THE EPIDEMIOLOGY OF MENTAL RETARDATION:CHALLENGES AND OPPORTUNITIES

IN THE NEW MILLENNIUM

Helen Leonard*1,2 and Xingyan Wen3

1Centre for Child Health Research, The University of Western Australia, Telethon Institute

for Child Health Research, West Perth, Australia2Disability Services Commission, Western Australia

3Australian Institute of Health and Welfare, Canberra, Australia

There are a number of problems and challenges in relating thescience of epidemiology to mental retardation (MR). These relate to howMR is defined and classified and how these definitions may change overtime. These as well as other differences in ascertainment sources and meth-ods need to be considered when comparing MR prevalence over time andplace. On the other hand, advances in technology also provide new andefficient methods of data collection both by data linkage and by use ofweb-based methods to study rare diseases.

While prevalence studies have not been individually reviewed, wehave examined the range of data including recent studies relating to howprevalence differs according to age, gender, social class and ethnicity. Someproblems with available etiological classification systems have been identi-fied. Recent etiological studies, most of which use different classificationsystems, have been reviewed and explanations have been postulated toaccount for differences in results. Individual risk factors for MR are consid-ered whilst the option of considering a population as opposed to a high riskstrategy to MR prevention is raised. This might well involve improving thesocial milieu surrounding the occurrence of individual risk factors.

The impact of biotechnological advances such as antenatal and neo-natal screening and assisted reproduction on MR are discussed. The issue ofhow inequalities in access to technology may impact on case identificationand even have the potential to further widen inequalities is raised. Theimportance of extending the use of epidemiological tools to study thesocial, health and economic burden of MR is also emphasized. However, inorder to apply to MR the “prevention-intervention-research” cycle, whichsurely underpins all epidemiology, it is vital to ensure that the methodolog-ical challenges we raise are adequately addressed. © 2002 Wiley-Liss, Inc.MRDD Research Reviews 2002;8:117–134.

Key Words: mental retardation intellectual disability/impairment; etiol-ogy; Down syndrome; data linkage

INTRODUCTION

The definition, classification, and measurement of mentalretardation (MR) have involved considerable contro-versy over time. Some would argue that the lumping

together of so many different underlying disorders and patho-logical processes within a single entity should not even beconsidered. The name assigned to this group of conditions variesinternationally [Haveman, 1996], and other related terms include“general learning disorder,” “mental handicap,” “learning disabili-ty,” “intellectual handicap,” and “intellectual disability.” There is

currently discussion in the United States about changing to a namethat better portrays those affected [Luckasson and Reeve, 2001].

Epidemiology has been defined as “the study of the dis-tribution and determinants of health-related states or events inspecified populations and the application of the study to thecontrol of health problems” [Last, 1995]. Applying the conven-tional principles of epidemiology to the study of MR helpsexamine how common MR is in the community; whetherincidence or prevalence has changed over time; and whethercertain population subgroups (according to gender, socio-eco-nomic status, ethnicity, place of residence) are more or less likelyto be affected. In terms of control of the “health problem,”epidemiological methods help identify those causal determinantswhere prevention is possible and then whether the appropriateintervention has had an impact on prevalence. Epidemiologistscan evaluate quality of life and access to medical and othersupports for people with MR and their families. Nevertheless,there are many challenges in applying epidemiological principlesto the study of a field with lack of consensus on “conceptual-ization, classification and terminology” [Fryers, 1992].

This article focuses on issues relating to definition andmethodology but also provides an overview of challenges, op-portunities and new roles for epidemiology in the study of MR.The discussion covers material published since the last compre-hensive review [Murphy et al., 1998] but earlier literature in-cluding the work of Roeleveld et al. [1997] is referenced whererelevant. Examples from the experience in Western Australia(WA) [Wellesley et al., 1991; 1992; Bower et al., 2000; Leonardet al., 2002] are used to illustrate some of these issues throughoutthe text.

*Correspondence to: Dr Helen Leonard, TVW Telethon Institute for Child HealthResearch, PO Box 855, West Perth, WA 6872, Australia.E-mail: hleonard@cyllene.uwa.edu.Received 21 May 2002; Accepted 23 May 2002Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/mrdd.10031

MENTAL RETARDATION AND DEVELOPMENTAL DISABILITIESRESEARCH REVIEWS 8: 117–134 (2002)

© 2002 Wiley-Liss, Inc.

DEFINITIONS ANDCLASSIFICATIONS OF MR

Whilst case definition is funda-mental to its epidemiology [Haveman,1996], there has been debate over thedefinition and classification of MR forseveral decades. This debate has focussedon both conceptual approaches and keyelements for case identification. Wen[1997] recently reviewed some interna-tionally recognized and widely adoptedMR definitions and classifications andemphasized the need for further im-provement and standardization. Inconsis-tency in data collection with great differ-ences in reported prevalence of MR maybe partially attributable to the revisions andvariations in some major definition andclassification systems.

VARIATIONS IN MAJORDEFINITION ANDCLASSIFICATION SYSTEMS

The traditional approach considersMR as a characteristic of a person and acondition with the source of difficultylying essentially within the individual.This approach tends to define MR onthe basis of either a medical model or astatistical model. The medical model fo-cuses on pathology, which defines MRby the presence of pathological symp-toms. The statistical model defines MRby identifying a certain group of the pop-ulation as “abnormal,” using comparisonof an individual’s performance and theperformance of a standardized normgroup. The statistical model measures se-verity of MR by standardized tests, suchas intelligence quotient (IQ) tests. How-ever, the latest (ninth) revision of thedefinition and classification of MR bythe American Association on Mental Re-tardation (AAMR) has taken significantsteps away from a clinically oriented per-spective towards a multidimensional ap-proach in defining MR [Luckasson et al.,1992]. Although the new revision main-tains the three key criteria—low generalintellectual functioning as measured byIQ score, difficulties in adaptive behav-ior, and the conditions manifesting be-fore age 18—it puts more emphasis onfunctional and environmental consider-ations, and less emphasis on an individu-al’s deficiency. Rather than relying onlyon IQ and adaptive behavior measures,the AAMR definition gives more cre-dence to the “state” of interaction be-tween the individual and the social envi-ronment. Under the new AAMRdefinition, the concept of adaptive be-havior is expanded with the specificationof ten applicable skill areas, relating to

age-appropriate functioning of the indi-vidual in the community (Table 1).

Severity of MR had been conven-tionally based on the statistical distributionof IQ scores. The new AAMR definitionreplaced the previous classification of se-verity with a new concept of intensity ofsupport required, assuming that a person’slevel of needed supports parallels the indi-vidual’s (intellectual) limitations (Table 1).Such descriptions of severity are consideredto be more functional, relevant, and ori-ented to service delivery and outcomesthan the labelling classification used previ-ously [Luckasson et al., 1992]. The AAMRhas also made changes in the upper limit ofIQ score in defining subaverage intellectualfunctioning (Table 1). The IQ cut-offscore was set up to 84 (one standard devi-ation below the mean) in the fifth revision[Heber, 1961], and was reduced to approx-imately 70 (two standard deviations) in thesixth and eighth revisions [Grossman, 1973;

1983]. The latest (ninth) revision [Luckas-son et al., 1992] defines significantly sub-average as “IQ standard scores of approxi-mately 70 to 75 or below” (Table 1). TheAmerican Psychiatric Association (APA)has modified its recent (fourth) versions ofThe Diagnostic and Statistical Manual ofMental Disorders (DSM-IV) by incorpo-rating the ten adaptive skill areas of the newAAMR manual into its general definitionof MR. Nevertheless, DSM-IV has re-tained the conventional severity levels ofintellectual impairment based on IQ scoresand maintained an IQ cut-off point at 70 orbelow [American Psychiatric Association,1994].

In the International Statistical Clas-sification of Diseases and Related HealthProblems (ICD-10), apart from IQ scoresand functional ability, need for support isalso included as one of the indicatorsdifferentiating mild MR from severe MR[World Health Organization (WHO),

1992; 1993]. The ICD-10 manual alsopoints out that intellectual abilities andrehabilitation may change over time andmay improve by training and rehabilita-tion, so diagnosis should be based on thecurrent levels of functioning. The 1996ICD–10 Guide to Classification in Men-tal Retardation suggests the use of stan-dard scales as measurements of socialfunctioning [O’Brien, 2001b]. The “in-tellectual impairments” concept in Inter-national Classification of Impairment,Disabilities and Handicap (ICIDH) cov-ers a wide range of impairments and syn-dromes, involving impairments in intel-ligence, memory and thinking. MR isconsidered as one of the sub-categories ofintelligence impairments. The ICIDHdefinition of intellectual impairments ex-cludes impairments of language andlearning [World Health Organization(WHO), 1980]. In the revised version ofICIDH—International Classification ofFunctioning, Disability and Health(ICF), MR is classified as part of intellec-tual functions, together with intellectualgrowth, intellectual retardation and de-mentia while memory, thought andhigher level cognitive functions are ex-cluded [WHO, 2001]. Neither of thetwo WHO manuals (ICD-10, ICIDH orICF ) has specified an age as a cut-offpoint for the developmental period todefine MR, in contrast to the AAMRdefinition that requires the diagnosis ofMR before age 18. ICD-10 refers to thecondition as ‘especially characterized byimpairment of skills manifested duringthe developmental period,’ while ICIDHseems to refer to the general populationof all ages.

DIVERSITY IN KEY CRITERIAFOR CASE IDENTIFICATIONAND CLASSIFICATION

The debate over the key criteria todefine cases has implications for epidemi-ological research because prevalence es-timates may be influenced by changes inthese criteria. Zigler and colleagues[1984] felt that MR should be definedonly on the basis of cognitive skills andthat inclusion of social adaptation, a mea-sure of interaction between the child andthe environment, would make it difficultto assess the true prevalence. Barnett[1986] on the other hand agreed that thefundamental property of MR was “cog-nitive inefficiency” but felt that to opera-tionalise this, the cultural context andenvironment had to be taken into ac-count. In practice, adaptive behavior ismore likely to be ignored in epidemio-logical research of MR. Many studies useIQ scores as the sole criterion to estimate

The debate over the keycriteria to define caseshas implications for

epidemiological researchbecause prevalenceestimates may be

influenced by changes inthese criteria.

118 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

Tab

le1.

Com

par

ison

ofD

iffe

rent

Ver

sions

ofA

AM

RD

efinitio

ns

ofM

enta

lR

etar

dat

ion*

Ter

mFi

fthre

visio

n[H

eber

,19

61]

Sixt

hre

visio

n[G

ross

man

,19

73]

Eig

hth

revi

sion

[Gro

ssm

an,

1983

]N

inth

revi

sion

[Luc

kass

onet

al.,

1992

]

Gen

eral

defin

i-tio

nSu

bave

rage

gene

ral

inte

llect

ual

func

tioni

ngw

hich

orig

inat

esdu

ring

the

deve

lopm

enta

lpe

-ri

odan

dis

asso

ciat

edw

ithim

-pa

irm

ent

inad

aptiv

ebe

hav-

iour

.

Sign

ifica

ntly

suba

vera

gege

nera

lin

telle

ctua

lfu

nctio

ning

ex-

istin

gco

ncur

rent

lyw

ithde

ficits

inad

aptiv

ebe

havi

our

and

man

ifest

eddu

ring

the

deve

lopm

enta

lpe

riod

.

Sign

ifica

ntly

suba

vera

gege

nera

lin

tel-

lect

ual

func

tioni

ngre

sulti

ngin

oras

soci

ated

with

conc

urre

ntim

pair

-m

ents

inad

aptiv

ebe

havi

our

and

man

ifest

eddu

ring

the

deve

lopm

en-

tal

peri

od.

Subs

tant

ial

limita

tions

inpr

esen

tfu

nctio

ning

.It

isch

arac

teri

sed

bysig

nific

antly

suba

vera

gein

telle

ctua

lfu

nctio

ning

,ex

istin

gco

ncur

-re

ntly

with

rela

ted

limita

tions

intw

oor

mor

eof

the

follo

win

gap

plic

able

adap

tive

skill

area

s:co

mm

unic

atio

n,se

lf-ca

re,

hom

eliv

ing,

soci

alsk

ills,

com

mun

ityus

e,se

lf-di

-re

ctio

n,he

alth

and

safe

tyfu

nctio

nal

acad

em-

ics,

leisu

re,

and

wor

k.M

enta

lre

tard

atio

nm

anife

sts

befo

reag

e18

.Su

bave

rage

Gre

ater

than

one

stan

dard

devi

a-tio

nbe

low

the

mea

n.Si

gnifi

cant

lysu

bave

rage

:tw

oor

mor

est

anda

rdde

viat

ions

belo

wth

em

ean.

Sign

ifica

ntly

suba

vera

ge:

defin

edas

anIQ

of70

orbe

low

onst

anda

rdise

dm

easu

res

ofin

telli

genc

e;co

uld

beex

tend

edup

war

dth

roug

hIQ

75or

mor

e,de

pend

ing

onth

ere

liabi

lity

ofth

ein

telli

genc

ete

stus

ed.

Sign

ifica

ntly

suba

vera

gein

telle

ctua

lfu

nctio

n-in

g:de

fined

asan

IQst

anda

rdsc

ore

ofap

-pr

oxim

atel

y70

to75

orbe

low

.

