association of sociodemographic characteristics of children with intellectual disability in western...

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Social Science & Medicine 60 (2005) 1499–1513

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Association of sociodemographic characteristics of childrenwith intellectual disability in Western Australia

Helen Leonarda,b,�, Beverly Pettersona,b, Nicholas De Klerka, StephenR. Zubrickc, Emma Glassond, Richard Sanderse, Carol Bowera

aCentre for Child Health Research, Telethon Institute for Child Health Research, The University of Western Australia,

P.O. Box 855, West Perth, WA 6872, AustraliabDisability Services Commission, Western Australia, Australia

cCurtin Centre for Developmental Health, Institute for Child Health Research, AustraliadSchool of Population Health, The University of Western Australia, Australia

eDepartment of Education and Training, Western Australia, Australia

Abstract

The social determinants of intellectual disability (ID) are poorly understood, particularly in Australia. This study has

investigated sociodemographic correlates of ID of unknown cause in Western Australian born children. Using record

linkage to the Western Australian Maternal & Child Health Research Database, maternal sociodemographic

characteristics of children with ID (of unknown cause) born between 1983 and 1992 (n ¼ 2871) were compared with

those of children without ID (n ¼ 236; 964). Socioeconomic indices for areas based on the census district of mother’sresidence were also included in the analysis. Aboriginal mothers (OR=2.83 [CI: 2.52, 3.18]), teenagers (OR=2.09 [CI:

1.82, 2.40]) and single mothers (OR=2.18 [CI: 1.97, 2.42]) were all at increased risk of having a child with mild or

moderate ID. Children of mothers in the most socioeconomically disadvantaged 10% had more than five times the risk

of mild and moderate ID compared with those in the least disadvantaged 10% (OR=5.61 [CI: 4.42, 7.12]). Fourth or

later born children were also at increased risk (OR=1.82 [CI: 1.63, 2.02]). The results of the study have implications

both for further aetiological investigation as well as service provision for children with ID. Furthermore, many of the

sociodemographic correlates identified in this study, particularly in the mild/moderate category of ID, are potentially

modifiable, opening up opportunities for primary prevention.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Intellectual disability; Sociodemographic determinants; Epidemiology; Mental retardation; Australia

e front matter r 2004 Elsevier Ltd. All rights reserve

cscimed.2004.08.014

ing author. Centre for Child Health Research,

of Western Australia, Telethon Institute for

esearch, P.O. Box 855, West Perth, WA 6872,

+61-8-9489-7789; fax: +61-8-9489-7700.

ess: [email protected] (H. Leonard).

Introduction

Intellectual disability (ID) or learning disability, as it

is known in the UK, involves significant cognitive

impairment and deficits in adaptive behaviour that are

manifested during childhood. The terminology used

varies internationally (Haveman, 1996) with learning

disability and mental retardation being those currently

favoured in the UK and US, respectively. Other

d.

www.elsevier.com/locate/socscimed

ARTICLE IN PRESSH. Leonard et al. / Social Science & Medicine 60 (2005) 1499–15131500

synonyms include general learning disorder, mental

handicap and intellectual handicap. Because develop-

ment of the complex systems which contribute to

cognitive function occurs pre- and peri-conceptionally,

antenatally, through infancy and into childhood, the

factors which can contribute to a deficit in intellectual

ability are varied in their nature and timing. ID itself is a

binary outcome but is often represented as an ordinal

variable according to level of severity. Although defining

a cut-off point at which we determine whether or not an

individual has an ID could be considered rather a crude

way of measuring cognitive ability, it does have some

advantages. Despite the fact that definitions for ID

change across time and place (Leonard & Wen, 2002), it

is still easier to separate out children who do or do not

meet these definitions than it is to identify the much

larger group of children with milder and more subtle

cognitive deficits. This latter group, at least in the US

(Blair & Scott, 2002), appears to be increasing con-

siderably in numbers.

ID comprises a heterogeneous group of disorders,

some of which have distinct biomedical causes, but for a

substantial proportion (Leonard & Wen, 2002) there is

no defined cause, and this group has been classified in a

variety of ways such as ‘‘isolated’’ (Decoufle & Boyle,

1995) or ‘‘unspecified’’ (Stromme & Magnus, 2000) ID.

It has also been hypothesised that there are two

distribution curves for IQ, one following a Gaussian

distribution with a mean of 100 and a second represent-

ing ‘‘organic’’ damage with a much lower IQ distribu-

tion (Zigler, Balla, & Hodapp, 1984). With increasing

awareness, particularly over the last decade, of the role

of social determinants in population health (Kawachi &

Kennedy, 1997) we suspect that the pathways to ID are

likely to be much more complex than originally

supposed. A better understanding of the sociodemo-

graphic and biological determinants is needed in order

to identify and implement both preventive and manage-

ment strategies especially for the group of children with

currently unexplained ID. We believe that such in-

formation may also have relevance to understanding the

determinants of the neurodevelopmental problems being

experienced by the much larger group of children with

learning difficulties who are academically challenged.

Using the Western Australian Maternal Child

Health Research Database (MCHRDB) (Stanley, Read,

Kurinczuk, Croft, & Bower, 1997) this article aims to

investigate the sociodemographic factors associated with

ID of unknown cause in children born in Western

Australia (WA).

Methods

The 3400 children with ID born in WA between 1983

and 1992 and alive in 2000 and whose births could be

linked to the MCHRDB form the case group for this

study. Ascertainment from multiple sources including

agencies providing general, medical and educational

services for this group of children has been previously

described in a study which found an ID prevalence of

14.3 per 1000 (Leonard, Petterson, Bower, & Sanders,

2003). Cases identified from Disability Services Com-

mission (DSC), the main government agency providing

services for people with ID in WA, were considered to

have ID if: (i) their IQ waso70 on formal testing; or (ii)they had a condition known to be associated with ID

(e.g. Down syndrome); or (iii) they were clearly

documented as having ID in their DSC records. Cases

ascertained only from educational sources were defined

as such ‘‘if they demonstrated significant deficits in

adaptive behaviour and academic achievement and

demonstrated intellectual functioning two or more SD

below the mean on an approved measure of cognitive

functioning.’’ For DSC cases, ID severity was obtained

from IQ testing where available, and otherwise was

based on the level assigned by the medical officer. Cases

were classified in keeping with DSM IV recommenda-

tions (American Psychiatric Association, 1994) and thus,

according to which psychological test was used, as

‘‘Mild’’ (IQ 50–55 to 69); ‘‘Moderate’’ (IQ 35–40 to

40–54); and ‘‘Severe (including profound)’’ (IQ o35 or40). Similar severity levels were used by the educational

sources but in most data provided, mild and moderate

were collapsed into one category. Hence, for ease of

comparison of data from the different sources in this

study, ID severity was categorised as mild/moderate,

severe, and unspecified (for those children for whom no

severity level was assigned).

