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How Where You Live Affects Your Health
1. Location, Location, Location
How Where You Live in the US
Affects Your Health
Francine Laden, ScD
Mark and Catherine Winkler Associate Professor of
Environmental Epidemiology
Harvard School of Public Health
Channing Laboratory, Brigham and Women’s Hospital
2. Overview
The study of environmental epidemiology –
issues with exposure assessment
Location as a “measure” of exposure
Aggregate data
Individual data
Examples from my research group
Ultraviolet radiation
Air pollution
Built environment
3. Cohort Studies in the Examples
The Nurses’ Health Study (NHS)
The Harvard Six Cities Study
The Trucking Industry Particle Study (TrIPS)
The US Renal Data System (USRDS)
The Nurses’ Health Study II (NHSII)
4. Environment is all that
surrounds us, food we eat,
soil we live on, buildings we
dwell in, work we do, society
we are a part of.
5. Environmental Exposures
My working definition
Exposures that are outside of ourselves
Experienced passively
Natural and unnatural extras
Common factors
Ubiquitous
Low levels with a tight range
Small effects
7. Self-reports of Proxies of Exposure
Where do you
live now, and
Do you spend
Are you
where smoky
time in
bars?
exposed to
dust or fumes
did you live
in your job? then?
Do you drink
tap water?
12. Aggregate Data
Country, Region, State, County, City
Exposure = Location
Exposure = Aggregate value of an
environmental exposure
e.g. ultraviolet light, urbanicity, air pollution
14. Geographic Information System
(GIS)
integrates hardware, software, and data for
capturing, managing, analyzing, and displaying
all forms of geographically referenced
information
But first of all, the exposure of interest has to
have been
Measured and mapped
in the right space and
at the right time
20. The Nurses’ Health Study
121,700 women
Enrolled in 1976
Biennial follow-up
Information specific
to location
Biennial mailing
address
State at birth, age
15 and age 30
22. Breast Cancer
Region Age-adjusted Multivariate*
at baseline Cases HR (95%CI) HR (95% CI)
South 222 reference reference
Northeast 1103 1.08 (0.93–1.24) 1.12 (0.97–1.30)
Midwest 353 1.08 (0.91–1.27) 1.09 (0.92–1.29)
California 327 1.24 (1.05–1.47) 1.18 (1.00–1.40)
Postmenopausal breast cancers, *adjusted for known breast cancer risk
factors
Laden et al. JNCI 1997;89:1373-8
23. Rheumatoid Arthritis
Region Multivariate
at baseline Cases HR (95% CI)
West 121 reference
Midwest 161 1.33 (1.05-1.69)
Mid-Atlantic 392 1.30 (1.05-1.60)
New England 137 1.42 (1.10-1.82)
Southeast 21 1.20 (0.75-1.91)
Costenbader et al. Arch Intern Med. 2008;168(15):1664-70
25. Ultraviolet Radiation
Exposure a
function of time of
day, cloud cover,
haze, ozone
concentrations,
latitude and altitude
26. Skin Cancer
women living in the same state at birth, age 15, age 30
Cancer UV rank HR (95% CI)
Melanoma Low 1
Medium 1.26 (0.97-1.63)
High 1.12 (0.72-1.72)
SCC Low 1
Medium 1.61 (1.31-1.98)
High 2.07 (1.55-2.77)
BCC Low 1
Medium 1.24 (1.16-1.31)
High 1.30 (1.18-1.43)
Qureshi et al. Arch Intern Med 2008;168:501-7
27. Non-Hodgkin Lymphoma
Time point UV rank HR (95% CI) P for trend
Birth Low 1 <0.01
Medium 1.21 (1.03-1.42)
High 1.18 (0.97-1.43)
Age 15 Low 1 <0.01
Medium 1.17 (1.00-1.38)
High 1.21 (1.00-1.47)
Baseline Low 1 0.02
Medium 1.01 (0.88-1.16)
High 1.11 (0.95-1.29)
Bertrand et al. in preparation
29. Harvard 6 Cities Study
Portage
(Madison) Watertown
#
(Boston)
#
Steubenville
#
St. Louis
Topeka #
#
#
Kingston-Harriman
(Knoxville)
30. Cities Defined By
Various components of air pollution
Particles: total (TSP), inhalable (PM10), fine (PM2.5)
Sulfate particles
Aerosol acidity
Sulfur dioxide
Nitrogen dioxide
Ozone
Measured at a central location
Averaged over the study period
32. Continued Follow-up 1998
1.3 S
1.2 H
L
Rate Ratio
1.1 H
T W S
1 P T
L
0.9
0.8
W Period 1
Period 2
0.7
0 5 10 15 20 25 30 35
PM2.5 mg/m3
Laden et al. AJRCCM 2006;173:667-72
33. PM Inhalation
Lungs
• Inflammation
Heart
• Oxidative stress Blood
• Accelerated progression • Altered rheology
• Altered cardiac
autonomic function
and exacerbation of COPD • Increased coagulability
• Increased respiratory symptoms • Translocated particles
• Increased dysrhythmic
• Effected pulmonary reflexes • Peripheral thrombosis
susceptibility
• Reduced lung function • Reduced oxygen saturation
• Altered cardiac
repolarization
•Increased myocardial
ischemia
Systemic Inflammation
Oxidative Stress
• Increased CRP
• Proinflammatory mediators
Vasculature • Leukocyte & platelet activation
Brain
• Atherosclerosis,
accelerated progression of and • Increased cerebrovascular
destabilization of plaques ischemia
• Endothelial dysfunction
• Vasoconstriction and Hypertension
There are multiple mechanistic pathways with complex interactions and interdependencies
35. 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
36. 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
37. Street Conceptual model:
connectivity
Effects of the built
Residential or
population environment on physical
Physical density
activity Access to
activity and obesity
environment physical activity
resources
Physical
Access, density,
and diversity of
activity Morbidity
destinations Obesity /
Mortality
Supermarkets
Access and grocery
/ stores
density Dietary
food
retail Convenience intake
Food stores
environment
Access Sit-down
/ restaurants
density
food
Fast-food * Food retail and food service facilities could also
service
restaurants be physical activity destinations.