Ass

essm

ent

proc

edur

eG

ener

alin

telle

ctua

lfu

nctio

ning

:m

aybe

asse

ssed

byon

eor

mor

eof

the

stan

dard

ised

test

sde

velo

ped

for

the

purp

ose.

Sam

eas

Heb

er.

Sam

eas

Heb

erfo

rin

telle

ctua

lfu

nc-

tioni

ng.

Ada

ptiv

ebe

havi

our

as-

sess

edby

clin

ical

asse

ssm

ent

and

stan

dard

ised

scal

es.

Mul

tidim

ensio

nal

appr

oach

incl

udin

ga

thre

e-st

eppr

oced

ure

for

diag

nosin

g,cl

assif

ying

and

dete

rmin

ing

the

need

edsu

ppor

ts.

Dev

elop

men

tal

peri

odA

ppro

xim

atel

y16

year

s.U

pper

age

limit

of18

year

s.B

etw

een

conc

eptio

nan

dth

e18

thbi

rthd

ay.

Man

ifest

ing

befo

reag

e18

.

Ada

ptiv

ebe

-ha

viou

rIm

pair

men

tin

adap

tive

beha

v-io

ur:

refe

rsto

the

effe

ctiv

enes

sof

the

indi

vidu

alto

adap

tto

the

natu

ral

and

soci

alde

man

dsof

his

envi

ronm

ent.

May

bere

flect

edin

:1.

mat

urat

ion,

2.le

arni

ng,

3.so

cial

adju

stm

ent.

Defi

ned

asef

fect

iven

ess

orde

gree

with

whi

chth

ein

divi

dual

mee

tsth

est

anda

rds

ofpe

rson

alin

depe

nden

cean

dso

cial

resp

onsib

ility

expe

cted

ofhi

sag

ean

dcu

ltura

lgr

oup.

May

bere

flect

edin

the

follo

win

gar

eas:

Dur

ing

infa

ncy

and

early

child

hood

:1.

sens

ory-

mot

orsk

ills

deve

lopm

ent,

2.co

m-

mun

icat

ion

skill

s,3.

self-

help

skill

s,4.

soci

alisa

tion

Dur

ing

child

hood

and

early

adol

esce

nce:

5.ap

plic

atio

nof

basic

aca-

dem

ics

inda

ilylif

eac

tiviti

es,

6.ap

plic

atio

nof

appr

opri

ate

reas

onin

gan

dju

dgm

ent

inm

aste

ryof

the

envi

ronm

ent,

7.so

cial

skill

sD

urin

gla

tead

oles

cenc

ean

dad

ult

life:

8.vo

ca-

tiona

lan

dso

cial

resp

onsib

ilitie

san

dpe

rfor

man

ces.

Impa

irm

ents

inad

aptiv

ebe

havi

our

refe

rsto

signi

fican

tlim

itatio

nsin

anin

divi

dual

’sef

fect

iven

ess

inm

eetin

gth

est

anda

rds

ofm

atur

atio

n,le

arn-

ing,

pers

onal

inde

pend

ence

,or

so-

cial

resp

onsib

ility

that

are

expe

cted

for

his

orhe

rag

ele

vel

and

cultu

ral

grou

p.M

aybe

refle

cted

inth

esa

me

area

sas

1973

.

10ad

aptiv

esk

illar

eas:

com

mun

icat

ion,

self-

care

,ho

me

livin

g,so

cial

skill

s,co

mm

unity

use,

self-

dire

ctio

n,he

alth

and

safe

ty,

func

-tio

nal

acad

emic

s,le

isure

,an

dw

ork.

The

rele

vant

skill

sw

ithin

each

adap

tive

skill

area

may

vary

with

chro

nolo

gica

lag

e,so

that

asse

ssm

ent

offu

nctio

ning

mus

tbe

refe

renc

edto

the

pers

on’s

chro

nolo

gica

lag

e.

Leve

lsof

se-

veri

tyB

orde

rlin

ere

tard

atio

nIQ

68–8

4—

—In

tens

ities

ofsu

ppor

ts:

Mild

reta

rdat

ion

IQ52

–67

Mild

reta

rdat

ion

IQ52

–67

Mild

reta

rdat

ion

IQ50

–55

toap

prox

-im

atel

y70

Inte

rmitt

ent

Mod

erat

ere

tard

atio

nIQ

36–5

1M

oder

ate

reta

rdat

ion

IQ36

–51

Mod

erat

ere

tard

atio

nIQ

35–4

0to

50–5

5Li

mite

d

Seve

rere

tard

atio

nIQ

20–3

5Se

vere

reta

rdat

ion

IQ20

–35

Seve

rere

tard

atio

nIQ

20–2

5to

35–4

0E

xten

sive

Prof

ound

reta

rdat

ion

IQ�

20Pr

ofou

ndre

tard

atio

nIQ

�20

Prof

ound

reta

rdat

ion

IQbe

low

20or

25Pe

rvas

ive

Uns

peci

fied

*Sou

rces:

Gro

ssm

an19

83;

Patt

onet

al.,

1990

;Lu

ckas

son

etal

.,19

92;

Wen

,19

97.

119MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

prevalence of MR. This is partly becausethere are no totally objective and stan-dardized measures of social adaptive be-havior, particularly in different socioeco-nomic and cultural environments. Forinstance, because of inconsistencies andlack of standardization of testing, adap-tive functioning was not included in thepopulation-based Atlanta study of MRprevalence [Yeargin-Allsopp et al., 1992].The authors acknowledged that childrenmight have been included based on IQcriteria who would have been excluded byadaptive functioning criteria.

Replacing the levels of IQ scorewith intensities of supports in severitymeasures in the new AAMR manual hasbeen criticized in that it makes it moredifficult to differentiate groups with mildfrom those with severe or profound MR,a division thought by some [MacMillanet al., 1993] but not by others [Schalocket al., 1994] to separate organic and non-organic etiologies. McLaren and Bryson[1987] claimed that this distinction is notclear cut and that there is likely to be anetiological role from the interaction ofpsychosocial and biological factors.Greenspan [1999] on the other hand feltthat the severity levels previously usedwere of little value from an educationalperspective and should be replaced with amore descriptive term such as “evidenceof inability to benefit from skilled readinginstruction.” The recent AAMR revisionhas also been criticized because sub-av-erage intellectual functioning has beendefined as “an IQ score of approximately70 to 75 or below.” MacMillan et al.[1993; 1995] described this range as im-precise and capable of targeting an addi-tional 2.8% of the population with IQsbetween 71 and 75. They argued that thisdefinition has been designed as a tool foradvocacy rather than one to meet theneeds of clinicians and researchers.

A binary categorization of severityof MR by IQ level is often used, with anIQ � 50 being considered as severe MR(SMR) [Kiely, 1987; Decoufle andBoyle, 1995; Roeleveld et al., 1997].However, in accordance with the previ-ous AAMR [Grossman, 1993] andDSM-IV [American Psychiatric Associa-tion, 1994] definitions, most Australianstudies, including the Western Australianstudy [Wellesley et al., 1992], have used adefinition of severe MR as IQ � 35 orIQ � 40. Thus, the use of different IQcut-off points and groupings for severeMR means that care is required whencomparing severity levels across studies.

Estimates of MR prevalence canalso vary according to whether psycho-metric or diagnostic criteria or educa-

tional experience are used to define cases[Hansen et al., 1980], with minoritygroups often disproportionally affected[Reschly and Jipson, 1976; McMillan etal., 1993]. Using the prospective River-side California study [Mercer, 1973],Hansen et al. [1980] showed that theestimated MR prevalence could be re-duced from 2% to 1% by the inclusion ofadaptive behavior as one of the criteria.The use of assessment of adaptive behav-ior or nonverbal intellectual measureshelps to reduce false positives [Reschlyand Jipson, 1976; McMillan et al., 1993],with the reduction more marked inAfrican-Americans and Hispanics. Thefindings in our Australian study [Leonard

et al., 2002a) of a doubling in the prev-alence of MR and a marked increase inthe proportion of indigenous childrenwhen educational sources are also in-cluded are consistent with those from theRiverside study.

The implications for epidemiolog-ical research of having a stable definitionor classification system that will allowcomparison over time and place are crit-ical. Taxonomy in this field is particularlydifficult because professionals and con-sumers come from a range of back-grounds and have different purposes suchas advocacy, education, medical care andservice provision. Fryers [1987; 1992]stressed the importance of epidemiolog-

ical taxonomy with universally acceptedclassification systems, categorizations andstandardized ways of measuring. Hestated that there is a need for a conceptualframework that would relate the differentdefinition and classification systems in away amenable to scientific investigationand that the tool for this was the thenICIDH, which puts more weight on therelationship between the individual andthe environment. ICD-10 provides anetiological framework and diagnosticclassifications of diseases, disorders andother health conditions, and this is nowcomplemented by the additional infor-mation provided by International Classi-fication of Functioning, Disability andHealth (ICF) (ICIDH-2) [World HealthOrganization (WHO), 2001] on func-tioning and person-environmental inter-action. Information about diagnosis plusfunctioning provides a broader and moremeaningful picture of MR in the popu-lation.

FURTHER METHODOLOGICALISSUES AFFECTINGPREVALENCE

Differences in definition and ascer-tainment methods including type of datasources and purpose behind data collec-tion [Hansen et al., 1980] are also criticalconsiderations in the epidemiology ofMR. They certainly account for some ofthe variation in prevalence [Roeleveld, etal., 1997; Larson et al., 2001]. Both Kiely[1987] and Roeleveld et al. [1997] haveused various criteria including method ofcase ascertainment and classification toevaluate the validity of previously pub-lished prevalence studies. Cases should beascertained from entire populations, andnot limited only to individuals receivingselected specialty services (e.g., hospital-based services) or living in institutions.Such individuals may not be representa-tive of the larger population of affectedindividuals. On the other hand registersset up for the purpose of population-based ascertainment [Fryers and Mackay,1979] can also be at risk of overascertain-ment if cases who are deceased or whosestatus is otherwise changed (e.g., bymoving out of the study area) are notremoved [Larson et al., 2001]. Roeleveldet al. [1997] differentiated between “trueprevalence,” i.e., “the total number ofmentally retarded people in a popula-tion,” and “ascertained prevalence,” i.e.,“the number of cases recorded by theauthorities.” However, Yeargin-Allsoppet al. [1992] found that when quality andquantity of services are high and multipleadministration data sources are used, ad-ministrative prevalence closely approxi-

The implications forepidemiological research

of having a stabledefinition or classification

system that will allowcomparison over time and

place are critical.Taxonomy in this field is

particularly difficultbecause professionals andconsumers come from a

range of backgrounds andhave different purposes

such as advocacy,education, medical careand service provision.

120 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

mates “true prevalence.” Yeargin-Allsoppet al. [1992] have described a “multiplesource” approach to ascertainment of MR.They carried out a comprehensive epide-miological study of children with develop-mental disabilities using several differentdata sources including school hospital andother service records. Most children wereascertained from education data sources.They did not identify through the hospitalsystem any cases that were not ascertainedelsewhere.

Technological advances have nowmade linkage of multiple data sources apowerful tool in epidemiological studiesand provide significant opportunities forthe etiological investigation of MR[Boussy and Scott, 1993; Bower et al.,2000]. Using record linkage involvingthe birth notifications register, a contin-uous census system known as the CentralPopulation Register, the Cause-of DeathRegister, the Statistical Database forChild Care Support and regional MRregisters Gissler et al. [1998; 1999] foundthe cumulative incidence of MR to be6.1 per 1000 in children aged 0–7 years.By linking service agency records with apopulation cohort of 4,590,333 Califor-nian births Croen et al. [2001] found aprevalence of 5.2 per 1000. They usedthese data to investigate the contribution

of various maternal and infant character-istics to the etiology of MR of unknowncause. Records of Western Australianchildren with MR identified from a reg-ister were linked with three educationaldata sources and with the 240,358 birthsbetween 1983 and 1992 [Leonard et al.,2002a]. The prevalence of MR was14.2/1000 for children aged 6–16 years.

PREVALENCEAs discussed in early sections, the

considerable variations in the estimates ofprevalence across countries and regionsfrom 2 to 85/1000 [Roeleveld et al.,1997] may be attributable to the varia-tions in major classification systems andthe diversity in study operational defini-tions and methodologies. Nevertheless,many reviews of international epidemio-logical studies suggested that the preva-lence of severe MR (SMR) is approxi-mately 3 to 4 per 1000 in children[Starza-Smith, 1989; Roeleveld et al.,1997] and in adults [Reschly, 1992] inboth developed and developing countries[Kiely, 1987]. Figures 1 and 2, showingthe prevalence of SMR and MMR, havebeen adapted from Roeleveld et al.[1997] and modified by the inclusion ofrecent studies F, G, S, C and L [Fernell etal., 1996; Fernell et al., 1998; Stromme

and Valvatne, 1998; Gissler et al., 1999;Cans et al., 1999; Leonard et al., 2002a].The addition of the new studies does notalter the pattern for mild MR (MMR)(IQ 50–70) which was already very vari-able. Roeleveld et al. [1997] had previ-ously commented that it was not knownwhether this variability represented truedifferences or differences in methodol-ogy. However some of the more recentpopulation-based studies suggest a lowerprevalence of SMR than the 3.8/1000average previously found [Roeleveld etal., 1997]. As already discussed for a bi-nary categorisation of severity the natureof the recent Western Australian data[Leonard et al., 2002a] made it necessaryto use a lower IQ cut-off point (IQ � 35or 40 according to psychological test) todefine SMR. Therefore one might ac-cordingly expect the Western Australianestimate to be lower for SMR and higherfor MMR in comparison to other stud-ies. In defining SMR previously fromWestern Australian data [Wellesley et al.,1992], Roeleveld et al. [1997] includedas severe children who had been classifiedin the original study as moderate (IQ35–54)—a categorisation not availablefrom the present data. However, the pre-vious estimate of SMR (IQ � 35) forchildren born in Western Australia be-

Fig. 1. Prevalence of SMR in children of school age 1960–2002.

121MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

tween 1967 and 1976 of 1.6/1000[Wellesley et al., 1992] is extremely closeto our current estimate of 1.4/1000. Whatis different is the prevalence of what wehave defined as MMR which has almostdoubled from 5.4 to 10.6 per 1000 by theinclusion of education department datausing record linkage. These examples un-derscore the importance of attention todefinitions and ascertainment methods inthe interpretation of results particularly indata relating to different time periods.

AGEThe selection of population age

groups at risk (children, adults, the el-derly or total population) will result indifferent estimates of prevalence. Opin-ion concerning the value of examiningage-specific MR prevalence rates varies[Kiely, 1987; Roeleveld et al., 1997].Data from the Australian Bureau of Sta-tistics disability survey (1993) showedthat the age-specific prevalence rate in-creased with age. It peaked at age 10–14years, declined slightly among adoles-cents, and then fell markedly amongadults [Wen, 1997]. This pattern is con-sistent with the findings from other in-ternational studies [Kiely 1987; McLarenand Bryson, 1987]. The dramatic in-

crease and marked fall of the reportedage-specific prevalence rates across agegroups may not necessarily mirror varia-tions in the actual prevalence among thepopulation. Rather, it probably reflectsdifferences in case ascertainment. Thehigh prevalence of MR among childrenat school age demonstrates the great im-pact of the education system on caseidentification. The Metropolitan AtlantaDevelopmental Disabilities Study se-lected the age of ten years for case ascer-tainment, as the investigators felt thatsome disabilities might not become ap-parent until this age.

The age variation may also be dueto the ability of adults with MMR toadapt to the demands of society with thepassage of time. The adaptive behaviorcriterion was originally added to the IQassessment in the fifth revision of theAAMR manual [Heber, 1961] to removethe false positives which were specific toMMR and only identifiable through theschool system. As also noted by Murphyet al. [1995], it is important to recognizethat IQ scores can change over time bothin individuals and groups. An IQ test is ameasure not of innate intellectual func-tioning but of performance on a set ofskills defined by test instruments. Hence,

any accumulated experience or improve-ment in performance a person brings tothe test situation will be reflected in theIQ score. This phenomenon could ac-count for the fact that in the WesternAustralian study, although there was littledifference in the eligibility criteria for thetwo ascertainment sources, a proportionof children identified though the stateeducation services had been previouslyevaluated by the state disability servicesbut did not meet the case definition forthe study [Leonard et al., 2002a]. Thedifferentials in mortality between peoplewith MR and the general populationmay also account, to some extent, for thelower prevalence of MR among the adultpopulation.

GENDERA higher prevalence of MR among

male children has been noted [Richard-son et al., 1986]. Drews et al. [1995]found that males were between 1.6 to 1.7times more likely to experience MMR,SMR, isolated MR or MR accompaniedby other neurological disorders than fe-males (Table 2). Croen et al. [2001]found that the relative risk for males forMMR (1.9) was greater than for SMR(1.4) (Table 2) for MR of unknown eti-

Fig. 2. Prevalence of MMR in children of school age 1960–2002.

122 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

ology. Analysis of data from the Austra-lian population disability survey revealedthat the gender difference increased withage up to 15 years, after which the dif-ference then decreased substantially[Wen, 1997]. Among people aged 40 andover, there was no consistent pattern ofgender difference across age groups. It isalso reported that the marked gender dif-ferences in prevalence among Canadianchildren, apparent prior to 12 years, be-come insignificant after that age [McLarenand Bryson 1987].

In terms of biological factors, thegender difference in prevalence is oftenattributed to X linked conditions [Tari-verdian and Vogel, 2000; Chelly andMandel, 2001], including both Fragile Xas well as unidentified X linked condi-tions [Partington et al., 2000]. A maledisadvantage is also seen in relation toneonatal mortality. Among infants withvery low birthweight, mortality for boyswas 22% compared with 15% for girls[Stevenson et al., 2000]. Moreover, Za-ren et al. [2000] found that maternalsmoking had a proportionally greaterdetrimental effect on male than femalefetal growth. More recently Matte et al.[2001] also found that the association be-tween birthweight and IQ was strongerin males than females, once again sup-porting this male disadvantage. The samemale excess is seen in attention deficithyperactivity disorder, the commonest ofthe conditions contributing to the appar-ent epidemic in diagnosed psychosocialdisorders over the past two decades[Kelleher et al., 2000]. Using the termpercentage of expected birth weight forgestational age as a measure of appropri-ateness of fetal growth, Zubrick et al.[2000] have demonstrated the associationbetween low birth weight, also a riskfactor for MR, and an increase in childmental health problems with boys againmuch (38%) more likely to have a prob-lem. It seems plausible that shared causalpathways will contribute to a continuumof neurodevelopmental and psychologi-cal outcomes which include MR and inwhich there is an increased male suscep-tibility.

Since no gender differences werefound for MMR in the testing situationin a study of 950 nonreferred Arizonachildren [Reschly and Jipson, 1976] it isalso possible that there are factors thatmake boys more likely to be referred forservices and hence identified as havingMR. The Western Australian study alsofound that boys with Down syndrome(DS) had significantly lower WeeFIMscores than girls, though it is not clearwhether this is biologically or socially

Tab

le2.

Pre

vale

nce

ofM

Rin

Sel

ecte

dStu

die

sU

nder

take

nin

the

Pas

tD

ecad

e

Publ

icat

ion

Loca

tion

Num

ber

ofca

ses

Sour

cepo

pula

tion

Prev

alen

cepe

r10

00M

ale

tofe

mal

era

tio

Rac

eC

ompa

riso

ngr

oup:

Afr

ican

-Am

eric

anor

In-

dige

nous

Aus

tral

ian

Mat

erna

lE

duca

tion

orSo

cioe

cono

mic

stat

us

Wel

lesle

yet

al.

[199

2]W

este

rnA

ustr

alia

1602

210,

789

7.61

(7.2

–8.0

)1.

51.

5(1

.2–1

.8)

Can

set

al.

[199

9]Fr

ance

-3re

gion

s11

5032

5,34

73.

5(3

.3–3

.7)

1.4

SMR

(IQ

�50

)H

ouet

al.

[199

8]T

aiw

an11

892

423,

000

2.8

(2.8

–2.9

)1.

4St

rom

me

and

Hag

berg

[199

8]A

kers

hus

Cou

nty,

Nor

way

185

30,0

376.

2(5

.3–7

.0)

1.3

Whe

nSE

Sdi

chot

omise

dR

Rfo

rM

MR

com

pare

dw

ithSM

R2.

6fo

rch

ildre

nof

low

SES

pare

nts

0.2

for

16�

year

s(is

olat

edM

R)

refe

renc

e12

year

sM

urph

yet

al.

[199

5]A

tlant

a,U

SA10

7489

534

12.0

(11.

3–12

.7)

MM

R1.

6M

MR

1.8

Dre

ws

etal

.[1

995]

Yea

rgin

-Alls

opp

etal

.[1

995]

SMR

1.7

SMR

1.4

Dec

oufle

and

Boy

le[1

995]

Cro

enet

al.

[200

1]C

alifo

rnia

,U

SA23

,956

4590

333

5.2

(5.2

–5.3

)*M

MR

1.9

(adj

uste

d)*M

MR

1.5

(adj

uste

d)*M

MR

0.3

for

post

-gra

duat

eed

ucat

ion

refe

renc

e�

high

scho

ol(a

djus

ted)

*SM

R1.

4(a

djus

ted)

*SM

R1.

6(a

djus

ted)

*SM

R0.

4fo

rpo

st-g

radu

ate

educ

atio

nre

fere

nce

�hi

ghsc

hool

(adj

uste

d)Le

onar

det

al.

[200

1]W

este

rnA

ustr

alia

3426

2403

5814

.2(1

3.8–

14.7

)M

MR

1.6

MM

R2.

6SM

R1.

5SM

R1.

7G

issle

ret

al.

[199

9]Fi

nlan

d36

760

,254

6.1

(5.5

–6.7

)1.

4Pa

rtin

gton

etal

.[2

000]

New

Sout

hW

ales

,A

ustr

alia

527

172,

914

3.04

(IQ

�50

)1.

4

*MR

ofun

know

net

iolo

gy

123MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

determined [Leonard et al., 2001]. Gissleret al. [1999] studied gender differences ina range of childhood morbidities and dis-abilities including MR (Table 2). In ad-dition to the clearly increased risk forMR, males were over twice as likely tohave delayed development and to requirespecial education. These differences per-sisted after adjustment for health relatedvariables. This raises the issue of the pos-sible social determinants of gender differ-ences and the potential for interventionin the educational system.

SOCIAL CLASSIn addition to the demographics of

age and sex, MR prevalence can also beinfluenced by social, economic, cultural,racial/ethnic and other environmentalfactors. Many studies have consistentlyfound that the prevalence of MMR wasstrongly associated with low socioeco-nomic status [Drews et al., 1995], andsome suggested that MMR was rarelyfound in the highest socioeconomicgroups, unless accompanied by evidenceof organic damage. In a Norwegian studyrepresenting 30,037 births, children ofparents with lower socioeconomic statuswere found to have a significantly in-creased risk of MMR but not of SMR[Stromme and Magnus, 2000] (Table 2).

The Metropolitan Atlanta study(Table 2) results support the idea of twodistinct types of MR: isolated MR,which is mainly influenced by social anddemographic factors; and MR with otherneurological conditions, which is largelyaffected by biological or pathological fac-tors [Drews et al., 1995]. In a furtheranalysis of the same Atlanta data, Dec-oufle and Boyle [1995] confirmed thestrong inverse relationship between ma-ternal education and isolated MR (Table2). These findings were in keeping withthe previous hypothesis of Zigler et al.[1984] that there are two distributioncurves, one following the Gaussian curvewith a mean of 100 and a second repre-senting “organic” damage, with a muchlower IQ distribution. Yet, in a very largepopulation-based study of 4,590,333 birthsthat focused on MR of unknown etiology,Croen et al. [2001] found a relationshipbetween low maternal education and bothMMR and SMR (Table 2). However, de-spite this finding, 59% of MR of unknownetiology occurred in children of womenwith college and postgraduate education.This was in contrast to the findings ofDrews et al. [1995], who found that iso-lated MMR was rare in children of moth-ers with the highest median income andmore than 12 years of education.

International comparisons indicatethat, in general, lower prevalence esti-mates of MMR have been reported fromScandinavia [Hagberg et al., 1981;Stromme and Valvatne, 1998]. When theOrganization for Economic and Cooper-ative Development graphed the relation-ship between parental level of education,a proxy for socioeconomic status, andyouth literacy scores, Sweden was foundto score highest with the most shallowgradient [Willms, 1997]. This is anotherexample of the correlation between thelevel of social inequality or equitable in-come distribution and a range of healthand other outcomes [Kawachi andKennedy, 1997]. In contrast to the de-veloped world, the impact of MR indeveloping countries has been less wellstudied, although it is likely to be muchmore important and the opportunities forprevention much greater. Worldwide io-

dine deficiency is the leading cause ofpreventable MR and its elimination iswithin reach with enormous benefits[Delange et al., 2001]. Moreover Chi-nese studies show that genetic factorsmay also underly the susceptibility to io-dine deficiency [Wang et al., 2000]. Inthe meantime, epidemiological data re-lating to MR in China with its 1.3 billionpopulation are limited [Sonnander andClaesson, 1997]. However, in Bangla-desh it has been shown that the associa-tion with socioeconomic status wasmuch more marked for MMR than forSMR [Islam et al., 1993].

ETHNICITYIn parallel with their greater use of

special education services, there is a gen-erally higher prevalence of MR amongAfrican-American children as comparedwith children of other racial groups

[Yeargin-Allsopp et al., 1995; Murphy etal., 1995]. MMR was more common inAfrican-American children in Atlanta, af-ter controlling for selected socioeco-nomic and demographic factors such assex, maternal age, birth order, maternaleducation and economic status (Table 2)[Yeargin-Allsopp et al., 1995]. More re-cent California data [Croen et al., 2001]also showed an increase of about 50% inAfrican-American children having MRof unknown etiology as compared toother racial groups (Table 2). Findingswith respect to the Australian indigenouspopulation are similar to the African-American population in Atlanta [Leonardet al. 2002] (Table 2).

In assessing the reasons for racialdifferences in prevalence it is importantto consider both methodological issuesand potential confounders including casedefinitions, study design, demographiccomposition of the study population,maternal factors, early intervention ef-forts and other social, economical andcultural factors. Researchers have specu-lated that unmeasured confounders suchas maternal intelligence and housing den-sity may have contributed to their find-ings. Indeed, although controlling for allconfounders is difficult many of the mea-surable ones have been examined in in-dividual studies [Yeargin-Allsopp et al.,1995; Croen et al., 2001]. However, in arecent comparative analysis of changes inchildren’s IQ scores in two socioeco-nomically disparate communities, it wasfound that growing up in a racially seg-regated and disadvantaged community,more than individual and familial factors,may contribute to a decline in children’sIQ scores in early school years [Breslau etal., 2001].