Children registered with DSC are assigned a medical

diagnosis using the Heber version of the American

Association on Mental Retardation (AAMR) classifica-

tion system (Heber, 1961). Additional diagnostic in-

formation on children who were only ascertained from

educational sources was limited to cases with ID

associated with autism. For this project diagnostic codes

assigned to study subjects were categorised as biomedi-

cal or otherwise based loosely on the terminology used

by Yeargin-Allsopp, Murphy, Cordero, Decoufle, and

Hollowell (1997). Biomedical diagnoses include genetic

conditions (chromosomal and Mendelian), recognised

teratogenic effects such as congenital infections and

birth defects, neonatal and postneonatal infections,

trauma and other events (e.g. neoplasm). Like Year-

gin-Allsopp et al. (1997) we excluded diagnostic

categories (e.g. preterm birth) that are associated with

but are not necessarily a sufficient cause of ID.

However, we also excluded their category 2a (intrauter-

ine/intrapartum) as we did not feel this group should be

considered a true biomedical cause. A biomedical cause

was assigned for 15.6% (529 cases), of which Down

syndrome, accounting for 39.4%, was the largest single

ARTICLE IN PRESSH. Leonard et al. / Social Science & Medicine 60 (2005) 1499–1513 1501

diagnostic entity. Post-natal injury (18 cases) and

primary microcephaly (11 cases) were the only other

entities individually responsible for more than 2% of the

cases. Of the remaining 2871 cases, 213 (7.4%) were

categorised as severe, 2106 (73.4%) as mild-moderate,

and for 361(12.6%) the severity of the ID was

unspecified. A further 191 (6.7%) had been given an

autistic spectrum disorder (ASD) diagnosis in addition

to their ID by one or more of the data providers. We

report here on the sociodemographic characteristics of

these 2871 cases compared with the remainder of the

children born in WA between 1983 and 1992 and alive in

2000 and not identified as having an ID (n ¼ 236; 964).From our clinical experience we felt that it was

justifiable to classify the 361 cases with an unspecified

level of severity in the mild-moderate category.

Infant characteristics examined were sex and birth

order and maternal characteristics ethnicity, age group,

marital status, height, country of birth, and health

insurance status. The Accessibility/Remoteness Index of

Australia (ARIA), based on the mother’s postcode of

residence at the time of the infant’s birth, was used as an

indicator of geographical remoteness from major service

centres (Department of Health and Aged Care, 2001).

ARIA measures accessibility to services by calculating

road distances to population centres of varying sizes.

This index has five categories: highly accessible, acces-

sible, moderately accessible, remote and very remote. In

addition to ARIA, three socioeconomic indices for areas

(SEIFA) relating to relative ‘‘socioeconomic disadvan-

tage’’, ‘‘economic resources’’ and ‘‘education and

occupation’’ are available as summary measures for

each census Collection District (CD) from the Austra-

lian Bureau of Statistics 1996 Census of Population and

Housing (Australian Bureau of Statistics, 1998). They

provide an aggregated measure of socioeconomic well-

being which can be used to categorise people living in

the CD. In the MCHRDB these measures are recorded

according to the CD associated with the mother’s

address at the time of the child’s birth. Thus these

indices represent a ‘‘neighbourhood level’’ measure

which can be used as a proxy for an individual’s

measure. The SEIFA values for each of the three indices

were grouped into percentile categories (490%,75–90%, 50–75%, 25–50%, 10–25%, o10%) based onpopulation level data for WA. Other categories were

compared with the most advantaged 10%. Paternal

occupation was the only uniquely paternal variable

used. It was grouped into eight categories according to

the principal Australian Standards for Classification of

Occupation codings (Castles, 1991). Two additional

categories representing those who were unemployed and

those for whom there was no information were also

included.

As might be expected in a data set with 239,835 child

records a proportion of the data was missing. Missing

data ranged from 0.14% (maternal age) to 11.6%

(SEIFA indices 1984–1992). SEIFA indices were not

available for 1983. For paternal occupation we elected

to treat missing information as a separate category,

because we hypothesised as others have (Williams &

Decoufle, 1999), that absence of details about the father

is important information to include in the analysis.

Stata (StataCorp., 1999) was used for statistical

analysis. Proportions by each level of categorical

variables for cases with mild-moderate ID, severe ID

and ID associated with ASD were compared with the

population group. Odds ratios and 95% confidence

intervals were calculated using logistic regression for

each ‘‘case’’ group separately. Agreement between the

SEIFA indices was assessed by Pearson’s correlation

coefficient. The importance of the different sociodemo-

graphic characteristics was examined with stepwise

modelling. Birth year was included for most analyses

because of potential confounding. The final ‘‘best’’

models were re-estimated using multilevel models to

allow for the different levels of variability between

individuals, families and CDs (Rabe-Hesketh, Skrondal,

& Pickles, 2002).

Results

Infant characteristics

As shown in Table 1 children with ID were more

likely to be male with the risk increasing from mild-

moderate (OR=1.55 [CI: 1.43, 1.68]) to severe

(OR=1.83 [CI: 1.38, 2.43]) to ASD (OR=3.97 [CI:

2.78, 5.69]). The risk of mild-moderate ID was increased

for fourth and fifth born (OR=1.68 [CI: 1.47, 1.91]) and

increased further for later born infants (OR=3.13[CI:

2.50, 3.91]). For severe ID the pattern was similar with

an increased risk for fourth and fifth born (OR=1.80

[CI: 1.77, 2.77]) and even higher (OR=3.42 [CI: 1.65,

7.10]) for later born infants. With ASD there was no

increased risk associated with later birth order.

Maternal age, marital status and stature

The distribution of maternal age group, marital status

and stature by degree of ID is shown in Table 2.

Compared with women aged 25–29 years, those under

20 years were significantly more likely to have a child

with a mild-moderate ID (OR=2.09 [CI: 1.82, 2.40]), as

were those aged 20–24 years (OR=1.53 [CI: 1.38, 1.69]).

A slightly increased risk for mothers aged 20–24 years

was also seen for severe ID (OR=1.45 [CI: 1.04, 2.01]).

