Modeling the Determinants of Health in Complex Policy Environments: A System Dynamics Perspective

Modeling the Determinants of
Health in Complex Policy
Environments: A System Dynamics
Perspective
Aziza Mahamoud
Bob Gardner
February 14, 2013
Centre for Research on Inner City Health
1

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
22/14/2014 | www.wellesleyinstitute.com

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

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

Canadians With Chronic Conditions
Who Also Report Food Insecurity
5

We live in a world that is increasingly
more complex, dynamic &
interconnected
6
Better tools are needed to help us understand and
manage complexity!

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

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

9
“All models are wrong, but some are useful”
George E. P. Box
Robustness in the Strategy of Scientific
Model Building, 1979

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

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.

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)

Problem
Definition
Identifying
Problem
Causes
Focus on Policy
Levers
Model
formulation, testi
ng & evaluation
Knowledge
Translation
Applying the System Dynamics Perspective
Mental
Model

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

Model Framework
Population health outcomes
Death rate Disability Chronic illness
Social determinants of health interventions
Social cohesion
Health care
access
Affordable
housing
Income/jobs Behavioural
Changing health & social conditions
Adverse
Housing
Low
Income
Social
cohesion
unhealthy
behaviour
Poor health
care access
Disability
Chronic
illness
death

Model Scope
Population: City of Toronto
Distinguishes 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 – 2046
Age: 25-64

Outcome measures & definitions
Unhealthy 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 living
Mortality: age-standardized death rate
Adverse housing: overcrowding (insufficient bedrooms)
Social cohesion: feeling “strong sense of community
belonging "

Data Sources and Parameter Estimation
All 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

Dynamic Hypothesis
19
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 %
Unhealthy
behaviour %
Poor access to
primary care %
Disabled %
Chronically ill %
Death rate
Social
cohesion %
Adverse
housing %
Employment/income
interventions
Health care
interventions
Behavioural
interventions
Social cohesion
interventions
Housing
interventions

Feedback loops in the model
20
- Blue arrows have reinforcing (+) effects
- Red arrows have stabilizing (-) effects
- Large + signs depict positive feedback loop
% Low-income
Prevalence of
disability
Prevalence of
chronic illness
Prevalence of
unhealthy behaviour
& obesity
Poor health care
access %
Adverse
housing
Social cohesion
interventions
+
Health care access
interventions
Unhealthy
behaviour
interventions
Housing
interventions
Social cohesion
-
-Employment/income
interventions
-
-
-
-

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

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

Model Uses
1. planning, strategizing and advocating for improving
population health outcomes
2. 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 knowledge
6. strengthening community dialogue

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

Interface & Scenario Demonstration

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?

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).

© The Wellesley Institute
www.wellesleyinstitute.com
Acknowledgement
Collaborators
1. Jack Homer, Homer Consulting
Modeling
2. Dianne Patychuck, Steps to
Equity
Data collection
3. Carey Levinton, Equity Magic
Structural equation modeling
Advisors
1. Nathaniel Osgood, University of
Saskatchewan
2. Peter Hovmand, Washington
University
3. Bobby Milstein, US CDC

THANK YOU
Please visit us at
www.wellesleyinstitute.com

Modeling the Determinants of Health in Complex Policy Environments: A System Dynamics Perspective

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