מצגת מתוך הרצאה שקיימה פרופסור פרנסין ליידן בכנס סביבת המגורים ובריאותינו: האם הדשא של השכן בריא יותר? שהתקיים ב- 30 במאי, 2011

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

6. Exposure Assessment

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?

8. Location, Location, Location

9. Defining and
Measuring Location

10. Aggregate Data
Country, Region, State, County, City
 Exposure = Location

11. Breast Cancer Mortality Rates

12. Aggregate Data
Country, Region, State, County, City
 Exposure = Location
 Exposure = Aggregate value of an
environmental exposure
 e.g. ultraviolet light, urbanicity, air pollution

13. Individual Data
Residential Address

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

15. Where You Live
in the US Affects
Your Health

16. Region
State
City
Residence

18. Region
State
City
Residence

19. US Census Regions

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

21. The NHS: Addresses 1986-2006

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

24. Region
State
City
Residence

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

28. Region
State
City
Residence

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

31. Total
Mortality
1972-1991
Dockery et al. NEJM 1993; 329: 1753-9

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

34. The Built Environment

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)

42. Region
State
City
Residence

43. All-cause Cardiovascular
Outcomes
Mortality
Diabetes
Rheumatoid
Arthritis
Kidney
Function
Cognitive
Decline

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

47. EPA PM10 Monitors - 1985

48. EPA PM10 Monitors – 2000

49. Distribution of PM in the Airways
EHP 2006

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

52. Hart et al. EHP 2009 117:1690–6

53. Exposure to PM10, S02 and NO2
the Trucking Industry Particle Study
PM10 (6 µg/m3) SO2 (4 ppb) NO2 (8 ppb)
Cause of % Increase % Increase % Increase
Death Cases (95%CI) (95%CI) (95%CI)
All Cause 2,816 9.7% 10.6% 14.9%
(5.2%,14.5%) (4.6%,16.9%) (9.9%,20.2%)
Lung 475 4.7% 9.1% 7.3%
Cancer (-5.9%,16.5%) (-4.6%,24.8%) (-3.9%,19.9%)
CVD 972 7.6% 9.6% 10.9%
(-0.2%,16.0%) (-0.4%,20.7%) (2.7%,19.8%)
Respiratory 184 8.2% 25.1% 20.1%
Disease (-8.6%,28.0%) (2.0%,53.5%) (0.9%,42.8%)
Non-truck drivers
Controlling for job title
Hart et al. AJRCCM 2011;183:73-78

54. End-stage Renal Disease

55. End-Stage Renal Disease
 Hypotheses:
 Particularly vulnerable to adverse effects of PM
 More severe anemia with higher exposures
 Greater resistance to erythropoiesis stimulating
agents (EPO)
 Preliminary results:
 ↑ anemia with ↑ annual average PM10
 ↑ EPO dose with ↑ annual PM10

56. PM10 PM2.5
PM10-2.5
Yanosky et al. Atmos Env 2008;42:4047-62;
Yanosky et al. EHP 2008;117:22-9

57. Yanosky et al. Atmos Env 2008;42:4047-62

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

60. Effect Modification BMI and Smoking
Fatal CHD and PM10
Puett et al. AJE 2008: 168:1161–68

61. Diabetes
Particulate Matter Distance to Road
1 IQR ↑ HR (95% CI) 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
Adjusted for age, season,
year, state, smoking , BM,
hypertension, alcohol intake,
physical activity, and diet.
Puett et al. 2010 EHP epub ahead of print

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

66. Personal Level Built Environment

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

72. Conclusions

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