Chiara Badaloni Roma, 15 Oct EXPOSURE TO TRAFFIC AIR POLLUTION IN A CASE-CONTROL STUDY OF CHILDHOOD LEUKAEMIA

LEUCEMIA Background (1/2): LEUKAEMIA POSITIVE NEGATIVE

Background (2/2): LUR SATELLITE DATA

Test the hypothesis that air pollution is associated with Leukaemia in childhood Objective

Study design (1/3) THE SETIL CASE - CONTROL STUDY It was designed to identify environmental factors that may contribute to childhood leukaemia

Study design (2/3) Diagnoses: Leukaemia from Italian register (AIEOP) SELECTION OF THE CASES Period of observation: 1 August 1998 to 31 July 2001 Inclusion criteria: The cancer cases were 0-10 year old Residents in 16 Italian regions: 8 regions in the NORD, 4 regions in the CENTRE, 2 in the SOUTH and the 2 ISLAND Italian population

Study design (3/3) SELECTION OF THE CONTROLLS Randomly selected from the children population residing in each region. Free from cancer Two controls were randomly sampled per each leukaemia case Matching: date of birth, sex and region of residence.

Geocoding of cases and controls (all residences) Address Locator

EXPOSURE ASSESSMENT GIS variables grid 4x4km from Hybrid model: Satellite data and LUR grid 100x100m NO 2 PM 2.5 from Dispersion model: MINNIPM 2.5 from Dispersion model: MINNI PM 10 O 3

GIS variables Distance from major roads (m) MAJORROADLENGTH (m): sum of road length of major roads within a 100m buffer (major roads are defined as class 0-5 from the central road network)

MINNI: (National Integrated Model to support the international negotiation on atmospheric pollution) Dispersion model : MINNI Calculates concentration and deposition fluxes of air pollutions. Combines information in terms of alternative emission scenarios, alternative air quality scenarios and abatement costs. Atmospheric Transfer Matrices (ATM) RAIL (RAINS Atmospheric Inventory Link) PM 2.5 Exposure: Spatial resolution 4x4km

Dispersion model : MINNI Spatial resolution 4x4km PM 2.5 Exposure:

Hybrid model: SATELLITE DATA and LUR

Satellite data OMI (Ozone Monitoring Instrument) Annual data MODIS (Moderate-resolution Imaging Spectroradiometer) + MISR (Multi-angle Imaging SpectroRadiometer) NO 2 PM 10 broad scale meteorological variables for temperature and wind-speed were incorporated in the database. O3O3 Output: the aerosol optical depth (AOD) Aggregation of Spatial resolution 10x10km

LUR LUR: Land Use Regression Model Tool for estimating pollutant levels and exposure at a fine spatial scale was constructed on the basis of: a series of common source predictor variables compiled in GIS NO 2 = b0b0 b - b + b + b - Spatial resolution 100x100m pollution measurements from routine monitoring

LUR monitoring from the European EuroAirnet network reported in Airbase European level AIRBASE, annual means, >=75% data capture 80% for training 20% for validation

LUR Predictor variables

LUR: Validation Final Models Pollutant/YearVariablesModel building (n=1612)Validation (n=398) Adj R2SEEAdj R2SEE (ug/m3) NO Surface NO satellite (ppb) minroad Semi-natural land majroad Total built up PM Surface PM (ug/m3) Y_COORD minroad ALT MAJROAD canopy O altitude localroad_10000 Topographic exposure (on a hill or in a valley) Summer temp Annual wind-speed High density residential land Low density residential land Distance to sea forest_1000 Nres_5000 (other build up) Agriculture_ buffer: 100,200,300,400,500,600,800,1000,1200,1500,2000,2500,3000,3500,4000,5000,6000,7000,8000,10000

Statistical analysis (1/2) - Outcome: Leukaemia - Exposure: Quartile Linear Trend per 400 m (5°-95° pct) range Majorroadlength Quartile Distance from major roads - Matching: Date of birth, sex and region of residence Conditional Logistic Regression - Confounder: parental educational level

Conditional Logistic Regression Statistical analysis (2/2) - Outcome: Leukaemia - Exposure: Quartile Linear Trend per 35.9 μg/m 3 (5°-95° pct) range PM 2.5 Quartile Linear Trend per 20.6 μg/m 3 (5°-95° pct) range Quartile Linear Trend per 32.7 μg/m 3 (5°-95° pct) range Quartile Linear Trend per 40.7 μg/m 3 (5°-95° pct) range NO 2 PM 10 O 3

Case: Leukaemia: 683 Controls: Geocoding Birth residence Case: Leukaemia: 675 Controls: Exclusion of subjects with missing value for any of the exposures Case: Leukaemia: 625 Controls: 965 Results: descriptive

Results: Exposure as linear term

Results: Exposures in Quartile

The large SETIL study allowed to evaluate the association between childhood cancer and air pollution using several indexes available at national level. There was no sign of association between the estimated exposures to pollutants and childhood leukaemia when adjusting for the matching factors and parental education. Key points

Limitations: - Power problems - Exposure misclassification due to the large grid of national dispersion models - Only residence at birth considered here