To review the issues and methodologies in epidemiologic time series studies of daily counts of mortality and hospital admissions and illustrate some of the methodologies. This is a review paper with an example drawn from hospital admissions of the elderly in Cleveland, Ohio, USA. The central issue is control for seasonality. Both over and under control are possible, and the use of diagnostics, including plots, is necessary. Weather dependence is probably non-linear, and adequate methods are necessary to adjust for this. In Cleveland, the use of categorical variables for weather and sinusoidal terms for filtering season are illustrated. After control for season, weather, and day of the week effects, hospital admission of persons aged 65 and older in Cleveland for respiratory illness was associated with ozone (RR = 1.09, 95% CI 1.02, 1.16) and particulates (PM10 (RR = 1.12, 95% CI 1.01, 1.24), and marginally associated with sulphur dioxide (SO2) (RR = 1.03, 95% CI = 0.99, 1.06). All of the relative risks are for a 100 micrograms/m3 increase in the pollutant. Several adequate methods exist to control for weather and seasonality while examining the associations between air pollution and daily counts of mortality and morbidity. In each case, care and judgement are required.