Ambient air pollution and respiratory health effects in mail carriers.

Mail carriers represent an occupational group suffering from respiratory symptoms and lung function impairment. Although environmental conditions may play role, information on the effects of air pollution exposure in this population is lacking. The present study was conducted in Athens, Greece, in order to investigate the adverse effects of long-term air pollution exposure on respiratory outcomes in mail carriers. A total of 226 mail carriers and 73 office employees were enrolled. Information on respiratory symptoms, medical, occupational, residential and smoking history was obtained through a questionnaire. Flow-volume curves were performed in the workplace using a portable spirometer. Individualised personal exposure assessment has been applied based on long-term residential and occupational subject history linked with geographical air pollution distribution. Furthermore, personal measurements were obtained for forty-one mail carriers using NO(2) and O(3) passive samplers, assuming that current air pollution exposure is sufficiently representative of long-term, previous exposure to make a plausible link with current health status. The analysis based on exposures estimated on the basis of residential and work addresses showed that the most exposed to PM(10) postal workers have rhinitis at a higher rate (OR=1.67, 95% CI: 1.01-2.75). In mail carriers there is indication that those exposed to higher concentrations of Omicron(3) or PM(10) have a greater possibility to present rhinitis (OR=1.63, 95% CI: 0.93-2.88 and OR=1.70, 95% CI: 0.96-3.03, respectively). The effect of O(3) on rhinitis became even more apparent in the analysis based on exposures assessed by personal measurements (OR=6.74, 95% CI: 1.24-36.55). Exposure to NO(2) was significantly associated with decrements in lung function. For office employees the exposure to air pollutants was not associated to any adverse respiratory outcome. Our findings suggest that air pollution is a contributing factor for the occurrence of rhinitis and lung function impairment in mail carriers.

Seasonal confounding in air pollution and health time-series studies: effect on air pollution effect estimates.

A major statistical challenge in air pollution and health time-series studies is to adequately control for confounding effects of time-varying covariates. Daily health outcome counts are most commonly analysed by Poisson regression models, adjusted for overdispersion, with air pollution levels included as a linear predictor and smooth functions for calendar time and weather variables to adjust for time-varying confounders. Various smoothers have been used so far, but the optimal strategy for choosing smoothers and their degree of smoothing remains controversial. In this work, we evaluate the performance of various smoothers with different criteria for choosing the degree of smoothing in terms of bias and efficiency of the air pollution effect estimate in a simulation study. The evaluated approaches were also applied to real mortality data from 22 European cities. The simulation study imitated a multi-city study. Data were generated from a fully parametric model. Model selection methods which optimize prediction may lead to increased biases in the air pollution effect estimate. Minimization of the absolute value of the sum of the partial autocorrelation function of the model's residuals (PACF), as a criterion to choose the degree of smoothness, gave the smallest biases. The penalized splines (PS) method with a large number of effective dfs (e.g. 8-12 per year) could be used as the basic, relatively conservative, analysis whereas the PS and natural splines in combination with PACF could be applied to provide a reasonable range of the effect estimate.