Translational science fostering integration

The predictive validity of the AEDI: Predicting later cognitive

and behavioural outcomes.

Assoc Prof Sally BrinkmanACER 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

Vision

0 1 2 3 7654

High

Low

Years

Habitual ways of respondingEmotional

control

Symbol

Peer social skillsNumbers

Hearing

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

Pre-school years School years

Language

EDI

 

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 self-

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

 

• 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

 

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 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)

 

Best predictors:• AEDI (all domains)• WAI

Worst predictors:• PEDS• SDQ

Predictive Validity: Outcome is Literacy

 

Best predictors:• WAI• AEDI (all domains)

Worst predictors:• PEDS• SDQ

Predictive Validity: Outcome is Maths

 

Best predictors:• WAI• AEDI (all domains)

Worst predictors:• PEDS• SEIFA

Predictive Validity: Outcome is Behaviour

 

  Outcome Measures at ~ 8 years of ageAEDI Measure SDQ ARS

Language and Literacy

ARSMathematics

Vulnerable on one or more of the AEDI Domains. (Australian National Progress Measure)

Spec = 0.86Sens = 0.34NPV = 0.94PPV = 0.20

Spec = 0.88Sens = 0.65NPV = 0.94PPV = 0.48

Spec = 0.88Sens = 0.65NPV = 0.94PPV = 0.45

Predictive Validity – Study 1

 

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

 

0

10

20

30

40

50

60

70

None One Two Three Four Five

% p

erfo

rmin

g po

orly

on

NA

PLA

N i

n yr

7

Number of AEDI domains vulnerable on in first year of school

NAPLAN Reading yr 7

NAPLAN Numeracy yr 7

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

Predictive Validity – Study 2

 

  NAPLANYear 3

NAPLANYear 5

NAPLANYear 7 

EDI Domains 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

.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

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– Gestational age– Mothers occupational status (ASCO)– Fathers occupational status (ASCO)– Mothers smoking status

AUC=0.72

 

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

Relationship between the AEDI

and Socio-Economic Position (SEP)

Social Inequality in Child Health and Development in South Australia2009

Targeted Programsby high social disadvantage

Proportionate Universal Programsthat increasingly addresses barriers across the social gradient

Targeted Programsby high developmental

vulnerabilityUniversal Programs

Barriers to uptake

Social Disadvantage

Dev

elop

men

tal v

ulne

rabi

lity

High Low

High

Low

 

Social Disadvantage

Dev

elop

men

tal v

ulne

rabi

lity

High Low

High

Low

 

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

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?

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.

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