Chapter 14: Systems Thinking: From Single Solutions to One Health
Introduction to Systems Thinking
usefulness of reductionist thinking
science-based disciplines
complexity of problems
population perspective
systems thinking as a complement
reductionist thinking
looks at 1 fact or variable at a time
reduces the problem to one potential "cause" and one "effect"
"magic bullet" or "miracle cure" approach
one answer to improve outcomes
Effective for specific factors like contributory causes of disease
Builds on reductionist thinking, not replacing it
As problems become more complex, need to evaluate all factors and their interconnections
Understanding Systems
what is a system?
O'Connor & McDermott: Systems maintain existence through the interaction of parts (ex: the human body)
Systems VS Heap
Definition: An interacting group of items forming a unified whole
A system has interconnected parts, while a heap is just a collection
Changes in a system affect its structure; arrangement is irrelevant in a heap
Behavior Depends on Structure: A system's behavior is determined by the overall structure
Systems Thinking in Population Health
Steps for Complete Systems Analysis
Systems thinking applied to health
tools for system analysis
Population health has shifted toward systems thinking
Diagrams/Graphics: Visual tools to represent systems
Initial 3 Steps in Systems Analysis
- identify key influences/interventions of an outcome
- indicate the relative strength of each influence
- determine how influences interact when more than one is present
dynamic approach
additional steps in systems analysis
Complex Systems: Systems rely on feedback loops to maintain stability
Feedback Loops: Positive or negative impacts on outcomes
Bottlenecks: Identify points where systems are slowed down
Leverage Points: Identify opportunities for major improvements in outcomes
- Identify dynamic changes through feedback loops
- Identify bottlenecks that limit effectiveness
- Identify leverage points to improve outcomes
One Health Approach
Systems Diagrams
Health Disparities and Systems Thinking
Applying Systems Thinking to Population Health
How to Use System Diagrams
(+) or (-) signs indicate whether factors increase or decrease subsequent outcomes
Arrows indicate relationships between factors
Five Key Questions for Systems Thinking
- How can systems thinking incorporate interactions between factors to understand disease etiology?
- How does it account for interactions between diseases?
- How can it help identify bottlenecks and leverage points for health improvement?
- How can it help develop strategies for simultaneous interventions?
- How does it help look at processes as a whole for better intervention planning?
Example Applications
Social Determinants of Health: Understanding the broad impact of societal factors
Syndemics: The interaction of multiple diseases, like HIV leading to other conditions
One Health Initiative
Global Factors Affecting Health
What is One Health?
Historical Context: Rudolph Virchow coined "zoonotic disease," which is transmitted from animals to humans (e.g., anthrax, SARS, HIV)
Interconnection between human health, animal health, and ecosystem health
The rise of RNA viruses (e.g., HIV, SARS, COVID-19) and antibiotic resistance
Human Health Linked to Animal Health: Health threats can cross species barriers, and ecological changes impact disease transmission
Environmental change, population growth, and economic disparities accelerate disease spread.
Addresses microbiological changes, antibiotic resistance, population changes, and ecosystem shifts
importance of interconnectedness in addressing health threats and diseases
Barriers in Health Disparities Research
Systems Thinking and Modeling
Benefits of Systems Approaches
Challenges and Limitations
Future of Systems Approaches
Traditional analytical methods focus on independent effects, limiting the scope of research
Systems thinking allows for a more comprehensive understanding of health disparities
Formal Models and Simulations: Test and refine conceptual models
Identifying Intervention Points: Understanding underlying dynamics
Visualizing Dynamic Processes: Helps to explore complex interactions
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Stimulates innovation in research.
Generates new research questions.
Provides learning opportunities for stakeholders.
Contrasts alternative hypotheses for causes of health disparities.
Systems approaches complement, but don’t replace, traditional methods
Models should answer meaningful questions, not just serve as simulations
Focus on how dynamic factors at different levels contribute to health disparities
Shift from partitioning individual vs. group effects to understanding interconnections
Systems thinking leads to clearer, more actionable policies