final published online 5-20-15

Autism
1–10
© The Author(s) 2015
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/1362361315585055
aut.sagepub.com
Perhaps the most important issue in the identification and
treatment of children with autism spectrum disorder
(ASD) is the need for early diagnosis and early, special-
ized intervention (Baghdadli et al., 2003; Corsello, 2005;
Yeargin-Allsopp et al., 2003). Research has shown that
early, specialized intervention can result in significant
developmental progress compared with beginning inter-
vention at later ages (Dawson et al., 2010; Harris and
Handleman, 2000; Sallows and Graupner, 2005; Szatmari
et al., 2003). Several researchers suggest that improve-
ment as a result of early treatment results in considerable
cost savings to both families and the systems that serve
them (Jacobson et al., 1998; Jacobson and Mulick, 2000;
Jarbrink and Knapp, 2001).
Despite evidence that ASD can be detected within the
first 2years of life, early identification has not yet become
the norm (Goin and Myers, 2004; Moore and Goodson,
2003; Volkmar et al., 2004, 2005). Rather, the median age
of diagnosis is around the fourth birthday (CDC, 2014).
This delay in identification is even greater in low-
income, minority communities. Racial, ethnic, and eco-
nomic disparities in access to healthcare in general have
been well documented and may result from factors such as
limited availability of services in the community, financial
hardship, diverse cultural beliefs, and social prejudice
(Fouad et al., 2010; Mandell et al., 2009). Children from
families of low socio-economic status (SES) receive a
diagnosis of ASD at a later age, on average, than more
affluent children (Durkin et al., 2010; Fountain et al.,
2010). African-American/Black (hereafter referred to as
Black) and Hispanic/Latino/Spanish (hereafter referred to
as Hispanic) children are diagnosed with ASD at later ages
than White children, often regardless of their economic
status (Mandell et al., 2002, 2007, 2009). When Black or
Screening for autism spectrum disorder
in underserved communities: Early
childcare providers as reporters
Yvette M Janvier1, Jill F Harris2, Caroline N Coffield3,
Barbara Louis4, Ming Xie5, Zuleyha Cidav5 and
David S Mandell5
Abstract
Early diagnosis of autism typically is associated with earlier access to intervention and improved outcomes. Daycares
and preschools largely have been ignored as possible venues for early identification. This may be especially important for
minority children in the United States who are typically diagnosed with autism later than White children, limiting their
access to early specialized interventions and possibly resulting in poorer outcomes. Early childcare providers within
underserved communities completed autism screening tools for a sample of low-risk young children (n=967) in their
programs. Early childcare providers returned screening tools for 90% of the children for whom parental consent had been
received. A total of 14% of children screened positive for autism spectrum disorder and 3% of the sample met criteria for
autism spectrum disorder. Among those who screened positive, 34% were lost to follow-up. Findings suggest that early
childcare providers can effectively screen young children for autism spectrum disorder in preschool/daycare settings,
thus improving access to early diagnosis and reducing potential healthcare disparities among underserved populations.
Keywords
autism spectrum disorders, early childcare providers, preschool children, screening, underserved
1Children’s Specialized Hospital, Toms River, NJ, USA
2Children’s Specialized Hospital, Fanwood, NJ, USA
3Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
4The Gifted Child Clinic, NJ, USA
5University of Pennsylvania, Philadelphia, PA, USA
Corresponding author:
Jill F Harris, Children’s Specialized Hospital, 330 South Avenue,
Fanwood, NJ 07023, USA.
Email: jharris@childrens-specialized.org
585055AUT0010.1177/1362361315585055AutismJanvier et al.
research-article2015
Original Article
at CHILDRENS SPECIALIZED HOSPITAL on May 20, 2015aut.sagepub.comDownloaded from

2 Autism
Hispanic children are diagnosed at comparable ages to
their White peers, they are more likely to have intellectual
disability (CDC, 2014), suggesting greater severity in clin-
ical presentation. In many cases, other diagnoses are given
instead of a diagnosis of ASD (Mandell et al., 2007, 2009).
