How to Improve Air Quality? - pneumologie.de · How to Improve Air Quality? Research, Evaluation and Societal Change A Symposium oftheGerman RespiratorySociety September 19, 2019

How to Improve Air Quality?

Research, Evaluation and Societal Change

A Symposium of the German Respiratory Society

September 19, 2019 at the Berlin-Brandenburg Academy of Sciences and Humanities

Programm | Program

10:00 – 10:10 Grußwort | Welcome Note

10:10 – 10:30 Anmoderation | Introduction

10:30 – 11:10 Wie steht es um unsere Atemluft?

Zum Stand der Schadstoffbelastung in Deutschland | Air Quality in Germany Today

11:10 – 11:30 Kaffeepause | Coffee break

11:30 – 12:10 Air Quality and Health: Insights from Epidemiology |

Wie verhalten sich Krankheitsraten zu verschmutzter Luft? Aktuelle epidemiologische Forschungsergebnisse

12:10 – 12:50 Air Quality and Health: Insights from Toxicology |

Wie wirken Luftschadstoffe auf den Körper? Diskutierte Wirkmechanismen

12:50 – 13:20 Mittagspause | Lunch

13:20 – 13:55 Deriving Conclusions on Air Pollution Effects:A Critical Evaluation of Current Research Methods | Von der Schadstoffexposition zum Gesundheitsrisiko:Kritische Methodenbewertung

13:55 – 14:30 Research Perspective and Institutional Output:

The Policy Impact of Methodology | Von der Methodik zur Politik Methodische Ansätze und deren Bedeutung für die Policy von Institutionen

14:30 – 14:45 Kaffeepause | Coffee break

14:45 – 15:30 Keynote: Was Sie schon immer über den Klimawandel

wissen wollten | All you ever wanted to know about climate change

15:30 – 16:15 Abschlussdiskussion | Round Table Discussion

Was tun fur bessere Luft? | Symposium der DGP | 19. September 2019

Prof. Dr. Joel SchwartzT.H. Chan School of Public Health, Harvard University

Deriving Conclusions on Air Pollution Effects: A Critical Evaluation of Current Research Methods


What Is Air Pollution?

| Prof. Dr. Joel Schwartz


How do we know that Air Pollution is bad for Health?I will talk about NO2, Ozone, and PM2.5 because NO2 makes ozone and PM2.5 (nitrate and OC particles)



If You Reduce Particles

Fewer People Die

Fewer People get Sick


We have thousands of studies showing that air pollution causes deaths, Hospitalizations for respiratory and cardiovascular disease, lower lung function, increased susceptibility to Respiratory infections etc.

How do we decide it is causal?



Basic Approaches

1. Look for coherence between Epidemiology of hard outcomes,Epidemiology of biomarkers and intermediate outcomesToxicology of mechanistic pathways, biomarkers and intermediateoutcomes.

2. Use newer designs that mimic randomized trials forobservational data



For example, we have epidemiology showing that air pollutionincreases deaths from respiratory disease, hospital admissionsfor respiratory disease, increased symptoms of respiratory disease, and increased lung inflammationWe have toxicology showing increased lung inflammation, decreasedresistance to infection, increased burden of S pneumonia, etc.

This all fits together


Idea of Causal Modeling

• Take Observational Data

• Make it look like a Randomized Control Trial

• Then we can believe it is causal

–How can we do this?– Make the distribution of covariates (other factors)

among people exposed to a the same as among people exposed to a′.

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0 5 10 15 20 25 30 35

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PM2.5 m/m3

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Relative Risk of Death in Six US Cities during Two Follow-up Periods

Laden 2006


Pope Smelter Strike Study

• Smelter Strike Reduced particles in the U.S. Southwest

• 2.5 ug/m3 decrease in PM2.5

• There was 2.5% Reduction in Mortality during the strike

• Avol Lung Function Growth in Southern California Children’s Cohort

• Children who moved to more polluted areas had slower growth

• Children who moved to less polluted areas had faster growth


Pope, Amer J Public Health 1989; 79: 623

Natural Experiments

• Lleras-Muney (2010) looked at all U.S. military

families stationed in the United States over 11 years.

• About 1/3 of families are relocated each year to new

bases, based on military need, presumably unrelated

to air pollution levels or health.

• 114,612 children < age 2-5, 1.2% hospitalized each

year for respiratory disease

• They found a 10 ppb increase in annual ozone

increased hospitalization by an additional 0.35% (95%

CI 0.05, 0.65%), that is, from 1.2% to 1.55%

Differences in Differences

• New Jersey replaced its toll booths with electronic tolling

• Walker and Currie considered two groups, those living within 1.5km of the toll plaza, and those living near the highway, but further (2-10 km) from the plaza

• They controlled for individual and small area covariates, but what about omitted confounders?

