modeling the determinants of health in complex policy environments: a system dynamics perspective
DESCRIPTION
This presentation for the Centre for Research on Inner City Health addresses the need to develop modeling tools to understand complex systems and the social determinants of health. Bob Gardner, Director of Policy Aziza Mahamoud, Research Associate, Systems Science and Population Health www.wellesleyinstitute.com Follow us on twitter @wellesleyWITRANSCRIPT
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Modeling the Determinants of Health in Complex Policy
Environments: A System Dynamics Perspective
Aziza MahamoudBob Gardner
February 14, 2013
Centre for Research on Inner City Health
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Objective
• Background• Introduction to simulation models and
system dynamics• Overview of urban health model and user
interface• Hands-on experience with using the urban
health model and interface• Discussion
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The Problem to Solve: Systemic Health Inequities in Ontario
•there is a clear gradient in health in which people with lower income, education or other indicators of social inequality and exclusion tend to have poorer health •+ major differences between women and men•the gap between the health of the best off and most disadvantaged can be huge – and damaging•impact and severity of these inequities can be concentrated in particular populations and neighbourhoods
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these health inequities are based in structured social and economic inequality – social determinants of health
• income inequality and poverty• inequitable access to childcare and
early development resources• precarious employment, unsafe
work• racism, social exclusion• inadequate and unaffordable
housing • decaying social safety nets
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Canadians With Chronic Conditions Who Also Report Food Insecurity
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We live in a world that is increasingly more complex, dynamic & interconnected
Better tools are needed to help us understand and manage complexity!
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Health Inequities = ‘Wicked’ Problems
• this means they are:• shaped by many inter-related and inter-dependent factors • in constantly changing social, economic, community and policy environments• action has to be taken at multiple levels -- by many levels of government, service
providers, other stakeholders and communities• solutions are not always clear and policy agreement can be difficult to achieve• effects take years to show up
• have to be able to understand and navigate this complexity to develop solutions
• we need to be able to:• identify the connections between multiple factors → the key pathways to change →
the mechanisms or levers that drive change along these pathways• specify the outcomes expected and the preconditions for achieving them• understand how to deploy these levers in specific social, institutional and policy
contexts
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Systems Approach at Wellesley Institute
WI has been working with stakeholders to explore the use of systems thinking and modeling to• inform our understanding of the complexities of
the social determinants of health• identify, assess and develop effective policy
alternatives to advance health equity• consider how new approaches like this can be
informed by and connected to community perspectives and policy needs
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“All models are wrong, but some are useful”
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George E. P. BoxRobustness in the Strategy of Scientific Model Building, 1979
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Why Develop Simulation Models?
• Systems are complex• Help us be explicit about our mental models• Theory building and testing• A virtual world to design and assess
intervention strategies• Tool for stakeholder engagement• Identify gaps in our knowledge of how a
system works
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Systems Dynamics: What is it?
• Field developed by Jay. W. Forrester at MIT in the 1950s
• “The use of informal maps and formal models with computer simulation to uncover and understand endogenous sources of system behavior” (Richardson, 2011)
Richardson, G.P. (2011). Reflections on the foundations of system dynamics. System Dynamics Review, 27(3), 219-243.
