To forecast mortality related to most of the major cause clusters we use the regression models and associated beta coefficients prepared for the GBD project (Mathers and Loncar 2006).  Age, sex, cause, and country-specific mortality rate is a function of income (using GDP per capita as a proxy), adult education, technological progress. For specific death causes, smoking impact (for malignant neoplasms, cardiovascular disease, and respiratory disease) or body mass index (for diabetes only) add to the causality; see the discussion of flow charts and equations for information on the determination within IFs of smoking and smoking impact and of body mass index and obesity.

A number of parameters control technology in the distal functions.  In the default mode (hlmortmodsw = 1), IFs modifies the technology (time) coefficient in recognition of slower than expected historical progress in many countries, an approach developed in the Global Burden of Disease (GBD project).  Those country differences are controlled by hltechbase , hltechlinc, and hltechssa.  Setting the switch value to 0 activates an alternative IFs project approach to the impact of those parameters. 

The user can also affect the mortality patterns directly with several parameters, including mortm , which allows simultaneous manipulation of all causes of death and hlmortm , which facilitates manipulation of each cause of death separately.   Hlmortcdchldm changes the rates of all communicable diseases for children aged 5 and younger, while hlmortcdadltm affects rates of death from communicable diseases for adults aged 15-49. 

Based on the mortality level, it is possible to compute the years of life lost to each cause of death (HLYLL).  Using WHO-based estimates, IFs links mortality also to years of living with disability (HLYLD).  The sum of the two is disability-adjusted life years lost (HLDALYS). 

The forecast of mortality in this figure, dependent almost entirely on distal factors, is not actually the final calculation in the model.  See the discussion of the entry of proximate drivers into the discussion of population attributable mortality fractions (PAFs), in interaction with distal-based mortality, for the rest of the story. 

Because of the importance of smoking impact in the distal driver formulation, it is important that we elaborate that term.  Body mass index is, at this point, only linked to diabetes and we discuss that in the context of the PAFs.