DAILY MORTALITY AND AIR-POLLUTANTS - FINDINGS FROM KOLN, GERMANY

Study objective and design - For the APHEA study, the short term effec ts of air pollutants on human health were investigated in a comparable way in various European cities. Daily mortality was used as one of th e health effects indicators. This report aims to demonstrate the steps in epidemiological model building in this type of time series analysi s aimed at detecting short term effects under a poisson distribution a ssumption and shows the tools for decision making. In addition, it ass esses the impact of these steps on the pollution effect estimates. Set ting - Koln, Germany, is a city of one million inhabitants. It is dens ely populated with a warm, humid, unfavourable climate and a high traf fic density. In previous studies, smog episodes were found to increase mortality and higher sulphur dioxide (SO2) levels were connected with increases in the number of episodes of croup. Participants, materials and methods Daily total mortality was obtained for 1975-85. SO2, tota l suspended particulates, and nitrogen dioxide (NO2) data were availab le from two to five stations for the city area, and size fractionated PM(7) data from a neighbouring city. The main tools were time series p lots of the raw data, predicted and residual data, the partial autocor relation function and periodogram of the residuals, cross correlations of prefiltered series, plots of categorised influences, chi(2) statis tics of influences and sensitivity analyses taking overdispersion and autocorrelation into account. Results and conclusions - With regard to model building, it is concluded that seasonal and epidemic correction are the most important steps. The actual model chosen depends very mu ch on the properties of the data set. For the pollution effect estimat es, trend, season, and epidemic corrections are important to avoid ove restimation of the effect, while an appropriate short term meteorology influence correction model may actually prevent underestimation. When the model leaves very little overdispersion and autocorrelation in th e residuals, which indicates a good fit, correction for them has conse quently little impact. Given the model, most of the range of SO2 value s (5th centile to 95th centile) led to a 3-4% increase in mortality (s ignificant), particulates led to a 2% increase (borderline significant , less data than for SO2), and NO2 had no relationship with mortality (measurements possibly not representative of actual exposure). Effects were usually delayed by a day.