As noted by Yeargin-Allsopp et al.[1995], children from minority culturesare also more likely to be labelled ashaving MR as a result of cultural differ-ences including socially different behav-ior and culturally inappropriate IQ tests[Zigler, 1987]. Reschly and Jipson [1976]showed that by the application of non-verbal intellectual measures the racial dif-ference in MR (IQ � 70) was muchreduced. Kearins [2000] has providedguidelines on how Australian indigenouschildren could be more appropriately as-sessed. The rate of MR for Australianindigenous children was nearly threetimes greater in children identifiedthrough the educational system than inchildren attending the state disability ser-vice [Leonard et al., 2002]. This couldeither indicate inappropriate labelling ofindigenous children in the educationalsystem or it could mean that a higher

It seems plausible thatshared causal pathways

will contribute to acontinuum of

neurodevelopmental andpsychological outcomeswhich include MR andin which there is an

increased malesusceptibility.

124 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

proportion of eligible indigenous thanCaucasian children were not accessingdisability services. We were unable tocompare the prevalence of MR by eth-nicity in children diagnosed before andafter school entry. However, we did findthat there was an increasing trend withage and that only 10% of seven-year-oldas compared with 14.7% of twelve-year-old children in our cohort were indige-nous (unpublished data).

Yeargin-Allsopp et al. [1995] con-sidered possible prenatal or postnatal fac-tors which may be increasing the risk ofMR in African-American children.These included the increased prevalenceof maternal conditions such as diabetes,hypertension and chronic renal disease, apattern also mirrored in the Australianindigenous population [McLennan andMadden, 1999]. Possible postnatal childfactors included elevated lead levels andanemia. A more recent study [Hurtado etal., 1999] reviewed by Pollitt [1999] usedrecord linkage to show that for each dec-rement in haemoglobin the risk of mildto moderate MR increased by 1.28-foldafter controlling for birthweight, mater-nal education, sex, maternal age, race andchild’s entry age into the program. Otheretiological pathways for the racial differ-ences may involve intergenerational fac-tors as has been postulated in the AfricanAmerican population [Chapman andScott, 2001]. It is likely that these issueswill need to be addressed by changes insocial policy rather than by implementa-tion of individual interventions.

ETIOLOGY

Issues Relating to Some EtiologicalClassification Systems

The fifth revision of the AAMRclassification system [Heber, 1961] placedemphasis on the presumed cause of MRand combined etiologies such as DS(which could be considered a sufficientcause of MR) with component causes,such as intrauterine growth retardation.It did not clearly provide a map to de-termine which diagnoses should be con-sidered “genetic” and which “multifac-torial.” Nor was it clear how diagnosticcategories can be translated into prenatal,perinatal, postnatal and unknown group-ings. The ninth revision of the AAMRclassification manual [Luckasson et al.,1992] updates the etiologic categories,but under prenatal causes still includesteratogens and maternal diseases that mayonly be a contributing factor to the causalpathway. It also indicates as postnatalRett syndrome and a range of neurode-generative disorders whose etiology is

clearly genetic. Although manifestingpostnatally, these conditions should beclassified as prenatal in origin. Therefore,the most recent revision of the AAMRclassification system is still not ideal anddoes not adequately reflect our presentunderstanding of biological mechanisms.

Wilska and Kaski [1999] haveadapted the methods and systems used toassist with the work up of individuals withMR in Finland over the last two decades todevelop an etiological classification systembased predominantly on timing. They feelthat their model is as equally appropriate foretiological investigation as it is for clinicalevaluation, and that it is applicable both toservice providers and society. Their classi-fication system can be depicted as a multi-dimensional etiological tree. It has five ma-jor divisions: 1) genetic, CNS multiple andmalformation syndromes of unknown ori-

gin; 2) external prenatal disorders 3)paranatal disorders; 4) postnatal disorders;and 5) unknown etiology. These can bethen be divided into further groups, sub-groups and individual diagnoses. This sys-tem has the flexibility to allow extrabranches to be added when new geneticdisorders are identified. A universally ac-cepted, consistent, and reliable etiologicalclassification system for MR and one flex-ible enough to include recently elucidateddisorders would be helpful both from aclinical and epidemiological perspective. Itwould assist with the investigation of thestill sizeable proportion of undefined MR,some of which is likely to be genetic.

Recent Etiological Studies of MRUsing the system of Heber et al.

[1961], Wellesley et al. [1991] designated72% of cases ascertained in their popula-

tion-based study as having a prenatal,perinatal or postnatal etiology (Tables 2and 3). Hou et al. [1998] used the clas-sification system reported by Crocker[1989] that, like the AAMR system[Luckasson et al., 1992], is also based onthe timing of the insult (i.e., prenatal,perinatal, postnatal and unknown) in alarge etiological population-based studyin Taiwan (Tables 1 and 2). A geneticetiology was identified in 54.7 % of thecases, including 16.2% with multifactorialinheritance. DS accounted for 82.4% andFragile X 12.3% of the chromosomal ab-normalities. Contiguous gene syndromes,particularly Angelman and Prader Willi,were the next most common.

Using medical information fromthe department of special education,Cans et al. [1999] investigated childrenaged 7–16 years with SMR (IQ � 50)with and without cerebral palsy (CP)from a population base of 325,347 chil-dren (Tables 2 and 3). They assignedcases to etiological groupings: definite(25%, e.g., trisomy 21, metabolic disor-ders); suspected (25.8%, e.g., fetopathy orfetal asphyxia); and unknown (49.3%)and also classified whether or not CP waspresent. The results suggested differencesin the underlying pathogenic factors be-tween those with and without CP.

Fernell [1998] studied the cause ofSMR (defined as an IQ below 50 to 55)in a population of 14138 Swedish chil-dren (Table 3). The etiology of twothirds of cases was identified as arisingprenatally, 4/64 from the perinatal periodand 3/64 postnatally while in the remain-der it was unclassified. Stromme andHagberg [2000] recently investigatedMR etiology from a larger populationbase of 30,037 Norwegian children (Ta-bles 2 and 3). Using multiple sources,they identified 185 children who wereclinically investigated using a rigorous di-agnostic work-up. Cases were subdi-vided into “biopathological” and “un-specified” and classified according totiming of the event as in the study ofHou et al. [1998]. Compared with the50.3% that Cans et al. [1999] assigned toa known or suspected etiology, theyidentified a “determined” biopathologi-cal etiology in 62/79 (78.5%) cases withSMR. However this group included anunknown category relating to unspeci-fied syndromes and brain anomalies. Asdid Hou et al. [1998], Cans et al. [1999]and Fernell [1998], they found DS to bethe most common single entity whilstother specific genetic MR syndromesand fetal alcohol syndrome were lesscommon with a prevalence in the regionof 1:10,000–15,000 children but with

A universally accepted,consistent, and reliableetiological classificationsystem for MR and one

flexible enough to includerecently elucidateddisorders would behelpful both from a

clinical andepidemiological

perspective.

125MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

Tab

le3.

Rec

ent

Etiolo

gica

lStu

die

sofM

R

Loca

tion

Cas

epo

p-ul

atio

nSo

urce

ofca

ses

Etio

logi

cal

clas

sifi-

catio

nC

omm

oncl

inic

alen

titie

sPr

opor

tion

with

diag

nosis

Wor

k-up

Wel

lesle

yet

al.

[199

2]W

este

rnA

ustr

alia

1602

Aut

hori

tyfo

rIn

telle

c-tu

ally

Han

dica

pped

Pers

ons

IQ�

70

Heb

erD

own

synd

rom

e(1

3.9%

),sp

ecifi

cae

tiolo

gies

not

othe

rwise

de-

fined

.

72%

(pre

nata

l,pe

rina

tal

and

post

nata

l)

Use

dav

aila

ble

diag

-no

ses

Can

set

al.

[199

9]Fr

ance

-3re

gion

s11

50D

epar

tmen

tal

Com

mis-

sion

for

Spec

ial

Edu

-ca

tion

Kno

wn,

susp

ecte

dor

unkn

own

Dow

nsy

ndro

me

(16%

)25

%(k

now

n)26

%(s

uspe

cted

)U

sed

diag

nost

ic&

med

ical

info

rmat

ion

from

heal

thpr

acti-

tione

rsH

ouet

al.

[199

8]T

aiw

an11

892

Spec

ial

scho

ols

&in

sti-

tutio

nsPr

enat

al,

peri

nata

l,po

stna

tal

orun

know

n[C

rock

er,

1989

]

Dow

nsy

ndro

me

(13%

),Fr

agile

X(1

.9%

),Pr

ader

Will

i(0

.5%

)A

n-ge

lman

s,N

oon-

ans

(0.2

9%)

68%

(pre

nata

l,pe

rina

tal

and

post

nata

l)

Pane

lof

clin

icia

nsre

-vi

ewed

fam

ilyhi

s-to

ry&

med

ical

reco

rds.

Cyt

ogen

etic

&m

olec

ular

stud

ies.

Neu

roim

agin

gfo

rst

ruct

ural

defe

cts

Stro

mm

ean

dH

egbe

rg[1

998]

Ake

rshu

sC

ount

y,N

orw

ay18

5M

ultip

leso

urce

sB

iopa

thol

ogic

alor

unsp

ecifi

edD

own

synd

rom

e(9

.5%

),fe

tal

alco

-ho

lsy

ndro

me

(1.7

%),

Frag

ileX

(Aor

E)

(1.7

%)

Will

iam

syn-

drom

e(1

.7%

)

66%

(pre

nata

l,pe

rina

tal

and

post

nata

l)14

%un

dete

rmin

ed

Kar

yoty

ping

,m

etab

olic

scre

enin

g,ne

uroi

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126 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

wide confidence intervals. Prevalence ofFragile X (A or E) (2% of children withMR) was consistent with the findings ofthe much larger Taiwan study [Hou etal., 1998]. The three cases of fetal alcoholsyndrome gave an estimated prevalenceof 1 per 10,000 births, significantly lowerthan the 28–46 per 10,000 live birthsreported in the United States [Sampsonet al., 1997; Bagheri et al., 1998]. Thisstudy, like others, identified the presenceof neurological impairments such as epi-lepsy and CP in 20% and 14% of thepopulation respectively. They alsoshowed that microcephaly (head circum-ference less than 2.5th percentile) waseight times more common than in thegeneral childhood population.

Partington et al. [2000] reviewedthe clinical diagnosis in 429 cases of MR,aged 4–18 years, whose parents were liv-ing in 1990–1991 in five different areasin New South Wales (the most populatedAustralian state) [Einfeld and Tonge,1996]. Cases were classified according towhether they had a firm causal, a descrip-tive, or an unknown diagnosis (Tables 2and 3). As in the study of Stromme andHagberg [2000], clinical and laboratoryinvestigations were instituted (althoughthe extent of these is not documented).The prevalence of discrete diagnostic en-tities was consistent between the twostudies with four cases each of tuberoussclerosis and Fragile X syndrome andthree of Prader Willi, Angelman andRett syndrome. The authors note, how-ever, that their population is biased to-wards individuals with an IQ � 50where one would expect a higher prev-alence of identifiable syndromes.

Yeargin-Allsopp et al. [1997] pre-viously reviewed biomedical causes ofMR in their Atlanta population-basedstudy (Tables 2 and 3). Unlike Strommeand Hagberg [2000] and Partington et al.[2000], but like Cans et al. [1999], theyrelied completely on available recordsfrom multiple sources to designate theprobable cause. They used a hierarchialsystem based on timing of the event sim-ilar to Stromme and Hagberg [2000] andHou et al. [1998] but with more struc-ture and detail. They were able to iden-tify a defined biomedical cause in 22% ofall cases (13.4% of MMR and 33.2% ofSMR). This proportion is much lowerthan that of Hou et al. [1998] andStromme and Hagberg [2000], but moreconsistent with the recent Californianrecord linkage study [Croen et al., 2001].

For the cohort of 1,560 WesternAustralian children identified from thestate disability service and born between1983 and 1992 the syndromes, other than

frank chromosomal abnormalities, inwhich three or more live cases wereidentified were tuberous sclerosis, PraderWilli, Williams, Fragile X, Noonan,Rett, Angelman, Sanfilippo, Aarskog,Cornelia de Lange, neurofibromatosisand Coffin Lowry [Leonard, unpublisheddata]. With a denominator of just under aquarter of a million births and ten themaximum number of cases for any disor-der, the prevalence would not exceed1:25,000 for any of these conditions.Thus, apart from DS, the proportion ofMR cases affected by individual biomed-ical entities is very small. Most of thesewould be genetic syndromes with overone thousand entries for MR on OnlineMendelian Inheritance in Man in 2001[OMIM, 2001].

