EDUCATION CORNER Time series regression studies in environmental epidemiology

Time series regression studies have been widely used in environmen-tal epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or dis-ease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollu-tion levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the gen-eral features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and model-ling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model