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System Dynamics Foundations• Complexity science • Focus on the whole rather than individual parts• Interdependency• Emergent behaviour• Stock and flow• Emphasis on feedback and non-linear thinking approach to
solving problems• Provides tools and techniques that can help us:
• Study a system from various perspectives• Look for emerging patterns and trends over time• Examine causes of policy failures and unintended consequences• Identify effective ways of intervening (leverage points)
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Problem Definition
Identifying Problem Causes
Focus on Policy Levers
Model formulation,
testing & evaluation
Knowledge Translation
Applying the System Dynamics Perspective
Mental Model
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Wellesley Urban Health Model• a computer-based systems dynamics simulation
model• helps us learn and understand the complex, and
dynamic interconnections between a select number of health & social factors
• allows us to test what impact our decisions (interventions) will likely have on population health outcomes under various assumptions • offers insight into how these effects could play out, and
over what timeframes
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Model Framework
Population health outcomes
Death rate Disability Chronic illness
Social determinants of health interventionsSocial cohesion Health care
accessAffordable
housing Income/jobs Behavioural
Changing health & social conditionsAdverse Housing
Low Income
Social cohesion
unhealthybehaviour
Poor health care access Disability Chronic
illness death
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Model ScopePopulation: City of TorontoDistinguishes people by:
• Ethnicity (Black, White, E Asian, SW Asian, Other)• Immigrant status (Recent, Established, Native-born)• Gender
Captures:• 5 areas of intervention: Healthcare access, Health behavior,
Income, Housing (lower & non-lower income), Social cohesion• Outcomes: Changes in overall deaths and health conditions,
and disparity ratios
Timeframe: 2006 – 2046Age: 25-6404/12/2023 | www.wellesleyinstitute.com
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Outcome measures & definitionsUnhealthy behaviour & obese: the prevalence of people
who are smokers or obese (POWER 2009). Chronic illness: having two or more of 12 chronic conditions
as specified by the Association of Public Health Epidemiologists in Ontario (POWER 2009)
Access to health care: the ease of getting an appointment for primary care
Disability: limitation in activities of daily livingMortality: age-standardized death rate Adverse housing: overcrowding (insufficient bedrooms) Social cohesion: feeling “strong sense of community
belonging "
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Data Sources and Parameter EstimationAll data or estimates broken out by 30 subgroups:
5 ethnicities x 3 immigrant statuses x 2 genders
Census 2001 and 2006, Ages 25-64• Population sizes• Disabled % (“often or sometimes”)• Low income• Adverse housing for lower income and higher income
Deaths per 1000 ages 25-64, City of Toronto combined 2000-05 (ethnic differences estimated, not available)
CCHS combined 2001-08 (4 cycles), Ages 25-64 • Chronically illness• Healthcare access• Unhealthy behaviour• Social cohesion
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Dynamic Hypothesis
The figure maps causal pathways in the model. The variables in red are the intervention options. The orange arrows indicate stabilizing effects, and blue arrows indicate reinforcing effects.
Low income %
Unhealthybehaviour %
Poor access toprimary care %
Disabled %
Chronically ill %
Death rate
Socialcohesion %
Adversehousing %
Employment/incomeinterventions
Health careinterventions
Behaviouralinterventions
Social cohesioninterventions
Housinginterventions
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Feedback loops in the model
- Blue arrows have reinforcing (+) effects- Red arrows have stabilizing (-) effects- Large + signs depict positive feedback loop
% Low-income
Prevalence ofdisability
Prevalence ofchronic illness
Prevalence ofunhealthy behaviour
& obesity
Poor health careaccess %
Adversehousing
Social cohesioninterventions
+
Health care accessinterventions
Unhealthybehaviour
interventions
Housinginterventions
Social cohesion
-
-Employment/incomeinterventions
-
-
-
-
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Hypothesis Testing• Multivariate regression analysis was conducted to
test causal connections and to produce effect estimates to parameterize the simulation model
• Conducting analysis at the subgroup level (not individual)
• treat each subgroup as a single observation• Controlling for demographic variables
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Limitations• Other important SDoH not included• Interventions are aggregate • Community support and care not captured• Lack of historical data to do trend analysis• Measurement issues associated with certain variables• Lack of projections for poverty and housing
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Model Uses1. planning, strategizing and advocating for improving
population health outcomes2. a learning tool to ground policy development & analysis
for dynamically interacting and complex SDoH• Introduce systems thinking
3. allows decision-makers to ask "what if" questions and test different courses of action
4. building a shared understanding and consensus among diverse groups with differing views on issues
5. eliciting stakeholder views and knowledge6. strengthening community dialogue
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How do interventions work?
• There are 5 intervention options to choose from• Interventions are ramped up over the period
2011-15 and stay in force through 2046• Range from 0 to 100%• Broad-based• For example:
• implementing 30% of the behavioural intervention reduces unhealthy behaviour by 30% in all population segments
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Interface & Scenario Demonstration
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Discussion Questions
• How could you imagine using the model?
• Who would you use the model with?
• What would need to be developed to facilitate that use?
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For more information
Mahamoud A. Roche B, Homer J. Modeling the Social Determinants of Health and Simulating Short-Term and Long-Term Intervention Impacts for the City of Toronto, Canada. Soc Sci Med (in press).
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© The Wellesley Institutewww.wellesleyinstitute.com 28
AcknowledgementCollaborators
1. Jack Homer, Homer ConsultingModeling
2. Dianne Patychuck, Steps to Equity
Data collection
3. Carey Levinton, Equity MagicStructural equation modeling
Advisors
1. Nathaniel Osgood, University of Saskatchewan
2. Peter Hovmand, Washington University
3. Bobby Milstein, US CDC
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