This presentation offers insight on systems approach in order to illustrate the complexities of the social determinants of health; and its effectiveness in identifying, assessing and developing effective policy alternatives to advance health equity. Aziza Mahamoud, Research Associate, Systems Science and Population Health Michael Shapcott, Director of Housing and Innovation www.wellesleyinstitute.com Follow us on twitter @wellesleyWI

1. Welcome to Fireside Chat # 250
December 9, 2011 1:00 – 2:00 PM Eastern Time
Wellesley Urban Health Model
Advisor on Tap:
Aziza Mahamoud, Research Associate, Wellesley Institute
Michael Shapcott, Director of Housing and Innovation, Wellesley Institute
www.chnet-works.ca
CHNET-Works! Hosts weekly Fireside Chats
For population health and stakeholder sectors
A project of
Population Health Improvement Research Network
University of Ottawa
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2. Advisor on tap
Name: Michael Shapcott
Title: Director, Housing & Innovation
Organization: Wellesley Institute
Coordinates: Michael@wellesleyinstitute.com
Brief bio: Michael manages the Wellesley Institute’s
knowledge mobilization and communications practice, and
leads the WI’s housing and homelessness work. He co-leads
the Wellesley Institute’s social innovation practice
Related website: www.wellesleyinstitute.com
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3. Advisor on tap
Name: Aziza Mahamoud
Title: Research Associate, Systems Science &
Population Health
Organization: Wellesley Institute
Coordinates: aziza@wellesleyinstitute.com
Brief bio: Aziza leads the Wellesley Institute’s systems science
and population health research work. She holds a Masters of
Public Health degree and has research experience in
communicable disease control & prevention and system
dynamics modeling of population health issues
Related website: www.wellesleyinstitute.com
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4. What part of Canada are you
from? √ on your province/territory
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5. What Sector are you from? Put a √ on your answer
Public Health Education/Research Provincial /Territorial
Faculty/Staff/Student Government/Ministry
Not-for-profit Health Practitioner Other
/
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7. Overview
• Background
• Introduction to systems dynamics
• Methods
• Findings
• Simulation scenarios
• Policy implications and roll out
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8. Wellesley Institute
• A Toronto-based non-profit and non-partisan
research and policy institute
• Focuses on population health advancement
through research on the social determinants
of health
• Collaborates with diverse communities to
develop practical and achievable policy
alternatives
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9. One: We live in a
complex, dynamic world
where everything is
connected to
everything else
We need better tools to help us
understand the connections 9

10. Two: There is an
increasing amount and
array of qualitative and
quantitative data
coming at us
We need better tools to help us
understand and use data
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11. Three: ‘Wicked’ policy problems
cannot be ‘solved’ with a program
here or an investment there… We
can’t just throw up our hands and
say it all is too complex. We need
models of policy thinking, strategic
investment, and service interventions
that address complex problems...
- Bob Gardner, Wellesley Institute
We need better tools to
understand interventions in
complex systems
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12. 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 and to
• 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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13. 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, p. 241)
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14. System Dynamics Foundations
• Complexity science
• Focus on the whole rather than individual parts
• Interdependency
• Emphasis on feedback and non-linear thinking approach
to solving problems
• Emergent patterns
• Provides tools and techniques that can help us and
system actors to study and learn about:
• Causes of policy failures and dynamic complexities
• Counterintuitive behaviour
• Leverage points & effective ways of changing system
structure
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15. Applying the System Dynamics Perspective
Problem
Definition
Implementation Identifying
& Knowledge Problem
Translation Mental Causes
Model
Model
Focus on Policy
formulation, testi
Levers
ng & evaluation
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16. 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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17. Model Framework
Changing health & social conditions
Adverse Low Social unhealthy Poor health Chronic
Disability death
Housing Income cohesion behaviour care access illness
Social determinants of health interventions
Health care Affordable
Social cohesion Income/jobs Behavioural
access housing
Population health outcomes
Death rate Disability Chronic illness
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18. 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, Healthy
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
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19. 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 of “strong sense of community "
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20. 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
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21. Overview of the modeling process
Population size by
Initial stakeholder Initial differences in social
determinants and health by ethnicity,
Population-wide
averages & disparity ethnicity, immigrant
meeting in 2010 Initial Dynamic Hypothesis
immigrant status, and gender ratios status, and gender
Social cohesion Social cohesion
interventions Death rate
Health care
interventions
Developed a reference Behavioral
Chronically ill %
interventions
group comprised of
Unhealthy behavior
domain experts, data & obese %
Poor access to
health care %
Disabled %
specialist, researchers, an
d internal team Education
interventions
Undereducated %
Low income % Adverse housing %
(by low/higher income)
Held several meetings General low
with the reference group & income trend General adverse
housing trends
Jobs/income Housing
modeler to interventions interventions
conceptualize, design, an
d evaluate model
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22. 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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23. Current Model Structure
Employment/income
interventions
Low income %
Health care Social cohesion
interventions interventions
Poor access to
primary care % Social Cohesion %
Unhealthy
behaviour %
Disabled % Housing
Behavioural interventions
interventions Death rate
Adverse housing %
Chronically ill %
j
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.
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24. Feedback loops in the model
Housing
interventions
Health care access
interventions Prevalence of
- chronic illness
Unhealthy
Prevalence of - behaviour
Poor health care
disability interventions
access %
-
Adverse
housing Prevalence of
unhealthy behaviour
& obesity
% Low-income
-
-
Employment/income -
interventions
Social cohesion + Social cohesion
interventions
- Both pink and blue arrows have reinforcing (+) effects
- Red arrows have stabilizing (-) effects
- Large + signs depict positive feedback loop
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25. Model Validation
- We are conducting confirmatory factor analysis
(structural equation modeling) to test how well our
current causal pathways in the model can be
reproduced
- Regenerate parameter estimates through this
method
- Preliminary findings suggest:
- model reproduces well, with the exception of a few
causal linkages
- most of the parameter estimates are similar to
current estimates and they are stable
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26. Limitations
Model Structure
• Interventions are exogenous
• Interventions are aggregate
• They apply equally to all population subgroups
• No aging
• Assuming independence of risk factors
Data challenges
• Lack of historical data to do trend analysis
• Measurement issues associated with certain variables
• Small sample size
• Lack of projections for poverty and housing
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27. Relationship between model
structure and behaviour
Simulation outcome:
Model behaviour
Model structure
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28. How 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%
• All intervention levers are applied equally to all
population segments
• For example:
• implementing 30% of the behavioural intervention
reduces gaps in unhealthy behaviour by 30%
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29. Impact of different levels of individual
interventions on chronic illness
we find that it takes 75% improvement Chronically ill popn age 25-64
in social cohesion (grey line) to yield the 480,000
same result as 25% improvement in
income (black line)
450,000
Higher levels of improvements in
420,000
housing (green) & unhealthy behaviour
(red) have decent effect on reducing
chronic illness 390,000
Different interventions play out 360,000
different times – effects of cohesion & 2006 2016 2026 2036 2046
Year
income are realized earlier, and housing
Baseline Cohesion75
before health behaviour Behaviour80 Income 25
Housing70
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30. The impact of income on chronic
illness prevalence by immigrant status
Prevalence of chronic illness
•Improvement in income (30%)
appears to have the greatest
impact in reducing chronic
illness prevalence for the
native-born population
segment (blue line) (15%)
•between recent (green line)
and established immigrants
(red line), the latter segment
seems to benefit the most
over the long term (13% 09/
12/
decrease) 201
1|
ww
w.w
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elle

