Air Pollution Reduction: Is it Real and Does it Work?

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Air Pollution Reduction:

Is it Real and Does it Work?

C. Arden Pope III

Mary Lou Fulton Professor of Economics

Presented at:

Frontiers in Spatial Epidemiology

Imperial College of London, Natural History Museum of London

November 5-6, 2012

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Illustration for Charles Dickens’

Sketches by Boz, by George Cruikshank, 1838

The chimney, smoke Stack, and smoke and fog (smog) have long played important iconic roles in the art and literature of London.

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Over London by Rail, 1870 Gustave Doré

Depicts the congested and polluted Industrial environment of 1870 London

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Coffee Stall by

Gustave Doré,

In a description of a

representative London Fog day (April 6, 1870) Doré’s friend Blanchard Jerrold say’s of an experience with Doré:

“We were well into the morning— and it was as dark as the darkest midnight. . . It was choking: it made the eyes ache.”

From:

London: A pilgrimage, 1872 and

Doré’s London: All 180 illustrations from London, A Pilgrimage, 2004

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Claude Monet, Houses of Parliament, London, 1903

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Claude Monet, Houses of Parliament, London, 1904

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Claude Monet, Waterloo Bridge, London, 1903— Zoom in on background of pollution

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Classical Economics

• Adam Smith— Wealth of Nations (1776),

free markets, competition, and the

metaphor of the “

invisible hand

”.

• “

Laissez-faire

”

• What about air pollution?

Smells like

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Meuse Valley, Belgium Smog, December 1-5, 1930 --60 deaths, 10 time expected

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Donora, PA

(Severe episode, Oct. 27-31, 1948)

From, Public Health Service, Bulletin No. 306, 1949

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Danora, PA. Noon, Oct 29, 1948.

http://www.weather.com/multimedia/videoplayer.html?clip=12603&collection=257&fro m=sitemap

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Donora, PA—administering oxygen --Approx. 20 deaths, 6 times expected

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London Fog Episode, Dec. 1952

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Loss of Faith in the doctrine of Laissez-faire

• No longer did air pollution “smell like money” but it

smelled like something unhealthy.

• In the UK, US, and elsewhere there were public policy

efforts to clean up the air and eliminate the “Killer Smog

Episodes.” (Clean air act)

• There was success at eliminating or reducing the

extreme air pollution episodes but air pollution remained

and we learned that even moderate air pollution had

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Brief methodological outline of air

pollution epidemiological studies:

Studies of short-term exposure



Episode



Population-based daily time-series



Panel-based acute exposure



Case-crossover

Studies of long-term exposure



Population-based cross-sectional



Cohort-based mortality



Cohort- and panel-based morbidity



Case-control studies

Intervention/natural experiment

Exploit temporal variability Exploit spatial variability Exploit both temporal and spatial variability

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Brief methodological outline of air



Episode



Case-crossover



Cohort-based mortality



Case-control studies

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Median PM_2.5 for aprox. 1980 8 10 12 14 16 18 20 22 24 26 28 30 32 34 A d ju s te d Mor ta lit y f o r 1 9 8 0 (De a th s /Y r/ 1 0 0 ,0 0 0 ) 600 650 700 750 800 850 900 950 1000

Mean sulfate for aprox. 1980

4 6 8 10 12 14 16 18 20 22 24 A d ju s te d Mor ta lit y fo r 1 9 8 0 (De a th s /Y r/ 1 0 0 ,0 0 0 ) 600 650 700 750 800 850 900 950 1000

Mean PM_2.5 for aprox. 1980

8 10 12 14 16 18 20 22 24 26 28 30 32 34 600 650 700 750 800 850 900 950 1000

Mean TSP for aprox. 1980

15 30 45 60 75 90 105 120 135 150 165 600 650 700 750 800 850 900 950 1000

Figure 1. Age-, sex-, and race-adjusted population-based mortality rates in U.S. cities for 1980 plotted over various indices of particulate air pollution.

Age-, sex-, and race- adjusted population-based mortality rates in U.S. cities for 1980 plotted over various indices of particulate air pollution (From Pope 2000).

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Brief methodological outline of air



Episode



Case-crossover



Case-control studies

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An Association Between Air Pollution and

Mortality in Six U.S. Cities

1993

Dockery DW, Pope CA III, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG Jr, Speizer FE.