Women who had never married (OR=2.18 [CI: 1.97,

2.42]) and women who were widowed, divorced or

separated (OR=2.40 [CI: 1.87, 3.07]) were more likely

to have a child with a mild-moderate ID than those who

ARTICLE IN PRESS

Table 1

Infant characteristics by degree of intellectual disability

Category Not

intellectually

handicapped

Mild-

moderate

intellectual

disability

OR 95% CI Severe

intellectual

disability

OR 95% CI Intellectual

disability

with autism

OR 95% CI

Gender of child

Female 115,735 940 1 73 1 37 1

(48.84%) (38.10%) (34.27%) (19.37%)

Male 121,229 1527 1.55 140 1.83 154 3.97

(51.16%) (61.90%) (1.43–1.68) (65.73%) (1.38–2.43) (80.63%) (2.78–5.69)

Birth order

First born 92,277 868 1 73 1 83 1

(38.94%) (35.18%) (34.27%) (43.46%)

2nd or 3rd born 121,067 1185 1.04 103 1.08 95 0.87

(51.09%) (48.03%) (0.95–1.14) (48.36%) (0.80–1.45) (49.74%) (0.65–1.17)

4th or 5th born 20,349 321 1.68 29 1.80 12 0.66

(8.59%) (13.01%) (1.47–1.91) (13.62%) (1.17–2.77) (6.28%) (0.36–1.20)

6th or later born 2958 87 3.13 8 3.42 0

(1.25%) (3.53%) (2.50–3.91) (3.76%) (1.65–7.10) (0%)

Missing 313 6 0 1

H. Leonard et al. / Social Science & Medicine 60 (2005) 1499–15131502

were married. Neither the risk of severe ID nor ASD

was significantly associated with marital status.

Shorter women (OR=1.51 [CI: 1.37, 1.67]) and

women of medium height (OR=1.15[CI: 1.03, 1.28])

were more likely than taller women to have a child with

a mild-moderate ID. The shortest women were also

significantly more likely to have a child with a severe ID

(OR=1.49 [CI: 1.05, 2.11]). These effects were not seen

for mothers of children with ASD.

Maternal ethnicity, country of birth and area of residence

Maternal ethnicity, birthplace and area of residence

by child’s degree of ID are shown in Table 3. A child

with a mild-moderate ID was born to 2.55% of

Aboriginal mothers compared with 0.98% of Caucasian

mothers (OR=2.83 [CI: 2.52, 3.18]). Severe ID was also

commoner, being found in the children of 0.14%

Aboriginal mothers compared with 0.08% of Caucasian

mothers (OR=1.67 [CI: 1.03, 2.71]). In contrast, the

odds of ASD in children born to Aboriginal mothers

was significantly less than in those born to Caucasian

mothers (OR=0.30 [CI: 0.09, 0.93]). There was no

significant difference between the odds of either mild-

moderate or severe ID for children of mothers of

‘‘other’’ ethnicity. The odds of ASD were greater in

children born to mothers of ‘‘other’’ ethnicity but not

significantly so (OR=1.49 [CI: 0.91, 2.46]). Mothers

born in the UK and Ireland (OR=0.73 [CI: 0.64,0.82]),

other parts of Europe (OR=0.73 [CI: 0.58, 0.92]), Asia

(OR=0.68 [CI: 0.56, 0.83]), and Africa and America

(OR=0.66 [CI: 0.49, 0.88]) were less likely to have a

child with a mild-moderate ID than Australian born

mothers. No significant effects were seen with severe ID.

However, there was a tendency for mothers of children

with ASD to be more likely to be born in Asia

(OR=1.53 [CI: 0.92, 2.53]). The direction of these

results held when Aboriginal women were excluded (and

remained statistically significant except for mothers born

in other parts of Europe). With respect to the ‘‘Index of

Remoteness’’ the only significant finding was that living

in a very remote area at the time of the infant’s birth

provided a protective effect against having a child with

ASD (OR=0.45 [CI: 0.21, 0.96]). The effect persisted

but was no longer significant when Aboriginal children

were excluded (OR=0.55 [CI: 0.25, 1.25]).

Socioeconomic factors

Based on the index of relative social disadvantage

mothers in the five more disadvantaged groups were at

significantly increased risk of having a child with a mild-

moderate ID compared with those in the most

advantaged 10% (Table 4). Those in the most dis-

advantaged 10% had more than five times the risk

(OR=5.61 [CI: 4.42, 7.12]). For both mild-moderate

and severe ID there was a monotonic increasing risk of

ID with increasing relative social disadvantage although

for severe ID this was significant only for the most

disadvantaged (OR=2.38[CI: 1.29, 4.38]). There was no

statistically significant association of relative social

disadvantage with ASD. A similar picture was seen with

the index of education and occupation and mild-

moderate ID (Table 4) with a nearly sixfold increased

ARTICLE IN PRESS

Table 2

Maternal age, marital status and stature by child’s degree of intellectual disability

Category Not

intellectually

handicapped

Mild-

moderate

intellectual

disability

OR 95% CI Severe

intellectual

disability


disability

with autism

OR 95% CI

Maternal age groupa (years)

o20 14,986 278 2.09 16 1.29 13 1.13

(6.32%) (11.27%) (1.82–2.40) (7.51%) (0.75–2.22) (6.81%) (0.62–2.04)

20–24 56,035 762 1.53 67 1.45 38 0.88

(23.65%) (30.89%) (1.38–1.69) (31.46%) (1.04–2.01) (19.90%) (0.59–1.31)

25–29 89,585 796 1 74 1 69 1

(37.81%) (32.27%) (34.74%) (36.13%)

30–34 56,897 461 0.91 35 0.74 56 1.22

(24.01%) (18.69%) (0.81–1.02) (16.43%) (0.50–1.11) (29.32%) (0.97–1.53)

35–39 16,812 141 0.94 20 1.44 12 0.93

(7.09%) (5.72%) (0.79–1.13) (9.39%) (0.88–2.36) (6.28%) (0.50–1.71)

439 2334 23 1.1 1 0.52 2 1.11

(0.98%) (0.93%) (0.73–1.68) (0.47%) (0.07–3.72) (1.05%) (0.27–4.54)

Missing 315 6 0 1

Marital statusa

Single 22,376 449 2.18 22 1.12 24 1.38

(9.44%) (18.20%) (1.97–2.42) (10.33%) (0.72–1.74) (12.57%) (0.90–2.12)

Widowed, divorced or

separated

2991 66 2.40 5 1.90 2 0.86

(1.26%) (2.68%) (1.87–3.07) (2.35%) (0.78–4.62) (1.05%) (0.21–3.48)

Married 211,282 1946 1 186 1 164 1

(89.16%) (78.88%) (87.32%) (85.86%)

Missing 315 6 0 1

Maternal height (cm)

4166 70,204 584 1 49 1 60 1

(29.63%) (23.67%) (23.00%) (26.17%)

4160o167 78,383 751 1.15 72 1.32 72 1.07

(33.08%) (30.44%) (1.03–1.28) (33.80%) (0.92–1.89) (31.25%) (0.76–1.51)

o161 86,593 1090 1.51 90 1.49 58 0.78

(36.54%) (44.18%) (1.37–1.67) (42.25%) (1.05–2.11) (40.34%) (0.55–1.12)

Missing 1784 42 2 1

aAt time of child’s birth.