Data from the 2003–2004 National Survey of Children’s
Health suggest that Black and Hispanic children are under-
diagnosed with ASD, especially those with less severe
symptoms (Liptak et al., 2008). Even when minority and
poor children have access to adequate primary care, their
physicians may not screen for autism or other develop-
mental delays, further contributing to disparity in age at
diagnosis (e.g. Liptak et al., 2008). Whatever the cause,
early signs of ASD may go unnoticed within these
communities.
To promote early identification, theAmericanAcademy
of Pediatrics recommends routine screening of young chil-
dren for developmental delays as part of well child visits
along with specific screening for autism at 18 and
24months or whenever caregivers express concern
(Johnson and Myers, 2007). Reported rates of develop-
mental screening by pediatricians using validated screen-
ing tools vary widely (Bethell et al., 2011; Zuckerman
et al., 2013). Even when pediatricians report screening for
developmental delays, few screen specifically for ASD
(Dosreis et al., 2006), and autism screening instruments
are neither widely nor systematically used in pediatric
practices or early intervention programs (Sices, 2007).
Disparities may also exist in availability of pediatricians
providing developmental or ASD screening in Spanish
(Zuckerman et al., 2013).
Reliance on healthcare professionals to identify autism
is therefore likely to result in missed cases, especially
among low-income and racial and ethnic minority families
in which parents may not recognize early signs of autism.
Early childhood providers are in an ideal position to
observe children’s physical, behavioral, and social devel-
opment. They also have more of an opportunity than most
parents to compare a specific child’s development to that
of other children in the same setting. Therefore, they may
be excellent candidates to recognize atypical development.
Branson et al. (2008) argued for the value of having com-
munity childcare workers screen the children they serve,
but little research has been conducted in this area.
While little attention has been placed on early child-
hood providers as screeners of ASD, elementary school
teachers have been found to be able to accurately nominate
children at risk of high functioning ASD in their class-
rooms (Hepburn et al., 2008). Constantino et al. (2007)
found that parents’ and teachers’ combined ratings were
highly effective in identifying children between the ages of
4 and 18years with pervasive developmental disorders.
Unfortunately, this study did not provide measures of
screening accuracy by the age of the child.
DeVincent et al. (2008) examined parent and teacher
screening of 3-to-5-year olds with and without ASD in
clinical and community samples of primarily White and
middle to upper SES families. Agreement between pre-
school teachers and parents was fairly high in this sample.
Good sensitivity and specificity were found for both par-
ents and preschool teachers. This study validated a new
Level 1 screener, did not report on the feasibility of screen-
ing for ASD within preschool classrooms, and did not
include lower income families.
In Belgium, Dereu et al. (2012) investigated early
childcare workers’ ability to screen for ASD. Childcare
workers could discriminate between children with and
without ASD as well as parents; however, none of the
screening instruments had both satisfactory sensitivity and
specificity, and the loss to follow-up was substantial.
Early identification of young children with ASD is
important. Early childcare providers are a natural access
point to such children, but there is limited research on the
feasibility of early childhood educators as screeners. The
value of such an approach may be even more salient for
low-income and racial/ethnic minority communities, in
which barriers to early diagnosis have been noted. The
purpose of this study was to investigate the feasibility of
early childcare providers screening for ASD within under-
served community daycare and preschool settings.