31. Outcomes from a Layered Sequence of Tests
Deaths per yr in age 25-64 Disabled popn age 25-64 Chronically ill popn age 25-64
3,000 240,000 480,000
DEATHS/YR DISABLED POP SICK POP
Poverty down 25%
Poverty down 25%
+ Poor cohesion down 50%
2,800 210,000 450,000 Poverty down 25%
+ Poor access down 50% (green)
+ Adverse behavior & housing down 50% (grey)
+ Poor cohesion
2,600 180,000 down 50% 420,000
+ Poor cohesion
down 50% (red)
+ Poor access down 50% (green)
+ Adverse behavior & housing down 50% (grey)
2,400 150,000 390,000
2,200 120,000 360,000
2006 2016 2026 2036 2046 2006 2016 2026 2036 2046 2006 2016 2026 2036 2046
Year Year Year
Income25x Income25x Income25x
Inc25Cohes50x Inc25Cohes50x Inc25Cohes50x
Inc25Cohes50Access50x Inc25Cohes50Access50x Inc25Cohes50Access50x
Inc25Allother50x Inc25Allother50x Inc25Allother50x
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32. Overall Findings
• Death rate reduction: Strongest influence is from
Healthcare Access
• Disability reduction: Strongest influences are from
Low Income and Cohesion, followed by Health care
Access.
• Chronic illness reduction: Strongest influences are
from Low Income and Cohesion, followed (but not
closely) by Adverse Housing.
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33. Bearing in mind…
• We acknowledge that the model does not include some of
important population health factors & intervention tactics
• Although preliminary analyses of the data and the model
produce a number of counter-intuitive findings, we must
remember to:
• exercise caution when interpreting the findings
• be cognizant of apparent data limitations – e.g. access to
primary care, social cohesion
• These findings also illustrate the need for further data
collection and improvement of current measurement
techniques to better inform simulation modeling
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34. Implications & Policy Considerations
• Getting at the roots of health disparities means understanding
& acting on fundamental structural inequalities
• The need to always consider the complex & dynamic nature of
SDoH interventions
• we can’t analyze or plan interventions around particular
determinant in isolation
• The most efficient policy is when the combined impact of
interventions is taken into account
• The need to recognize the role of strong and cohesive
communities in improving population health and well-being
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35. Implications & Policy Considerations
Cont’d
If income is fundamental and underlies other trends
and interventions:
• This doesn’t mean that the impact of other
determinants of health are insignificant
• These other determinants can have a major role in
mediating the effects of overall health disparities and
lived experience
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36. 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
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37. Stakeholder and public engagement
Ongoing engagement with wide range of stakeholders
including:
• decision-makers at various levels of government
• various organizations
• community partners
Plan to develop a web-based computer interface to make the
model more accessible and to engage users interactively
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38. Desktop interface
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40. Acknowledgement
Collaborators Internal Team
1. Jack Homer, Homer Consulting 1. Rick Blickstead
Modeling 2. Aziza Mahamoud
2. Dianne Patychuck, Steps to 3. Brenda Roche
Equity 4. Michael Shapcott
Data collection 5. Bob Gardner
3. Carey Levinton, Equity Magic
SEM
Advisors:
1. Nathaniel Osgood, University of
Saskatchewan
2. Bobby Milstein, US CDC
3. Peter Hovmand, Washington
University
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41. THANK YOU
Please visit us at
www.wellesleyinstitute.com

42. Thanks for joining in!
www.chnet-works.ca
Contact animateur@chnet-works.ca for
information about partnering with
CHNET-Works!
A project of
Population Health Improvement Research Network
University of Ottawa
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