The regression models used in the GBD project did not differentiate between-subject from within-subject variation. Particularly for cardiovascular-related outcomes in some age/sex groups, this model produced a perverse finding: a negative relationship between cardiovascular-related mortality and smoking impact (HLSMOKINGIMP). However our further statistical investigation showed, as expected, a positive relationship between cardiovascular-related mortality and HLSMOKINGIMP within a given country over time.
As such, we completed a more sophisticated mixed model regression analysis (using SAS, version 9.1) to capture both within and between-subject effects. We used the GBD mortality database described here, supplemented by our historical series of income per capita. All distal drivers were included as fixed effects, with random effects included for subject (country) and time (T). The revised coefficients (see Appendix Table 1) were used to forecast cardiovascular disease-related mortality. We created only one model for all countries (no separate low-income model) due to lack of data. Comparison with the original GBD models reveals fairly similar forecast outcomes overall. However, the positive change in the smoking/cardiovascular mortality relationship allows us to better examine how smoking intervention scenarios might impact cardiovascular-related mortality.
 Note that we did use historical estimates of education provided by the GBD project, instead of using the less complete historical series available through IFs. Future distal driver analysis may explore using alternate sets of education data, including those included in the IFs system.