Methods
Participants
Head Start programs in six medically underserved cities in
New Jersey with predominantly low-income, high racial/
ethnic minority populations were targeted. In order to
increase the potential sample, all state-licensed daycares in
those six target cities also were invited to participate. Both
Head Start and state-licensed daycare/preschools in these
target cities served a similar, primarily low-income popu-
lation. Head Start centers were contacted by phone with
email follow-up, and daycare/preschool sites were con-
tacted by email, with no follow-up. Head Start programs
from five of the six cities agreed to participate. Three addi-
tional state-licensed daycare/preschools located within
one of the targeted cities also participated, for a total of
eight sites across five cities. Each center’s 3- to 5-year-old
preschool classes were eligible to participate. One of the
Head Start sites and one of the daycare/preschools also
served 16- to 36-month-old children, and parents of these
children were invited to participate. A total of 1227 chil-
dren were enrolled in these programs, of whom 967 were
screened by their preschool teachers for this study.
Measures
Screening tools. Either the Modified Checklist for Autism
in Toddlers (M-CHAT; Robins et al., 2001) or the Social
Communication Questionnaire (SCQ; Rutter et al., 2003)
was administered, depending upon the age of the child.

Janvier et al. 3
Early childcare providers of children ages 16months to
29months completed the M-CHAT. Providers of children
ages 30–48months completed both the M-CHAT and
SCQ. Providers of children older than 48months com-
pleted the SCQ.
The M-CHAT (Robins et al., 2001) has been validated
as a Level 1 screening tool for ASD risk in children aged
16 to 30months. It consists of a 23-item parent-report
checklist examining children’s developmental milestones.
Previous studies have extended the M-CHAT age range to
48months when screening for ASD (e.g. Snow and
Lecavalier, 2008). A cut-off score of two failures on criti-
cal items or three failures on any item is considered posi-
tive for ASD risk. Reported sensitivity of the M-CHAT
ranges from 0.70 to 0.93 (Eaves et al., 2006; Kleinman
et al., 2008; Snow and Lecavalier, 2008; Wong et al., 2004)
with specificity ranging from 0.38 to 0.85 (Snow and
Lecavalier, 2008; Wong et al., 2004), although these stud-
ies drew from at-risk samples, which could influence sen-
sitivity and specificity. These studies also were not
specifically conducted with low-income or racially/ethni-
cally diverse groups. This study used teachers as M-CHAT
respondents, the psychometrics of which have not been
reported. For those children who scored positive on the
teacher M-CHAT, the M-CHAT Follow-Up Interview
(Robins et al., 2001) was also conducted. In this structured
interview, study personnel reviewed items that indicated
risk of ASD with the child’s parent. The parent was further
queried about each failed item or other items of concern,
and a cascading series of subsequent queries determined
whether the item was still considered failed.
The SCQ (Rutter et al., 2003) is a parent-report Level 1
screening measure forASD with items based on theAutism
Diagnostic Interview–Revised (ADI-R; Lord et al., 1994).
It consists of 40 items. A cut-off score of 15 is considered
positive for ASD risk. The SCQ is designed for children
ages 48months and over; however, previous studies have
extended the age range down to 30months when screening
for ASD (e.g. Snow and Lecavalier, 2008). The reported
sensitivity of the SCQ ranges from 0.70 to 0.90 and speci-
ficity ranges from 0.52 to 0.86 (Chandler et al., 2007;
Snow and Lecavalier, 2008) although these studies were
not specifically conducted with racially or ethnically
diverse populations. This study used early childcare pro-
viders as respondents, which is a novel use of the SCQ.
There is no formal follow-up interview available for chil-
dren who score positive on the SCQ.
Diagnosis
The Autism Diagnostic Observation Schedule (ADOS;
Lord et al., 1999) is a semi-structured play-based assess-
ment for ASD with standardized administration and scor-
ing. Children’s behavior is sampled through the use of
systematic probes for autism symptoms in communica-
tion, social interaction, play, and restricted and repetitive
behaviors. Algorithm criteria for ASD and autism classifi-
cation, based on communication, reciprocal social interac-
tion, and restricted interests summary scores, are provided.
In this study, the ADOS was used to classify children as
having autistic disorder/ASD, or not having autism/ASD.
