Modelling Primary Care -

Modelling the Early Life Course (MEL-C). A simulation tool for policy makers.

eGovPoliNetSeptember, 2012

Barry Milne, Peter Davis, Roy Lay-Yee, Jessica Thomas, Janet Pearson, Oliver MannionCOMPASS Research Centrewww.compass.auckland.ac.nz

The University of AucklandNew ZealandThe University of AucklandNew Zealand11OutlineWhat is MEL-C?GoalsMicrosimulationEnd Users

Demonstration

Next steps

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The University of AucklandNew Zealand22What is MEL-C?MEL-C is a 5-year MSI-funded research project (to be completed in 2013)1. Goals what are we trying to do?Develop a software application as a decision-support tool for policy-making2. Rationale why are we doing it?To improve policymakers ability to respond to issues concerning children and young people3. Means how are we doing it?By building a computer simulation model with data from existing longitudinal studies to quantify the underlying determinants of progress in the early life course

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The University of AucklandNew Zealand33Creating a virtual cohort using micro simulationWe start with a sample of peopleReal or syntheticA birth cohort of children (Christchurch Health & Development Study, CHDS) with individual attributes at the start (n=1265, born 1977)We then apply statistically-derived rules that allow us to create a virtual cohort (synthetic data) to age 13A sample of children with typical biographies over the life-courseWith allowance for variation around the average (via random allocation)

We then can simulate what might happen if policy were to change Impact on outcomes when we alter features in our synthetic data set

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The University of AucklandNew Zealand5Child characteristics(age)genderethnicity

Parental characteristicsage at birth of childethnicityeducation level

Socio-economic positionSES at birth of child(single-parent status at birth)

Employment e.g. parental employment, welfare dependencePsychosocial factorse.g. family functioning: change of parents, change of residenceHealth service usee.g. GP visits, hospital admissions,hospital outpatient attendances

Educatione.g. reading ability

Social/Justicee.g. Conduct disorder

Structural levelIntermediate levelOutcomeOther factorse.g. perinatal factorsBehavioural factorse.g. parental smokingFamily/household characteristicse.g. single-parent status, number of children, household sizeMaterial circumstancese.g. housing: accommodation type, owned-rented, bedrooms numberConceptual framework

The University of AucklandNew Zealand5Scenario testingTest what if scenariosProjection into the future; alternative settingsSimulate impact of policy change

Important role of end usersEngage key people from government agenciesAdopt a partnership approachUse their expertise to get better model & policy-relevant scenarios6

The University of AucklandNew Zealand6End Users Group7End Users Group:

Ministry of Social Development (MSD)

Ministry of Health (MOH)

Ministry of Education (MinEdu)

Ministry of Justice (MOJ)

The University of AucklandNew ZealandEnd User Meetings

May 2011 MSD - Important scenarios to be testedJuly 2011 MOH - Simulation of use of health services (0-5)Sept 2011 MinEdu - Simulation of conduct and education outcomes (5-13)Nov 2011 MOJ - Effect of simulation on whole distributionFeb 2012 MSD - Validation of toolApril 2012 MOH - Discussion of how tool might be used; by whomJune 2012 MinEdu - Report on presentations of MEL-C to other interested partiesAug 2012 Statistics New Zealand - Discussion of achievements to date and what is left to do

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The University of AucklandNew Zealand9Simulation tool - DemonstrationDemonstrate modelling the effect of various inputs on an education outcomes (reading) for the child across ages 8-13Interrogate system to check base rates of various inputs and outputsShow how inputs can be flexibly changedShow the effect of changing inputs on outputs.

The University of AucklandNew Zealand10

Simulation tool - Demonstration

The University of AucklandNew Zealand11Simulation tool - Demonstration

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The University of AucklandNew ZealandSimulation tool - Demonstration13

The University of AucklandNew ZealandSimulation tool - Demonstration14

The University of AucklandNew ZealandSimulation tool - Demonstration15

The University of AucklandNew ZealandSimulation tool - Demonstration16

The University of AucklandNew ZealandSimulation tool - Demonstration17

The University of AucklandNew ZealandSimulation tool - Demonstration18

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The University of AucklandNew ZealandNext stepsAnalyse additional dataCombine together:Christchurch Health and Development StudyDunedin Multidisciplinary Health and Development StudyPacific Islands Families StudyTe Hoe Nuku Roa StudyOther data sources as availableAnalyse as integrated dataset where possible; combine estimates where notPossibility of using estimates from published studiesExtending range of outcomes and period of life-course covered

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The University of AucklandNew ZealandNext stepsSynthetic representative base fileUsing 2006 Census data to create a representative synthetic unit record fileTool DevelopmentSubgroup scenariosAbility to compare unlimited number of scenariosMacro to run a range of scenarios (i.e., programmable, not just point and click)More (and better) graphical representations of base-scenario differences

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The University of AucklandNew ZealandNext stepsValidationCompare results against other datasets/national ratesCompare scenarios against intervention resultsDeploymentAvailable to users in policy making roleRegistration process with training mandatoryCaveats and pitfalls made explicitUser-support availableRemote desktop accessLess technical issues than web-based applicationUsers group might help future tool developmentFunding would be needed

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The University of AucklandNew Zealand