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IEc

Using AirNow-I data in

BenMAP-CE

A health burden analysis in Addis Ababa,

Ethiopia

Presented by:

William Raich, Industrial

Economics, Inc. (IEc)

23 November 2018

INDUSTRIAL ECONOMICS, INCORPORATED

Outline

• Background and motivation

• Objectives

• Methodology and data sources

• Results

• Discussion

2


Background: Addis Ababa, Ethiopia

• Population of 3.5 million

• Rapidly growing

economy and

urbanization

• Major sources of

ambient air pollution:

• Aging vehicle fleet

• Unpaved roads

• Solid waste burning


Vehicle PM Emissions in Addis Ababa


Addis Ababa Megacities Partnership

• Collaboration between

USEPA and Addis Ababa

EPA to advance local air

quality management

• Identify and address gaps

in monitoring and data

management

• Grow analytical capacity

via benefits analysis


Objectives

• Assess the current health burden in Addis Ababa

associated with fine particulate matter (PM2.5)

• Support air quality management planning at the

Addis Ababa Environmental Protection Agency


Methodology

• Use BenMAP-CE to estimate health impacts

associated with PM2.5 concentrations in Addis

• Health impact functions, e.g.:

∆𝑌 = 1 − 𝑒−𝛽∆𝑃𝑀 ∗ 𝑃𝑜𝑝 ∗ 𝑌0

Pollutant change (∆ PM)

Effect

estimate

(ß)

Health

impact

(∆ Y)

Population (Pop) Baseline incidence (Y0)


Data Sources

• Local data sources:

• Population (Ethiopian

Statistical Agency)

• Incidence (Ministry of

Health) → in progress

• Shapefiles (Urban

Planning Commission)

• International data

sources:

• Monitors (AirNow-I)

• Incidence (Global Burden

of Disease)

• Health impact functions

(IER and GEMM)

Population

Baseline incidence

ß Health Impact Functions

Monitored air quality

$ Valuation Functions


Data Inputs: Monitors

• No continuous PM2.5

monitors managed by

federal or local

governments in Addis

• 2 AirNow monitors in the

city!

• Located at the U.S.

Embassy and

International School

• Hourly PM2.5

concentrations since

2016

• Data processed to

create daily average

PM2.5 concentrations


Accessing AirNow Monitor Data


Monitored PM2.5 Concentrations (2016-2018)

0

10

20

30

40

50

60

70

PM

2.5

(u

g/m

3)

School Central Ethiopia Annual Standard WHO Annual Standard


Data Inputs: Health Impact Functions (IER)

• Integrated Exposure Response (IER)

• Derived as part of Global Burden of

Disease (GBD) study

• Quantifies PM2.5-related premature

deaths worldwide

• Cause-specific death: Ischemic heart

disease, stroke, chronic obstructive

pulmonary disorder, lung cancer,

and lower respiratory infection

• “Pooled” estimate of effect

coefficients from outdoor air

pollution, active smoking and

passive smoking studies

• Developed to characterize air

pollution at high concentrations

(e.g. >100 μg/m3)

From: Burnett, R. T., C. A. Pope, M. Ezzati, C. Olives, S. S. Lim, S. Mehta, H.

H. Shin, et al. 2014. “An Integrated Risk Function for Estimating the Global

Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure.”

Environmental Health Perspectives 122 (4): 397-403. doi:10.1289/ehp.1307049.


Data Inputs: Health Impact Functions (GEMM)

• Global Exposure Mortality Model

(GEMM)

• Quantifies PM2.5-related premature

deaths worldwide

• Cause-specific death: Ischemic heart

disease, stroke, chronic obstructive

pulmonary disorder, lung cancer,

and lower respiratory infection

• Non-communicable diseases (NCD) +

lower respiratory infection (LRI)

• “Pooled” estimate of effect

coefficients from cohort studies of

outdoor air pollution (no smoking

studies)

• Developed to characterize air

pollution at high concentrations and

simplify the approach and relax

assumptions with the IERFrom: Burnett, R. T., C. Hong, M. Scyskowicz, N. Fann, B. Hubbell, et al.

2018. “Global estimates of mortality associated with long-term exposure to

outdoor fine particulate matter.” PNAS, doi:10.1073/pnas.1803222115


BenMAP-CE Analysis

• Monitor rollback:

• 100% rollback of 2017 PM2.5 concentrations

• Background concentration: 0 μg/m3

• 2017 population

• 2016 GBD incidence

• Health impact functions:

• Cause-specific IER (ages 25-99)

• Cause-specific GEMM (25-99, with Chinese male cohort study)

• NCD + LRI GEMM (25-99, with Chinese male cohort study)


BenMAP-CE Air Quality Grid


Health Impact Results

Mortality Endpoint GEMM IER

Cerebrovascular disease 330 200

Chronic obstructive pulmonary disorder (COPD) 91 64

Ischemic heart disease (IHD) 640 340

Lower respiratory infection (LRI) 570 270

Lung cancer 30 18

Five causes of death (total) 1,700 880

Non-communicable diseases + LRI 3,000 -


Results

• Annual PM2.5 concentrations in Addis ranged from 29-

41 μg/m3 in 2017

• Long-term exposure to outdoor PM2.5 accounts for

880 – 3,000 premature deaths annually in Addis

• 57 percent of all PM2.5-related deaths are linked with

five causes (ordered by magnitude):

• Ischemic heart disease, lower respiratory infection, stroke,

chronic obstructive pulmonary disorder, and lung cancer

• GEMM estimates are 1.9 – 3.4 times higher than IER


Discussion

• Data-sparse environments:

• AirNow-I allows analysts to use local monitor data in areas

where government monitors may not exists

• International, meta-analytic health impact functions (IER,

GEMM) allow analysts to assess the impacts of pollution on

human health in areas with limited epidemiological research

• Next steps:

• Refine local data sources (e.g., incidence)

• Air quality management planning


Acknowledgments

• Project Team:

• Natalie Weiss, Meredith Amend, Stefani Penn, and Henry

Roman

Questions?

William Raich

[email protected]

Henry Roman

[email protected]

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