Licensed, experienced clinicians determined ASD sta-
tus (ASD or non-ASD) and clinical diagnosis based on
clinical observation, clinical interview including subject
and family medical history, results of the ADOS (Lord
et al., 1999), and review of the Diagnostic and Statistical
Manual of Mental Disorders (4th ed., rev.; DSM-IV-R) cri-
teria for Pervasive Developmental Disorder and related
disorders (American Psychiatric Association (APA),
1994). The principal investigator (PI) (author Y.M.J.) is
research-certified in the ADOS, while the other clinicians
had extensive formal clinical training and experience with
theADOS and received direct supervision from the PI. The
clinicians were not blind to screening status as only those
children who screened positive were evaluated.
Cognitive ability
Cognitive ability was estimated using the Mullen Scales of
Early Learning (MSEL; Mullen, 1995). The MSEL is a
comprehensive assessment of development normed for
ages birth to 5years, 8months. The Visual Reception sub-
test can serve as a proxy for cognitive development
(Thurm, 2008, personal communication). The measure of
interest in this study was Visual Reception t-score. A
t-score from 40 to 60 is within the average range, while
t-scores below 40 are considered to be indicative of below-
average ability.
Demographics
Parents completed a measure in English or Spanish of
child sex, age, race, and ethnicity; parent/guardian race,
ethnicity, and level of education; child’s health insurance;
and target child’s current or past receipt of early interven-
tion. The education level of the parent/guardian complet-
ing the form and child’s type of health insurance served as
proxy for SES.
Procedure
This study was reviewed and approved by the Western
Institutional Review Board. No compensation for partici-
pation was provided. Head Start and state-licensed daycare
directors were contacted by study staff and invited to par-
ticipate. All early childcare providers in the classrooms of
children who met the age requirement for inclusion in the
study were invited to participate. Center staff sent home
release of information forms to obtain consent from parents
for the screening. Early childcare providers completed the
M-CHAT and/or SCQ for those children for whom consent
was obtained. Parents completed the Demographic Forms.

4 Autism
To reduce burden on the childcare staff, study staff reviewed
forms for completeness and scored the forms. In the rare
instance of an incomplete provider form, study staff con-
tacted providers in person or by email to request the miss-
ing information. Parents of children who screened positive
for ASD on the provider completed M-CHAT or SCQ were
contacted by study staff by phone for follow-up. For those
with positive M-CHATs, the formal M-CHAT Follow-Up
Interview was administered. Those who continued to screen
positive were offered a diagnostic evaluation. Children
with positive SCQs were offered a diagnostic evaluation,
since there is no formal SCQ follow-up interview.
Diagnostic evaluations were conducted by a trained,
licensed developmental pediatrician, advanced practice
nurse, or doctoral level psychologist at the preschool/day-
care at a time convenient for the family. Parents were pre-
sent during the evaluation. The evaluations consisted of a
clinical interview that included review of child and family
medical history, observation, ADOS, MSEL Visual
Reception subtest, and review of DSM-IV-R checklist for
autistic and related disorders. Written evaluation reports
including observations, test results, diagnostic impression,
and recommendations were provided to each parent/guard-
ian. Recommendations included referrals for early inter-
vention or school services when applicable.
Data analysis
Feasibility of this screening process was determined by
examining the percentage of early childcare providers who
completed screening measures for their students. Initial
estimate of validity of this screening process was deter-
mined by comparing providers’ reports of ASD risk status
with subsequent clinical diagnosis of ASD.
Results
Parent consent to participate was received for 1080 chil-
dren (88%) of those enrolled at the target sites. Completed
teacher measures were received for 967 children (90% of
those for whom parents had provided consent). We received
completed measures from 104 early childcare providers
(90% of the educators at these sites). Of the early childcare
providers, 80% were from Head Start sites (n=83 provid-
ers) and 20% were from non-Head Start sites (n=21 pro-
viders). Participant demographics are shown in Table 1.
