Beyond Scaling Up

Pathways to Scaling up Health Services in Complex Adaptive

Systems

Ligia Paina & David Peters

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The Problems of Scaling Up

Many effective health interventions known, but are not reaching universal coverage

Not known which models for scaling up work best

How can global health initiatives take advantage of knowledge on scaling up?

Do we have the right models for scaling up?

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Models for Scaling Up Health Services: Two Views

Domain Scaling up to Reach the MDGs

Scaling up Innovations and Pilot Projects

Defining Concerns

“Becoming large”; more people reached

Expanding impact, becoming sustainable in quantitative, functional, organizational, political terms

Time Frame Short to medium term

Medium to long term

Funding Money is a binding constraint

Money is necessary but not sufficient

Absorptive Capacity

Ability to spend external funds

Ability to find a fit between capabilities of beneficiaries, programs, and organizations

Subramanian et al (2010). Under review4

Misalignment between scaling up assumptions and health system behavior

Scaling up assumptions

Linear, blueprint process

Simplistic, deterministic

Standardized methods for predicting human and financial resources

Little adaptation to emerging issues

Health system behavior

Highly heterogeneous groups of actors

Multiple levels, services, and functions

Dynamic change

Rooted in unique local context

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Complex Adaptive Systems (CAS): Pathways to Scaling Up

CAS involve large number of interacting agents with adaptive capabilities in changing environment Not conventionally “controlled” Not fully predictable Unintended consequences frequent

Health systems behave like CAS

Scaling up is better understood through CAS phenomena

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Why CAS Phenomena are Relevant to Scaling Up Intervention that may work on a small scale or

in one context cannot be simply replicated elsewhere on a large scale

“Control” over behaviors of communities and providers is limited in real world

Large efforts can produce small effects, and small stimuli can create large changes

Implementation is highly variable and changing

Even simple public health interventions involve complex social interventions

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Path dependence: “History matters”

Single events can have system-wide effects that persist for a long time

Outcomes sensitive to initial conditions and bifurcations/choices along the way

Complicates predictions of a system’s evolution

Example: Can’t cut & paste reforms

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Feedback loops: “Vicious” and “Virtuous” Circles

An output of a process within the system is fed back into the same system

Used to analyze variations in supply and demand for health services

Example: health & poverty

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Scale-free networks Networks which are dominated by

few hubs with an unlimited number of preferentially attached links

Provide insights into system entry points and the diffusion of knowledge, technology, and practices

Example: Spread of HIV

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Emergent behavior The whole is greater than sum of parts:

the spontaneous creation of order – small entities jointly contribute to complicated behaviors

Health system actors self-organize in response to rapid changes, new policies

Example: Boda Boda drivers organize to transport women for ANC and delivery

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Phase transitions Tipping points that occur when

radical changes take place in features of health system parameters as they reach certain critical points

Threshold effects and sometimes abrupt changes happen in health systems

Example: Rapid adoption of a policy stalled for years.

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How CAS Can Inform Scaling Up

Better understanding of dynamics between the health system, contextual factors, and population health

Identify root causes of variations in service delivery

Identify multi-sectoral factors which promote the diffusion of innovation in complex systems

Better understanding of intended and unintended consequences

New tools and approaches to understand and facilitate decision-making

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Relevant Theories and Methodologies

Systems science Non-linear dynamics

and chaos theory Systems theory and

cybernetics Chaos theory Theory of critical

phenomena

Agent-based modeling Network analysis Scenario modeling Sensitivity analysis Statistics of extreme

events Non-equilibrium

statistics (physics) Large-scale data

mining

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Revisiting assumptions behind scaling up and other rapid health system change

Understand dynamic health system relationships

Involve key, multi-sector policy and planning stakeholders

Ensure flexibility to adapt to emerging issues Recognize local conditions Maintain vision for long-term sustainability

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Lessons to be learned Scaling up is not predictable or controlled:

scrap the blueprint Employ “theories of change” to build local

organizational, functional, and political capabilities

Should develop sustainable institutions Use “learning by doing” approaches: use data,

engage key stakeholders, problem-solving strategies

Identify constraints and complex pathways

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