H. Leonard et al. / Social Science & Medicine 60 (2005) 1499–1513 1503

risk when the poorest endowed 10% were compared

with the highest scoring 10% (OR=5.70 [CI: 4.38,

7.42]). The pattern with severe ID was similar with a

significantly increased risk (OR=2.62 [CI: 1.36, 5.02])

for the lowest 10%. The odds ratios with ASD for

education and occupation were minimally raised but

only statistically significant when the group between the

75% and 90% was compared with those in the highest

10%. In terms of economic resources (Table 4) the

results for mild-moderate and severe ID were similar to

those for the other indices. As would be expected the

three SEIFA indices were correlated (0.68 for education

and occupation and economic resources; 0.76 for

education and occupation and socioeconomic disadvan-

tage; 0.90 for socioeconomic disadvantage and economic

resources). There were increased odds ratios for both

mild-moderate and severe ID for all paternal occupa-

tions compared with fathers who were in professional

employment (Table 5). The highest risk for both was

when fathers were not in the work force followed by

those with missing information. There was no consistent

or significant association between paternal occupation

and ASD. Compared with mothers who had private

insurance those who did not were at significantly

increased risk of having children with mild-moderate

(OR=2.38 [CI: 2.19, 2.60]) and severe (OR=2.05 [CI:

1.54, 2.71]) ID and to a lesser extent ASD (OR=1.44

[CI: 1.01, 1.78]).

In a logistic regression model with mild-moderate ID

as the outcome all independent variables other than the

SEIFA indices of relative socioeconomic disadvantage

and economic resources remained significant (Table 6).

The odds ratio for Aboriginal maternal ethnicity was

1.54 [CI: 1.30, 1.81]. Compared with women aged 20–24

years there continued to be a significant protective effect

for women in older age groups. Single or widowed,

ARTICLE IN PRESS

Table 3

Maternal ethnicity, birthplace and area of residence by child’s degree of intellectual disability

Category Not

intellectually

handicapped

Mild-

moderate

intellectual

disability

OR 95% CI Severe

intellectual

disability


disability

with autism

OR 95% CI

Maternal ethnicity

Caucasian 210,079 2004 1.00 181 1.00 170 1.00

(88.65%) (81.23%) (84.98%) (89.01%)

Aboriginal 12,517 338 2.83 18 1.67 3 0.30

(5.28%) (13.79%) (2.52–3.18) (8.45%) (1.03–2.71) (1.57%) (0.09–0.93)

Other 14,054 119 0.88 14 1.16 17 1.49

(5.93%) (4.82%) (0.74–1.07) (6.57%) (0.67–1.99) (8.90%) (0.91–2.46)

Missing 314 6 0 1

Maternal birth place

Australia, New Zealand,

Papua New Guinea

160,181 1866 1 159 1 137 1

(67.60%) (75.64%) (74.65%) (71.73%)

UK and Ireland 33,175 281 0.73 24 0.73 23 0.81

(14.00%) (11.39%) (0.64–0.82) (11.27%) (0.47–1.12) (12.04%) (0.52–1.26)

Europe (other) 8663 74 0.73 10 1.16 7 0.94

(3.66%) (3.00%) (0.58–0.92) (4.69%) (0.61–2.20) (3.66%) (0.44–2.02)

Asia 13,024 103 0.68 9 0.70 17 1.53

(5.50%) (4.18%) (0.56–0.83) (4.23%) (0.36–1.36) (8.90%) (0.92–2.53)

Africa and America 5881 45 0.66 6 1.03 4 0.80

(2.48%) (1.82%) (0.49–0.88) (2.82%) (0.45–2.32) (2.09%) (0.29–2.15)

Missing 16,040 98 5 3

Index of remotenessa

Highly accessible 170,929 1746 1 160 1 153 1

(72.13%) (70.77%) (75.12%) (80.10%)

Accessible 17,523 205 1.14 14 0.85 10 0.64

(7.39%) (8.31%) (0.99–1.32) (6.56%) (0.49–1.47) (5.24%) (0.34–1.21)

Moderately accessible 19,640 217 1.08 17 0.92 14 0.80

(8.29%) (8.80%) (0.94–1.25) (7.98%) (0.56–1.52) (7.33%) (0.46–1.38)

Remote 7380 66 0.88 4 0.58 3 0.45

(3.11%) (2.68%) (0.68–1.12) (1.88%) (0.31–1.10) (1.57%) (0.14–1.42)

Very remote 17,409 196 1.10 12 0.74 7 0.45

(7.35%) (7.94%) (0.95–1.28) (5.63%) (0.41–1.32) (3.66%) (0.21–0.96)

Missing 4083 37 6 4



divorced or separated marital status continued to be a

risk factor. Protection was still shown in mothers born

in the UK and Ireland, other parts of Europe and Asia.

The pattern seen in the univariate analysis with paternal

occupation also held up in the multivariate analysis. On

the other hand for severe ID (Table 7) only factors

relating to birth order, gender of child, health insurance

status and paternal occupation remained significant with

the highest odds ratios being for infants who were sixth

or later born (OR=2.76 [CI: 1.32, 5.76]) and fathers not

in the work force (OR=2.58 [CI: 1.20, 5.56]). With ASD

(Table 8) the only significant effects in the model were

male sex (OR=3.95 [CI: 2.76, 5.66]), Aboriginality

(OR=0.29 [CI: 0.09, 0.91]) and birth year (OR=1.14

[CI: 1.08, 1.20]).

It was important to check the validity of these

models (Tables 6–8) allowing for the different levels of

variability within people, families and CDs, particularly

where some of the socioeconomic variables were only

measured at the CD level, while the remainder were

measured at the family level. Two sets of 2-level (subject

and family; subject and CD) models were estimated

for each response. None of the overall conclusions

were affected with estimates the same to 3 significant

digits and standard errors the same to 2 digits. Because

of the large sample size, it was not possible to fit the

full 3-level model to any outcome, however, a 3-level

model was estimated for the autism model of Table 8

using a 1% sample of controls and there were no

appreciable differences to the model estimates, although

ARTICLE IN PRESS

Table 4

Socioeconomic indices based on mother’s residence at time of infant’s birth by child’s degree of intellectual disability

Category Not

intellectually

handicapped

Mild-

moderate

intellectual

disability

OR 95% CI Severe

intellectual

disability


disability with

autism

OR 95% CI

Index of relative social disadvantagea (%)

490 19,242 81 1 15 1 17 1

(8.12%) (3.28%) (7.04%) (8.90%)

75–90 32,068 202 1.50 28 1.12 19 0.67

(13.53%) (8.19%) (1.16–1.94) (13.15%) (0.60–2.10) (9.95%) (0.35–1.29)

50–75 44,928 393 2.08 35 1.00 38 0.96

(18.96%) (15.93%) (1.63–2.64) (16.43%) (0.55–1.83) (19.90%) (0.54–1.70)

25–50 47,248 534 2.68 42 1.14 45 1.07

(19.94%) (21.65%) (2.12–3.39) (19.72%) (0.63–2.06) (23.56%) (0.62–1.88)