For many genetic syndromes, diag-nosis in the past was made on clinicalgrounds often with the assistance of spe-cific criteria [Trevathan and Moser,1988; Williams, 1995]. Diagnostic testsare now available, at least in the devel-oped world, for most of these disorderseither by fluorescent in situ hybridiza-tion, DNA testing including methylationstudies or gene sequencing. Moreoversubmicroscopic deletions in the subtelo-meric chromosomal regions, now beingfound to be associated with MR [deVries et al., 2001] may occur in 3.6% ofpreviously undiagnosed cases [Baker etal., 2002]. X linked genes have also nowbeen identified, many within the last fiveyears, in fifteen syndromes causing SMR,and in a further eight MMR may or maynot be present [Chelly and Mandel,2001]. The commonest of these wouldbe Fragile X and Rett syndrome affecting1:4,000 males [Turner et al., 1996] and1:10,000 females [Leonard et al., 1997],respectively. As well as the Methyl CPGbinding protein 2 in the latter [Amir etal., 1999], other recently identified genesinclude the doublecortin gene in X–linked lissencephaly [Gleeson et al.,1998] and the Ring box/B-box proteingene in the Opitz G/BBB syndrome[Quaderi et al., 1997]. It is also worthy tonote that MECP2 mutations are nowbeing found in males with neurodevel-opmental disorders [Clayton-Smith et al.,2000; Couvert et al., 2001] and in new-born or nonprogressive encephalopathy[Wan et al., 1999; Villard et al., 2000;Imessaoudene et al., 2001], as well as inX linked mental retardation [Orrico etal., 2000; Meloni et al., 2000; Couvertet al., 2001]. This has led Couvert et al.[2001] to suggest that MECP2 muta-tions could be as common a cause ofMR as Fragile X syndrome and for

Dotti et al. [2002] to characterise a newtype of X linked MR.

Variation in Prevalence of KnownEtiologies

Variation between studies in theprevalence of cases with a known etiol-ogy is likely to be influenced both byhow a “known etiology” is defined, bythe extent of the clinical investigationsundertaken and possibly by the specialtyof the diagnostician. Although Yeargin-Allsopp et al. [1997] acknowledge thatfactors such as intrauterine growth retar-dation (IUGR) (also included as causal inthe system used by Hou et al. [1998]) andlow birth weight are risk factors for MR,they do not consider that they meet thecriteria for a biomedical cause. Neverthe-less, their proportion of unequivocal ge-netic causes would still seem lower thanin the studies of Hou et al. [1998] andStromme [2000]. Despite the fact that theAtlanta study had a larger base populationthan the Norwegian study no cases ofWilliams, Angelman, Rubenstein Taybior even Rett syndrome were identified,suggesting perhaps some under diagnosis.On the other hand fetal alcohol syn-drome, although still only representing2% of MMR, was the second common-est cause after DS. This was in approxi-mate agreement with the findings ofStromme and Hagberg [2000] of 3 casesout of 178 children with mild and severeMR but much lower than the ratesquoted by Sampson et al. [1997]. Therecould be a number of other reasons forthe variation in the prevalence and dis-tribution of known etiological entities indifferent populations. It could be thatdifferences in the prevalence of geneticconditions are real and related both to thegenetic characteristics and rates of con-sanguinity in the underlying population[Hutchesson et al., 1998]. On the otherhand it may be that “idiopathic or unde-termined” MR is commoner in somecommunities because of underlying psy-chosocial or unidentified environmentaldeterminants. Consequently discrete bio-medical causes would represent a smallerproportion of all cases. Alternatively, be-cause cases with unknown etiology aremore likely to be identified through theeducational than the medical system,they may have been better ascertained instudies using multiple sources.

INDIVIDUAL RISK FACTORSFOR MR

Atlanta cases were also categorizedaccording to the presence of associatedconditions, disabilities and non-CNS de-fects as well as risk factors such as low

127MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

birthweight, intra-uterine growth retar-dation (IUGR) and maternal conditions[Yeargin-Allsopp et al., 1997]. Potentialand confirmed risk factors for MR nowinclude low birthweight, preterm birth[Mervis et al., 1995], multiple births[Croen et al., 2001], as well as maternalexposures and conditions such as smok-ing [Drews et al., 1996], alcohol [Amer-ican Academy of Pediatrics, 2000], thy-roid disease [Haddow et al., 1999], andurinary tract infection [Camp et al.,1998; McDermott et al., 2001].

Low birthweight has been the moststudied risk factor. Mervis et al. [1995]found that low birthweight children overall had nearly three times the risk of MR.However the risk was higher for verylow birthweight (� 1,500 g) childrenthan for moderately low birthweight(1,500–2,499 g) children, and higher forSMR than MMR. Children with normalbirth weight who were born pre-termwere also at increased risk of MR. Sim-ilarly, Camp et al. [1998] found that upto 16.5% of infants with a birthweightof � 2,000g had MR at age 7 years. Atthe age of five years Californian childrenwho had been low birthweight had amore than threefold risk of MMR [Mc-Dermott et al., 1993]. Kok et al. [1998]followed very preterm SGA infants fornine years and found that they weremore likely to require special educationthan appropriate for gestational age in-fants. Paz et al. [2001] on the other handfound that 17-year-olds who were bornat term with growth restriction hadslightly lower IQ levels than normallygrown infants after adjusting for a rangeof confounders. These included maternaland paternal education, maternal age, pa-rental smoking, ethnic origin, socioeco-nomic status, birth order, pregnancy anddelivery factors. In the very large popu-lation based study of Croen et al. [2001]low birthweight was the strongest pre-dictive factor for both MMR and SMRof unknown cause, but in the study ofCans et al. [1999] it was only associatedwith SMR accompanied by CP.

Meta-analysis has shown a two foldincrease in the risk of low birth weightinfants to smoking mothers [English etal., 1995]. Therefore, this may have con-siderable implications for MR preven-tion. Drews et al. [1996] however suggestthat maternal smoking may increase theoccurrence of MR by mechanisms otherthan low birthweight. A recent reportusing data from the National Collabora-tive Perinatal Project [McDermott et al.,2001] also found an increased relative riskof 1.4 (CI � 1.01–1.95) of MR or de-velopmental delay in the children of

mothers who had a urinary tract infectionin the third trimester.

In keeping with the population-based approach to prevention discussedby Rose [2001], Matte et al. [2001] haverecently shown that a small shift in thedistribution of birthweight will have amuch greater impact on population dis-tribution of intellectual functioning thana specific focus on the prevention of lowbirth weight per se. Similarly it is likelythat social policy aimed at reducing pov-erty levels in the community may have amuch greater impact than focus on indi-vidual risk [Chapman and Scott, 2001].The detrimental influence on IQ ofgrowing up in a disadvantaged and ra-cially segregated environment under-scores the importance of social policy inthe prevention of MR.

FURTHER AREAS TO EXPLORE

Biotechnological AdvancesAffecting Prevalence of MR

The prevalence of MR can be re-duced by primary or secondary preven-tion. In primary prevention the interven-tion results in a reduction in theoccurrence of new cases, e.g., immuni-zation to prevent congenital rubella syn-drome [Stanley et al., 1986]. Both ante-natal and neonatal screening are forms ofsecondary prevention. Antenatal screen-ing using a variety of different methods[Wald et al., 1999] can identify pregnan-cies with increased risk of DS. Neonatalscreening allows conditions to be de-tected at a stage when treatment can beinstituted and MR prevented. In con-trast, tertiary prevention which aims atreducing disability associated with MRcould actually improve survival andhence increase prevalence.

Because DS is the commonestknown cause representing 14–15% ofMR [Bower et al., 2000], changes in itsincidence or prevalence may have thegreatest impact on overall prevalence ofMR. Parallel with the temporal increasein median age at childbirth in recent de-cades in developed nations has been anincrease in the proportion of trisomy 21conceptions [Alberman et al., 1995]. Thiswill be offset by the increased use ofmaternal serum screening, prenatal diag-nosis and termination [O’Leary et al.,1996]. However, while the birth preva-lence of DS is likely to fall, overall prev-alence may not because of improved sur-vival [Leonard et al., 2000b]. Surgicaltreatment of congenital heart disease hashad a major impact on infant survivalwhilst better health management and

care are likely to affect survival intoadulthood.

The use of artificial reproductivetechnology, which is increasing overtime, is another factor with possible im-plications for MR [Society for AssistedReproductive Technology and Ameri-can Society for Reproductive Medicine,2000; Menezo et al., 2000]. At thepresent time, research results in relationto the finding of developmental problemsin children born by assisted conceptionare contradictory [Bowen et al., 1998;Bonduelle et al., 1998; Sutcliffe et al.,2001; Stromberg et al., 2002] with twoof these four studies finding an associa-tion between delayed development andconception by intracytoplasmic sperm in-jection or in vitro fertilisation. Moreover,using record linkage, an increase in birthdefects has recently been identified inWestern Australian children conceived byassisted reproductive technology [Hansenet al., 2002].

On the other hand, implementa-tion of newborn screening programs, atechnological advance practiced for overthirty years, have virtually eliminatedMR due to phenylketonuria [Abadie etal., 2001] and congenital hypothyroidism[Kurinczuk et al., 2002] in developedcountries. Similar to preventing congen-ital rubella syndrome, immunization pro-grams are likely to have a major impacton MR due to post natal infections suchas Haemophilus influenzae b [Bower etal., 1994; 1998].

However, these biotechnologicaladvances are very much confined to theWestern world. Although some preven-tion activities may have been establishedin many developing countries, there areoften no overall systematic interventionprograms and service activities are oftenfragmented and less than adequate [Son-nander and Claesson, 1997; Durkin et al.,2000]. Moreover, the vaccination [Shannand Steinhoff, 1999] and neonatal screen-ing programs [Gu and Chen, 1999] prac-ticed in developed nations are less likely tobe available.

Inequality in Access to Diagnosisand its Impact on CaseIdentification

As is likely with assisted concep-tion, access to technological interven-tions, advanced medical care and exten-sive diagnostic work-ups may not beuniform across groups in society. Theeffect is to create a potential bias whichmay increase or decrease the likelihoodof identifying cases for epidemiologic re-search. For instance, Brett et al. [1994]noted that amniocentesis use was more

128 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

common in white than black women.The sequelae to this possible discrimina-tion in the availability of prenatal diag-nosis could be a relative increase in thebirth prevalence of DS in those groups insociety with least resources to care forsuch children. In the same way that ac-cess to prenatal diagnosis [Brett et al.,1994] and neonatal intensive care [Allenet al., 2000] may not be uniform, accessto diagnostic investigations may alsovary. Syndromes causing MR tend to beindividually rare and some may only bediagnosed as a process of exclusion aftermultiple investigations. The cost of thesecan be considerable and in some coun-tries could be prohibitive without med-ical insurance. For example, in Norway,Stromme and Hagberg [2000] com-mented that limited resources curtailedthe full extent of their planned telomericprobe investigation. An apparently lowerprevalence of certain birth defects hasbeen reported in Aboriginal children[Bower et al., 1989]. Thus a lower prev-alence of the diagnosis of specific entitiesin some populations could be influencedby a reduced access to investigations orthe diagnostic process. Epidemiologicaldata on single disorders and, in fact, as wehave noted earlier, on all MR dependheavily on the diagnosis having beenmade. This may be more or less likely incertain sociodemographic groups and it isimportant to be aware of this potentialbias which may either increase or de-crease the likelihood of identifying cases.Whilst Slone et al. [1998] found thathospital referrals for MMR were under-represented in low socioeconomic areas,disadvantaged children may be morelikely to be labelled as MR [Zigler et al.,1984].

FURTHER APPLICATIONS OFEPIDEMIOLOGY TO MR

Using as a model some specificconditions associated with MR includingDS and X linked MR, Alberman et al.[1992] have illustrated how trends in MRmight be projected. In doing so, theirprimary purpose was to provide informa-tion for UK local authorities for the plan-ning of health, education and social ser-vices for this population. To predicttrends for each condition, it is necessaryto take into account known risk factors(and the sociodemographic trends whichaffect them), possible prenatal interven-tions, current prevalence, future esti-mated prevalence, survival, associatedhealth problems and facilities required foraccommodation, education and place-ment. Unfortunately, other than for DS,much of these data are incomplete both

for individual and for groups of disorders.This is partly because for such rare con-ditions insufficient cases will be gener-ated from individual centers and possiblybecause clinicians may be reluctant toengage in population-based research.The epidemiology of MR involves morethan just the study of incidence and prev-alence and needs to start examining thesocial and economic burden of MR.

It is now evident that over andabove state funded resources, the extracare required to be provided by the fam-ilies of disabled children is substantial[Roberts and Lawton, 2001]. Time costsinvolved for parents in the care of chil-dren with and without disabilities wererecently compared [Curran et al., 2001].It was found that the care needs of se-verely disabled children far surpass non-disabled children, making it impossiblefor the majority of mothers to return topaid employment. Thus it is important tonote that factors other than birth preva-lence contribute to the social and eco-nomic burden associated with specificMR disorders. These factors includefunctional dependence, morbidity andlife expectancy, areas about which par-ents may often have many questions[O’Brien, 2001a]. For instance whilstone would expect only one affected girlwith Rett syndrome to be born for everyten with Down syndrome (DS), in termsof functioning and need for health andsupport services, Rett syndrome is gen-erally much more severe. We have al-ready shown that in Rett syndrome theWeeFIM score, a measure of functionalability, is less than one third of the valueattained by children with DS [Leonard etal., 2001; 2002b]. Dykens and Hodapp[2001] have also illustrated how func-tioning in different domains may vary inspecific genetic MR syndromes and pos-tulate that this may lead us to a furtherunderstanding of the development ofthese processes in people without MR.Examples given are Down, Williams andFragile X syndromes. Specific differencesin behavioral phenotypes are now beingrecognized [Moldavsky et al., 2001] andmay also have implications for the dy-namics of families affected by specific dis-orders and for types of services required[Fidler and Hodapp, 1999]. About twoout of five children with MR have co-morbid psychopathology according toStromme and Diseth [2000] and Einfeldand Tonge [1996]. Using a random sam-ple from a population-based register andrecognized diagnostic schedules, theprevalence in adults seems to be evengreater with one in two affected and “be-havior disorder” cited as the commonest

entity [Cooper and Bailey, 2001]. MRepidemiology might benefit from the de-velopment of new measures which donot just reflect numbers of cases butwhich incorporate the presence or ab-sence of psychopathology and the degreeof functional ability. These two domainscan impact substantially on the burdenassociated with MR. Indeed the study ofhow both specific and generic servicesimpact the lives of people with MR andthe rationale, if any, behind the changesoccurring in service provision are impor-tant areas for epidemiological research.

