Barry Milne, Peter Davis, Roy Lay-Yee, Jessica Thomas, Janet Pearson, Oliver Mannion

1. 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 Mannion COMPASS Research Centre www.compass.auckland.ac.nz

2. Outline • What is MEL-C? • Goals • Microsimulation • End Users • Demonstration • Next steps 2

3. What 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-making 2. Rationale … why are we doing it? • To improve policymakers’ ability to respond to issues concerning children and young people 3. 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 3

4. Creating a ‘virtual cohort’ – using micro simulation • We start with a sample of people • Real or synthetic • A 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 13 • A sample of children with typical biographies over the life-course • With 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 4

5. Child characteristics • (age) • gender • ethnicity • Parental characteristics • age at birth of child • ethnicity • education level • Socio-economic position • SES at birth of child • (single-parent status at birth) Health service use e.g. GP visits, hospital admissions, hospital outpatient attendances Education e.g. reading ability Social/Justice e.g. Conduct disorder Employment e.g. parental employment, welfare dependence Psychosocial factors e.g. family functioning: change of parents, change of residence Conceptual framework Structural level Intermediate level Outcome Family/household characteristics e.g. single-parent status, number of children, household size Material circumstances e.g. housing: accommodation type, owned-rented, bedrooms number Behavioural factors e.g. parental smoking Other factors e.g. perinatal factors 5

6. Scenario testing • Test “what if” scenarios • Projection into the future; alternative settings • Simulate impact of policy change • Important role of end users • Engage key people from government agencies • Adopt a partnership approach • Use their expertise to get better model & policy-relevant scenarios 6

7. End Users Group • End Users Group: Ministry of Social Development (MSD) Ministry of Health (MOH) Ministry of Education (MinEdu) Ministry of Justice (MOJ)

8. End User Meetings • May 2011 – MSD - Important scenarios to be tested • July 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 distribution • Feb 2012 – MSD - Validation of tool • April 2012 – MOH - Discussion of how tool might be used; by whom • June 2012 – MinEdu - Report on presentations of MEL-C to other interested parties • Aug 2012 – Statistics New Zealand - Discussion of achievements to date and what is left to do

9. Simulation tool - Demonstration • Demonstrate modelling the effect of various inputs on an education outcomes (reading) for the child across ages 8-13 • Interrogate system to check base rates of various inputs and outputs • Show how inputs can be flexibly changed • Show the effect of changing inputs on outputs. 9

10. Simulation tool - Demonstration

11. Simulation tool - Demonstration

12. Simulation tool - Demonstration

13. Simulation tool - Demonstration

14. Simulation tool - Demonstration

15. Simulation tool - Demonstration

16. Simulation tool - Demonstration

17. Simulation tool - Demonstration

18. Simulation tool - Demonstration

21. Next steps • Analyse additional data • Combine together: • Christchurch Health and Development Study • Dunedin Multidisciplinary Health and Development Study • Pacific Islands Families Study • Te Hoe Nuku Roa Study • Other data sources as available • Analyse as integrated dataset where possible; combine estimates where not • Possibility of using estimates from published studies • Extending range of outcomes and period of life-course covered

22. Next steps • Synthetic representative base file • Using 2006 Census data to create a representative synthetic unit record file • Tool Development • Subgroup scenarios • Ability to compare unlimited number of scenarios • Macro to run a range of scenarios (i.e., programmable, not just point and click) • More (and better) graphical representations of base-scenario differences

23. Next steps • Validation • Compare results against other datasets/national rates • Compare scenarios against intervention results • Deployment • Available to users in policy making role • Registration process with training mandatory • Caveats and pitfalls made explicit • User-support available • Remote desktop access • Less technical issues than web-based application • Users group might help future tool development • Funding would be needed