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 ID: 1040583
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1. Translational science fostering integration The predictive validity of the AEDI: Predicting later cognitive and behavioural outcomes.Assoc Prof Sally BrinkmanACER Conference August 2014
2. Presentation StructureBackground.Predictive validity – 2 studies.Inequality in child development and predictive strength.Conclusions.International interlude (if time).
3. What is the AEDI?Teacher checklist,5 DomainsTriennial Census
4. Sensitive Periods in Early Brain DevelopmentVision01237654HighLowYearsHabitual ways of respondingEmotional controlSymbolPeer social skillsNumbersHearingSensitivityGraph developed by Council for Early Child Development (ref: Nash, 1997; Early Years Study, 1999; Shonkoff, 2000.) Pre-school yearsSchool yearsLanguageEDI
5. Past reliability and validity studiesTeacher to parent inter rater reliabilityTeacher to teacher inter rater reliabilityRepeat testing intra rater reliabilityConstruct and concurrent validityRasch psychometric property analysesIndigenous and minority culture validation studiesSchools and the AEDI studyPublications downloadable from: www.aedc.gov.au, www.offordcentre.com/readiness
6. How does the AEDI predict outcomes through school?
7. Predictive ValiditySTUDY 1: LSAC – 2004 Wave 1 4year old cohortSTUDY 2: NMHS – 2003 EDI cohort
8. Predictive Validity – Study 1Longitudinal Study of Australian Childrennationally representative sample two cohorts of Australian children: 5,104 infants and 4,976 four year olds first wave of the LSAC commenced in May 2004face-to-face interviews with parents, parent self-completed questionnaires, interviewer observation, direct child assessment, and teacher completed questionnaires
9. 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 QLD717 children with complete data in Wave 1523 children with complete teacher and parent data in Wave 3 (72.6%). Predictive Validity – Study 1
10. Predictive Validity – Study 1Even 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 1Instruments collected at ~4 years during Wave 1AEDISDQPPVTWAIPEDSPedsQLGlobal Health
12. Predictive Validity – Study 1Teacher completed instruments collected at ~8 years during Wave 3SDQ Academic Rating Scale (literacy)
13. 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. Sensitivity & Specificity (looking backwards)
14. Best predictors:AEDI (all domains)WAIWorst predictors:PEDSSDQPredictive Validity: Outcome is Literacy
15. Best predictors:WAIAEDI (all domains)Worst predictors:PEDSSDQPredictive Validity: Outcome is Maths
16. Best predictors:WAIAEDI (all domains)Worst predictors:PEDSSEIFAPredictive Validity: Outcome is Behaviour
17. Outcome Measures at ~ 8 years of ageAEDI MeasureSDQARSLanguage and LiteracyARSMathematicsVulnerable on one or more of the AEDI Domains. (Australian National Progress Measure)Spec = 0.86Sens = 0.34NPV = 0.94PPV = 0.20Spec = 0.88Sens = 0.65NPV = 0.94PPV = 0.48Spec = 0.88Sens = 0.65NPV = 0.94PPV = 0.45Predictive Validity – Study 1
18. Predictive Validity – Study 2North Metro Health ServicePopulation widePre-primary (avg 5.6 years)2003Original EDIIndividually data linked to education records (DET WA)Govt schools onlyBiased (transience)WALNA yr3, NAPLAN yr5 and NAPLAN yr7
19. Source: Brinkman et. al. in Child Indicators (2013)Predictive Validity – Study 2
20. NAPLANYear 3NAPLANYear 5NAPLANYear 7 EDI DomainsNumeracyReadingNumeracyReadingNumeracyReading Physical well-being.23**.22**.25**.22**.24**.24**Social competence.24**.24**.22**.24**.24**.27**Emotional maturity.17**.16**.12**.16**.15**.19**Language and cognitive development.42**.40**.37**.40**.39**.40**Communication skills and general knowledge.36**.34**.30**.34**.28**.39**Total Score.36**.35**.32**.35**.32**.38**Predictive Validity – Study 2
21. How do perinatal factors predict the AEDI?
22. Predictive Validity – Perinatal onto the AEDISA Linked Data set at the individual level2003 to 2004 birth populationDevelopmentally vulnerable on the 2009 AEDIStrongest predictors at birth:Childs genderGestational ageMothers occupational status (ASCO)Fathers occupational status (ASCO)Mothers smoking statusAUC=0.72
23. Predictive Validity – Perinatal onto the AEDISensitivity: % 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 Australia2009Targeted Programsby high social disadvantageProportionate Universal Programsthat increasingly addresses barriers across the social gradientTargeted Programsby high developmental vulnerabilityUniversal ProgramsBarriers to uptakeSocial DisadvantageDevelopmental vulnerabilityHighLowHighLow
26. Social DisadvantageDevelopmental vulnerabilityHighLowHighLow Changes in South Australian Community (LGA) AEDI results Vulnerable on 1 or more domain from 2009 - 2012
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 CohortSource: Brinkman, Sincovich, Gregory 2013
30. Reflections
31. The pertinent questions to askWhat 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 outcomesTake away message – improve school readiness for all with a progressive universalist approach from birth to school age.
33. International interlude
34. International InterludeLicensingCostsProtection’s around programingVsGreater good / Public ownershipImproving local systemsLocal capacity buildingInternational comparable and locally relevant
35. Tonga – locally mapped TeHCI