Extrapolation is the process of estimating conditions outside the range of available data, by extending observable trends in these data. As a means of rapid screening, prior to integrated assessments, it can be used in a wide range of ways, for example:

• to derive dose-response functions at extremes (high or low) doses, where direct observations have not been made;
• to estimate exposures  or health impacts under business-as-usual scenarios by extending current trends into the future;
• to estimate exposures or health impacts under alternative (e.g. policy) scenarios, by rescaling available data to reflect the likely new conditions.

A wide range of extrapolation methods is available, of varying complexity and statistical rigour (see links to left).  In each case, however, extrapolation may be subject to a number of uncertainties and errors. 

In particular, care is needed to ensure that:

• the observed trends in the data are valid and unbiased: biases tend to occur, especially, when the data set is small or clustered either in space or time; 
• the possibility of non-linearity (including thresholds or step-changes) is allowed for;
• allowance is made for the influence of external factors (e.g. social or environmental determinants) which may modify the observed trends;
• extrapolation remains within the range of plausible values and extrapolated estimates have plausible distributions (e.g. do not show large and unexpected skew or outliers).