Of the various specific health risks that the model treats, smoking has a special place because its impact is in the distal driver formulation of the IFs health model.  The figure shows that the impact is driven by the rate of smoking (differentiated by males and females) 25 years earlier, with the relationship controlled by an impact elasticity ( hlsmkel ).  The user can also posit as a nearer term (in the model immediate) impact by setting a switch for that ( hlsmkimeff ) at some fractional value of the full delayed impact–the value in the base case is 0.1 or 10 percent.  For analysis purposes, another switch ( hlsmimpsw ) can turn off the endogenous computation of smoking impact and leave it constant at the initial year value.

Smoking rate itself is computed in two different ways.  The basic formulation uses only the initial condition and a function linked to the simple and squared values of GDP per capita at PPP.  The more extended formulation is an algorithmic one based on the same general concept of a pattern that initially rises with GDP per capita, peaks, and then falls, but with a series of parameters that allow much more control over the stages. [1]   This staged algorithmic approach (see Lopez et al. 1994; Shibuya et al. 2005; Ploeg et al. 2009) is turned on with a switch ( hlsmokingstsw ).

Because control of tobacco is a major policy objective in many countries, there is also a representation of a tobacco control score on a 100-point scale ( hlsmokingtcs ) with an associated parameter to control the elasticity of smoking with that score ( hlsmokingtcsel ), as well as a multiplier on the score ( hlsmokingtcsm ).

Finally, there is a multiplier that allows direct manipulation of the smoking rate, again by sex ( hlsmokingm ).