Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol.

Abstract

BACKGROUND AND OBJECTIVES: Results from several studies over the past five years have shown that the current levels of pollutants in Europe and North America have adverse short term effects on health. The APHEA project aims to quantifying these in Europe, using standardised methodology. The project protocol and analytical methodology are presented here. DESIGN: Daily time series data were gathered for several air pollutants (sulphur dioxide; particulate matter, measured as total particles or as the particle fraction with an aerodynamic diameter smaller than a certain cut off, or as black smoke; nitrogen dioxide; and ozone) and health outcomes (the total and cause specific number of deaths and emergency hospital admissions). The data included fulfilled the quality criteria set by the APHEA protocol. SETTING: Fifteen European cities from 10 different countries with a total population over 25 million. METHODOLOGY: The APHEA collaborative group decided on a specific methodological procedure to control for confounding effects and evaluate the hypothesis. At the same time there was sufficient flexibility to allow local characteristics to be taken into account. The procedure included modelling of all potential confounding factors (that is, seasonal and long term patterns, meteorological factors, day of the week, holidays, and other unusual events), choosing the "best" air pollution models, and applying diagnostic tools to check the adequacy of the models. The final analysis used autoregressive Poisson models allowing for overdispersion. Effects were reported as relative risks contrasting defined increases in the corresponding pollutant levels. Each participating group applied the analyses to their own data. CONCLUSIONS: This methodology enabled results from many different European settings to be considered collectively. It represented the best available compromise between feasibility, comparability, and local adaptibility when using aggregated time series data not originally collected for the purpose of epidemiological studies.