Enhancing social policy outcomes: How important are structural factors?
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Enhancing social policy outcomes: How important are
structural factors?
Sociology Seminar Series21 May 2014
Roy Lay-Yee & TeamCOMPASS Research CentreUniversity of Auckland, New Zealandwww.compass.auckland.ac.nz
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Enhancing social policy outcomes
Social policy goal to improve outcomes for disadvantaged
Need to know most influential factors, i.e. best policy levers
An influential perspective is social determinants framework (WHO Commission 2008, Marmot Review 2010) - applied to health – argues primacy of structural factors - sociological
How do we show if structural factors are most important?
Further, how do we show if efforts to tackle disparities will make a difference to the most disadvantaged?
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What we are offering
Empirical model of what social factors influence outcomes
Captures social complexity, heterogeneity, change
Mechanisms contextualised in social system (theory)
Pathways may be amenable to intervention (policy)
Use simulation to test scenarios - ask ‘what if’ questions, i.e. impact of changing social determinants on outcomes
Model developed in health but applicable to other domains
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Outline
RationaleSocial determinants of healthData sourceOur conceptual modelResearch questions
MethodMicrosimulation
Policy applicationScenario testing – answering ‘what if’ questions
Conclusions4
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Social determinants (SDs) of health
Health disparities are rooted in SDs that confer differential vulnerability to poor health or exposure to conditions that produce poor health
Structural factors comprise SDs of health disparities (that are also SDs of health) while intermediary factors comprise other SDs of health (only)
Debate as to relative importance, as effective policy levers, of structural or intermediary factors
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Social determinants of health framework Solar & Irwin, 2010 (WHO)
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Data source
Christchurch Health & Development Study
Longitudinal study of birth cohort born in 1977
(on-going)
Used for our model - 1017 children from birth
to 10-years-old
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Our model of social determinants of child well-being
Structural factors (fixed)
Child• Gender• EthnicityParental• Age (at birth of child)• Ethnicity• EducationFamilial• Socio-economic position
(at birth of child)
Proxy indicators (modifiable)
Family composition• Single- or two-parent• Number of childrenIncome source• Parent employment• Welfare dependence
Intermediary factors (modifiable)
• Owned/ rented home• Overcrowding• Accommodation type• Change of parent• Change of residence• Parental smoking
Family doctor visits
Reading ability
Conduct problems
Outcomes
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Research questions
What is the effect of improving various factors (potential determinants) on child outcomes?
Q1. Are structural or intermediary factors more influential? Q2. Is there greater impact on socially disadvantaged groups?Q3. Do the same mechanisms operate for outcomes in a range of domains: GP visits, reading ability or conduct problems?
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What is microsimulation?
Begin with a starting sample of children Based on Christchurch Health & Development Study (CHDS)
Derive statistical equations from CHDS
Apply equations to starting sample to reproduce original CHDS patterns
A sample of children with typical synthetic biographies
We have created a virtual world
Predict what might happen if conditions were to changeChange the original settings to pose ‘what if’ scenarios
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Virtual versus real cohort: family doctor visits, reading ability, and conduct problems, by year of age
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Year Real cohort (CHDS)n=1017
Virtual cohort (simulated) n=1017
Absolute error Absolute error / CHDS mean
Family doctor visits (mean (95% CI)) 1 5.82 5.82 - -
2 5.34 5.28 0.06 -3 3.31 3.18 0.13 -4 3.13 3.15 0.02 -5 3.22 3.12 0.10 -6 3.35 3.32 0.03 -7 2.43 2.41 0.02 -8 2.14 2.15 0.01 -9 1.96 1.90 0.06 -10 1.65 1.68 0.03 -
All years 3.24 3.20 (3.15-3.25) 0.04 1.2% Reading ability: BURT score (mean (95% CI)) 8 45.3 45.3 - -9 54.4 54.7 0.3 -10 64.1 63.7 0.4 -11 72.8 71.9 0.9 -12 79.5 78.9 0.6 -13 85.2 84.6 0.6 -
All years 66.9 66.5 (65.7-67.4) 0.4 0.6% Conduct problems (mean (95% CI)) 6 10.6 10.6 - -7 24.6 24.8 0.2 -8 24.4 25.0 0.6 -9 24.7 25.3 0.6 -10 24.9 25.6 0.7 -
All years 21.8 22.3 (22.1-22.4) 0.5 2.3%
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What is microsimulation?
Begin with a starting sample of children Based on Christchurch Health & Development Study (CHDS)
Derive statistical equations from CHDS
Apply equations to starting sample to reproduce original CHDS patterns
A sample of children with typical synthetic biographies
We have created a virtual world
Predict what might happen if conditions were to changeChange the original settings to pose ‘what if’ scenarios
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Scenario testing
What if there was a policy intervention that changed social determinants? What would be its impact on outcome?
Base simulation (no change) vs. ‘improvement’ simulation (modifying factors in a direction expected to advantage people, e.g. father employed, family not welfare dependent)
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Scenario testing procedure
1. We ‘improved’ single factors and assessed the degree of impact on outcome (little effect)
2. We ‘improved’ multiple factors simultaneously (bigger effect)
3. We compared the relative effects of ‘improving’ structural and intermediary factors
4. We posed ‘best case scenarios’ by ‘improving’ structural and intermediary factors simultaneously
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Outcome: GP visits
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Increasing the number of visits per year – i.e. increasing access to GP care – is interpreted as an improvement in outcome
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Base-line shows social gradient
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Structural factors have greater effect than intermediary
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All groups benefit but more so for disadvantaged
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Gradient flattens – closing gap – structural factors important
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Outcome: Reading ability
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Increasing the reading score is interpreted as an improvement in outcome
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Gradient flattens – closing gap – structural factors important
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Outcome: Conduct problems
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Reducing the number of conduct problems per year is interpreted as an improvement in outcome
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Gradient flattens – closing gap – structural factors important
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Summary of results
Q1: Effect of modifiable structural factors is greater than of intermediary factors
Q2: Clear social gradient of impact with the benefits of intervention flowing disproportionately to the most disadvantaged
Q3: Similar findings for range of outcomes in different domains
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Conclusions
Our simulation model can be used to test scenarios
What if there was a policy intervention that changed social determinants? What would be impact on outcome?
Policy implications
Important to tackle (multiple) structural & intermediary determinants esp. structural
– argues for inter-sectoral policy? fundamental change?Social gradients of impact, more disadvantaged groups gain more benefit
– argues for progressive universalism?
Social policy can potentially make a difference to the most disadvantaged
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