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Translational science fostering integration

Translational science fostering integration. The predictive validity of the AEDI: Predicting later cognitive and behavioural outcomes. Assoc Prof Sally Brinkman ACER Conference August 2014. Presentation Structure Background. Predictive validity – 2 studies.

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Translational science fostering integration

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  1. Translational science fostering integration The predictive validity of the AEDI: Predicting later cognitive and behavioural outcomes. Assoc Prof Sally Brinkman ACER Conference August 2014

  2. Presentation Structure • Background. • Predictive validity – 2 studies. • Inequality in child development and predictive strength. • Conclusions. • International interlude (if time).

  3. What is the AEDI? • Teacher checklist, • 5 Domains • Triennial Census

  4. Sensitive Periods in Early Brain Development Pre-school years School years High Numbers Peer social skills Language Symbol Sensitivity Habitual ways of responding Emotional control Vision Hearing Low 1 2 3 4 5 6 7 0 Years EDI Graph developed by Council for Early Child Development (ref: Nash, 1997; Early Years Study, 1999; Shonkoff, 2000.)

  5. Past reliability and validity studies • Teacher to parent inter rater reliability • Teacher to teacher inter rater reliability • Repeat testing intra rater reliability • Construct and concurrent validity • Raschpsychometric property analyses • Indigenous and minority culture validation studies • Schools and the AEDI study • Publications downloadable from: www.aedc.gov.au, www.offordcentre.com/readiness

  6. How does the AEDI predict outcomes through school?

  7. Predictive Validity STUDY 1: LSAC – 2004 Wave 1 4year old cohort STUDY 2: NMHS – 2003 EDI cohort

  8. Predictive Validity – Study 1 • Longitudinal Study of Australian Children • nationally representative sample • two cohorts of Australian children: 5,104 infants and 4,976 four year olds • first wave of the LSAC commenced in May 2004 • face-to-face interviews with parents, parent self-completed questionnaires, interviewer observation, direct child assessment, and teacher completed questionnaires

  9. Predictive Validity – Study 1 • Of the original 4948 children participating in the 2004 Wave 1 (4 year old cohort), information was obtained for 89.7% (n=4332) in the Wave 3 2008 data collection. • AEDI – Nested Sample, children from WA, Vic and QLD • 717 children with complete data in Wave 1 • 523 children with complete teacher and parent data in Wave 3 (72.6%).

  10. Predictive Validity – Study 1 • Even gender divide, • 5.7% of children had ESL, • 1.1% of children were of Aboriginal descent, • 4% with medically diagnosed SN status, • Age gap between Wave 1 and Wave 3 ranges from 3yr 4mths through to 4yr 5mths (avg gap 3yr 8 mths).

  11. Predictive Validity – Study 1 • Instruments collected at ~4 years during Wave 1 • AEDI • SDQ • PPVT • WAI • PEDS • PedsQL • Global Health

  12. Predictive Validity – Study 1 • Teacher completed instruments collected at ~8 years during Wave 3 • SDQ • Academic Rating Scale (literacy)

  13. Sensitivity & Specificity (looking backwards) • Sensitivity the percentage of sick people who were correctly identified as having the condition. • Specificity the percentage of healthy people who were correctly identified as not having the condition.

  14. Predictive Validity: Outcome is Literacy • Best predictors: • AEDI (all domains) • WAI • Worst predictors: • PEDS • SDQ

  15. Predictive Validity: Outcome is Maths • Best predictors: • WAI • AEDI (all domains) • Worst predictors: • PEDS • SDQ

  16. Predictive Validity: Outcome is Behaviour • Best predictors: • WAI • AEDI (all domains) • Worst predictors: • PEDS • SEIFA

  17. Predictive Validity – Study 1

  18. Predictive Validity – Study 2 • North Metro Health Service • Population wide • Pre-primary (avg 5.6 years) • 2003 • Original EDI • Individually data linked to education records (DET WA) • Govt schools only • Biased (transience) • WALNA yr3, NAPLAN yr5 and NAPLAN yr7

  19. Predictive Validity – Study 2 Source: Brinkman et. al. in Child Indicators (2013)

  20. Predictive Validity – Study 2

  21. How do perinatal factors predict the AEDI?

  22. Predictive Validity – Perinatal onto the AEDI • SA Linked Data set at the individual level • 2003 to 2004 birth population • Developmentally vulnerable on the 2009 AEDI • Strongest predictors at birth: • Childs gender • Gestational age • Mothers occupational status (ASCO) • Fathers occupational status (ASCO) • Mothers smoking status AUC=0.72

  23. Predictive Validity – Perinatal onto the AEDI Sensitivity: % of cases of poor development identified according to the number of risk factors present perinatally % of total population of children according to the number of risk factors they have

  24. Relationship between the AEDI and Socio-Economic Position (SEP)

  25. Social Inequality in Child Health and Development in South Australia 2009 High Targeted Programs by high social disadvantage Proportionate Universal Programs that increasingly addresses barriers across the social gradient Universal Programs Barriers to uptake Targeted Programs by high developmental vulnerability Developmental vulnerability Low Social Disadvantage High Low

  26. Changes in South Australian Community (LGA) AEDI results Vulnerable on 1 or more domain from 2009 - 2012 High Developmental vulnerability Low Social Disadvantage High Low

  27. So - How does the AEDI predict school outcomes considering SEP?

  28. WHAT WE PREDICTED TO SEE. The famous Feinstein graph – 1970 British Birth Cohort Feinstein, L. (2003). Inequality in the Early Cognitive Development of British Children in the 1970 Cohort. Economica, 70 (73–97)

  29. WHAT DO WE SEE? Feinstein Replication with Australian Data – 2003 Perth AEDI Cohort Source: Brinkman, Sincovich, Gregory 2013

  30. Reflections

  31. The pertinent questions to ask • What has happened differently to the cohort born in 2003/2004 to the cohort born in 2006/2007 to the cohort born in 2009/2010? • How do we reduce inequality in child development?

  32. Translational science, fostering integration. Conclusions: The AEDI has shown to be a moderate to strong predictor of school based outcomes Take away message – improve school readiness for all with a progressive universalist approach from birth to school age.

  33. International interlude

  34. International Interlude • Licensing • Costs • Protection’s around programing • Vs • Greater good / Public ownership • Improving local systems • Local capacity building • International comparable and locally relevant

  35. Tonga – locally mapped TeHCI

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