Prediction of ozone-induced lung function responses in humans

The main purpose of this study was to evaluate the ability of a human exposure-response model, which describes ozone-induced changes in forced expiratory volume in 1 second (FEV(1)) across a wide range of dynamic exposure conditions, to predict responses in independent data. We first conducted an n-fold cross-validation of the model using samples of the original EPA data from which the model was developed. We then identified seven more recently published studies with controlled exposures to a wide range of ozone exposure patterns relevant to the current ambient ozone health standard and used the model to calculate the mean predicted responses for the exposure conditions of the individual studies that we compared to the mean observed responses reported in these studies. The n-fold cross-validation indicated good internal agreement between mean predicted and mean observed responses in the original data used to develop the model. The model accurately captured the patterns of response in each of the seven independent studies with a tendency to overpredict the observed responses by about 1 percentage point of FEV(1) decrement on average. We conclude that the model is currently capable of predicting human FEV(1) responses across a wide range of dynamic exposure conditions and anticipate further improvements in predictions with the addition of low-concentration exposure data.