1. Complex Adaptive Systems in Health
Applying system dynamics methods
Prof David Bishai
www.futurehealthsystems.org

2. Workshop objectives
• (Re-)introduce participants to CAS framework
• Focused hands-on, interactive experience with system
dynamics and related software
• Provide participants with a foundation for considering
modeling with system dynamics in their own research
• Discuss linkages between system dynamics and FHS
country work
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3. Workshop outline
• Intro to CAS
• Intro to System Dynamics (SD) and SD research
• Make your own model
• Discussion
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4. The FHS CAS Framework
A quick review

5. Systems Thinking: Key Concepts
• Parts of a system are interdependent
• Actions have consequences at multiple
levels
• Optimizing one part can lead to poor
overall system performance
• Organizational structures drive behavior
• Mental models influence actions
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6. Systems thinking in health systems involves
• Understand health systems actors,
functions, principles, purpose
• Make changes in financing, organization,
oversight
• Look for responses in actors, health
services, money, information
• Monitor effects on intended and
unintended outcomes
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7. Model for Understanding Health Systems
Changes as Complex Adaptive System
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8. Value added of CAS
• Challenges linear approaches and commonly held
assumptions
• Greater focus on relationships than simpler cause and
effect models
• Draws theoretical and methodological links from
multiple disciplines to help frame knowledge about
agents and their relationships
• Can suggests new stakeholders and opportunities for
intervention
• Draws a dynamic picture of forces affecting change and
their unintended consequences.
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9. Caveats of CAS
• CAS, being a collection of theories, is not always
“well defined or differentiated”
• Little empirical application to date
• Quantitative methodologies are complex
• The benefits of using CAS versus those of using
other theories has not been explored
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10. System dynamics
An introduction

11. Session objectives
• Broad introduction to System Dynamics
methods
• Present an application of SD methods to
public health dilemma (prevention vs.
cure)
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12. Systems concepts in health
 Most systems we model are composed of individuals
inside units
Units linked by institutions
Units linked by coherence or monitoring
Agents driven by incentives
 Contracts transmit incentives across units
Good contracts tie wanted incentives to easily
measured metrics
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13. Systems dynamics is …
 A set of tools and approaches used to study the behavior
of complex systems, particularly feedback loops
(reinforcing or balancing).
 Used to illustrate and model how simple systems exhibit
unexpected, nonlinear, dynamic behavior.
Predictive capabilities vs. identifying dynamic
responses
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14. Identifying states
 A “state” is a concrete stock variable that lends itself to
easy measurement
Number of drugs in stock
Number of patients in beds
Number of employees on payroll
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15. Diagramming States
State=Stock of
Drugs
States are diagrammed by rectangles:
Every rectangle represents a state variable
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16. Diagramming Flows
Inflow
State
Outflow
Rates are diagrammed by stopcocks:
Arrows inside stopcocks mean “flow”
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17. Diagramming Controls
Transport
Inflow cost
State
Black
market Outflow
demand
Controls are diagrammed by circles:
Arrows not in stop cocks are arrows of influence
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18. Importance of Diagram
 Can build mathematical model around each item in
diagram
 Level of state X
Xt+1 = Xt+Rate of Inflowt – Rate of Outflowt
Rate of inflow
Ratet+1 = F(Controlt) *Ratet
Control
Controlt+1=f(Controls, Levels, Rates)
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19. Tilting the balance between curing
and preventing.
A system dynamics model of unintended
consequences of aid in weakening health systems

20. Introduction
 Premise:
Investing in prevention (e.g. primary care, injuries) receives
less attention than investing in curative care for acute
illnesses
Understanding SD  Policies to optimize spending on
curative and preventive care
 Purpose: A SD model of how resource allocation decisions
impact the burden of disease and the health system
Simulated epidemics
Internal and external funds
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21. Methodology
• Vensim software
• Stock and flow diagram
Type of variable Definition
Box/Level variable Quantities which can accumulate
Rate Changes in quantity over time
Auxiliary variable Constants or other parameters
Connectors Illustrate dependencies between
variables
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22. A system dynamics model of unintended consequences
of aid in weakening health systems
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23. Initial model values
 Plausible, but not representative of a particular disease and/or
injury
Population: 800; stable
Disease A: infectious disease; can be cured by doctors
Disease B: fatal severe injury; can be prevented by
hygienists
Public funding allocated to curative and preventive care
Private funding from NGOs and A patients
Doctors and hygienists lobby for more resources from all
sources
 Designed to, as a whole, have the model start at equilibrium, for
better illustration of dynamic effects
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24. Subsystem 1: The population and
disease model
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25. Subsystem 2: Health resources
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26. Subsystem 3a: Doctor resource
allocation
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27. Subsystem 3b: Hygienist resource
allocation
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28. Methods
 Analyze cost and health effects of NGO donations
 NGOs programmed to
Donate $DA additional per incremental DALY from
disease A
Donate $DB additional per incremental DALY from
disease B
 Euler equation: Efficient allocation when DA=DB
 What happens when DA<DB or DA>DB ?
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29. Results Holding DA Fixed
Ordered Pairs
DA:DB
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30. Results Holding DB Fixed
Ordered Pairs
DA:DB
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31. Discussion
• After a threshold increasing donations on
behalf of curing diseases harms overall
population health
• Effects driven by the doctor’s lobby and a
zero-sum budget for prevention and cure
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32. Sensitivity Analysis 1
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33. Sensitivity Analysis 2
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34. Sensitivity analyses
 Qualitative results not sensitive to:
DALY weights
Except if DALY weights for A or B set to zero
Lobbying power weight parameters
Except if DALY weights for A or B set to zero
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35. Discussion
 This is not a model of real diseases or a real country
 Just a demonstration of zero-sum budgeting meeting the
basic asymmetric economics of health
Curing is more remunerative than preventing
 Is it real? (See above)
 Could there be places where the “cure” lobby is making
populations sicker?
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36. Examples of system dynamics research
• Atun, R. A., R. Lebcir, et al. (2005). "Impact of an effective
multidrug-resistant tuberculosis control programme in the
setting of an immature HIV epidemic: system dynamics
simulation model." Int J STD AIDS 16(8): 560-570.
• Clouth, #160, et al. (2009). Evaluating Health Care using
System Dynamics Modelling - a Case Study in
Schizophrenia. Stuttgart, Germany, Thieme.
• Rwashana, A. S., D. W. Williams, et al. (2009). "System
dynamics approach to immunization healthcare issues in
developing countries: a case study of Uganda." Health
Informatics J 15(2): 95-107.
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37. Future directions
• Examine NACCHO and ASTHO databases to assess
prevalence of a common prevention/cure budget
• Assess impact of PEPFAR donations for cure on
performance of preventive public health functions in
Africa
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