New methods are presented for analyzing repeated binary health measurements of [human] individuals exposed to varying levels of air pollution. The methods involve a separate logistic regression of response against environmental covariates for each individual. Parameters reflecting individual susceptibility to pollutants and weather are estimated using Cox''s regression techniques. The individual parameters are combined to yield summary estimates of environmental effects. The approach does not require independence of successive health measurements. It is illustrated with data on asthma and air pollution in the Los Angeles [California, USA] area.