Complex Adaptive Systems in Health

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  • ** in the introduction, it would be worthwhile to explain the value added of CAS framework – when to consider using it. ***
  • http://www.health.org.uk/public/cms/75/76/313/2590/Complex%20adaptive%20systems.pdf?realName=jIq8CP.pdfSome points on value added taken from link above
  • Some might argue that the underpinning principles are reasonably common sense and so the main value of this way of thinking is its ability to see through taken for granted approaches and delve deeper into the way people and organisations interact. This approach is a model for thinking about the world, not a way of predicting what will happen. Some authors suggest that thinking about things as complex adaptive systems opens up a variety of new options.
  • Drugs in stock Inflow from distribution chainOutflow to patientsOther outflows?
  • 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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