 12 13 14 15 16 17 18 19 20
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
Median of PM2.5 quintile, μg/m3
Ptrend = 0.11
Difference in global cognitive score change per
2 years, by increasing quintile of PM2.5
(ref: lowest quintile)
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol consumption.
1 year of age
Stronger association with measures of long-term exposure to PM2.5
Adjusted for age, education, husband's education, smoking history, physical activity, and alcohol consumption.
-0.040
-0.035
-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
Past month
Past 2 yrs Past 5
yrsSince 1989
Past yrDifference in global
cognitive score change per 2 years,
per 10 μg/m3
increase in PM2.5
1 year of age
Parkinson’s DiseasePM10 PM2.5
Quartiles (g/m3)
cases RR (95% CI)
Quartiles (g/m3)
cases RR (95% CI)
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)
P for trend 0.08 P for trend 0.18
Per 10 g/m3 515 1.16 (0.96-1.40)
Per 10 g/m3 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 Matter1 IQR ↑ HR (95% CI)
PM2.5 0.99 (0.92,1.08)
PM10-2.5 1.04 (0.98,1.11)
Distance to Road meters HR (95% CI)
<50 1.14 (1.03,1.27)
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
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)
Risk for each 10 μg/m3 increase in PM2.5 among 67,487 women in NHSII, 1993-2007; 5,814 cases
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
Physical activity
Obesity
Supermarkets and grocery
stores
Convenience stores
Fast-food restaurants
Sit-down restaurants
Access to physical activity resources
Access, density, and diversity of
destinations
Residential or population
density
Street connectivity
Access/density
food retail
Access/density
food service
Physical activity environment
Food environment
* Food retail and food service facilities could also be physical activity destinations.
Dietary intake
Conceptual model: Effects of the built environment on
physical activity and obesity
Morbidity /
Mortality
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)
Outcomeβ (95% CI)
1 SD (25.7) ↑ in DensityWeight BMI (kg/m2) -0.08 (-0.14, -0.02)
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)
Adjusted for age, smoking, race, and husband's education
James et al. AJPH in press
Weight Gain by Quintiles of Sprawl
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
Diffe
renc
e in
Rat
e of
Wei
ght G
ain
(lbs.
per y
ear)
Change in Walking METs
Spraw
l Quintile
1
Spraw
l Quintile
2
Spraw
l Quintile
3
Spraw
l Quintile
4
Spraw
l Quintile
5-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5Di
ffere
nce
in C
hang
e in
Wal
king
(M
ETs p
er y
ear)
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
• Jaime Hart• Philip Troped• Peter James• Jeff Yanosky• Steve Melly• Christopher Paciorek• Biling Hong
• Robin Puett• Jennifer Weuve• Donna Spiegelman• Marc Weisskopf• Natalia Palacios• Andrea Roberts• Andrew Kinlock