Generalized Additive Models in Environmental Health: A Literature Review

Abstract

Time series regression models are especially suitable in epidemiology for evaluating shortterm effects of time-varying exposures. Typically, a single population is assessed with reference to its change over the time in the rate of any health outcome and the corresponding changes in the exposure factors during the same period. In time series regression dependent and independent variables are measured over time, and the purpose is to model the existing relationship between these variables through regression methods. Various applications of these models have been reported in literature exploring relationship between mortality and air pollution (Katsouyanni et al. 2009; Wong et al. 2010; Balakrishnan et al. 2011); hospital admissions and air pollution (Peng et al. 2008; Zanobetti et Schwartz 2009; Lall et al. 2011); pollution plumes and breast cancer (Vieira et a. 2005); diet and cancer (Harnack et al. 1997); and mortality and drinking water (Braga et al. 2001). Different time series methods have been used in these studies, i.e. the linear models (Hatzakis et al. 1986) the log-linear models (Mackenbach et al. 1992), the Poisson regression models (Schwartz et al. 2004), and Generalized Additive Models (Dominici 2002; Wood, 2006). The Generalized Additive Models represent a method of fitting a smooth relationship between two or more variables and are useful for complex correlations, that not easily fitted by standard linear or non-linear models.