10–25 28,452 426 3.55 30 1.35 25 0.99

(12.01%) (17.27%) (2.80–4.51) (14.08%) (0.73–2.51) (13.09%) (0.54–1.84)

o10 17,784 420 5.61 33 2.38 21 1.34

(7.5%) (17.02%) (4.42–7.12) (15.49%) (1.29–4.38) (10.99%) (0.70–2.53)

Missing 47,242 411 30 26

Index of education and occupationa (%)

490 15,697 64 1 12 1 8 1

(6.62%) (2.59%) (5.63%) (4.19%)

75–90 30,292 169 1.37 24 1.04 34 2.20

12.78% (6.85%) (1.03–1.83) (11.27%) (0.52–2.07) (17.80%) (1.02–4.76)

50–75 55,949 459 2.01 49 1.15 47 1.65

(23.61%) (18.61%) (1.55–2.62) (23.00%) (0.61–2.15) (24.61%) (0.78–4.49)

25–50 44,739 546 2.99 31 0.91 38 1.67

(18.88%) (22.13%) (2.31–3.88) (14.55%) (0.47–1.77) (19.90%) (0.78–3.57)

10–25 24,551 388 3.88 30 1.60 20 1.60

(10.36%) (15.73%) (2.97–5.05) (14.08%) (0.82–3.12) (10.47%) (0.70–3.63)

o10 18,494 430 5.70 37 2.62 18 1.91

(7.80%) (17.43%) (4.38–7.42) (17.37%) (1.36–5.02) (9.42%) (0.83–4.39)

Missing 47,242 411 30 26

Index of economic resourcesa (%)

490 25,827 134 1 22 1 20 1

(10.90%) (5.43%) (10.33%) (10.47%)

75–90 36,440 243 1.29 28 0.90 18 0.64

(15.38%) (9.85%) (1.04–1.59) (13.15%) (0.52–1.58) (9.42%) (0.34–1.21)

50–75 47,183 480 1.96 34 0.85 51 1.40

(19.91%) (19.46%) (1.62–2.38) (15.96%) (0.49–1.45) (26.70%) (0.83–2.34)

25–50 40,250 488 2.34 45 1.31 36 1.15

(16.99%) (19.78%) (1.93–2.83) (21.13%) (0.79–2.19) (18.85%) (0.67–2.00)

10–25 25,334 373 2.84 33 1.53 21 1.07

(10.69%) (15.12%) (2.33–3.46) (15.49%) (0.89–2.62) (10.99%) (0.58–1.98)

o10 14,688 338 4.44 21 1.68 19 1.67

(6.20%) (13.70%) (3.63–5.42) (9.86%) (0.92–3.05) (9.95%) (0.89–3.13)

Missing 47,242 411 30 26

aBased on mother’s residence at time of child’s birth.


neither random effect was at all statistically sig-

nificant. The variance of the family random effect

was not significant in any of the 2-level models

whereas the variance of the CD effect was significant

for the mild model (Table 6) and the autism model

(Table 8).

Discussion

This study builds on previous work in which we

ascertained a population-based cohort of children with

ID of any cause-born in WA between 1983 and 1992

(Leonard et al., 2003). The current analysis used data

ARTICLE IN PRESS

Table 5

Paternal occupation and maternal health insurance status by child’s degree of intellectual disability

Category Not

intellectually

handicapped

Mild-

moderate

intellectual

disability

OR 95% CI Severe

intellectual

disability


disability

with autism

OR 95% CI

Paternal occupationa

Professional 33,185 126 1 16 1 29 1

(14.00%) (5.11%) (7.51%) (15.18%)

Para-Professional 9929 52 1.38 7 1,46 7 0.81

(4.19%) (2.11%) (1.00–1.91) (3.29%) (0.60–3.56) (3.66%) (0.35–1.84)

Administrative &

managerial

42,926 389 2.39 29 1.40 29 0.77

(18.11%) (15.77%) (1.95–2.92) (13.62%) (0.76–2.58) (15.18%) (0.46–1.29)

Tradesmen 77,294 940 3.20 69 1.85 53 0.78

(32.62%) (38.10%) (2.66–3.86) (32.39%) (1.07–3.19) (27.75%) (0.50–1.23)

Clerical 11,368 66 1.53 12 2.19 14 1.41

(4.8%) (2.68%) (1.13–2.06) (5.63%) (1.04–4.63) (7.33%) (0.74–2.67)

Sales & Service 11,750 75 1.68 9 1.59 10 0.97

(4.96%) (3.04%) (1.26–2.24) (4.23%) (0.70–3.60) (5.24%) (0.47–2.00)

Plant & machine

operators

13,863 187 3.55 17 2.54 10 0.83

(5.85%) (7.58%) (2.83–4.46) (7.98%) (1.28–5.04) (5.24%) (0.40–1.69)

Labourers 17,117 247 3.79 22 2.66 25 1.67

(7.25%) (10.01%) (3.05–4.70) (10.33%) (1.40–5.06) (13.09%) (0.98–2.85)

Not in work force 6944 154 5.84 13 3.88 6 0.99

(2.93%) (6.24%) (4.61–7.40) (6.1%) (1.87–8.10) (3.14%) (0.41–2.38)

Missing information 12,534 231 4.85 19 3.14 8 0.73

(5.29%) (9.36%) (3.90–6.04) (8.92%) (1.62–6.12) (4.19%) (0.33–1.60)

Health insurancea

Private health insurance 12,3632 779 1 75 1 86 1

(52.17%) (31.58%) (35.21%) (45.03%)

No private health

insurance

109,489 1645 2.38 136 2.05 102 1.34

(46.20%) (66.68%) (2.19–2.60) (63.85%) (1.54–2.71) (53.40%) (1.01–1.78)

Missing 3843 43 2 3



from the same cohort for children who have ID of

unexplained cause. Multivariate logistic regression

revealed numerous sociodemographic variables to be

associated with mild-moderate ID. Male children were

at higher risk of ID in all groups. However, mild-

moderate ID was associated with younger maternal age,

smaller maternal height, higher birth order, sole parent

status (single, widowed, divorced or separated), lack of

private health insurance, Aboriginal status of the

mother, non-participation in the labour force or lower

job classification levels for the father, and lower indices

of area education and occupation. In contrast severe ID

was only associated with higher birth order, lack of

private health insurance, and non-participation in the

labour force or lower job classification levels for the

father. ASD showed the fewest relationships with

sociodemographic variables—cases were less likely

where ethnicity was reported to be Aboriginal.

These results are both valuable and striking. They are

valuable because they provide the first comprehensive

population-based profile of sociodemographic charac-

teristics in an Australian sample of children with ID.

They are striking in that they demonstrate clear

associations between many modifiable sociodemo-

graphic characteristics and mild-moderate ID. In the

main, higher levels of social disadvantage and poverty

are particularly associated with the burden of mild-

moderate ID. Our findings of an elevated likelihood of

mild-moderate ID with sole parent status, early mother-

hood, low levels of education and no or low status

occupation bear striking similarity to findings reported

by Fujuira and Yamaki (2000) and Fujuira (1998).