Clinical epidemiology is also thescience used to evaluate both antenatal(e.g. DS [Gilbert et al., 2001]) and neo-natal (e.g., phenylketonuria [Abadie etal., 2001]) screening programs includingtheir economic analyses. Toledano-Al-hadef et al. [2001] have recently exam-ined the effectiveness, including an eco-nomic evaluation, of a fragile X carrierscreening program. Using a prevalence of1:113 for premutation or full mutationcarriers the program was deemed cost-effective. Tandem mass spectrometry[Pollitt, 1999] now allows for the testingat birth of multiple conditions simulta-neously. Although, like phenylketonuria,all conditions being tested are very rare,some do not fit the traditional criterionfor screening which states that early iden-tification will allow treatment to proceedso that further damage can be preventedor the burden of the disease reduced.Thus it is extremely important to con-sider the cost, yield and outcome, includ-ing negative effects, of both antenatal andneonatal screening programs [Peckhamand Dezateux, 1998; Nelson et al.,2001b; Gilbert et al., 2001]. Evaluationssuch as this, which can be the province ofthe clinical epidemiologist, should not berestricted to screening programs but canalso be applied to clinical investigations.Whilst no definite etiology is found inbetween 32% and 75% of children withMR, the number of laboratory and ra-diological investigations becoming avail-able is increasing exponentially. Thusthere is the need for clinical epidemiol-ogists to develop evidence-based proto-cols that will provide the optimum diag-nostic yield most efficiently.

For many genetic syndromes thenext stage in research after the identifica-tion of the gene involves describing thephenotypic diversity produced by differ-ent mutations in the same gene. In someX linked syndromes such as Rett [Amirand Zoghbi, 2000] and ATRX [Gibbonsand Higgs, 2000] syndromes the modu-lating effect of X inactivation status onthe female phenotype also has to be taken

129MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

into account [Van den Veyver, 2001].Studying the relationship between geno-type and phenotype is a new role for theclinical epidemiologist. In such studies itwill be important to take into accountthe confounding effects of age and othernongenetic influences on the phenotype.Population-based registers such as that inAustralia for Rett syndrome provide anexcellent source of cases for such studies[Leonard et al., 2000a] but unfortunatelysuch registers rarely exist for other syn-dromes. Cases tend to be selected forstudy based only on clinic referral pat-terns and may not be representative ofthe underlying population of affectedsubjects.

A recent breakthrough in the eti-ological investigation of MR is the find-ing of raised levels of specific neuropep-tides and neurotrophins in neonatalblood spots of children with MR andautism [Nelson et al., 2001a]. The pat-tern of elevation was similar for childenwith MR and autism and different fromcontrols and children with CP. Vasoac-tive intestinal peptide was the analytewhich most clearly discriminated chil-dren with MR or autism from controls.This innovative research took advantageof available stored Guthrie blood spotswhilst reports of placental pathology canalso provide useful information for etio-logical research [Viscardi and Sun, 2001].Thus it is also important for epidemiol-ogists to branch out and explore freshdata sources in the new millennium.

ADDITIONALMETHODOLOGICALCHALLENGES AND THE USEOF RECORD LINKAGE

The source of data used by Alber-man et al. [1992] for DS in their study offuture trends in severe learning disabiitywas a national register [Mutton et al.,1991]. Through a similar mechanism wehave studied survival [Leonard et al.,2000b], functioning [Leonard et al.,2001], morbidity and use of health ser-vices for the Western Australian DSchildhood population [Leonard andFletcher, 1999]. When such registers ex-ist, longitudinal data on these individualdisorders can be collected, providing thenecessary information to predict trendsand allowing good epidemiological stud-ies to be performed. One of the fewother syndromes in which the epidemi-ology has been well studied is Rett syn-drome [Kozinetz et al., 1993; Leonard etal., 1997; Hagberg and Hagberg, 1997].The Australian register allows us to col-lect an equivalent range of data for Rettsyndrome as we have in DS. It was as-

sisted by the establishment in Australia ofa unit [Gazarian et al., 1999], based onthe British Paediatric Surveillance Unit[Hall and Roberts, 1993] and designed tofacilitate research into rare childhood dis-orders. In addition to Rett syndrome,several other pediatric disorders causingor with the potential to cause MR, suchas Prader Willi syndrome, fetal alcoholsyndrome, congenital CMV, congenitalrubella, neonatal herpes simplex virus in-fection, invasive haemophilus influenzaeinfection, HIV infection and AIDS, areor have been studied using this mecha-nism [Australian Paediatric SurveillanceUnit, 2001]. Systems such as these have avital role in the epidemiology of disor-ders causing MR. Equally important,however, is an ethic amongst cliniciansthat values the importance of supportingthe collection of population-based datafor rare disorders. As the use of informa-tion technology has exploded over thelast decade, bioinformatics has developedas a new discipline. It provides significantopportunities for the study of conditionswhich are individually rare and may re-quire the pooling of international data toprovide adequate sample size. Harnessingweb-based technology both in the col-lection and appropriate dissemination ofinformation relating to MR is an excitingopportunity. Plans are underway to col-lect both molecular and clinical data onRett syndrome on an international basisby modelling the system already beingused for phenylketonuria [PhenylalanineHydroxylase Locus Knowledgebase].However, such advances will not bewithout their challenges especially asidentifiers are required when linking in-formation from different sources [Kruseet al., 2001].

Boussy and Scott [1993] have pro-vided a detailed overview of the applica-tion of record linkage to the investigationof MR. In a recent study using recordlinkage by Croen et al. [2001] the infantand maternal characteristics availablewere limited to birth weight, pluralityand birth order. In Western Australiathere is a unique system of linked data-bases relating to births, deaths, hospitalmorbidity and childhood disability [Stan-ley et al., 1997]. These data relating tobirths since 1980 are stored as the Mater-nal and Child Health Research Database.The specific linkage of these data to acomprehensive database of medical anddemographic information on childrenand adults with MR [Bower et al., 2000]provides a unique mechanism for etio-logical investigation of MR. It is note-worthy however that Gissler et al. [1998]stressed the need for researchers to be

proactive in communicating the impor-tance of health registers and linked dataprojects to epidemiological research. Inparticular it is imperative to ensure thatthe public is educated about the benefitsof epidemiological research and the issueof the impracticablility of gaining consentfor large record linkage studies [Holman2001].

As acknowledged by Louhiala[1995], whose study attempted to exam-ine changes in risk factors over time,there are as many methodological prob-lems associated with the etiological in-vestigation of MR as there are with itsdefinition. Use of multiple sources, asothers and we have done, helps to opti-mize ascertainment. As raised by Drewset al. [1995] the likely dependence onprevalence rather than incidence data as asource of cases is a particular difficulty foretiological investigation. There may bedifferential loss due to migration and tomortality. In an earlier Finnish study in-volving a cohort of 12,000 children fol-lowed till the age of 14 years [Rantakallioand von Wendt, 1985] this was over-come by comparing risk factors for MR,CP, epilepsy and death with those ofhealthy children. As a clear overlap be-tween the etiological determinants ofMR and neonatal and childhood death isbiologically plausible, this issue certainlyneeds to be taken into consideration. Afurther issue is that MR tends to be stud-ied as a dichotomous variable whilst inreality children with IQs in the range70–75 are likely to share similar risk fac-tors to those with an IQ of 65–69. Andonly the latter would be regarded ascases. In etiological investigations itmight be preferable, where feasible, asdone recently by Matte et al. [2001] totreat IQ as a continuous variable.

CONCLUSIONIn summary, there is established

evidence that the prevalence of MR doesdiffer by gender, maternal race, socioeco-nomic and educational status and is in-fluenced by birthweight, although thesize of the effect has varied among stud-ies. On the other hand, the positive re-lationships between MR and both ma-ternal smoking and maternal urinary tractinfection have only been reported in asmall number of studies [Drews et al.,1996; Camp et al., 1998; McDermott etal., 2001]. These are nevertheless areaswhere specific interventions could befeasible. However a better preventivestrategy may be the improvement of thesocial milieu surrounding the occurrenceof such risk factors. The determination ofattributable risks for individual factors can

130 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

help project the number of childrenlikely to be affected by MR in givenpopulations and hence allow the appro-priate planning of support, accommoda-tion, educational and medical services. Intandem with the ability to project futureneeds, it is very important to aim toestablish mechanisms by which temporaltrends can be measured. Keeping casedefinitions, ascertainment sources andmethods constant will allow prevalenceto be measured over time so that theeffects of changing and competing influ-ences can be monitored. Only if we areable to do this can the complete cycle ofa “prevention—intervention—research”sequence actually occur.

To implement this important “pre-vention—intervention—research” cycle,which surely underpins the role of epi-demiology in MR, it will be necessary toaddress all the important methodologicalissues we have raised. It is clear that use ofmultiple sources and record linkage areefficient strategies in this process. How-ever, the next step may be to establishsystems which allow linkages across gen-erations and jurisdictions [Chapman andScott, 2001]. Such research processes willhave the capacity to identify the inter-generational risk factors, which impacton child disadvantage and indicate howwe can use social policy to intervene.This sort of intersectoral collaboration isessential both in understanding the deter-minants of MR as well as in the provisionof the most appropriate and cost effectiveservices to these children and their fam-ilies. Thus many of the research questionsneeding to be answered in relation toMR fit well within the scope of the newdisciplines of both social and genetic ep-idemiology. There are exciting opportu-nities ahead to make major inroads intothis field-opportunities that must not bemissed whilst grappling with the meth-odological challenges we have raised. f

REFERENCESAbadie V, Berthelot J, Feillet F, et al. 2001. Neo-

natal screening and long-term follow-up ofphenylketonuria: the French database. EarlyHum Dev 65:149–158.

Alberman E, Nicholson A, Wald K. 1992. Severelearning disability in young children. London:The Wolfson Institute of Preventive Medi-cine.

Alberman E, Mutton D, Ide R, et al. 1995. Down’ssyndrome births and pregnancy terminationsin 1989 to 1993: preliminary findings. Br JObstet Gynaecol 102:445–447.

Allen MC, Alexander GR, Tompkins ME, et al.2000. Racial differences in temporal changesin newborn viability and survival bygestational age. Paediatr Perinat Epidemiol14:152–158.

American Academy of Pediatrics. 2000. Committeeon Substance Abuse and Committee on Chil-

dren with Disabilities. Fetal alcohol syndromeand alcohol-related neurodevelopmental dis-orders. Pediatrics 106:358–361.

American Psychiatric Association. 1994. Diagnosticand Statistical Manual of Mental Disorders.4th ed. Washington DC.

Amir RE, Van den Veyver IB, Wan M, et al. 1999.Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat Genet 23:185–188.

Amir RE, Zoghbi HY. 2000. Rett Syndrome:Methyl-CpG-Binding Protein 2 Mutationsand phenotype-genotype correlation. Am JMed Genet 97:147–152.

Australian Paediatric Surveillance Unit. 2001.Eighth Annual Report of the Australian Pae-diatric Surveillance Unit. Sydney: Division ofPaediatrics, Royal Australasian College ofPhysicians.

Bagheri MM, Burd L, Martsolf JT, et al. 1998. Fetalalcohol syndrome: maternal and neonatalcharacteristics. J Perinat Med 26:263–269.

Baker E, Hinton L, Callen DF, et al. 2002. Study of250 children with idiopathic metnal retarda-tion reveals nine cryptic and diverse subtelo-meric chromosome anomalies. Am J MedGenet 107:285–293.

Barnett WS. 1986. Definition and classification ofmental retardation: a reply to Zigler, Balla,and Hodapp. Am J Ment Defic 91:111–119.

Bonduelle M, Joris H, Hofmans K, et al. 1998.Mental development of 201 ICSI children at2 years of age. Lancet 351:1553.

Boussy CA, Scott KG. 1993. Use of database link-age methodology in epidemiological studiesof mental retardation. International Reviewof Research in Mental Retardation 19: 135–161.

Bowen JR, Gibson FL, Leslie GI, et al. 1998.Medical and developmental outcomes at 1year for children conceived by intracytoplas-mic sperm injection. Lancet 315:1529–1534.

Bower C, Forbes R, Seward M, et al. 1989. Con-genital malformations in Aborigines and non-Aborigines in Western Australia, 1980-1987.Med J Aust 151:245–248.

Bower C, Payne J, Condon R, et al. 1994. Sequelaeof Haemophilus influenzae type b meningitis inAboriginal and non-Aboriginal children un-der 5 years of age. J Paediatr Child Health30:393–397.

