Welcome to Fireside Chat # 250                          December 9, 2011 1:00 – 2:00 PM Eastern Time               Wellesl...
Advisor on tapName: Michael ShapcottTitle: Director, Housing & InnovationOrganization: Wellesley InstituteCoordinates: Mic...
Advisor on tapName: Aziza MahamoudTitle: Research Associate, Systems Science &   Population HealthOrganization: Wellesley ...
What part of Canada are you                     from? √ on your province/territory09/12/2011 | www.wellesleyinstitute.com ...
What Sector are you from? Put a √ on your answerPublic Health                                Education/Research      Provi...
09/12/2011 | www.wellesleyinstitute.com   6
Overview•    Background•    Introduction to systems dynamics•    Methods•    Findings•    Simulation scenarios•    Policy ...
Wellesley Institute• A Toronto-based non-profit and non-partisan  research and policy institute• Focuses on population hea...
One: We live in acomplex, dynamic worldwhere everything isconnected toeverything else              We need better tools to...
Two: There is anincreasing amount andarray of qualitative andquantitative datacoming at us               We need better to...
Three: ‘Wicked’ policy problemscannot be ‘solved’ with a programhere or an investment there… Wecan’t just throw up our han...
Systems Approach at Wellesley                     InstituteWI has been working with stakeholders to explore the use of sys...
Systems Dynamics: What is it?• Field developed by Jay. W. Forrester at MIT in  the 1950s• “The use of informal maps and fo...
System Dynamics Foundations• Complexity science• Focus on the whole rather than individual parts• Interdependency• Emphasi...
Applying the System Dynamics Perspective                                          Problem                                 ...
Wellesley Urban Health Model• a computer-based systems dynamics simulation  model• helps us learn and understand the compl...
Model Framework                        Changing health & social conditionsAdverse        Low          Social        unheal...
Model ScopePopulation: City of TorontoDistinguishes people by:      • Ethnicity (Black, White, E Asian, SW Asian, Other)  ...
Outcome measures & definitions  Unhealthy behaviour & obese: the prevalence of people     who are smokers or obese (POWER ...
Data Sources and Parameter Estimation All data or estimates broken out by 30 subgroups:     5 ethnicities x 3 immigrant st...
Overview of the modeling process                                                                                          ...
Hypothesis Testing• Multivariate regression analysis was conducted to  test causal connections and to produce effect  esti...
Current Model Structure                                                    Employment/income                              ...
Feedback loops in the model                                                   Housing                                     ...
Model Validation- We are conducting confirmatory factor analysis  (structural equation modeling) to test how well our  cur...
LimitationsModel Structure      • Interventions are exogenous      • Interventions are aggregate             • They apply ...
Relationship between model                structure and behaviour                                          Simulation outc...
How interventions work?• There are 5 intervention options to choose from• Interventions are ramped up over the period  201...
Impact of different levels of individualinterventions on chronic illnesswe find that it takes 75% improvement             ...
The impact of income on chronicillness prevalence by immigrant status                                          Prevalence ...
Outcomes from a Layered Sequence of Tests        Deaths per yr in age 25-64                        Disabled popn age 25-64...
Overall Findings  • Death rate reduction: Strongest influence is from    Healthcare Access  • Disability reduction: Strong...
Bearing in mind…• We acknowledge that the model does not include some of  important population health factors & interventi...
Implications & Policy Considerations• Getting at the roots of health disparities means understanding  & acting on fundamen...
Implications & Policy Considerations                 Cont’dIf income is fundamental and underlies other trends    and inte...
Model Uses1. planning, strategizing and advocating for improving   population health outcomes2. a learning tool to ground ...
Stakeholder and public engagementOngoing engagement with wide range of stakeholders  including:   • decision-makers at var...
Desktop interface09/12/2011 | www.wellesleyinstitute.com          38
09/12/2011 | www.wellesleyinstitute.com   39
AcknowledgementCollaborators                             Internal Team1.    Jack Homer, Homer Consulting        1.   Rick ...
