- Australian Council for Educational Research

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. Inequality in child development and predictive strength. Conclusions. International interlude (if time).

What is the AEDI? • • •

Teacher checklist, 5 Domains Triennial Census

Sensitive Periods in Early Brain Development Pre-school years

High

School years

Numbers Peer social skills Language

Symbol

Habitual ways of responding Emotional control Vision Hearing

Low

0

1

2

3

4

Years

5

6

EDI

Graph developed by Council for Early Child Development (ref: Nash, 1997; Early Years Study, 1999; Shonkoff, 2000.)

7

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 Rasch psychometric property analyses Indigenous and minority culture validation studies Schools and the AEDI study

•

Publications downloadable from: www.aedc.gov.au, www.offordcentre.com/readiness

How does the AEDI predict outcomes through school?

Predictive Validity

STUDY 1: LSAC – 2004 Wave 1 4year old cohort STUDY 2: NMHS – 2003 EDI cohort

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 selfcompleted questionnaires, interviewer observation, direct child assessment, and teacher completed questionnaires

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%).

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).

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

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

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.

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

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

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

Predictive Validity – Study 1 Outcome Measures at ~ 8 years of age AEDI Measure SDQ ARS ARS Language and Mathematics Literacy Vulnerable on one or Spec = 0.86 Spec = 0.88 Spec = 0.88 more of the AEDI Sens = 0.34 Sens = 0.65 Sens = 0.65 Domains. (Australian NPV = 0.94 NPV = 0.94 NPV = 0.94 National Progress PPV = 0.20 PPV = 0.48 PPV = 0.45 Measure)

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

Predictive Validity – Study 2 70

% performing poorly on NAPLAN in yr 7

NAPLAN Reading yr 7 60

NAPLAN Numeracy yr 7

50

40

30

20

10

0 None

One

Two

Three

Four

Number of AEDI domains vulnerable on in first year of school

Five

Source: Brinkman et. al. in Child Indicators (2013)

Predictive Validity – Study 2 NAPLAN Year 3

EDI Domains

NAPLAN Year 5

NAPLAN Year 7

Numeracy Reading Numeracy Reading

Numeracy Reading

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 Communication skills and general knowledge Total Score

.42**

.40**

.37**

.40**

.39**

.40**

.36**

.34**

.30**

.34**

.28**

.39**

.36**

.35**

.32**

.35**

.32**

.38**

How do perinatal factors predict the AEDI?

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 AUC=0.72 – Gestational age – Mothers occupational status (ASCO) – Fathers occupational status (ASCO) – Mothers smoking status

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

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

Social Inequality in Child Health and Development in South Australia 2009

Developmental vulnerability

High

Targeted Programs by high developmental Proportionate Universal Universal Programs Programs vulnerability Barriers to uptake addresses barriers across the social gradient that increasingly

Targeted Programs by high social disadvantage

Low High

Social Disadvantage

Low

Changes in South Australian Community (LGA) AEDI results Vulnerable on 1 or more domain from 2009 - 2012

Developmental vulnerability

High

Low High

Social Disadvantage

Low

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

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)

WHAT DO WE SEE? Feinstein Replication with Australian Data – 2003 Perth AEDI Cohort

Source: Brinkman, Sincovich, Gregory 2013

Reflections

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?

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. Translational science, fostering integration.

International interlude

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

Tonga – locally mapped TeHCI