Methods:



14-16 yr prospective follow-up of 8,111 adults living in

six U.S. cities.



Monitoring of TSP PM

₁₀

, PM

_2.5

, SO

₄

, H

+

, SO

2

, NO

2

, O

3

.



Data analyzed using survival analysis, including

Cox Proportional Hazards Models.



Controlled for individual differences in: age, sex, smoking,

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Average Polluted cities Highly Polluted cities Clean cities

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Cox Proportional Hazards Survival Model

Cohort studies of outdoor air pollution have commonly used the CPH Model to relate survival experience to exposure while simultaneously controlling for other well known mortality risk factors. The model has the form



(

)



)

(

)

(

₀

(

)

(

)

(

t

x

exp

t

l

T

i

l

i









Hazard function or instantaneous probability of death for the ith

subject in the lth strata. Baseline hazard function, common to all subjects within a strata.

Regression equation that modulates the baseline hazard. The vector X_i(l)

contains the risk factor information related to the hazard function by the regression vector β which can vary in time.

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Adjusted risk ratios (and 95% CIs) for

cigarette smoking and PM

2.5

Cause of

Death

Current Smoker,

25 Pack years

Most vs. Least

Polluted City

All

2.00

(1.51-2.65)

1.26

(1.08-1.47)

Lung

Cancer

8.00

(2.97-21.6)

1.37

(0.81-2.31)

Cardio-pulmonary

2.30

(1.56-3.41)

1.37

(1.11-1.68)

All

other

1.46

(0.89-2.39)

1.01

(0.79-1.30)

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Particulate Air Pollution as a Predictor of Mortality in a

Prospective Study of U.S. Adults

Pope CA III, Thun MJ, Namboodiri MM, Dockery DW, Evans JS, Speizer FE, Heath CW Jr.

1995

Methods:

Linked and

analyzed ambient air

pollution data from

51-151 U.S. metro areas

with risk factor data for

over 500,000 adults

enrolled in the

ACS-CPSII cohort.

Clark Heath Michael Thun

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Adjusted mortality risk ratios (and 95% CIs) for cigarette

smoking the range of sulfates and fine particles

Cause of

Death

Current

Smoker

Sulfates

Fine

Particles

All

2.07

(1.75-2.43)

1.15

(1.09-1.22)

1.17

(1.09-1.26)

Lung

Cancer

9.73

(5.96-15.9)

1.36

(1.11-1.66)

1.03

(0.80-1.33)

Cardio-

Pulmonary

2.28

(1.79-2.91)

1.26

(1.16-1.37)

1.31

(1.17-1.46)

All other

1.54

(1.19-1.99)

1.01

(0.92-1.11)

1.07

(0.92-1.24)

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Legal uncertainty largely

resolved with 2001

unanimous ruling by the

U.S. Supreme Court.

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Dan Krewski Rick Burnett Mark Goldberg and 28 others

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Cox Proportional Hazards Survival Model

Cohort studies of outdoor air pollution have commonly used the CPH Model to relate survival experience to exposure while simultaneously controlling for other well known mortality risk factors. The model has the form



(

)



)

(

)

(

₀

(

)

(

)

(

t

x

exp

t

l

T

i

l

i









Hazard function or instantaneous probability of death for the ith

subject in the lth strata. Baseline hazard function, common to all subjects within a strata.

Regression equation that modulates the baseline hazard. The vector X_i(l)

contains the risk factor information related to the hazard function by the regression vector β which can vary in time.

Note: We have extended this basic model to deal with random effects (spatial

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Further extension of the Cox Proportional Hazards model to allow for random effects (spatial clustering) and to include spatial socioeconomic and

environmental (contextual) variables at two spatial levels (zip code and MSA). Contextual variables included: Poverty (%), Income disparity (Gini), Median

Household income, Unemployment (%), Not white (%), Grade 12 education (%), Air conditioning (%).