However, unlike these authors, our findings are also

notable for the high and independent association of

ethnicity (i.e. Aboriginality) with mild-moderate ID.

This may reflect uncontrolled sources of variation—

ARTICLE IN PRESS

Table 6

Sociodemographic characteristics associated with mild-moderate intellectual disability in a logistic regression model

Variable Category Odds ratio 95% CI p value

Sex Male 1.67 1.52–1.83 o0.001

Birth order First born 1

2nd or 3rd birth 1.15 1.04–1.28 0.009

4th or 5th born 1.65 1.39–1.94 o0.0016th or later born 2.63 1.98–3.48 o0.001

Maternal age group o20 years 1.22 1.08–1.37 0.001

20–24 years 1.25 1.04–1.50 0.019

25–29 years 1

30–34 years 0.96 0.84–1.10 0.536

35–39 years 1.05 0.86–1.28 0.647

439 years 0.87 0.52–1.45 0.593

Marital status Married 1

Single 1.29 1.12–1.49 o0.001Widowed, divorced or separated 1.37 1.03–1.83 0.031

Maternal height 4166cm 1

4160o167 cm 1.03 0.91–1.16 0.662

o161 cm 1.32 1.18–1.48 o0.001

Ethnicity Caucasian 1.00

Aboriginal 1.54 1.30–1.81 o.001Other 0.92 0.67–1.26 0.599

Maternal birth place Australia, New Zealand, Papua New Guinea 1

UK & Ireland 0.78 0.68–0.90 0.001

Europe (other) 0.73 0.56–0.96 0.023

Asia 0.72 0.51–1.01 0.058

Africa & America 0.99 0.72–1.37 0.951

Index of remoteness Highly accessible 1

Accessible 0.84 0.70–1.00 0.053

Moderately accessible 0.86 0.72–1.02 0.086

Remote 0.62 0.44–0.85 0.004

Very remote 0.72 0.59–0.88 0.001

Health insurance status No private health insurance 1.47 1.31–1.65 o0.001

Index of education &; occupation 490% 1

475–90% 1.17 0.86–1.57 0.315

50–75% 1.44 1.09–1.89 0.010

25–50% 1.79 1.36–2.36 o0.00110–25% 2.05 1.54–2.72 o0.001o10% 2.54 1.91–3.39 o0.001

Paternal occupation Professional 1

Para-professional 1.31 0.91–1.89 0.149

Administrative & managerial 1.75 1.37–2.24 o0.001Tradesmen 2.33 1.86–2.92 o0.001Clerical 1.47 1.05–2.06 0.026

Sales & service 1.62 1.18–2.22 0.003

Plant & machine operators 2.40 1.84–3.14 o0.001Labourers 2.59 2.00–3.35 o0.001Not in work force 3.40 2.57–4.51 o0.001Missing information 2.98 2.28–3.90 o0.001

Birth year 0.99 0.97–1.01 0.180


ARTICLE IN PRESS

Table 7

Sociodemographic characteristics associated with severe intellectual disability in a logistic regression model


Sex Male 1.84 1.39–2.45 o0.001

Birth order First born 1

2nd or 3rd birth 1.08 0.80–1.46 0.615

4th or 5th born 1.60 1.03–2.48 0.035

6th or later born 2.76 1.32–5.76 0.007

Health insurance No private health insurance 1.75 1.29–2.37 o0.001

Paternal occupation Professional 1

Para-professional 1.22 0.47–3.12 0.683

Administrative and managerial 1.23 0.67–2.28 0.504

Tradesmen 1.55 0.89–2.68 0.121

Clerical 2.17 1.03–4.60 0.042

Sales and service 1.49 0.66–3.38 0.339

Plant and machine operators 2.02 1.01–4.04 0.046

Labourers 2.02 1.05–3.90 0.036

Not in work force 2.58 1.20–5.56 0.015

Missing information 2.20 1.11–4.35 0.024

Birth year 1.00 0.95–1.06 0.878

Table 8

Sociodemographic characteristics associated with intellectual disability and ASD in a logistic regression model


Sex Male 3.95 2.76–5.66 o0.001

Ethnicity Caucasian 1.00

Aboriginal 0.29 0.09–0.91 0.034

Other 1.43 0.87–2.36 0.157

Birth year 1.14 1.08–1.20 o0.001


particularly lack of direct measures of family poverty

and circumstance—and further research is required to

assess the precise meaning of this finding. Certainly the

documented levels of profound disadvantage experi-

enced by Australian indigenous people (Zubrick et al.,

2004) make this finding important in terms of the sheer

scale of social disadvantage as well as plausible at the

level of its impact on biology.

There are no equivalent population-based data in

Australia and elsewhere population-based research on

ID has only been reported from a limited number of

centres in Atlanta (Yeargin-Allsopp, Murphy, Oakley,

& Sikes, 1992), Florida (Blair & Scott, 2002; Chapman,

Scott, & Mason, 2002) and California (Croen, Grether,

& Selvin, 2001). Whilst some studies have separated out

isolated or unspecified ID from co-developmental or

biopathological ID (Drews, Yeargin-Allsopp, Decoufle,

& Murphy, 1995; Stromme & Magnus 2000) others have

focussed only on mild ID (Yeargin-Allsopp, Drews,

Decoufle, & Murphy, 1995). In our study, we have been

able to identify specifically those children without a

designated cause and categorise them according to their

level of ID. The proportion of children in this study who

were categorised as having a biomedical cause for their

ID (15.6%) is similar to the 22% identified by

Yeargin–Allsopp, using similar criteria, and lower than

other studies that were more liberal in their definition of

a medical diagnosis (Leonard & Wen, 2002). Unlike

others (Croen et al., 2001), we have also been able to

consider those with ID and ASD as a separate group.

The range of factors we have been able to examine both

individually and in a multivariate analysis is much more

extensive than reported in other studies which have

generally included eight or less factors (Yeargin-Allsopp

et al., 1995; Croen et al., 2001; Drews et al., 1995; Blair

& Scott, 2002) and sometimes focussed on only one or


two factors such as maternal education and age (Chap-

man et al., 2002). Moreover, we have been able to

include measures such as maternal height, place of birth

and indigenous status which have rarely been previously

reported. This investigation has only been possible

because of the infrastructure of linkable population-

based databases available in Western Australia, which

include a comprehensive set of variables relating to the

mother at the time of her infant’s birth. The ability to

supplement these population data with both aggregated

measures of socioeconomic well being from the Aus-

tralian Census and measures of accessibility to services

makes them particularly valuable.

