Quantifying Health Benefits with Local Scale Air Quality Modeling

Presentation to CMASOctober 7th, 2008

Bryan Hubbell, Karen Wesson and Neal FannU.S. EPA

Jonathan I. LevyHarvard School of Public Health 1

Overview

• Summarize how we estimate human health benefits with local air quality modeling data– The basic steps in an

Environmental Benefits Mapping and Analysis Program (BenMAP) benefits analysis

– Matching the health data with the scale of the air quality data

• Understand how local-scale benefits are influenced by:– Resolution of exposure estimates– Scale of baseline incidence rates– Geographic specificity of health

impact functions

• Discuss directions for future research 2

Step One: Derive Health Impact Functions from Epidemiology

Literature

Ln(y) = Ln(B) + ß(PM)

Incidence (log scale)

PM concentrationLn(B)

∆ Y = Yo (1-e -ß∆ PM) * Pop

ß - Effect estimate

Yo – Baseline Incidence

Pop – Exposed population

Health impact function

Epidemiology Study

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Baseline air quality Post-policy air quality

Estimate air quality change

Estimate population exposure

Match exposure

with baseline incidence

rate

ß Apply the effect estimate to quantifyhealth impacts

Health Benefits

Step Two: Apply the Health Impact Function to Estimate Benefits

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National-Scale Modeling Calls for Coarse-Scale Health Inputs

Coarse-scale air quality modeling

Regional or national-scale Baseline incidence and ß

estimate

Coarse-scale population exposure

Regional or national Incidence count 5

Local-Scale Modeling Calls for Location-Specific Health Inputs

Fine-scale air quality modeling

Regional or national-scale Baseline incidence and ß estimate

Fine-scale population exposure

Local Incidence count

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Comparing Population-Weighted Air Quality Changes at 12km and 1km

Air quality change * Populationat 12km

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Air quality change * Populationat 1km

Exposure Estimates Sensitive to Air Quality

Modeling Scale

• PM2.5 Population-weighted air quality change highly variable– 12km: -0.037 µg/m3

– 1km: -0.715 µg/m3

• Summary conclusions:– 12km and 1km population

exposure different – Population exposure affected

by proximity of population centers to changes in grid-level air quality

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Assessing the Importance of Baseline Incidence Rate Scale

• We calculate health impacts relative to some baseline rate

• Local analysis calls for local incidence rates

• Michigan DEQ provided ZIP-level rates for:– Respiratory hospitalizations

• Asthma• Chronic Lung Disease• Pneumonia

– Non-fatal heart attacks– Acute Bronchitis– Chronic Bronchitis

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Chronic Bronchitis Rate Varies by Age and Location in Detroit

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Location Ages

Value (per

10,000)

National 27+ 37.8

Detroit

0 to 19No

reported cases

20 to 64

4 to 49

65 to 99

50 to 390

The Level and Distribution of Avoided Chronic Bronchitis Cases is Sensitive to the Incidence Rate

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Change in chronic bronchitis using national incidence rate

Change in chronic bronchitisusing Detroit incidence rate

O3 Benefits are Sensitive to the Scale of the Health Impact

Function

Original Bell et al. (2004) mortality estimate

Detroit-specific Bell et al.

(2004) mortality estimate

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Local Health Impact Analyses are Data-

Intensive• How best can we use local-

scale air quality modeling when we lack local:– Health impact functions?– Incidence rates?

• Do you use local concentration-response functions when:– They exhibit poor statistical

power due to small population sizes?

– Are sometimes negative?– They lack statistical significance?

• At what scale do you violate the fundamental assumptions of epidemiology study?

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