THANK YOU    Please visit us atwww.wellesleyinstitute.com
Thanks for joining in!                                          www.chnet-works.ca                     Contact animateur@c...
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Wellesley Urban Health Model

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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

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  • So to echo Michael, addressing population health challenges does require grappling with great complexity, both dynamic and structural.A way of forcing us to think about the interconnections, to demonstrate it in our work, the SDOH we`ve chosen reflects where we put the emphasis
  • A problem solving methodology
  • dynamic complexities – co behaviour of system as a results of interactions of agents over timeCounterintuitive behaviour – unintended consequence, as a results of the distal feedback effects of our decisions and policies that we do not anticipate--where the unforeseen effects occur in the system when we intervene (systems response to change)Leverage points – finding where in the system should we interveneThe focus is on system structure, rather than events and patterns – with emphasis on questions such as what’s causing the events we are seeing and why are patters occurring Toolshelps us study and understand:how components of the systems are interrelated (identifying previously unknown relationships)How systems generate unexpected behaviour & why policies lead to failure (unintended consequences, policy resistance) which policies are most effective under different assumptions (serve as “what if” tool
  • It’s a reiterative process, a co-evolution process whereby our mental models are the centre, both transforming the process of modeling as well as being transformed by it as we become explicit about our assumptionsOften, the greatest value is gained through the modeling process as opposed to the models built, the end result....this is sometimes not so obvious as stakeholders may put all the emphasis on the outcome of the simulation
  • We began with the urban health model, in collaboration with Jack homer from the US, and built a simulation model that that captures the complex interrelations between diverse health conditions, health risk factors, and possible interventions . To test the likely health trajectories under different assumptions
  • We are testing our theory, or hypothesized causal relationships in the initial model to see if these are supported by our data, and how significant, strong, or weak the relationships are, and then we refine the dynamic hypothesis in a reiterative fashion
  • Dynamic hypotheses, or system structure diagram regarding causal structures underlying observed behavioursMore simplified model, with fewer feedbacks, and some change in causal pathways.
  • 4feedback loops and two delays – key concepts in system structureAll operating through income, and most through disability and some through chronic illness
  • We are doing a confirmatory analysis, using the pre-existing system structure, or theory to see how well it fits the data and to produce parameter estimates for the model the key difference from the standard regression model being that it is producing results taking into account all of the variables in the model.
  • We are assuming interventions operate exogenously, i.e. they are unidirectional, which means we are not capturing any feedback effects from the changing health conditions and determinants on the interventions themselvesMany of the challenges due lack of trend data - inability to reproduce the historical epidemiologic profile
  • So the next set of slides will illustrate simulation scenarios. So I would like to remind everyone that, what we’re doing is assessing how Structure of system determines dynamic behaviour, examining the diverse consequences of changes in one area of the system (intervention) to the whole system.
  • If income is fundamental and underlies other trends and interventions:This doesn’t mean that the impact of other determinants of health are insignificantThese other determinants can have a major role in mediating the effects of overall health disparities and lived experienceBut getting at the roots of health disparities does mean acting on fundamental structural inequalities
  • Not either or scenario, combination interventions yield the most optimum resultsPayoff policy result – comprehensive intervention strategy
  • Not either or scenario, combination interventions yield the most optimum resultsPayoff policy result – comprehensive intervention strategyIf you take each interventions, it can create substantial outcome, but a combination of intervention that don’t on their own have the greatest impact can yield a strong impact.