As depicted in Table 1, children’s ages ranged from 16
to 76months (M=50months, SD=9.3months). Of the sub-
jects, 56% were male. Of the children, 44% were Hispanic;
3% were White, not Hispanic; 19% were Black, not
Hispanic; and 2% were other, not Hispanic. Child race/eth-
nicity was not reported for 32% of subjects. Only one par-
ent did not report ethnicity for either the child or himself/
herself. All other missing data were from the racial catego-
ries. Parent race/ethnicity was similar to data reported for
children. Of children, 43% were enrolled in Medicaid and
9% had no insurance. A total of 38% did not report insur-
ance status. Of parent respondents, 40% had a 12th-grade
education or less and 39% did not report parent’s education
level. Very few children had received early intervention.
Children enrolled in Head Start were more likely than chil-
dren enrolled in other daycares and preschools to be Black
and less likely to be Hispanic. They were also more likely
to be insured through Medicaid and less likely to have pri-
vate insurance. Both these characteristics were frequently
not reported (Figure 1).
Of the 967 children screened by early childcare provid-
ers, 24% (n=232) were screened with both the M-CHAT
and SCQs, 12% (n=119) were screened with M-CHAT
only, and 64% (n=616) were screened with the SCQ only.
Of those screened with both measures, 11% screened posi-
tive on both measures (n=26). Of all 351 children screened
with the M-CHAT, 29% screened positive (n=101).
Seventy-five of those 101 children screened positive on
the M-CHAT only (and either negative on the SCQ or were
not given the SCQ). Of all 848 children screened with the
SCQ, 7% screened positive (n=58). Of those 58, 32
screened positive on the SCQ only (and either negative on
the M-CHAT or were not given the M-CHAT). In total, 26
screened positive on both the M-CHAT and SCQ. Overall,
14% of children screened scored as at risk on either the
M-CHAT, the SCQ, or on both measures (n=133).
Parents of the 133 children who screened positive on
either the M-CHAT or the SCQ were contacted for follow-
up. Due to incorrect phone/address or because they did not
respond to messages, 45 families (34%) were lost to fol-
low-up. Of the 88 contacted, 73% (n=64) had screened
positive on the M-CHAT and an M-CHAT Follow-Up
Interview was conducted. Based on the M-CHAT
Follow-Up Interview, 64% screened positive (n=41); 68%
of those agreed to a full evaluation (n=28) and 32%
declined evaluation (n=13). Of the 32 children who
screened positive on the SCQ, 63% agreed to evaluation
(n=20), 9% declined (n=3), 25% (n=8) were lost to fol-
low-up, and 3% (n=1) chose to have an evaluation con-
ducted through another agency.
Forty-eight children who screened positive on the
M-CHAT Follow-Up Interview or SCQ were scheduled
for evaluation. Two children did not show for the evalua-
tion. Of the 46 children evaluated, 65% were diagnosed
with ASD (n=30).
Of the 30 diagnosed with ASD, 9 had screened positive
on both the M-CHAT and SCQ, 14 had screened positive
on the SCQ and had no M-CHAT, 1 had screened positive
on the SCQ and negative on the M-CHAT, and 6 had
screened positive on the M-CHAT and negative on the
SCQ. None had a previous diagnosis of ASD, but 3 were
already classified as eligible for special education.
Of the 16 evaluated and found not to meet criteria for
ASD, 6 had screened positive on both the M-CHAT and

Janvier et al. 5
SCQ, 5 had screened positive on the SCQ and had no
M-CHAT, 1 had screened positive on the SCQ and nega-
tive on the M-CHAT, 3 had screened positive on the
M-CHAT and negative on the SCQ, and 1 had screened
positive on the M-CHAT and had no SCQ. In total, 13
were diagnosed with a speech/language disorder and 1
Table 1. Demographics, combined and by site.
