air pollution, poverty, and health (apph*) in ho chi minh city: assessing inequalities in exposure...
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Air Pollution, Poverty, and Health (APPH*) in Ho Chi Minh City:
Assessing Inequalities in Exposure and Health
EffectsSumi Mehta and Vu Xuan Dan
(on behalf of all APPH collaborators)
*TA 4714-VIE, funded by ADB, Health Effects Institute (HEI), and Government of
Vietnam
Why the poor may suffer more health effects from air pollution (global
perspective)• The poor may suffer higher
exposures to air pollution– Roadside exposures (occupational,
commuting, residential)– Small and medium scale enterprises /
Cottage industries– Use of solid fuels for cooking
• The poor may be more susceptible to air pollution
– Poorer nutrition / immunosuppression– Higher incidence of ‘diseases of
poverty’– Lack of timely or comprehensive
access to health care
Limited evidence from the West, but clear need to
explore in AsiaExtrapolation of developed-
country research to other populations is challenging, often inadequate – Different pollution sources
and mixes – Different competing risk
factors– Population characteristics
addressed / overlooked– Health effects addressed /
overlooked– Epidemiologic evidence
base often only at low end of the exposure-response curve•HEI reanalysis of US Cohort studies
Studying APPH: Why HCMC?
• High quality data on air, health, and SEP
• Qualified and enthusiastic local collaborators– Dept of Public Health– HCMC Environmental
Protection Agency (HEPA):
– Children’s Hospitals – Bureau of Statistics
• A rapidly developing country in the midst of the environmental risk transitionSmith and Ezzati, 2005
Ho Chi Minh City
• Over 9 million people• Rapid growth: ~
200,000 people / year from 1999 - 2004
• >50% pop in Vietnam < 25 years of age
• ~3 million motorcycles, 500,000 cars
• Consistently high temperature (28.19°C,SD 1.41), humidity (73.7%, SD 7.48)
Studying APPH in HCMC: Objectives
1. Develop feasible approaches to studying air pollution, poverty, and health
• Methods appropriate for HCMC context• Methods suitable for use in other cities -
promote building an evidence base across Asian cities
2. Develop infrastructure for future studies of the health effects of air pollution in HCMC
• Technical capacity (epidemiologic methods, exposure assessment, analysis)
• Resources (data integration, equipment)
Studies on the health effects of air pollution in Asia, 1980 – Sep 2007*
*Public Health and Air Pollution in Asia – Science Access on the Net (PAPA-SAN)www.healtheffects.org/international
2 Project Components
C1. Hospital Study• Routinely collected data
from 2003-2005
C2. Household Study• Prospective data collection
from 2007-08
Summary of hypotheses tested
H1: the poor experience greater health impacts from ambient air pollution (C1 and C2)
H2: the poor live in areas with more ambient air pollution (C2)
H3: exposures of the poor are more closely linked to ambient air pollution (C2)
H4: the poor are more vulnerable to ambient air pollution (C1)
H5: the poor are more exposed to air pollution (C2)
C1: Hospital Study
• Assess the effects of short-term exposure to air pollution on hospital admissions from 2003-2005 for acute lower respiratory infections (ALRI) in young children (<5 years of age) of HCMC– ALRI is a leading cause of death in
young children of developing countries– Over 28,000 admissions for ALRI in
children during the study period• Assess whether there are there different
effects by socio-economic position (SEP), i.e. in poor children vs. other children
Daily Ambient Pollutant
Concentrations (city level)
Daily Hospital Admissions for ALRI
Preliminary Results• PM10, Ozone, NO2 and SO2 associated
with increased hospital admissions for
ALRI in young children of HCMC.– Ozone effects consistently higher
than PM effects. – Results consistent across analytic
techniques (time series and case crossover analyses)
• Greatest challenge: around 60% of ALRI
admissions occur during the rainy
season, highest pollutant concentrations
are observed in the dry season– How do we address negative
confounding in the rainy season???
Do effects differ by SEP?
– Impact of air pollution does not appear to differ by SEP (neither individual or group classification). However:
• Data from hospital financial records gives an underestimate of poverty status
• Analysis uses a single, daily, city-wide average concentration to represent population average exposure
• If exposure measurement error differs by social class, the ability to assess differences by SEP will be compromised
Air Quality Monitoring Station, District 2, HCMC
A HCMC Neighborhood in Transition, 2006
C2: Household study: the relationship between
personal and ambient exposures in HCMC
• Estimate personal exposures to air pollution among the poor and the non-poor•ambient air pollution •other sources (cooking with
solid fuels, cottage industries)
• Estimate prevalence of child and adult respiratory symptoms in HCMC
• Assess local perceptions of environment
Issues ExploredIssues Explored• What is the correlation between
ambient air pollution concentrations and personal exposures? – Is this correlation different for the
poor vs. the non-poor?• Are the mean total exposures of the
poor higher? • Are the poor’s exposures to ambient
air pollution higher?• Are the poor’s personal exposures to
other sources of air pollution higher?
Exposure Assessment
• Focus: primary caregivers of children < 5
• Repeated (<10) measurements of daily average
personal exposure over wet and dry seasons
– PM2.5 and PM10
– NO/NO2
– Elemental carbon
• Special attention to traffic exposure, incense,
cottage industries (street food), tobacco smoke
• Parallel monitoring at fixed site monitors for
comparison of personal vs. ambient
concentrations
Household Sampling Scheme
ST
AG
E 1
Ho
use
ho
ld
Su
rvey
self-reported information on household
assets,expenditure, respiratory symptoms
prevalence, perceptions
ST
AG
E 2
Exp
osu
re
Ass
essm
ent
16 households (higher SES)
16 households (lower SES)
16 households (higher SES)
16 households (lower SES)
respeated measurements of daily
personal exposure (PM2.5, PM10, Nox), time-
activity patterns
District Binh Thanh 500 households
District 2 500 households
Are the poor more exposed?
Personal PM 2.5 Exposures, by District and SES*Personal PM 2.5 Exposures, by District and SES*
*Results of first 6 household visits
How do exposures of the poor and non-poor correlate with the ambient air
quality data?
Nonpoor 0.74Poor 0.32
Median Correlation for PM2.5 - First 6 Visits
Intended Implications for Policy and Health Impact
Assessment• Global contribution:
• development of approaches to explore the relationship among air pollution, poverty, and health
• address key scientific questions: higher exposures, different pollutant mixtures, climatic conditions, and disease distributions
• Regional contribution: • locally relevant base of air pollution and
health science• focus on ALRI, a ‘disease of poverty’
responsible for a substantial burden of disease in children
• increased local capacity for future research
HCMC well equipped to conduct future research on health effects of
air pollution
• Targeted technical assistance: training on study design, data management, statistical analysis
• Necessary infrastructure for personal exposure assessment (monitoring and analysis)
– Personal exposure monitoring• PM and elemental carbon (gravimetric
samples)
• NO/NO2 (passive samples)
– Exposure Assessment laboratory (controlled temperature, relative humidity, glove box, microbalance, reflectometer, ion chromatography unit)
What next?• Vietnam has addressed class-related disparities on
many levels most inequities (i.e. nutritional deficiencies, differential access to health care) that confer increased susceptibility to air pollution may have been alleviated already– Will continued rapid economic growth broaden
inequities in exposures (and health effects as a result) to air pollution?
• What are the implications for replication of methods and /or design of multi-city or regional research?– Different patterns of poverty / inequality result
in different manifestations of exposure and susceptibility
– How can targeted monitoring be used to characterize and estimate exposure misclassification in routinely collected data?
