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Smoking Rate and Smoking Impact


The ultimate purpose of forecasting smoking (HLSMOKING) by country/region r and sex p is to forecast smoking impact (HLSMOKINGIMP) by country/region, age category a, and sex p.  We provide some background on the general approach surrounding smoking impact and the some specific elements of its implementation in IFs (some of the background comes directly from Hughes et al. 2011: 41-42).

In 1992 Peto et al. proposed a method for calculating the proportion of deaths caused by smoking that was not dependent on statistics on prevalence of tobacco consumption.  This method involved developing an indicator for accumulated smoking risk termed the smoking impact ratio (SIR). Ezzatti and Lopez (2004: 888) defined the SIR as “population lung cancer mortality in excess of never-smokers, relative to excess lung cancer mortality for a known reference group of smokers.” In other words, the ratio is derived by comparing actual population lung cancer mortality with the expected lung cancer mortality in a reference population of nonsmokers. Because the SIR is derived from age-sex lung cancer mortality it can also provide an indication of the “maturity” of the smoking epidemic (the extent to which the population had been exposed to tobacco in the past (Ezzati and Lopez 2004: 888). Once the SIR has been determined, one can then use it to estimate the proportions of deaths from other diseases attributable to smoking (Peto et al. 1992).

For the GBD project, Mathers and Loncar developed country-level smoking impact (SI) projections to 2030 (Mathers and Loncar 2006; and Mathers and Loncar, Protocol S1 Technical Appendix, n.d.) and used them as part of their distal-driver formulation.  The SI projections rely upon expert judgment, and it was not possible for the IFs project to improve on them; thus we used those projections without change.

Forecasting beyond 2030 required, however, that the IFs project extend those series, taking into account a long lag between smoking rates and smoking impact. We therefore wanted smoking rates themselves to drive our approach. The development of a structural forecast system for those rates involved several main steps. First, we created a historical series of estimated smoking rates. Second, we constructed cross-sectional relationships that suggest expected rates of smoking based on GDP per capita at PPP for males and females separately. Third, we initialized a moving average rate of change in smoking rate with the compound rate of change between 1995 and 2005 and used that as the basis for forecasting longer-term. Finally, for forecasting smoking impact longer term we used the same process in reverse that we had earlier used to estimate the historical smoking series, that is, we calculated smoking impact from smoking rate using a 25-year lag.

In more recent work (beyond that supporting the Hughes et al. 2011 volume, we have introduced an alternative approach to forecasting change in smoking rate over time, one that uses a structural (and heavily algorithmic) smoking stages model.  In the sections below we discuss the four steps of the original model and then the revised approach to forecasting smoking rates (work in progress).