We found it necessary to compute historical smoking rates because we found historical smoking rate data (taken from WHO) to be exceptionally sparse, and we needed to understand the patterns and trajectory of smoking behavior over time as a subsequent basis for forecasting. We may revisit this in the future because we now have data from the WDI for 1977 and more recent years.
We built the historical imputed smoking series on the most recent smoking rate data point of each country and the smoking impact forecasts of the Global Burden of Disease (GBD). Those GBD forecasts of smoking impact cover the period from 2005 through 2030, provide considerable country coverage, and represent age in four quite large categories: 30-44, 45-59, 60-69, and 70 and older. These can be found in our tables SeriesHealthSmokingImpactMales30to44, SeriesHealthSmokingImpactMales45to59, SeriesHealthSmokingImpactMales60to69, SeriesHealthSmokingImpactMales70to100 in IFsHistSeries.mdb (and the same four tables for females).
Assuming a direct 25-year lag between smoking rate and smoking impact, we used year-to-year percentage changes in the smoking impact series to change smoking rates before and after our smoking data point. In spite of the simplicity of this approach, and the fact that smoking impact reflects more than smoking rates, we found that the constructed series tended to match relatively well when more than one historical point for smoking rate existed.
This is done in a procedure invoked under the IFs menu option Extended Features called Generate Historical Smoking Rate Estimates. The procedure starts by estimating historic smoking rates by age category (4 categories corresponding to the 4 smoking impact age categories of the GBD forecasts) and sex assuming a lag of 25 years (that is, filling in the historical smoking series from BaseYear – 25 to Base Year using GBD smoking impact data from Base Year to Base Year + 25). Then an all-age estimate for smoking rate is found with a simple average across the 4 smoking impact age categories. Next we compute an additive shift factor for each country to match the most recent WHO smoking rate data (from SeriesHealthSmokingPrevalenceWHOFemales% and the same table for males), and then we apply the same shift factor to smoking rate data for previous years. In cases where there are no smoking rate data we compute aggregated shifts using WHO Regions and apply the regional shift to the member country(ies) with no data. The final result of this process are 25 year-long series on smoking rates in the tables SeriesHealthSmokingMales%SI and SeriesHealthSmokingFemales%SI in IFsHistSeries.mdb.