  • As part of our communication
  • Wellesley Urban Health Model

    1. 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 InstituteMichael 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 1
    2. 2. Advisor on tapName: Michael ShapcottTitle: Director, Housing & InnovationOrganization: Wellesley InstituteCoordinates: Michael@wellesleyinstitute.comBrief 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 practiceRelated website: www.wellesleyinstitute.com09/12/2011 | www.wellesleyinstitute.com 2
    3. 3. Advisor on tapName: Aziza MahamoudTitle: Research Associate, Systems Science & Population HealthOrganization: Wellesley InstituteCoordinates: aziza@wellesleyinstitute.comBrief 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 issuesRelated website: www.wellesleyinstitute.com09/12/2011 | www.wellesleyinstitute.com 3
    4. 4. What part of Canada are you from? √ on your province/territory09/12/2011 | www.wellesleyinstitute.com 4
    5. 5. What Sector are you from? Put a √ on your answerPublic Health Education/Research Provincial /Territorial Faculty/Staff/Student Government/MinistryNot-for-profit Health Practitioner Other / 09/12/2011 | www.wellesleyinstitute.com 5
    6. 6. 09/12/2011 | www.wellesleyinstitute.com 6
    7. 7. Overview• Background• Introduction to systems dynamics• Methods• Findings• Simulation scenarios• Policy implications and roll out09/12/2011 | www.wellesleyinstitute.com 7
    8. 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 alternatives09/12/2011 | www.wellesleyinstitute.com 8
    9. 9. One: We live in acomplex, dynamic worldwhere everything isconnected toeverything else We need better tools to help us understand the connections 9
    10. 10. Two: There is anincreasing amount andarray of qualitative andquantitative datacoming at us We need better tools to help us understand and use data 10
    11. 11. Three: ‘Wicked’ policy problemscannot be ‘solved’ with a programhere or an investment there… Wecan’t just throw up our hands andsay it all is too complex. We needmodels of policy thinking, strategicinvestment, and service interventionsthat address complex problems... - Bob Gardner, Wellesley Institute We need better tools to understand interventions in complex systems 11
    12. 12. Systems Approach at Wellesley InstituteWI 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 needs09/12/2011 | www.wellesleyinstitute.com 12
    13. 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)09/12/2011 | www.wellesleyinstitute.com 13
    14. 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 structure09/12/2011 | www.wellesleyinstitute.com 14
    15. 15. Applying the System Dynamics Perspective Problem Definition Implementation Identifying & Knowledge Problem Translation Mental Causes Model Model Focus on Policy formulation, testi Levers ng & evaluation09/12/2011 | www.wellesleyinstitute.com 15
    16. 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 timeframes09/12/2011 | www.wellesleyinstitute.com 16
    17. 17. Model Framework Changing health & social conditionsAdverse Low Social unhealthy Poor health Chronic Disability deathHousing Income cohesion behaviour care access illness Social determinants of health interventions Health care AffordableSocial cohesion Income/jobs Behavioural access housing Population health outcomes Death rate Disability Chronic illness09/12/2011 | www.wellesleyinstitute.com 17
    18. 18. Model ScopePopulation: City of TorontoDistinguishes people by: • Ethnicity (Black, White, E Asian, SW Asian, Other) • Immigrant status (Recent, Established, Native-born) • GenderCaptures: • 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 ratiosTimeframe: 2006 – 2046Age: 25-6409/12/2011 | www.wellesleyinstitute.com 18
    19. 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 "09/12/2011 | www.wellesleyinstitute.com 19
    20. 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 cohesion09/12/2011 | www.wellesleyinstitute.com 20
    21. 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 interventionsDeveloped a reference Behavioral Chronically ill % interventionsgroup comprised of Unhealthy behaviordomain experts, data & obese % Poor access to health care % Disabled %specialist, researchers, and internal team Education interventions Undereducated % Low income % Adverse housing % (by low/higher income)Held several meetings General lowwith the reference group & income trend General adverse housing trends Jobs/income Housingmodeler to interventions interventionsconceptualize, design, and evaluate model 09/12/2011 | www.wellesleyinstitute.com 21
    22. 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 variables09/12/2011 | www.wellesleyinstitute.com 22
