Once we have an historical rate of smoking, the next step is forecasting it so as to drive forecasts of smoking impact beyond the 2030 of the GBD estimates. Forecasting smoking in IFs is actually a two-step process beginning with the construction of
cross-sectional relationships for expected rates of smoking for males and females separately, based on GDP per capita at PPP.
Female smoking rate cross-sectional relationship:
Male smoking rate cross-sectional relationship:
Where Smok_Rate by country/region r, sex p, and year t is an initial estimate of smoking rate (percent), and GDPPCP is GDP per capita at PPP ($1,000). Regression results are kept constant after they reach a GDPPCP of $30,000 (females) or $50,0000 (males).
The cross-sectional calculation is initial in part because it will not produce results consistent with the data for countries, even in the first or base year of a model run. In the first year an additive shift is computed for both male and female smoking rates to reconcile 2010 values with regression results. In future model years, the additive shift is evaluated based on regression results: if it is positive (thus producing the forecasted value to be above the expected value given by the regression) or for all non-high-income countries (initial GDPPCP <= 25k) the shift is converged to 0 over 100 years. If it is negative and the country is high income, the shift is kept constant for the entire run horizon. The resulting adjusted shift is added to the smoking rate produced by the regression.