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Sources Data/ periods Model Strata Individual risk factor variables Ecological/contextual spatial variables Spatial smoothing? % increase/10 µg/m3 All- Cardio- Cause pulmonary Lave/Seskin 1977, Ozkaynak/ Thurston 1987 U.S. pop.-based mortality rates OLS or WLS cross-sectional regression models

NA None Various including:

Population, population density, percent elderly, none white, poverty levels, etc. No ~ 9 ---- Pope et al. AJRCCM 1995 Krewski et al. HEI 2000 ACS CPS-II cohort/ 1982-1989 Standard Cox proportional hazards model Age, Sex, Race smoking, education, marital status, BMI, alcohol, dust/fume exposure None No 7 (4-10) 12 (7-17) Pope et al. JAMA 2002 ACS CPS-II cohort/ 1982-1998 Random Effects Cox proportional hazards model Age, Sex, Race smoking, education, marital status, BMI, alcohol, occupation/ occupational exposure, diet None Yes 6 (2-11) 9 (3-16) Krewski et al. HEI 2009 ACS CPS-II cohort/ 1982-2000 Random Effects Cox proportional hazards model Age, Sex, Race smoking, education, marital status, BMI, alcohol, occupation/ occupational exposure, diet Poverty, income disparity, household income, unemployment,

none white, education, air conditioning—at two levels of spatial scale (Zip Code, Metro)

No 6 (4-8) 13 (10-16)

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P ercent i ncreas e in mort al il y ri sk (95% C I) -10 0 10 20 30 40 50 60 70 80 90 100 110 120 180 190

All Cause CPD CVD IHD

S ix Ci ti es (Do c k er y et al 19 93 ; K rew s k i et al . 20 00 ; La de n et al . 20 08 ) S ix Ci ti es, M ed ic ar e (E fi m et al . 20 08 ) ACS ACS Osl o, m en an d w om en , 51 -70 ( Na ess et al . 20 07 ) ACS A HS M OG, M al es (M c Do nn el l et al . 20 00 ) V A , Hy pe rt en s iv e, M al es (Li pf er t et al . 20 06 ) 11 CA c ou nt y , el de rl y ( E nst rom 20 05 ) Fr en c h P A A RC ( Fi lleu l et al . 20 05 ) Du tc h Co ho rt ( B ee len et al . 20 08 ) S ix Ci ti es S ix Ci ti es A CS LA LA LA W om en 's He al th Ini ti at iv e (M ill er et al . 20 07 ) A HS M OG, Fem al e (Ch en et al . 20 05 ) Osl o, m en an d w om en , 71 -90 ( Na ess et al . 20 07 ) Ge m an w om en ( Ge hr ing et al . 20 06 ) U. S . M ed ic ar e; E ast /Ce nt ral /W est ( Zeg ar et al . 20 08 ) A CS M ed ic ar e (E fi m et al . 20 08 ) A HS M OG, M al es (M c Do nn el l et al . 20 00 ) Fr en c h P A A RC ( Fi lleu l et al . 20 05 ) Ge m an w om en ( Ge hr ing et al . 20 06 ) Du tc h Co ho rt ( B ee len et al . 20 08 ) Nu rs es' He al th S tud y ( P ue tt et al . 20 08 ) Nu rs es' He al th S tud y ( P ue tt et al . 20 08 ) P op e et al . 19 95 ; K rew s k i et al . 20 00 ; P op e et al . 20 02 , 20 04 ; J er ret t et al . 20 05 ; K rew s k i et al . 20 09 ) Ca l. T ea c he rs S tud y ( Ostr o et al . 20 09 ) Ca l. T ea c he rs S tud y ( Ostr o et al . 20 09 ) Ca l. T ea c he rs S tud y ( Ostr o et al . 20 09 )

Other cohort studies have shown associations between exposure

to fine PM and increased risk of cardiovascular death

.

Six-Cities studies ACS studies Other studies

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All Cause CPD CVD IHD

Joel Kaufman Lianne Sheppard

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All Cause CPD CVD IHD Lung Cancer

Nurses’ Health Study:

•Puett et al. Am. J. Epidemiology 2008 Stronger association with CVD than with all cause.

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All Cause CPD CVD IHD Lung Cancer

Netherlands, Germany, Norway studies: Beelen et al. EHP 2008

Gehring et al. Epidemiology 2006

Naess et al. Am. J. Epidemiology 2007 Again, positive associations, generally Stronger for cardiovascular disease. Brunekreef (summary paper)

JESEE 2007

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All Cause CPD CVD IHD

U.S. Medicare Cohort studies:

•Eftim et al. Epidemiology 2008 •Zegar et al. EHP 2008

Cohorts of Medicare participants cities of the 6-cities and ACS study, plus all U.S.