We acknowledge the study’s limitations. To define a

group of children with ID of unexplained cause, we

excluded those children in whom there was a clear

known biomedical cause. However, for the majority of

the 1766 cases identified from educational sources, a

medical diagnosis was not provided. Therefore, if some

of these cases had a medical diagnosis that was not

known to us they could be contaminating our case group

and diluting or exaggerating the effects we are seeing.

There is also a lack of consistency in the literature on

how ID level should be categorised (Leonard & Wen,

2002). We concede that in some of our cases the level

was assigned by clinical observation rather than

psychometric testing (Leonard et al., 2003). In our study

(Leonard et al., 2003), cases were assembled using

record linkage and it is possible that there could be a

small proportion of incorrect matches as well as a

proportion of records which should have been linked

but were not. We realise that for some variables in the

MCHRDB there are missing data but for most variables

this accounted for less than 1%. One exception is the

SEIFA indices where data are missing on �11% of cases

for the 9 years in which they are available. However, the

estimates we obtained in our multivariate analysis were

little different when we restricted the dataset to cases

without these missing data. Unlike many of the US

studies (Croen et al., 2001; Drews et al., 1995; Decoufle

& Boyle, 1995; Chapman et al., 2002; Hollomon,

Dobbins, & Scott, 1998) we did not have a direct

measure of maternal education. We also elected not to

use maternal occupation as a variable because one-third

of cases had missing information (probably because

mother was not working at time of delivery) and we felt

that paternal occupation was more reliable. The only

variable thus specifically relating to education is the

SEIFA index of education and occupation. It is

important to understand that the SEIFA score assigned

to an individual represents a score created by using

principal components analysis from census information

of people living in a specific CD (McCracken, 2001). In

our study it is the CD where the mother was living at the

time of her infant’s birth. There has been criticism that

in Australia too much reliance is being placed on these

indices as a measure of the socioeconomic condition

without sufficient account being taken of their short-

comings (McCracken, 2001). Nevertheless we did find

that all the indices correlated fairly well with our

measures of paternal occupation and we feel that in

the absence of other indicators they provide useful

information. Finally, in comparison, for example, with

the Californian studies (Croen et al., 2001; Croen,

Grether, & Selvin, 2002) our population is relatively

small but the factors that we are able to examine are

more comprehensive.

In our previous publication (Leonard et al., 2003) we

specifically hypothesised on the determinants of the

racial differences that we found. We considered the role

of both potential biological pathways which might be

occurring antenatally and post-natally and issues relat-

ing to differential ascertainment of indigenous children.

In the latter context, a US study has further investigated

the relationship between sociodemographic character-

istics and ethnic disproportionality in ID using data

from the US Department of Education Office for Civil

Rights (Oswald, Coutinho, Best, & Nguyen, 2001).

Although the authors found that poverty was positively

associated with ID, both overall and within specific

racial groups, they also found that there were high

absolute levels of ID in African–Americans in low-

poverty communities and suggested, as we also did, that

there could be a bias involving inappropriate identifica-

tion of these children.

The relationships we found between maternal age and

mild-moderate ID are in keeping with those of Chap-

man et al. (2002). They found that the population

attributable risk for educationally mentally handicapped

(the equivalent of our mild-moderate ID) was highest

for mothers aged under 25 years with 12 or less years of

education. In contrast neither Williams and Decoufle

(1999) nor Drews et al. (1995) found evidence of an

increased risk for either ‘‘isolated’’ or ‘‘co-developmen-

tal’’ ID nor did Croen et al. (2001) for ID of unknown

cause in the children of teenage mothers. The latter

group agreed that their results could be affected by

misclassification into the unknown category of cases

with a biomedical cause as they were unable to validate

their diagnostic information. However, we would also

expect any bias in our study to be in the equivalent

direction, that is, the inclusion of children in the

unknown group for whose medical diagnosis we do

not have information. Thus this would not explain the

disparity with our findings.

Our findings in relation to birth order are consistent

with those reported elsewhere. Decoufle and Boyle

(1995) found that children with ID overall were likely

to be of higher birth order whilst Drews et al. (1995)

found that in an adjusted analysis this effect was only

seen for children with isolated ID (both mild and

severe). Also in an analysis limited to children with ID


of unknown cause Croen et al. (2001) found in their

adjusted analysis that there was an increased risk for

mild ID for second and subsequent born children.

Marital status has not always been reported in

previous studies (Drews et al., 1995). However, Williams

and Decoufle (1999) did find that the proportion of

children with isolated ID where the father was ‘‘absent’’

at birth (defined as missing information on all three

paternal demograhic variables (ethnicity, age and

educational level)) was twice that of control children.

Blair and Scott (2002) also found that the mother being

unmarried at birth was one of the individual risk factors

(RR=1.45 [CI: 1.40, 1.51]) for a child requiring learning

disability educational support placement by the age of

12–14 years. We found that the increased risk for mild-

moderate ID for unmarried mothers persisted in the

logistic regression model.

Compared with children of mothers who had less than

high school education, there was a protective effect for

ID (both mild and severe of unknown cause) for

children whose mothers were high school graduates or

had tertiary qualifications in the large Californian study

carried out by Croen et al. (2001). Using a model in

which being white and having 12 years of education was

the referent group Decoufle and Boyle (1995) also found

that both for the children of black mothers and the

children of white mothers with less than 10 years

education there was a similar increased risk of about

nine-fold for ‘‘isolated MR’’. Similarly Drews et al.

(1995) found that the greatest effects of low maternal

education were on mild and isolated ID rather than that

which was either severe or associated with other

neurological conditions. Unlike the other studies which

used population-based comparisons, Stromme and

Magnus (2000) compared the risk of mild as opposed

to severe ID by level of socioeconomic status (assigned

according to information from parental interview)

within their case group and found that the risk of mild

ID increased with lower socioeconomic status. Chap-

man et al. (2002) found that compared with children of

mothers with 412 years education, those with mothersofo12 years education had an increased risk (RR=10.9[CI: 9.6, 12.3]) of ‘‘educable mental handicap’’ and an

increased risk (RR=3.2 [CI: 2.6, 3.8]) of ‘‘trainable

mental handicap’’. Hollomon et al. (1998) found that

children with low maternal education (equivalent to

o12 years education) as their only risk factor were 1.55(1.45–1.65) times more likely to be receiving special

education than the reference group and attributed 19%

of the need for special education to this factor alone. In

our study using the index of education and occupation

as our only measure of maternal education our findings

were similar with the increased risk for mild-moderate

greater than that for severe ID.

Using a combination of factors including maternal

age, education and marital status to define low socio-

economic status Blair and Scott (2002) found that

children with socioeconomic disadvantage were at

increased risk of educational support placement for

learning disability. However, to our knowledge few

studies have been able to use a composite measure of

socioeconomic well being such as we have. Yet the

results we are seeing using a neighbourhood measure as

a surrogate for an individual measure are consistent with

other recent work from Florida (Yale, Scott, Gross, &

Gonzalez, 2003). These authors found that the effect of

living in a low-income area is a better predictor of poor

childhood outcome than maternal education per se.