Toll Booth

Suppose we take the difference in health after Vs before the change

• In the neighborhood near the toll booths this will control for unmeasured confounders that change slowly over time within the neighborhood (exercise, diet, blood pressure, etc.)

• What if an unmeasured confounder did change between the two years (e.g. new medicine). If the time varying omitted confounders change similarly in the other two neighborhoods, then their change over time can serve as a control for those omitted confounders

• The difference between post-pre in the near group and post-pre in the other two groups is this difference in differences estimate

• Birthweights increased when the tolls booths were removed

In the Northeastern U.S. (Yitshak-Sade et al 2019)

In the Lazio region of Italy (Renzi 2019)

In both cases PM2.5 predicted increased death rates

Abu Awad (2019) looked at all persons aged 65+ enrolled in the U.S. Medicare ProgramShe only compared movers (10 million) who left the same neighborhood, but moved to different locationsThe change in exposure was independent of SES, education, sex, smoking, prior hospital admissions, etcIf this holds true for unmeasured confounders this is randomized

assignment

A 10 µg/m3 increase in PM2.5 produced a 1.21 (1.20, 1.22) Hazard ratio for dying


Sometimes we can find a variable

That is responsible for some of the variation in exposureBut that is not plausibly related to confounders or to the health outcome

For example, wind blows local pollution out of town, but is not related to daily smoking, diet, exercise, or other predictors of death that day

Also, it is possible to use weights to make exposure independent of potential confounders

I applied both approaches to look at NO2 and PM2.5 and daily deaths in 135 U.S. cities


Weighting

• Suppose people with high cholesterol tend to have higher exposure.

• However, there are still some people with high cholesterol who have low exposure

• Suppose there are only half as many high-low as high-high

• Then if we give twice as much weight in the analysis to the high cholesterol low exposure group, exposure will be independent of cholesterol.

• Now it does not matter how health depends on cholesterol

• We can do this kind of weighting for all covariates

• If there are no omitted confounders this looks like a randomized trial

Results

• There were a total of 319,767 days with no missing data, and on those days there were 7,296,250 deaths.

Model Effect Size 95% CI

IV (PM/NO2) 1.6% per 10 μg/m3 1.2%, 2.0%

Negative Control -0.1% per 10 μg/m3

-0.4%, 0.2%

MSM PM2.5 0.83% per 10 μg/m3

0.31%, 1.35%

MSM NO2 2.26% per 10 ppb 1.36%, 3.15%

MSM PM2.5 < 25 μg/m3

0.92% per 10 μg/m3

0.40%, 1.45%

Particles Induce oxidants in the Heart and Lung

• Gurguera et al EHP 2003)

Chemiluminescence (CL) of lung, heart, and liver after various durations of Particle exposure.


Maimonides

• “Comparing the air of cities to the air of deserts and arid lands is like comparing waters that are befouled and turbid to waters that are fine and pure. In the city, because of the height of its buildings, the narrowness of its streets, and all that pours forth from its inhabitants …the air becomes stagnant, turbid, thick, misty and foggy… If there is no choice in this matter, for we have grown up in cities and have become accustomed to them, you should select from the cities one of open horizons… endeavor to at least dwell at the outskirts of the city.”



Beijing Particle Mask Study

• People walked the same route twice

• Once wearing a mask that filtered out particles, once without the mask

• Blood pressure was 3 mmHg lower when they were wearing the mask

• ST segment depression was less wearing the mask

• Note this was Traffic Pollution


This Also worked for Randomized trials of HEPA air filtration to remove particles

• Brauner (2008) reported an 8% improvement in endothelial function with filtered air in homes.

• Chuang (2017) found true home filtration produced lower concentrations of CRP and 8-OHdG in blood, and lower blood pressure than in homes getting the sham filter.

• Mauad exposed mice to either ambient air, or filtered air to remove particles. Lung function in the particle-exposed mice, with average particles concentrations of 16.8 μg/m3, was lower than in the mice with filtered air, with particle concentrations of 2.9 μg/m3.

Effect of putting animals in filtered air on oxidative stress in their heart, lung, and liver

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How to Improve Air Quality? - pneumologie.de · How to Improve Air Quality? Research, Evaluation and Societal Change A Symposium oftheGerman RespiratorySociety September 19, 2019