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

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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

http://www.stmichaelshospital.com/crich/

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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

2/14/2014 | 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


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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


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!


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”


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).


© 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


THANK YOUPlease visit us at

www.wellesleyinstitute.com


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

Technology

health ofthe

numberof health social

health equity

systemic health inequities

wellesley urban health

social exclusion inadequate

social safety nets45282013

system dynamics review