Difference between revisions of "Health"

From Wiki
Jump to: navigation, search
(Created page with "The most recent and complete health model documentation is available on Pardee's [http://pardee.du.edu/ifs-health-model-documentation website]. Although the text in this inter...")
 
Line 1: Line 1:
 
The most recent and complete health model documentation is available on Pardee's [http://pardee.du.edu/ifs-health-model-documentation 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 Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.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 most recent and complete health model documentation is available on Pardee's [http://pardee.du.edu/ifs-health-model-documentation 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 Although the text in this interactive system is, for some IFs models, often significantly out of date, you may still find the basic description useful to you.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.&nbsp; Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project[http://www.du.edu/ifs/help/understand/health/index.html#footnote <span style="color: #990000" data-mce-mark="1"><sup>[1]</sup> </span> ], formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.&nbsp; However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).&nbsp; Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.&nbsp; 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.
+
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.&nbsp; Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project[http://www.du.edu/ifs/help/understand/health/index.html#footnote <span style="color: #990000" data-mce-mark="1"><sup>[1</sup></span>]<span style="color: #990000" data-mce-mark="1"></span> ], formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.&nbsp; However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).&nbsp; Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.&nbsp; 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.&nbsp; 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.&nbsp; 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 http://www.ifs.du.edu/contact/index.aspx].
 
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.&nbsp; 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.&nbsp; 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 http://www.ifs.du.edu/contact/index.aspx].
Line 30: Line 30:
 
*Other unintentional injuries
 
*Other unintentional injuries
 
*Intentional injuries
 
*Intentional injuries
<header><hgroup>
+
 
 
== Structure and Agent System: Health ==
 
== Structure and Agent System: Health ==
</hgroup></header>
+
 
 
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"
 
{| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"
 
|-
 
|-
Line 50: Line 50:
 
| style="text-align: left;  padding-left: 10px" align="center" | <div>Distal driver formulations driven by income, education, and time as a proxy for technological advance</div><div>&nbsp;</div><div>Proximate driver formulations driven by various social patterns and behaviors</div>
 
| style="text-align: left;  padding-left: 10px" align="center" | <div>Distal driver formulations driven by income, education, and time as a proxy for technological advance</div><div>&nbsp;</div><div>Proximate driver formulations driven by various social patterns and behaviors</div>
 
|-
 
|-
| <div>'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div>(illustrative, not comprehensive)</div>
+
| <div>'''Key Agent-Class Behavior&nbsp;''' '''Relationships'''</div><div>(illustrative, not comprehensive)<br/></div>
| style="text-align: left;  padding-left: 10px" align="center" | <div>Behavior related to proximate drivers such as smoking, indoor solid fuel use, obesity</div>
+
| style="text-align: left;  padding-left: 10px" align="center" | <div>Behavior related to proximate drivers such as smoking, indoor solid fuel use, obesity<br/></div>
 
|}
 
|}
  
 
<header><hgroup>
 
 
== Dominant Relations: Health ==
 
== 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.&nbsp;
 
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.&nbsp;
  
 
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.&nbsp; 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.&nbsp; 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.
 
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.&nbsp; 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.&nbsp; 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.&nbsp; Formulations based on distal drivers (the GBD methodology) sit at its core.&nbsp; 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.&nbsp; For example, distal driver formulations tend to produce forecasts of constantly decreasing death rates.&nbsp; 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.<span style="color: #990000"><sup>[http://www.du.edu/ifs/help/understand/health/dominant.html#footnote <span style="color: #990000">[1]</span> ] </sup></span>
+
The figure shows the general structure.&nbsp; Formulations based on distal drivers (the GBD methodology) sit at its core.&nbsp; 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.&nbsp; For example, distal driver formulations tend to produce forecasts of constantly decreasing death rates.&nbsp; 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.<span style="color: #990000"><sup>[http://www.du.edu/ifs/help/understand/health/dominant.html#footnote <span style="color: #990000">[1</span>] ] [[File:Health1.png|border|right]]</sup></span>
</div>
+
<span style="color: #990000"><sup></sup></span>
 +
A hybrid model can therefore help us identify opportunities for interventions to improve health futures. These interventions might also occur in the form of super-distal drivers (for example, policy-driven human action with respect to health systems).&nbsp; The sociopolitical and environmental modules in IFs act in part as super-distal foundations for variables such as undernutrition and indoor air pollution which, in turn, facilitate analyses of proximate risk factors and human action around them.&nbsp;
 +
 
 +
The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.
 +
 
 +
<span style="color: #990000">[1]</span> It is partly for this reason that the creators of the GBD models added exogenous specification of smoking impact to the otherwise mostly monotonically (one-direction only) changing specifications.
 +
<header><hgroup>
 +
== Health Flow Charts ==
 +
</hgroup></header>
 +
=== Overview&nbsp; ===
 +
 
 +
Mortality from most causes of death is a function of a small number of distal or deep drivers and a larger number of proximate or more immediate drivers.&nbsp; For two specific mortality types, however, specifically deaths from AIDS and vehicle accidents, there are more specialized representations that rely on a number of more cause-related drivers.
 +
<header><hgroup>
 +
== Distal Drivers and Basic Indicators ==
 +
</hgroup></header>
 +
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).&nbsp; 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.
 +
<span style="color: #990000"><sup></sup></span><br/></div>

Revision as of 06:01, 16 January 2017

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

Structure and Agent System: Health

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

Dominant Relations: Health

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 ]
Health1.png

A hybrid model can therefore help us identify opportunities for interventions to improve health futures. These interventions might also occur in the form of super-distal drivers (for example, policy-driven human action with respect to health systems).  The sociopolitical and environmental modules in IFs act in part as super-distal foundations for variables such as undernutrition and indoor air pollution which, in turn, facilitate analyses of proximate risk factors and human action around them. 

The integrated nature of the IFs modeling system further allows us to think about feedback loops between health outcomes and larger development variables such as economic progress and population structure.

[1] It is partly for this reason that the creators of the GBD models added exogenous specification of smoking impact to the otherwise mostly monotonically (one-direction only) changing specifications. <header><hgroup>

Health Flow Charts

</hgroup></header>

Overview 

Mortality from most causes of death is a function of a small number of distal or deep drivers and a larger number of proximate or more immediate drivers.  For two specific mortality types, however, specifically deaths from AIDS and vehicle accidents, there are more specialized representations that rely on a number of more cause-related drivers. <header><hgroup>

Distal Drivers and Basic Indicators

</hgroup></header> 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.