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Complex Adaptive Systems in Health

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Complex Adaptive Systems in Health

  1. 1. Complex Adaptive Systems in HealthApplying system dynamics methodsProf David Bishai www.futurehealthsystems.org
  2. 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 work1
  3. 3. Workshop outline • Intro to CAS • Intro to System Dynamics (SD) and SD research • Make your own model • Discussion2
  4. 4. The FHS CAS FrameworkA quick review
  5. 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 actions4 4
  6. 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 outcomes5
  7. 7. Model for Understanding Health Systems Changes as Complex Adaptive System6
  8. 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.7
  9. 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 explored8
  10. 10. System dynamicsAn introduction
  11. 11. Session objectives • Broad introduction to System Dynamics methods • Present an application of SD methods to public health dilemma (prevention vs. cure)10
  12. 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 metrics11 11
  13. 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 responses12
  14. 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 payroll13 13
  15. 15. Diagramming States State=Stock of Drugs States are diagrammed by rectangles: Every rectangle represents a state variable14
  16. 16. Diagramming Flows Inflow State Outflow Rates are diagrammed by stopcocks: Arrows inside stopcocks mean “flow”15 15
  17. 17. Diagramming Controls Transport Inflow cost State Black market Outflow demand Controls are diagrammed by circles: Arrows not in stop cocks are arrows of influence16
  18. 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)17 17
  19. 19. Tilting the balance between curing and preventing. A system dynamics model of unintended consequences of aid in weakening health systems
  20. 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 funds19
  21. 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 variables20
  22. 22. A system dynamics model of unintended consequences of aid in weakening health systems21
  23. 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 effects22
  24. 24. Subsystem 1: The population and disease model23
  25. 25. Subsystem 2: Health resources24
  26. 26. Subsystem 3a: Doctor resource allocation25
  27. 27. Subsystem 3b: Hygienist resource allocation26
  28. 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 ?27
  29. 29. Results Holding DA Fixed Ordered Pairs DA:DB28
  30. 30. Results Holding DB Fixed Ordered Pairs DA:DB29
  31. 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 cure30
  32. 32. Sensitivity Analysis 131
  33. 33. Sensitivity Analysis 232
  34. 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 zero33
  35. 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?34
  36. 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): 95-107.35
  37. 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 Africa36

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