Combined sites Head Start Other daycares Head Start versus other
daycares, p value
Child demographics N=967 N=795 N=172
Child gender
Female (N=430) 44% 45% 44% 0.801
Male (N=537) 56% 55% 56%
Child race
White not Hispanic (N=25) 3% 3% 2% <0.001
Black not Hispanic (N=185) 19% 20% 15%
Other not Hispanic (N=23) 2% 3% 1%
No race reported, not Hispanic (N=307) 32% 34% 21%
Hispanic (N=426) 44% 40% 62%
No race or ethnicity reported (N=1) <1% <1% 0%
Parent race
White not Hispanic (N=31) 3% 3% 3%
Black not Hispanic (N=182) 19% 20% 15%
Other not Hispanic (N=22) 2% 3% 0%
No race reported, not Hispanic (N=339) 35% 38% 22%
Hispanic (N=392) 41% 36% 60%
No race or ethnicity reported (N=1) <1% <1% 0%
Parent education level
Less than 9th grade (N=78) 8% 6% 16%
Some high school (N=99) 10% 9% 15%
High-school grad (N=216) 22% 23% 22%
Some college (N=137) 14% 15% 12%
College grad (N=39) 4% 4% 3%
Graduate school (N=20) 2% 2% 4%
Not reported (N=378) 39% 41% 28%
Primary language
English (N=491) 51% 50% 52% 0.654
Spanish (N=476) 49% 50% 48%
Age in months (Mean=50, SD=9.3)
16–30months (N=40) 4% 4% 3% 0.790
31–60months (N=793) 82% 82% 81%
>60months (N=115) 12% 11% 14%
Not reported (N=19) 2% 2% 2%
Health insurance
Medicaid (N=420) 43% 45% 38% <0.001
Private commercial (N=59) 6% 5% 12%
Other government insurance (N=10) 1% 1% 0%
Other (N=23) 2% 2% 3%
None (N=91) 9% 6% 23%
Not reported (N=364) 38% 41% 24%
Child history of early interventiona N=794 N=171
Yes (N=34) 4% 4% 2% 0.167
No (N=931) 96% 96% 98%
Child currently receiving early interventiona N=794 N=171
Yes (N=29) 3% 3% 4% 0.358
No (N=936) 97% 97% 96%
aMissing data for two children.

6 Autism
Figure 1. The results of the completed provider screens.

Janvier et al. 7
child was also diagnosed as at risk of attention deficit/
hyperactivity disorder. Of these 14 children, 2 had also
been classified as eligible for special education. Two of the
children who did not meet criteria for ASD were given no
clinical diagnosis and were determined to be functioning
within normal limits. Overall, 3% of the total sample
screened by teachers was diagnosed with ASD.
Table 2 shows chi-square comparison of screening
results and site type (Head Start vs other daycare/pre-
school). Site (Head Start vs other sites) was associated
with the pass/fail rate only when both the M-CHAT and
SCQ (p=0.035) were administered to the same child.
Mullen scores were obtained for 44 of the 46 children
who received an evaluation. The overall mean t-score was
38.8 (below average). The difference in average Mullen
score between those with and without ASD (36 vs 42) was
not statistically significant (data not shown).
Discussion
In this study of autism screening conducted in community
preschools with a low-income, minority population, 14%
of children screened were identified as at-risk based on
early childcare provider report. Of those at risk, 23% met
criteria for ASD, 12% did not meet criteria for ASD, and
the remainder no longer showed ASD risk after a follow-
up interview, declined the evaluation, or were lost to fol-
low-up. Overall, 3% of the total sample met diagnostic
criteria for ASD. Significantly, none of the children diag-
nosed in this study had been previously identified as hav-
ing ASD.