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Age 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 S urv iv al P robabilit y 0 10 20 30 40 50 60 70 80 90 100

Baseline survival curve, life expect.=76.4

1.25 RR from Pollut, ages < 45 unaffected; life expect.=73.9 1.25 RR from Pollut., infants unaffected; life expect.=73.5 1.25 RR from Pollut., infants affected; life expect. = 73.4 2.00 RR from Smoking from age 20; life expect.=67.9

Survival curves based on projected U.S life tables and alternative excess risk assumptions

Note: 1.15 RR from Polut., infants uneffected; life expect=74.6 (curve not shown)

Pope 2000 Brunekreef 1997

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Brief methodological outline of air



Episode



Case-crossover



Case-control studies

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Southern California Children’s Health Study

Effects of air pollution on

children’s health, especially

lung function growth.

David Bates, Advisor

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Southern California Children’s Health Study, has shown that

air pollution impacts lung development in children.

Children living in cities with higher air pollution and living near major traffic sources showed greater deficits in lung function growth.

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Brief methodological outline of air



Episode



Case-crossover



Case-control studies

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Steubenville Topeka Watertown Kingston St. Louis Portage

Reduction in fine particulate air pollution:

Extended follow-up of the Harvard Six Cities Study

Laden, Schwartz, Speizer, Dockery, 2006

Follow up analysis of the Six-Cities cohort:

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Copper Smelter Strike, Mid July 1967 – March 1968

Adapted from: Trijonis, John. Atmospheric Environment 1979

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Age 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 S urv iv al P robabilit y 0 10 20 30 40 50 60 70 80 90 100

Baseline survival curve, life expect.=76.4

1.25 RR from Pollut, ages < 45 unaffected; life expect.=73.9 1.25 RR from Pollut., infants unaffected; life expect.=73.5 1.25 RR from Pollut., infants affected; life expect. = 73.4 2.00 RR from Smoking from age 20; life expect.=67.9

Survival curves based on projected U.S life tables and alternative excess risk assumptions

Note: 1.15 RR from Polut., infants uneffected; life expect=74.6 (curve not shown)

Pope 2000 Brunekreef 1997

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- Matching PM_2.5 data for 1979-1983 and 1999-2000 in 51 Metro Areas

- Life Expectancy data for 1978-1982 and 1997-2001 in 211 counties in 51 Metro areas - Evaluate changes in Life

Expectancy with changes in

PM_2.5 for the 2-decade period

of approximately 1980-2000.

Fine-Particulate Air Pollution and Life Expectancy in the United States

C. Arden Pope, III, Ph.D., Majid Ezzati, Ph.D., and Douglas W. Dockery, Sc.D.

January 22, 2009

Do cities with bigger improvements in air quality have bigger

improvements in health, measured by life expectancy?

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Covariates included in the regression models

Changes in socio-economic and demographic variables (from U.S. Census Data):

Per capita income

Population

5-yr in-migration

High-school graduates

Urban population

Black proportion of population

Hispanic proportion of population

Proxy cigarette smoking variables—available for all 211 counties

COPD mortality rates

Lung Cancer mortality rates

Survey-based metro-area estimates of smoking prevalence National Health Interview Survey (1978-1980)

Behavioral Risk Factor Surveillance System (1998-2000)

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Clustered standard errors (clustered by the 51 metro areas) were estimated for all models except for analysis that included only the 51 largest counties in each metro area.

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A 10 µg/m

3

decrease in PM

_2.5

was associated with

a

7.3 (± 2.4) month

increase in life expectancy.

This increase in life expectancy persisted even after controlling for socio-economic, demographic, or smoking variables

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Reduction in PM_2.5, 1980-2000 0 2 4 6 8 10 12 14 Chan g e in LE , 19 80 s - 1 99 0s 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 22 47 49 45 4 10 17 19 46 48 6 43 50 24 21 36 8 34 20 7 25 1 11 12 14 44 51 27 3 28 30 32 13 18 26 9 29 23 37 38 40 15 33 5 2 35 31 16 39 41 42 Reduction in PM_2.5, 1980-2000 0 2 4 6 8 10 12 14 Residual changes in LE cont rollin g f or cov ariat es -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 22 47 49 45 4 10 17 19 46 48 6 43 50 24 21 36 8 34 20 7 25 1 11 12 14 44 51 27 3 28 30 32 13 18 26 9 29 23 37 38 40 15 33 5 2 35 31 16 39 41 42

A

B

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Air Pollution Reduction:

Is it Real and Does it Work?

Yes