Does outdoor or indoor air pollution cause more respiratory disease? Evidence from

the Central European Study on Air Pollution and Respiratory Health

(CESAR Study).

Central European Study of Air Pollution and Respiratory Health

Tony Fletcher, London School of Hygiene and Tropical Medicine, London. UK

Brunekreef B, Houthuijs D, Fabianova E, Lebret E, Leonardi G, Gurzau E, Nikiforov B, Rudnai P, Volf J,

Zejda J.

CESAR National Research Teams

Bulgaria: National Centre of Hygiene, Bojidar Nikiforov

Czech Republic: Regional Institute of Hygiene Ostrava, Jaroslav Volf

Hungary: National Institute of Public Health, Alan Pintér and Peter Rudnai

Poland: Institute of Occupational Medicine and Environmental Health, Jan Zejda

Romania: Environmental Health Center, Eugen Gurzau

Slovakia: Regional Specialized Institute of Public Health Banska Bystrica, Eleonorá Fabiánová

United Kingdom: LSHTM, Tony Fletcher, Giovanni Leonardi and Sam Pattenden

The Netherlands: WAU, : Bert Brunekreef and Gerard Hoek

The Netherlands: RIVM, Erik Lebret, Annelike Dusseldorp and Danny Houthuijs

CESAR - AIMS:

Central European Study of Air Pollution and Respiratory Health

Establish comparable base-line data on:children’s respiratory health air pollution, including PM10 and PM2.5environment and health risk perceptions

Investigate effects on respiratory health of:air pollutionindoor and other risks factors

Capacity building:

(epidemiological) research methodsintroduction of QA/QC methods

Central European Study of Air Pollution

and Respiratory Health

1994-1997 EC - PHARE Programme

1999-2000 EC - INCO Copernicus

European Funding for CESAR:

Study characteristics

Cross-sectional study among children aged 7 - 11 year in 6 countries

Four (five) study areas per country: 25 study areas

Selection of study areas within countries based on differences in air

pollution levels and in dominant local sources

Participation of about 1,000 children per study area

Current concentration of PM10 and PM2.5 measured in all study areas

Assessment of respiratory health endpoints and potential confounders

at individual level

CESAR Study areas

Central European Study of Air Pollution and Respiratory Health

Methods

• 24 hour sampling, once every six days, during Nov 1995 - Oct 1996

• background sampling site

• Harvard impactors with cut-off points at 2.5 and 10 µm

• preparation and analysis in one central laboratory per country

• Questionnaire respiratory symptoms and conditions: based on items from WHO, ISAAC and ATS in children 7 - 11 years old

• Base-line pulmonary function test (FVC and FEV1) in children age 9 - 11

• Information on risk factors and potential confounders collected by questionnaire

Questionnaire based health endpoints

• Cough on most days for at least 3 months consecutively in the last autumn-

winter season

• Any cough symptom over life time (combination)

• Any wheeze symptom in the last 12 months (combination)

• Any wheeze symptom over lifetime (combination)

• Bronchitis doctor diagnosed, ever

• Bronchitis in last 12 months

• Asthma doctor diagnosed, ever

• Asthma attacks in last 12 months

• Medication use for a breathing trouble in last 12 months

Risk factors in model

• Age, sex

• country

• current # of smokers in the home

• use of gas range or oven for

heating in winter

• use of unvented gas, oil or

kerosene heater

• ever moisture stains or mould in

the home over lifetime of child

• Furniture with chipboard

• Reported frequency of traffic

passing the house

• Consumption of fruit,

vegetables and fish

• education of the mother

• occupation of the father

• Parental history of wheeze,

asthma, inhalant allergy,

eczema or hay fever

Statistical analyses

• Assessment of current annual average concentrations for PM10,

PM2.5 and coarse fraction

• Two stage regression of area-specific means/logits after

adjustment for potential individual confounders

• Random effects models at taking into account within country

correlations for estimating pollution effect

• Attributable fraction: calculation of attributable fractions from

logistic regression models

Numbers in study

•total population: 20271

•3470 (1 Country) dropped for lack of PM data

•2899 dropped for missing values in one or more variables in the models

•subjects used in these analyses: 13902

CESAR - 25 Study areas

Central European Study of Air Pollution and Respiratory Health

BulgariaSofia suburb Thermal power stationSofia centre TrafficVratza Chemicals,Assenovgrad Metallurgical

Czech RepublicOstrava centre Local heating, traffic Ostrava -Vitkovice Iron works, power, coke Ostrava - Poruba No local sourcesOstrava - Radvanice Iron works, coke oven

HungaryCegled No local sourcesDorog Local heat., power plant, pharmac. Eger Local heat., intense traffic, agric. Tata Local heating, moderate trafficTatabanya Local heating, coal/ oil power

PolandKedzierzyn - Kozle Chemical plantKielce Clean, recreational areaPszczyna Clean areaSwietochlowice Metallurg., coal, chemical

RomaniaBucharest Traffic, local heatingPloiesti Petrochemical, chemicalsBaja Mare Metallurgical industryTirgu Mures Chemical industry

SlovakiaBanska Bystrica suburb No local sourcesBanska Bystrica centre Traffic, cement plantZilina Chemical, paper factoriesBratislava Traffic, local heating

Association of PM with respiratory healthoutcomes

The next two slides illustrate provisional results of therelationships between particulate pollution and adjusted

prevalence of respiratory symptoms. Detailed numerical valueswill be available in a forthcoming publication

The subsequent slide illustrates the calculation of attributablefractions for a limited number of exposure factors and one

outcome factor. Detailed results will be available in aforthcoming publication

Cough and PM2.5 by study area

an

y co

ugh

sym

pto

ms

ever

(%

)

PM2.5-concentration (µg/m3)

30 40 50 60 70

0

20

40

60

B BBB

CCC C

HHH

HH

PP P

P

S

S SS

Wheeze and PM2.5 by study area

an

y w

hee

ze s

ympt

om

s ev

er (

%)

PM2.5-concentration (µg/m3)

30 40 50 60 70

10

20

30

40

50

BB

BB

CCC

CH

HH HH

PP

PP

SSS

S

Example of some risk factors for Wheeze: Prevalence, Odds ratios and Attributable fractions

Variable Level Prev. % OR 95%CI AFs %

Air pollution 29 µg/m3 567 µg/m3 5 - 95 1.49 1.07 -2.07 11.3

Traffic intensity None 52Light 29 1.16 1.03 -1.31 3.0Medium 12 1.18 1.03 -1.35 1.4Heavy 6 1.17 1.05 -1.31 0.7

Traffic 5.1

Heating with Gas Oven No 96Yes 4 1.04 0.85 -1.28 0.1

Kerosene heater No 96Yes 4 1.32 1.05 -1.67 0.8

Indoor combustionsources

0.9

Conclusions

Central European Study of Air Pollution and Respiratory Health

• attributable fractions are a helpful indicator for interpreting these results and could be used more widely

• parental history of respiratory illness and indicators of socioeconomic status are important contributors to symptom prevalence

• air pollution is more important for some symptoms than indoor combustion sources, ETS or dampness

• the presence of chipboard furniture is very prevalent and appears to be associated with substantial attributable fractions for some symptoms