Results of this study suggest that early childcare pro-
viders can screen children in their classes using traditional
ASD screening tools (M-CHAT and SCQ). Almost all edu-
cators agreed to participate suggesting that the screening
model is feasible. Among families that agreed to an evalu-
ation, 65% of those who had screened positive had autism,
providing preliminary evidence of the predictive validity
of this screening model. One advantage of this model is
that screening is not limited only to children for whom
concerns have been raised. All children in the preschool or
daycare classes can be screened.
It is important to note that 35% (n=16) of the children
who screened positive for ASD and were evaluated were
found not to have ASD. Of these false-positive screening
results, 88% (n=14) received another clinical diagnosis,
most typically speech-language disorder. This suggests
that population-based ASD screening may identify chil-
dren with ASD as well as those with other significant
developmental concerns who may warrant services. This is
important when considering potential costs of population-
based screening.
The cost of the screening tools is low and the M-CHAT
is free. The M-CHAT is also relatively quick to complete,
although the SCQ is lengthier. Having preschool teachers
screen the children they serve increases awareness of
autism and is an efficient way to determine which children
require further assessment. No formal training was pro-
vided to early childcare providers about how to complete
the screening tools. Such training is also not typically pro-
vided to parent/caregivers prior to completion of screen-
ers. This suggests that early childcare providers may
successfully screen children in their care without receiving
formal training on use of the M-CHAT or SCQ.
The screening measures used in this study were origi-
nally designed for use by parents rather than by early
childcare providers. The measures were chosen based on
common use in this age group and also because adequate
alternative provider-report measures were unavailable for
this age group. While study personnel scored the measures
Table 2. Screening and evaluation results.
Combined sites Head start Other daycares Head start versus
other daycares, p value
Screening results
M-CHAT or SCQ N=967 N=795 N=172
Pass (N=834) 86% 85% 91% 0.035
Fail (N=133) 14% 15% 9%
M-CHAT N=301 N=50
Pass (N=250) 71% 70% 80% 0.139
Fail (N=101) 29% 30% 10%
SCQ N=702 N=146
Pass (N=790) 93% 93% 96% 0.151
Fail (N=58) 7% 7% 4%
Evaluation results
Autism diagnosis (46 evaluations) N=46 N=37 N=9 0.919
Yes (N=30) 65% 65% 67%
No (N=16) 35% 35% 33%
M-CHAT: Modified Checklist for Autism in Toddlers; SCQ: Social Communication Questionnaire.

8 Autism
and conducted the follow-up interview, when applicable,
in order to reduce burden on early childcare staff, future
research could vary the involvement of early childcare
providers. If childcare workers also scored the screening
tools, conducted follow-up interviews, and assisted in
making referrals for evaluation and services, this screening
model may empower childcare staff, reduce costs, and
capitalize on existing relationships between staff and fami-
lies which might impact the likelihood of family follow
through with obtaining further evaluations and services.
Having providers complete the screening tools but con-
ducting follow-up interviews with parents may have intro-
duced bias and may have contributed to the percentage
who screened negative on M-CHAT follow-up interview
or who declined evaluation despite screening positive on
the follow-up interview. However, this process provided
parents opportunity to refute or confirm provider concerns.
While it is possible that parents may have denied child
symptoms that were of concern and would have qualified
the child for further evaluation, in actual clinical practice,
parents must consent to the evaluation. Thus, early child-
care provider screening may be a necessary, but not suffi-
cient step for identifying children at risk of ASD.
Screening in preschools and daycares eliminates a bar-
rier to access by bringing the service to the location where
many children spend a significant amount of time. The
proportion of children who screened positive but were lost
to follow-up was high, however. This may be typical for
low-income, minority populations where frequent change
of address or phone may be common. Mistrust and other
cultural issues may also have been factors in lost to follow-
up rate. Alternatively, high rates of loss to follow-up might
be characteristic of screening for autism in community
preschool settings. For example, in Dereu et al.’s (2011)
study of screening for autism in Belgian community child-
care settings, 50% of parents whose children had initially
screened as at-risk did not comply with repeated request
for follow-up. Race and SES were not reported. The high
rate of loss to follow-up in these studies is problematic
because these children had screened at risk of ASD and
screening is only the first step in assessing risk status and
connecting children and families to needed services.
