Wellesley Urban Health Model

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Welcome to Fireside Chat # 250December 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.caCHNET-Works! Hosts weekly Fireside Chats

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University of Ottawa

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Advisor on tapName: Michael ShapcottTitle: Director, Housing & InnovationOrganization: Wellesley InstituteCoordinates: [email protected] 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

09/12/2011 | www.wellesleyinstitute.com

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Advisor on tapName: Aziza MahamoudTitle: Research Associate, Systems Science & Population

HealthOrganization: Wellesley InstituteCoordinates: [email protected] 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.com09/12/2011 | www.wellesleyinstitute.com

What part of Canada are you from?

√ on your province/territory


What Sector are you from? Put a √ on your answer

/

Public Health Education/ResearchFaculty/Staff/Student

Provincial /Territorial Government/Ministry

Not-for-profit Health Practitioner Other



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Overview

• Background• Introduction to systems dynamics• Methods• Findings • Simulation scenarios• Policy implications and roll out


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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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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

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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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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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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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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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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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Problem Definition

Identifying Problem Causes

Focus on Policy Levers

Model formulation,

testing & evaluation

Implementation & 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 interventions

Social cohesion Health care access

Affordable housing Income/jobs Behavioural

Changing health & social conditions

Adverse Housing

Low Income

Social cohesion

unhealthybehaviour

Poor health care access Disability Chronic

illness death


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Model Scope

Population: 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, Healthy behavior,

Income, Housing (lower & non-lower income), Social cohesion• Outcomes: Changes in overall deaths and health conditions,

and disparity ratios

Timeframe: 2006 – 2046Age: 25-6409/12/2011 | 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 of “strong sense of community "


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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


Population size byethnicity, immigrantstatus, and gender

Disabled %

Undereducated %

Chronically ill %

Poor access tohealth care %

Unhealthy behavior& obese %

Low income %

Health careinterventions

Behavioralinterventions

Educationinterventions

Jobs/incomeinterventions

General lowincome trend General adverse

housing trends

Population-wideaverages & disparity

ratios

Housinginterventions

Adverse housing %(by low/higher income)

Social cohesioninterventions

Social cohesionDeath rate

Initial differences in socialdeterminants and health by ethnicity,

immigrant status, and gender

Initial stakeholder meeting in 2010

Developed a reference group comprised of domain experts, data specialist, researchers, and internal team

09/12/2011 | www.wellesleyinstitute.com 21

Held several meetings with the reference group & modeler to conceptualize, design, and evaluate model

Initial Dynamic Hypothesis

Overview of the modeling process

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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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Current Model Structure


Social Cohesion %

Adverse housing %

Low income %

Poor access toprimary care %

Death rate

Unhealthybehaviour %

Chronically ill %

Disabled %

Employment/incomeinterventions


HousinginterventionsBehavioural

interventions

Health careinterventions

jThe 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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Feedback loops in the model


- Both pink and 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


+

Health care accessinterventions

Unhealthybehaviour

interventions

Housinginterventions

Social cohesion

-

-Employment/incomeinterventions

-

-

-

-

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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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LimitationsModel 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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Relationship between model structure and behaviour

Model structure

Simulation outcome: Model behaviour


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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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Impact of different levels of individual interventions on chronic illnesswe find that it takes 75% improvement in social cohesion (grey line) to yield the same result as 25% improvement in income (black line)

Higher levels of improvements in housing (green) & unhealthy behaviour (red) have decent effect on reducing chronic illness

Different interventions play out different times – effects of cohesion & income are realized earlier, and housing before health behaviour

Chronically ill popn age 25-64

480,000

450,000

420,000

390,000

360,000

2006 2016 2026 2036 2046Year

BaselineBehaviour80Housing70

Cohesion75Income 25


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The impact of income on chronic illness prevalence by immigrant status

•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% decrease)

09/12/2011 | www.wellesleyinstitute.com09/12/2011 | www.wellesleyinstitute.com

Prevalence of chronic illness

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Deaths per yr in age 25-64

3,000

2,800

2,600

2,400

2,200

2006 2016 2026 2036 2046Year

Income25x Inc25Cohes50x Inc25Cohes50Access50x Inc25Allother50x

Disabled popn age 25-64

240,000

210,000

180,000

150,000

120,000

2006 2016 2026 2036 2046Year


Chronically ill popn age 25-64

480,000

450,000

420,000

390,000

360,000

2006 2016 2026 2036 2046Year


Outcomes from a Layered Sequence of Tests

+ Poor cohesion down 50%

Poverty down 25%

DISABLED POP SICK POPDEATHS/YR

+ Poor access down 50% (green)+ Adverse behavior & housing down 50% (grey)

Poverty down 25%

Poverty down 25%

+ Poor cohesion down 50%

+ Poor cohesion down 50% (red)

+ Poor access down 50% (green)+ Adverse behavior & housing down 50% (grey)


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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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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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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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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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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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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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Desktop interface



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AcknowledgementCollaborators

1. Jack Homer, Homer ConsultingModeling

2. Dianne Patychuck, Steps to Equity

Data collection

3. Carey Levinton, Equity MagicSEM

Advisors:4. Nathaniel Osgood, University of

Saskatchewan5. Bobby Milstein, US CDC6. Peter Hovmand, Washington

University

Internal Team

1. Rick Blickstead2. Aziza Mahamoud3. Brenda Roche4. Michael Shapcott5. Bob Gardner


THANK YOUPlease visit us at

www.wellesleyinstitute.com

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University of Ottawa


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Health & Medicine

Wellesley Urban Health Model