www.futurehealthsystems.org

Complex Adaptive Systems in HealthApplying system dynamics methodsProf David Bishai

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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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Workshop outline

• Intro to CAS

• Intro to System Dynamics (SD) and SD research

• Make your own model

• Discussion

The FHS CAS FrameworkA quick review

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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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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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Model for Understanding Health Systems Changes as Complex

Adaptive System

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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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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

System dynamicsAn introduction

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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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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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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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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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Diagramming States

State=Stock of Drugs

States are diagrammed by rectangles:Every rectangle represents a state variable

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Diagramming Flows

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State

Inflow

Outflow

Rates are diagrammed by stopcocks:Arrows inside stopcocks mean “flow”

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Diagramming Controls

State

Inflow

Outflow

Controls are diagrammed by circles:Arrows not in stop cocks are arrows of influence

Black market demand

Transport cost

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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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A system dynamics model of unintended consequences of aid in weakening health

systems

Tilting the balance between curing and preventing.

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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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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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A system dynamics model of unintended consequences of aid in weakening health

systems

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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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Subsystem 1: The population and disease model

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Subsystem 2: Health resources

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Subsystem 3a: Doctor resource allocation

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Subsystem 3b: Hygienist resource allocation

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Methods

Analyze cost and health effects of NGO donationsNGOs 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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Results Holding DA Fixed

Ordered Pairs DA:DB

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Results Holding DB Fixed

Ordered Pairs DA:DB

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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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Sensitivity Analysis 1

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Sensitivity Analysis 2

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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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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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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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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