    23. 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 % jThe figure maps causal pathways in the model. The variables in red are the intervention options. The orange arrows indicatestabilizing effects, and blue arrows indicate reinforcing effects.09/12/2011 | www.wellesleyinstitute.com 23
    24. 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 loop09/12/2011 | www.wellesleyinstitute.com 24
    25. 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 stable09/12/2011 | www.wellesleyinstitute.com 25
    26. 26. LimitationsModel Structure • Interventions are exogenous • Interventions are aggregate • They apply equally to all population subgroups • No aging • Assuming independence of risk factorsData challenges • Lack of historical data to do trend analysis • Measurement issues associated with certain variables • Small sample size • Lack of projections for poverty and housing09/12/2011 | www.wellesleyinstitute.com 26
    27. 27. Relationship between model structure and behaviour Simulation outcome: Model behaviour Model structure09/12/2011 | www.wellesleyinstitute.com 27
    28. 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%09/12/2011 | www.wellesleyinstitute.com 28
    29. 29. Impact of different levels of individualinterventions on chronic illnesswe find that it takes 75% improvement Chronically ill popn age 25-64in social cohesion (grey line) to yield the 480,000same result as 25% improvement inincome (black line) 450,000Higher levels of improvements in 420,000housing (green) & unhealthy behaviour(red) have decent effect on reducingchronic illness 390,000Different interventions play out 360,000different times – effects of cohesion & 2006 2016 2026 2036 2046 Yearincome are realized earlier, and housing Baseline Cohesion75before health behaviour Behaviour80 Income 25 Housing7009/12/2011 | www.wellesleyinstitute.com 29
    30. 30. The impact of income on chronicillness prevalence by immigrant status Prevalence of chronic illness•Improvement in income (30%)appears to have the greatestimpact in reducing chronicillness prevalence for thenative-born populationsegment (blue line) (15%)•between recent (green line)and established immigrants(red line), the latter segmentseems to benefit the mostover the long term (13% 09/ 12/decrease) 201 1| ww w.w09/12/2011 | www.wellesleyinstitute.com 30 elle
    31. 31. Outcomes from a Layered Sequence of Tests Deaths per yr in age 25-64 Disabled popn age 25-64 Chronically ill popn age 25-643,000 240,000 480,000 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 cohesion2,600 180,000 down 50% 420,000 + Poor cohesion down 50% (red) + Poor access down 50% (green) + Adverse behavior & housing down 50% (grey)2,400 150,000 390,0002,200 120,000 360,000 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 09/12/2011 | www.wellesleyinstitute.com 31
    32. 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.09/12/2011 | www.wellesleyinstitute.com 32
    33. 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 modeling09/12/2011 | www.wellesleyinstitute.com 33
    34. 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-being09/12/2011 | www.wellesleyinstitute.com 34
    35. 35. Implications & Policy Considerations Cont’dIf 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 experience09/12/2011 | www.wellesleyinstitute.com 35
    36. 36. 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 thinking3. allows decision-makers to ask "what if" questions and test different courses of action4. building a shared understanding and consensus among diverse groups with differing views on issues5. eliciting stakeholder views and knowledge6. strengthening community dialogue09/12/2011 | www.wellesleyinstitute.com 36
    37. 37. Stakeholder and public engagementOngoing engagement with wide range of stakeholders including: • decision-makers at various levels of government • various organizations • community partnersPlan to develop a web-based computer interface to make the model more accessible and to engage users interactively09/12/2011 | www.wellesleyinstitute.com 37
    38. 38. Desktop interface09/12/2011 | www.wellesleyinstitute.com 38
    39. 39. 09/12/2011 | www.wellesleyinstitute.com 39
    40. 40. AcknowledgementCollaborators Internal Team1. Jack Homer, Homer Consulting 1. Rick Blickstead Modeling 2. Aziza Mahamoud2. Dianne Patychuck, Steps to 3. Brenda Roche Equity 4. Michael Shapcott Data collection 5. Bob Gardner3. Carey Levinton, Equity Magic SEMAdvisors:1. Nathaniel Osgood, University of Saskatchewan2. Bobby Milstein, US CDC3. Peter Hovmand, Washington University09/12/2011 | www.wellesleyinstitute.com 40
    41. 41. THANK YOU Please visit us atwww.wellesleyinstitute.com
    42. 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 Ottawa09/12/2011 | www.wellesleyinstitute.com 42
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