Time-series regression models to study the short-term effects of environmental factors on health
Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants in Madrid for the period 1995-1998, of how these methods are employed.
|Date of creation:||Mar 2004|
|Date of revision:|
|Contact details of provider:|| Postal: FCEE. Campus Montilivi. 17071 Girona. Spain.|
Web page: http://www.udg.edu/depec
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:udg:wpeudg:011. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Germà Coenders)
If references are entirely missing, you can add them using this form.