38. 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
39. County Sprawl in the NHS
Mean=109.5
SD=26.4
Range=62.6-352.1
Higher sprawl = more compact county
40. :
Sprawl Index and
BMI/Physical Activity
Cross-sectional analysis 2000
β (95% CI)
Outcome 1 SD (25.7) ↑ in Density
Weight 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. in preparation
41. Sprawl Index and
Overweight/Obesity
Survival analysis 1986-2006
Among the women who were not overweight
(BMI 25-30) or obese (BMI ≥30) at baseline
HR for each 1 SD ↑ in Density
Overweight: HR 0.96 (95% CI: 0.95, 0.98)
Obesity: HR 0.95 (95% CI: 0.94, 0.97)
44. Distance to Major Road
Census Road Classifications
A1 (primary roads, typically
interstates, with limited
access)
15 m fr A2,
A2 (primary major, non-
510 m fr A1
interstate roads) 163 m fr A2,
A3 (smaller, secondary
645 m fr A1
roads, usually with more
85 m fr A2
than two lanes)
220 m fr A2
45. Rheumatoid Arthritis
Distance to A1-A3 Person
(meters) Cases 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
Hart et al. EHP 2009;117: 1065-1069
46. EPA Air Quality System (AQS)
Database of measurements of air pollutant
concentrations throughout the US
Criteria Air Pollutants
PM10, PM2.5, CO, NO2, SO2, O3, Pb
Hazardous Air Pollutants (HAPS)
Organic compounds and toxic metals
Dates of PM measurements:
PM10 – 1985 on
PM2.5 – 1999 on
50. 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
Annual average models PM10, NO2 and SO2
Monthly average models PM10 and PM2.5
51. Annual Modeling of PM10 and NO2
The Trucking Industry Particle Study
Hart et al. EHP 2009 117:1690–6
58. All-cause Mortality and PM10
Northeastern Region 1992-2004
16% increase 1.30
with a 10 μg/m3
↑ in 12-month 1.20
Hazard Ratio
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
59. Mortality and Coronary Heart Disease –
10 μg/m3 ↑ Fine and Coarse PM
HR (95% CI)
Outcome PM2.5 PM10-2.5
1.29 0.96
All-cause mortality
(1.03,1.62) (0.82,1.12)
1.10 1.01
First CHD
(0.76,1.60) (0.78,1.31)
2.13 0.91
Fatal CHD
(1.07,4.26) (0.56,1.48)
0.71 1.06
Non-fatal MI
(0.44,1.13) (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
62. 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
63. Cognitive Decline
NHS participants ≥ 70 yrs old 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, Distance to Road
Assessed different averaging periods
64. Long-term Exposure to PM10 in
Relation to Cognitive Decline
Ptrend = 0.003
Adjusted for age, education, husband’s education, long-term physical activity
and long-term alcohol consumption
Weuve et al. in preparation
65. Stronger Association with Measures of
Long-term Exposure
Δ in cognitive
0.010
score per 0.005
10 μg/m3 0.000
↑ in PM10 -0.005
Past 5 yrs Since 1989
-0.010
-0.015 Past month
-0.020
-0.025
Past yr Past 2 yrs
-0.030
Weuve et al. in preparation
67. 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
68. 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.
69. Street Connectivity
Intersection Count:
Number of
intersections within
each buffer
70. Subjective Measures:
yes/no questions
Shops, stores and markets are within easy walking
distance of my home
My neighborhood has free or low cost recreation
facilities, such as parks, walking trails, bike paths,
recreation centers, playgrounds, public swimming pools,
etc.
There are sidewalks on most of the streets in my
neighborhood
The crime rate in my neighborhood makes it unsafe to
go on walks at night.
71. Neighborhood Environment and Meeting
Physical Activity Recommendations
Attribute OR (95% CI)
Crime-Unsafe to Walk at Night 0.80 (0.74, 0.87)
Shops/Stores Easy Walking Distance 1.41 (1.36, 1.47)
Sidewalks on Most Streets 1.22 (1.18, 1.27)
Free/Low Cost Recreation Facilities 1.33 (1.28, 1.38)
Adjusted for age, race, ethnicity, BMI categories, and husband's education
Meeting physical activity recommendation by
walking ≥ 500 MET-minutes/week.
Troped et al. submitted
73. 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
74. 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
Location data can be a powerful tool to
incorporate assessment of environmental
exposures into a variety of study designs
75. Acknowledgments:
Kimberly Bertrand, M. Alan Brookhart, Douglas
Dockery, Mathilda Chiu, Karen Costenbader, Miguel
Craig, Mary Davis, Chris Garcia, Eric Garshick, Diane
Gold, Sue Hankinson, Jaime Hart, David Hunter, ISEE,
Elizabeth Karlson, Susan Korrick, Petros Koutrakis,
Peter James, Steve Melly, Lucas Neas, Chris Paciorek,
Robin Puett, Abrar Qureshi, Eric, Rimm, Joel Schwartz,
Tom Smith, Frank Speizer, Donna Spiegelman, Meir
Stampfer, Sheila Stewart, Helen Suh, Philip Troped,
Veronica Vieira, Scott Weiss, Jennifer Weuve, Jeff
Yanosky, Barbara Zuckerman…
Funding sources: US EPA, HEI, NIEHS, NCI, FAMRI, Harvard
Catalyst Program