Health

The most recent and complete health model documentation is available on Pardee's website. Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.

The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.  Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project^[1 ], formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.  However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).  Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.  Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.

This documentation supplements the third volume of the PPHP series, “Improving Global Health,” (Hughes et al, 2011) by providing technical details of health model integration into the IFs system.  It includes the specific equations used to forecast outcomes and drivers, relative risk values for proximate drivers, and data manipulations related to model initialization and projection.  We intend the IFs model to be fully transparent to all users, and invite comments and questions at http://www.ifs.du.edu/contact/index.aspx.

Cause groups in IFs

 Group I – Communicable, Maternal, Perinatal, and Nutritional Conditions

• Diarrheal diseases
• Malaria
• Respiratory infections
• HIV/AIDS
• Other Group I causes

 Group II – Noncommunicable Diseases

• Malignant neoplasms
• Cardiovascular diseases
• Digestive diseases
• Chronic respiratory diseases
• Diabetes
• Mental health
• Other Group II causes

 Group III – Injuries

• Road traffic accidents
• Other unintentional injuries
• Intentional injuries

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Structure and Agent System: Health

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System/Subsystem	Health
Organizing Structure	Hybrid structure using distal driver formulations supplemented by proximate drivers; integrated with larger IFs systems such as population and governance
Stocks	Population by age-sex; stunted population; HIV prevalence
Flows	Births, mortality and morbidity
Key Aggregate  Relationships  (illustrative, not comprehensive)	Distal driver formulations driven by income, education, and time as a proxy for technological advance   Proximate driver formulations driven by various social patterns and behaviors
Key Agent-Class Behavior  Relationships (illustrative, not comprehensive)	Behavior related to proximate drivers such as smoking, indoor solid fuel use, obesity

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Dominant Relations: Health

</hgroup></header> Health forecasting systems typically can help us either (1) to understand better where patterns of human development appear to be taking us with respect to global health, giving attention to the distribution of disease burden and the patterns of change in it; or (2) to consider opportunities for intervention and achievement of alternative health futures, enhancing the foundation for decisions and actions that improve health. 

Broad structural models (e.g., that of the Global Burden of Disease or GBD) assist in the first purpose by relating deep or distal development drivers to outcomes.  More specialized structural formulations and the inclusion of proximate drivers open the door to the second, allowing for consideration of interventions in the pursuit of alternate health futures.  A more hybrid and integrated model form like that of IFs can help with both purposes and provide a richer overall picture of alternative health futures.

The figure shows the general structure.  Formulations based on distal drivers (the GBD methodology) sit at its core.  There is no inherent reason, however, that income, education and time (the distal drivers of the GBD approach) should be equally capable of helping us forecast disease in each of the major categories (let alone each of the specific diseases) that the GBD models examine.  For example, distal driver formulations tend to produce forecasts of constantly decreasing death rates.  Yet we know, for instance, that smoking, obesity, road traffic accidents, and their related toll on health tend to increase in developing societies among those who first obtain higher levels of income and education; with further societal spread of income and education, at least smoking and road traffic deaths (and perhaps also obesity) typically decline.^[1 ]