Bower C, Condon R, Payne J, et al. 1998. Mea-suring the impact of conjugate vaccines oninvasive Haemophilus influenzae type b infec-tion in Western Australia. Aust N Z J PublicHealth 22:67–72.

Bower C, Leonard H, Petterson B. 2000. Intellec-tual disability in Western Australia. J PaediatrChild Health 36:213–215.

Breslau N, Chilcoat HD, Susser ES, et al. 2001.Stability and change in children’s intelligencequotient scores: a comparison of two socio-economically disparate communities. Am JEpidemiol 154:711–717.

Brett KM, Schoendorf KC, Kiely JL. 1994. Differ-ences between black and white women in theuse of prenatal care technologies. Am J ObstetGynecol 170:41–46.

Camp BW, Borman SH, Nichols PL, et al. 1998.Maternal and neonatal risk factors for mentalretardation: defining the ’at risk’ child. EarlyHuman Development 50:159–173.

Cans C, Wilhelm L, Baille MF, et al. 1999. Aetio-logical findings and associated factors in chil-dren with severe mental retardation. DevMed Child Neurol 41:233–239.

Chapman DA, Scott KG. 2001. The impact ofmaternal intergenerational risk factors on ad-

verse developmental outcomes. Develop-mental Review 21:305–325.

Chelly J, Mandel JL. 2001. Monogenic causes ofX-linked mental retardation. Nat Rev Genet2:669–680.

Clayton-Smith J, Watson P, Ramsden S, et al.2000. Somatic mutation in MECP2 as a non-fatal neurodevelopmental disorder in males.Lancet 356:830–832.

Cooper S, Bailey N. 2001. Psychiatric disordersamongst adults with learning disabilities-prev-alence and relationship to ability level. IrJ Psych Med 18:45–53.

Couvert P, Bienvenu T, Aquaviva C, et al. 2001.MECP2 is highly mutated in X-linked mentalretardation. Hum Mol Genet 10:941–946.

Crocker AC. 1989. The causes of mental retarda-tion. Pediatr Ann 18:623–636.

Croen LA, Grether JK, Selvin S. 2001. The epide-miology of mental retardation of unknowncause. Pediatrics 107:E86.

Curran AL, Sharples PM, White C, et al. 2001.Time costs of caring for children with severedisabilities compared with caring for childrenwithout disabilities. Dev Med Child Neurol43:529–533.

Decoufle P, Boyle CA. 1995. The relationship be-tween maternal education and mental retar-dation in 10-year old children. Annals of Ep-idemiology 5:347–353.

Delange F, de Benoist B, Pretell E, et al. 2001.Iodine deficiency in the world: Where do westand at the turn of the century? Thyroid11:437–447.

de Vries BB, White SM, Knight SJ, et al. 2001.Clinical studies on submicroscopic subtelo-meric rearrangements: a checklist. J MedGenet 38:145–150.

Dotti MT, Orrico A, De Stefano N, et al. 2002. ARett syndrome MECP2 mutation that causesmental retardation in men. Neurology 58:226–230.

Drews CD, Yeargin-Allsopp M, Decoufle P, et al.1995. Variation in the influence of selectedsociodemographic risk factors for mental re-tardation. American Journal of Public Health85:329–334.

Drews CD, Murphy CC, Yeargin-Allsopp M, et al.1996. The relationship between idiopathicmental retardation and maternal smoking dur-ing pregnancy. Pediatrics 97: 547–553.

Durkin M, Khan N, Davidson L, et al. 2000. Pre-natal and postnatal risk factors for mental re-tardation among children in Bangladesh. Am JEpidemiol 152:1024–1033.

Dykens EM, Hodapp RM. 2001. Research inmental retardation: toward an etiologic ap-proach. Journal of Child Psychology & Psy-chiatry & Allied Disciplines 42:49–71.

English DR, Holman CDJ, Milne E, et al. 1995.Commonweath Department of Human Ser-vices and Health, Canberra.

Einfeld SL, Tonge BJ. 1996. Population prevalenceof psychopathology in children and adoles-cents with intellectual disability .1. Rationaleand Methods. J Intellect Disabil Res 40:91–98.

Fernell E. 1996. Mild mental retardation in schoolchildren in a Swedish suburban municipality:prevalence and diagnostic aspects. Acta Pae-diatr 85:584–588.

Fernell E. 1998. Aetiological factors and prevalenceof severe mental retardation in children in aSwedish municipality: the possible role ofconsanguinity. Dev Med Child Neurol 40:608–611.

Fidler DJ, Hodapp RM. 1998. Importance of ty-pologies for science and service in mentalretardation. Ment Retard 36:489–495.

131MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

Fombonne E. 1999. The epidemiology of autism: areview. Psychological Medicine 29: 769–786.

Fryers T. 1987. Epidemiological issues in mentalretardation. Journal of Mental Deficiency Re-search 31:365–384.

Fryers T. 1992. Epidemiology and taxonomy inmental retardation. Paediatr Perinat Epide-miol 6:181–192.

Fryers T, Mackay RI. 1979. The epidemiology ofsevere mental handicap. Early Hum Dev3:277–294.

Gazarian M, Wiliams K, Elliot E, et al. 1999. Eval-uation of a national surveillance unit. Ar-chives of Diseases in Children 80:21–27.

Gibbons RJ, Higgs DR. 2000. Molecular-clinicalspectrum of the ATR-X syndrome. Am JMed Genet 97:204–212.

Gilbert RE, Augood C, Gupta R, et al. 2001.Screening for Down’s syndrome: effects,safety, and cost effectiveness of first and sec-ond trimester strategies. BMJ 323:423–425.

Gissler M, Hemminki E, Louhiala P, et al. 1998.Health registers as a feasible means of measur-ing health status in childhood-a 7-year fol-low-up of the 1987 Finnish birth cohort.Paediatr Perinat Epidemiol 12:437–455.

Gissler M, Jarvelin MR, Louhiala P, et al. 1999.Boys have more health problems in childhoodthan girls: follow-up of the 1987 Finnish birthcohort. Acta Paediatr 88:310–314.

Gleeson JG, Allen KM, Fox JW, et al. 1998. Dou-blecortin, a brain-specific gene mutated inhuman X-linked lissencephaly and doublecortex syndrome, encodes a putative signalingprotein. Cell 92:63–72.

Greenspan S. 1999. What is meant by mental re-tardation? International Review of Psychiatry11:6–18.

Grossman F. 1973. Manual on terminology andclassification in mental retardation. Washing-ton DC: American Association on MentalDeficiency.

Grossman F. 1983. Classification in mental retarda-tion. Washington DC, American Associationon Mental Deficiency.

Gu XF, Chen RG. 1999. Current status of neonatalscreening in China. J Med Screen 6: 186–187.

Haddow JE, Palomaki GE, Allan WC, et al. 1999.Maternal thyroid deficiency during preg-nancy and subsequent neuropsychological de-velopment of the child. N Engl J Med 341:549–555.

Hagberg B, Hagberg G. 1997. Rett-Syndrome -Epidemiology and geographical variability.European Child & Adolescent Psychiatry6:5–7.

Hagberg B, Hagberg G, Lewerth A, et al. 1981.Mild mental retardation in Swedish schoolchildren: I. Prevalence. Acta Paediatr Scand70:441–444.

Hall SM, Roberts C. 1993. Occasional Papers: TheBritish Paediatric Surveillance Unit. The Bul-letin of the Royal College of Pathologists82:12-16.

Hansen H, Belmont L, Stein Z. 1980. Epidemiol-ogy. Mental Retardation and Devlop Disabil-ities 11:21–54.

Hansen M, Kurinczuk J, Bower C, et al. 2002. Therisk of major birth defects after intracytoplas-mic sperm injection and in vitro fertilisation.N Engl J Med 346:725–730.

Haveman MJ. 1996. Epidemiological issues inmental retardation. Current Opinion in Psy-chiatry 9:305–311.

Heber R. 1961. Modifications in the manual onterminology and classification in mental retar-dation. Am J Ment Defic 64:499–500.

Holman CD. 2001. The impracticable nature ofconsent for research use of linked administra-tive health records. Aust N Z J Public Health25:421–422.

Hou JW, Wang TR, Chuang SM. 1998. An epi-demiological and aetiological study of chil-dren with intellectual disability in Taiwan.J Intellect Disabil Res 42:137–143.

Hurtado EK, Claussen AH, Scott KG. 1999. Earlychildhood anemia and mild or moderatemental retardation. American Journal of Clin-ical Nutrition 69:115–119.

Hutchesson AC, Bundey S, Preece MA, et al. 1998.A comparison of disease and gene frequenciesof inborn errors of metabolism among differ-ent ethnic groups in the West Midlands, UK.J Med Genet 35:366–370.

Imessaoudene B, Bonnefont JP, Royer G, et al.2001. MECP2 mutation in non-fatal, non-progressive encephalopathy in a male. J MedGenet 38:171–174.

Islam S, Durkin MS, Zaman SS. 1993. Socioeco-nomic status and the prevalence of mentalretardation in Bangladesh. Ment Retard 31:412–417.

Kawachi I, Kennedy B. 1997. Socioeconomic de-terminants of health: Health and cohesion:why care about income inequality? BMJ 314:1037.

Kearins J. 2000. Children and Cultural DifferencesIn: Dudgeon P, Garvey D, Pickett H, eds.Working with indigenous Australians: ahandbook for psychologists. Perth, WesternAustralia: Gunada Press.

Kelleher KJ, McInerny TK, Gardner WP, et al. 2000.Increasing identification of psychosocial prob-lems: 1979-1996. Pediatrics 105:1313–1321.

Kiely M. 1987. The prevalence of mental retarda-tion. Epidemiol Rev 9:194–218.

Kok JH, den Ouden AL, Verloove-Vanhorick SP,et al. 1998. Outcome of very preterm smallfor gestational age infants: the first nine yearsof life. Br J Obstet Gynaecol 105: 162–168.

Kozinetz CA, Skender ML, MacNaughton N, et al.1993. Epidemiology of Rett syndrome: apopulation-based registry. Pediatrics 91:445–450.

Kruse RL, Ewigman BG, Tremblay GC. 2001.The zipper: a method for using personal iden-tifiers to link data while preserving confiden-tiality. Child Abuse Negl 25:1241–1248.

Kurinczuk JJ, Bower C, Lewis B, et al. 2002.Congenital hypothyroidism in Western Aus-tralia 1981-1998. J Paediatr Child Health 38:187–191.

Larson SA, Lakin KC, Anderson L, et al. 2001.Prevalence of mental retardation and devel-opmental disabilities: estimates from the1994/1995 National Health Interview SurveyDisability Supplements. Am J Ment Retard106:231–252.

Last JM. 1995. A Dictionary of Epidemiology.New York: Oxford University Press.

Leonard H, Fletcher JM. 1999. Inherited metabolicdiseases: beyond newborn screening. Med JAust 170:573–574.

Leonard H, Bower C, English D. 1997. The prev-alence and incidence of Rett syndrome InAustralia. European Child & Adolescent Psy-chiatry 6:8–10.

Leonard H, Fyfe S, Danielle D, et al. 2000a. Usinggenetic epidemiology to study Rett syn-drome: the design of a case control study.Paediatr Perinat Epidemiol 14:85–95.:

Leonard H, Fyfe S, Leonard S, et al. 2001. Func-tional status, medical impairments, and reha-bilitation resources in 84 females with Rettsyndrome: a snapshot across the world from

the parental perspective. Disabil Rehabil 23:107–117.

Leonard H, Petterson B, Bower C, et al. 2002a.Prevalence of intellectual disability in West-ern Australia. Paediatr Perinat Epidemiol (inpress).

Leonard S, Bower C, Petterson B, et al. 2000b.Survival of infants born with Down’s syn-drome: 1980-96. Paediatr Perinat Epidemiol14:163–171.

Leonard S, Bower C, Petterson B, et al. 1999.Medical aspects of school-aged children withDown syndrome. Dev Med Child Neurol41:683–388.

Leonard S, Msall M, Bower C, et al. 2002b. Thefunctional status of school-aged children withDown syndrome. J Paediatr Child Health (38:160–165).

Luckasson R, Coulter DL, Polloway EA, et al.1992. Mental retardation: Definition, classifi-cation, and systems of supports. WashingtonDC: American Association on Mental Retar-dation.

Louhiala P. 1995. Risk indicators of mental retar-dation: changes between 1967 and 1981. DevMed Child Neurol 37:631–636.

Luckasson R, Reeve A. 2001. Naming, defining,and classifying in mental retardation. MentRetard 39:47–52.

Matte TD, Bresnahan M, Begg MD, et al. 2001.Influence of variation in birth weight withinnormal range and within sibships on IQ at age7 years: cohort study. BMJ 323: 310–314.

McDermott S, Cokert AL, McKeown RE. 1993.Low birthweight and risk of mild mental re-tardation by ages 5 and 9 to 11. PaediatrPerinat Epidemiol 7:195–204.

McDermott S, Daguise V, Mann H, et al. 2001.Perinatal risk for mortality and mental retar-dation associated with maternal urinary-tractinfections. J Fam Pract 50:433–437.

MacMillan DL, Gresham FM, Siperstein GN.1993. Conceptual and psychometric concernsabout the 1992 AAMR definition of mentalretardation. Am J Ment Retard 98: 325–335.

MacMillan DL, Gresham FM, Siperstein GN.1995. Heightened concerns over the 1992AAMR definition: advocacy versus precision.Am J Ment Retard 100:87–95.

