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