Maternal stature does not appear to have been

previously studied in relation to ID. Although variation

in body height is primarily due to genetic factors,

environmental factors such as nutrition, disease and

living conditions in childhood also impact on adult body

height, in both developing and developed countries

(Silventoinen, 2003). Maternal height as an indicator of

maternal nutrition has been studied in relation to

pregnancy outcome—particularly in developing coun-

tries and for the purpose of developing screening tools

which can be used to identify high-risk cases (Prasad &

Al-Taher, 2002; Kelly, Kevany, de Onis, & Shah, 1996).

However, decreasing maternal height has been shown to

be associated with an increased risk of neural tube

defects (Shaw, Todoroff, Schaffer, & Selvin, 2000),

neonatal encephalopathy (Ellis, Manandhar, & Costello,

2000) and small for gestational age infants (Clausson,

Cnattingius, & Axelsson, 1998).

Some of the factors we investigated such as maternal

country of birth, health insurance status and index of

geographical remoteness may have specific relevance for

Australia and may be less applicable to other countries

with different health systems and geographical situa-

tions. The study by Croen et al. (2001) was one of the

few to include maternal country of birth and found that

mother being born in the US other than in California, or

elsewhere in the world was protective for mild ID. This

‘‘healthy migrant effect’’ has been demonstrated in

Australia with overseas-born individuals experiencing

better health (Strong, Trickett, & Bhatia, 1998) and a

lowerrate of birth defects in their children (Kwon, 2000)

than their Australian-born contemporaries. The protec-

tive effect we saw was similar and was still present when

Aboriginal mothers were excluded from the case and

comparison group. The fact that there was a protective

effect for children whose mothers were living in remote

and very remote areas could reflect underascertainment

rather than a true effect. The findings according to

health insurance status are probably in keeping with the

protective effects of economic advantage.

To our knowledge ours is the first study to examine

specifically the sociodemographic factors associated

with ASD children who have ID. The descriptive

epidemiology of autism itself is still in its infancy.


However, Croen et al. (2002) have recently reported on

the sociodemographic and other correlates of what they

term as ‘‘full syndrome autism’’ in a Californian

population of three and a half million births. Their

definition excludes the children with Pervasive Develop-

mental Disorder Not Otherwise Specified (PDD-NOS)

whom we have included. In their study ID (usually

present in about three quarters of children with autism)

was only documented in 36% and thus they acknowl-

edged their major underreporting of this comorbidity

and inability to differentiate between autism with and

without ID. The profiles that we saw in our univariate

data for children with both ASD and ID (n ¼ 191) were

somewhat different from those with mild-moderate and

severe ID. The most obvious differences related to

maternal ethnicity, maternal height, geographical acces-

sibility, birth order and particularly paternal occupa-

tion. Some of these findings are consistent with the

Californian data on ‘‘full syndrome autism.’’

In WA for the past 5 years additional services have

been provided for children with an ‘‘autistic’’ label over

and above those provided to children with only ID. As

has been recognised elsewhere both for ID (Hansen,

Belmont, & Stein, 1980) and autism (Caronna & Halfon,

2003) ascertaining true incidence/prevalence and disen-

tangling it from service-driven estimates is an ongoing

challenge. It is quite feasible that the same presentation

in Aboriginal children is less likely than in Caucasian

children to progress to a full diagnostic assessment for

autism so that there is systematic underascertainment of

Aboriginal children. Croen et al. (2002) made a similar

comment in relation to Californian children whose

mothers were Mexican-born. This hypothesis would

also be consistent with our previous findings that

Aboriginal children were more likely to be identified

through the education system than through the organi-

sation which provides medical and support services for

intellectually disabled children (Leonard et al., 2003).

On the other hand it may be that there are environ-

mental risk factors for autism from which Aboriginal

children have up until now been protected. Only

by extending this study over a longer time period

and by including children with autism who do not have

ID will we be able to answer these very important

questions.

We have been able to confirm as shown by others that

certain social determinants such as maternal age, marital

status and education are associated with mild-moderate

ID. We have been able to look more thoroughly at other

variables including measures of socioeconomic well

being and maternal height which are also strongly

associated with mild-moderate and to a lesser extent

with severe ID. This study leads us in two directions.

The first is to ensure that our results are available to

inform policy, and specifically that those agencies

providing medical, therapy, educational and social

support services to these groups of intellectually disabled

children are made aware of our findings. This informa-

tion will be valuable in deciding such issues as which

schools are likely to require special resources and

staffing and on the distribution of support services in

the community. The second is to explore the pathways

by which the effects we are seeing are being mediated in

order to identify possible points of intervention. This

should be possible by using the MCHRDB to look at the

associations between these sociodemographic character-

istics, maternal health both prior to and during

pregnancy and various perinatal outcomes such as low

birth weight that may be leading to ID. For instance,

some of the effects we detect might be because of

maternal behaviours such as smoking, alcohol con-

sumption, and/or use of non-prescription drugs. There is

still much work to be done to understand these complex,

probably intergenerational relationships. It is likely that

any prevention will require multilevel intervention

involving major changes in social policy as well as

health education strategies and better pregnancy man-

agement for these mothers as well as early intervention

for their children.

We are not claiming that the burden of cases with

identified mild-moderate ID would be eliminated if

better early identification and prevention intervention

were available but, as Landesman Ramey and Ramey

(2002) point out, ‘‘y research findings clearly support

the conclusion that rates of mental retardation and

special education placements among children at socio-

demographic risk can be reduced by 50% or more. This

means that the most prevalent form of mental retarda-

tion—namely, mild mental retardation associated with

family conditions and not attributable to any known

biological cause—could be drastically reduced if early

intervention could be successfully targeted to those at

greatest risk (p 8)’’. Given the associations observed in

our data, this would most certainly be applicable to

those children in our sample who are distributed on the

milder end of our mild-moderate ID group. Interven-

tions are available and evidence supports their efficacy,

effectiveness and cost efficiency (Landesman Ramey &

Ramey, 2002). What is required is advocacy that leads to

effective community and political resolve to ensure that

such programs are developed and implemented as

intended.

Acknowledgments

We are grateful to Disability Services Commission,

Telethon Institute for Child Health Research, Depart-

ment of Education and Training, Catholic Education

Office, Association of Independent Schools Western

Australia and the Birth Defects Registry for assistance

with data collection and other aspects of the study


including organisational support. We would also like

particularly to thank Harry Bouckley, Kate Rowell,

Elvira Edwards, Jane Pavledis, Mairead McCoy, Tessa

Vincent, Maureen Thomson, Audrey Jackson, Peter

Cosgrove and Huan Ngyuen. We would also like to

acknowledge the special contribution provided by Jenny

Bourke. This work was funded by the National Health

and Medical Research Council (Program Grant #

003209 and Fellowship #172303 for CB).

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