McLaren J, Bryson SE. 1987. Review of recentepidemiological studies of mental retardation:prevalence, associated disorders, and etiology.Am J Ment Retard 92:243–254.

McLennan W, Madden R. 1999. The health andwelfare of Australia’s Aboriginal and TorresStrait Islander peoples. Canberra:AustralianBureau of Statistics, Australian Institute ofHealth and Welfare.

Meloni I, Bruttini M, Longo I, et al. 2000. Amutation in the Rett syndrome gene,MECP2, causes X-linked mental retardationand progressive spasticity in males. Am J HumGenet 67:982–985.

Menezo YJR,Veiga A, Pouly JL. 2000. AssistedReproductive Technology (ART) in hu-mans: facts and uncertainties. Theriogenology53:599–610.

Mercer J. 1973. The myth of 3% prevalence In:Eyman R, Myers C, Tarjan G, eds. Sociobe-havioural studies in mental retardation.Monographs of the American Association onMental Deficiency.

Mervis CA, Decoufle P, Murphy CC, et al. 1995.Low birthweight and the risk for mental re-tardation later in childhood. Paediatr PerinatEpidemiol 9:455–468.

Moldavsky M, Lev D, Lerman-Sagie T. 2001. Be-havioural phenotypes of genetic syndromes: a

132 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

reference guide for psychiatrists. J Am AcadChild Adolesc Psychiatry 40:749–761.

Murphy CC, Boyle C, Schendel D, et al. 1998.Epidemiology of mental retardation in chil-dren. Ment Retard Dev Disabil Res Rev4:6–13.

Murphy CC, Yeargin-Allsopp M, Decoufle P, etal. 1995. The administrative prevalence ofmental retardation in 10-year-old children inmetropolitan Atlanta, 1985 through 1987.Am J Public Health 85:319–323.

Mutton DE, Alberman E, Ide R, et al. 1991. Re-sults of first year (1989) of a national registerof Down’s syndrome in England and Wales.BMJ 303:1295–1297.

Nelson KB, Grether JK, Croen LA, et al. 2001a.Neuropeptides and neurotrophins in neonatalblood of children with autism or mental re-tardation. Ann Neurol 49:597–606.

Nelson RM, Botkjin JR, Kodish ED, et al. 2001b.Ethical issues with genetic testing in pediat-rics. Pediatrics 107:1451–1455.

O’Brien G. 2001a. Adult outcome of childhoodlearning disability. Dev Med Child Neurol43:634–638.

O’Brien G. 2001b. Defining learning disability:what place does intelligence testing havenow? Dev Med Child Neurol 43:570–573.

O’Leary P, Bower C, Murch A, et al. 1996. Theimpact of antenatal screening for Down syn-drome in Western Australia: 1980-1994. Aus-tralian and New Zealand Journal of Obstetrics& Gynaecology 36:385–388.

OMIM. 2001. Vol. 2001. Center for Medical Ge-netics, Johns Hopkins University (Baltimore,MD) and National Center for BiotechnologyInformation, National Library of Medicine(Bethesda, MD) World Wide Web URL:http://www.ncbi.nlm.nih.gov/omim/.

Orrico A, Lam C, Galli L, et al. 2000. MECP2mutation in male patients with non-specificX-linked mental retardation. FEBS Lett 481:285–288.

Partington M, Mowat D, Einfeld S, et al. 2000.Genes on the X chromosome are importantin undiagnosed mental retardation. Am J MedGenet 92:57–61.

Paz I, Laor A, Gale R, et al. 2001. Term infantswith fetal growth restriction are not at in-creased risk for low intelligence scores at age17 years. J Pediatr 138:87–91.

Peckham CS, Dezateux C. 1998. Issues underlyingthe evaluation of screening programmes. BrMed Bull 54:767–778.

Phenylalanine Hydroxylase Locus Knowledgebase.http://ww2.mcgill.ca/pahdb

Pollitt E. 1999. Early iron deficiency anemia andlater mental retardation. American Journal ofClinical Nutrition 69:4–5.

Pollitt RJ. 1999. Tandem mass spectrometryscreening: proving effectiveness. Acta Paedi-atr Suppl 88:40–44.

Quaderi NA, Schweiger S, Gaudenz K, et al. 1997.Opitz G/BBB syndrome, a defect of midlinedevelopment, is due to mutations in a newRING finger gene on Xp22. Nat Genet 17:285–291.

Rantakallio P, von Wendt L. 1985. Risk factors formental retardation. Archives of Disease inChildhood 60:946–952.

Reschly D. 1992. Mental retardation: conceptualfoundations, definitional criteria, and diag-nostic operations. In: Hooper S, Hynd G,Mattison R, eds. Developmental disorders:diagnostic criteria and clinical assessment.Hillsdale: Lawrence Erlbaum Associates.

Reschly DJ, Jipson FJ. 1976. Ethnicity, geographiclocale, age, sex, and urban-rural residence as

variables in the prevalence of mild retarda-tion. Am J Ment Defic 81:154–161.

Richardson SA, Katz M, Koller H. 1986. Sex dif-ferences in number of children administra-tively classified as mildly mentally retarded: anepidemiological review. Am J Ment Defic91:250–256.

Roberts K, Lawton D. 2001. Acknowledging theextra care parents give their disabled children.Child Care Health Dev 27:307–319.

Robinson EG, Rathbone GN. 1999. Impact ofrace, poverty, and ethnicity on services forpersons with mental disabilities: call for cul-tural competence. Ment Retard 37:333–338.

Roeleveld N, Zielhuis GA, Gabreels F. 1997. Theprevalence of mental retardation: a criticalreview of recent literature. Dev Med ChildNeurol 39:125–132.

Rose G. 2001. Sick individuals and sick popula-tions. Int J Epidemiol 30:427–432.

Sampson PD, Streissguth AP, Bookstein FL, et al.1997. Incidence of fetal alcohol syndrome andprevalence of alcohol-related neurodevelop-mental disorder. Teratology 56:317–326.

Schalock RL, Stark JA, Snell ME, et al. 1994. Thechanging conception of mental retardation:implications for the field. Ment Retard 32:181–193.

Shann F, Steinhoff MC. 1999. Vaccines for chil-dren in rich and poor countries. Lancet 354:SII7-SII11.

Slone M, Durrheim K, Lachman P, et al. 1998.Association between the diagnosis of mentalretardation and socioeconomic factors. Am JMent Retard 102:535–546.

Society for Assisted Reproductive Technology andAmerican Society for Reproductive Medi-cine. 2000. Assisted reproductive technologyin the Unites States: 1997 results generatedfrom the American Society for ReproductiveMedicine/Society for Assisted ReproductiveTechnology Registry. Fertility and Sterility74:641–653.

Sonnander K, Claesson M. 1997. Classification,prevalence, prevention and rehabilitation ofintellectual disability: an overview of researchin the People’s Republic of China. J IntellectDisabil Res 41:180–192.

Stanley FJ, Sim M, Wilson G, et al. 1986. Thedecline in congenital rubella syndrome inWestern Australia: an impact of the school girlvaccination program? Am J Public Health 76:35–37.

Stanley FJ, Read AW, Kurinczuk JJ, et al. 1997. Apopulation maternal and child health researchdatabase for research and policy evaluation inWestern Australia. Seminars in Neonatology2:195–201.

Starza-Smith A. 1989. Recent trends in prevalencestudies of children with severe mental retarda-tion. Disability, Handicap & Society 4:177–195.

Stevenson DK, Verter J, Fanaroff AA, et al. 2000.Sex differences in outcomes of very lowbirthweight infants: the newborn male disad-vantage. Arch Dis Child Fetal Neonatal Ed83:F182–185.

Stromberg B, Dahlquist G, Ericson A, et al. 2002.Neurological sequelae in children born afterin-vitro fertilisation: a population-based studyLancet 359:461–465.

Stromme P, Diseth T. 2000. Prevalence of psychi-atric diagnoses in children with mental retar-dation: data from a population-based study.Dev Med Child Neurol 42:266–270.

Stromme P, Hagberg G. 2000. Aetiology in severeand mild mental retardation: a population-based study of Norwegian children. Dev MedChild Neurol 42:76–86.

Stromme P, Magnus P. 2000. Correlations betweensocioeconomic status, IQ and aetiology inmental retardation: a population-based studyof Norwegian children. Soc Psychiatry Psy-chiatr Epidemiol 35:12–18.

Stromme P, Valvatne K. 1998. Mental retardationin Norway: prevalence and sub-classificationin a cohort of 30037 children born between1980 and 1985. Acta Paediatr 87:291–296.

Sutcliffe AG, Taylor B, Saunders K, et al. 2001.Outcome in the second year of life after in-vitro fertilisation by intracytoplasmic sperminjection: a UK case-control study. Lancet357:2080–2084.

Tariverdian G, Vogel F. 2000. Some problems inthe genetics of X-linked mental retardation.Cytogenet Cell Genet 91:278–284.

Toledano-Alhadef H, Basel-Vanagaite L, Magal N,et al. 2001. Fragile-X carrier screening andthe prevalence of premutation and full- mu-tation carriers in Israel. Am J Hum Genet69:351–360.

Trevathan E, Moser HW. 1988. Diagnostic Criteriafor Rett Syndrome. Annals of Neurology 23:425–428.

Turner G, Webb T, Wake S, et al. 1996. Preva-lence of fragile X syndrome. Am J Med Genet64:196–197.

Van den Veyver IB. 2001. Skewed X inactivationin X-linked disorders. Semin Reprod Med19:183–191.

Villard L, Cardoso AK, Chelly PJ, et al. 2000. Twoaffected boys in a Rett syndrome family: clin-ical and molecular findings. Neurology 55:1188–1193.

Viscardi RM, Sun CC. 2001. Placental lesion mul-tiplicity: risk factor for IUGR and neonatalcranial ultrasound abnormalities. Early HumDev 62:1–10.

Wald NJ, Huttly WJ, Hennessy CF. 1999. Down’ssyndrome screening in the UK in 1998. Lan-cet 354:1264.

Wan M, Lee SS, Zhang X, et al. 1999. Rett syn-drome and beyond: recurrent spontaneousand familial MECP2 mutations at CpG hot-spots. Am J Hum Genet 65:1520–1529.

Wang HY, Zhang FC, Gao JJ, et al. 2000. Apoli-poprotein E is a genetic risk factor for fetaliodine deficiency disorder in China. Molec-ular Psychiatry 5:363–368.

Wellesley D, Hockey A, Stanley F. 1991. Theaetiology of intellectual disability in WesternAustralia: a community-based study. DevMed Child Neurol 33:963–973.

Wellesley DG, Hockey KA, Montgomery PD, etal. 1992. Prevalence of intellectual handicapin Western Australia: a community study.Med J Aust 156:94–102.

Wen X. 1997. The definition and prevalence ofintellectual disability in Australia. Canberra:Australian Institute of Health and Welfare.

Williams CA. 1995. Angelmans Syndrome: Con-sensus for Diagnostic Criteria. Am J MedGenet 56:237–238.

Willms JD. 1997. Literacy Skills of CanadianYouth, Ottawa: Statistics Canada and HumanResources Development Canada.

Wilska M, Kaski M. 1999. Aetiology of intellectualdisability—the Finnish classification: develop-ment of a method to incorporate WHOICD-10 coding. J Intellect Disabil Res 43:242–250.

World Health Organisation (WHO). 1980. Inter-national classification of functioning, disabil-ity and health. Geneva: WHO.

World Health Organization (WHO). 1992. Inter-national statistical classification of diseases andrelated health problems: 10th revision, vol 1.Geneva: WHO.

133MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN

World Health Organization (WHO). 1993. Inter-national statistical classification of diseases an-drelated health problems: 10th revision, vol 2.Geneva: WHO.

World Health Organisation (WHO). 2001. Inter-national classification of functioning, disabil-ity and health. Geneva: WHO.

Yeargin-Allsopp M, Murphy CC, Oakley GP, etal. 1992. A multiple-source method forstudying the prevalence of developmentaldisabilities in children: the MetropolitanAtlanta Developmental Disabilties Study.Pediatrics 89:624 – 629.

Yeargin-Allsopp M, Drews CD, Decoufle P, et al.1995. Mild mental retardation in black andwhite children in metropolitan Atlanta: acase-control study. Am J Public Health 85:324–328.

Yeargin-Allsopp M, Murphy CC, Cordero JF, etal. 1997. Reported biomedical causes and as-sociated medical conditions for mental retar-dation among 10-year-old children, metro-politan Atlanta, 1985 to 1987. Dev MedChild Neurol 39:142–149.

Zaren B, Lindmark G, Bakketeig L. 2000. Ma-ternal smoking affects fetal growth more in

the male fetus. Paediatr Perinat Epidemiol14:118 –126.

Zigler E. 1987. The definition and classification ofmental retardation. Ups J Med Sci Suppl 44:9–18.

Zigler E, Balla D, Hodapp R. 1984. On the defi-nition and classification of mental retardation.Am J Ment Defic 89:215–230.

Zubrick SR, Kurinczuk JJ, McDermott BMC, etal. 2000. Fetal growth and subsequentmental health problems in children aged 4to 13 years. Dev Med Child Neurol42:14 –20.

134 MRDD RESEARCH REVIEWS ● EPIDEMIOLOGY OF MENTAL RETARDATION ● LEONARD & WEN