Access to follow-up evaluations is a substantial barrier
in many communities where there are limited diagnostic
resources. While we provided free diagnostic evaluation in
the community, 25% of those to whom this service was
offered declined. Therefore, barriers may continue to be
present in those communities even when families know
where to go for diagnostic evaluation, and proximity, cost,
and availability are not issues. Mistrust and cultural factors
that may affect lost to follow-up rate may also be barriers
in securing evaluations. Furthermore, parents may not
share the concerns of the early childcare providers, may
not understand the advantage of clarifying diagnosis, and
may worry about their children being labeled. It is quite
possible for early childcare providers to screen for ASD in
their classrooms, but additional strategies may be neces-
sary to ensure that at-risk children receive diagnostic eval-
uation. Parent outreach, education, and use of parent
cultural navigators may be possible strategies to reduce
these barriers.
While the M-CHAT and SCQ are commonly used ASD
screeners, sensitivity and specificity have primarily been
established based on at-risk samples and not with children
from low-income or racially or ethnically diverse popula-
tions. Future research is needed to determine sensitivity
and specificity of these measures when used in the general
population, when completed by childcare providers, and
when used in underserved communities.
There are some limitations to this study. Since the exact
number of non-Head Start daycares invited to participate
was not tracked, level of interest between Head Start and
non- Head Start settings in screening programs cannot be
compared in order to determine selection bias. Evaluations
were not offered to children who screened negative, so that
sensitivity, specificity, and positive predictive validity
could not be calculated. It should be noted, however, that
determining prevalence was not the focus of this study.
Additionally, the clinicians who evaluated the children in
this study were not blind to the child’s screening status
since evaluations were provided only to those children
who screened positive. As has been discussed, the lost to
follow-up rate in this study was significant and thus find-
ings should be interpreted with some caution. It is unclear
whether or how the evaluation results for children lost to
follow-up may have differed. The verbal subtests of the
Mullen Scales of Early Development were not adminis-
tered, and it is possible that performance on the verbal sec-
tions of the Mullen would have affected the clinical
diagnosis. Head Start conducts developmental screening
of enrolled children and is open to enrollment of those
with disabilities. While none of the children who received
evaluation had previously been diagnosed with or were
receiving services for autism, Head Start screening results
were not available for the children in this study. It would
be valuable to compare Head Start screening results to the
M-CHAT and SCQ in order to clarify the extent that spe-
cifically screening for ASD identifies children not previ-
ously felt to have developmental concerns. It is possible
that some of the children in this sample who did not receive
an evaluation may have been previously identified as at
risk of ASD, despite a low number reporting early inter-
vention involvement.
In this study, parents reported ethnicity for all but one
child while race was not reported for 32% of children. This
high percentage of missing racial data is consistent with
other studies of underserved children (e.g. Zuckerman et al.,
2014) and may suggest that identifying race is a sensitive
area for a significant percentage of parents in these popula-
tions. Reporting race may present challenges when ethnicity

Janvier et al. 9
is also being queried. Specifically, those of Hispanic/Latino/
Spanish origin may not identify with a particular race,
resulting in them reporting ethnicity but not race.
Regardless of methodological limitations, the model of
using early childcare providers as screeners for ASD in
their classrooms met the goal of identifying young chil-
dren with ASD, specifically young children in under-
served communities. Successful access to a pool of
underserved children was evidenced by a high number of
children from racial/ethnic minorities, the high percent-
age of children enrolled in Medicaid or with no insurance,
and the large proportion of their parents/guardians who
had a high school diploma or less. It appears that training
early childcare providers to screen young children in pre-
school and daycare classes for ASD may be a promising
method to improve access to early diagnosis in under-
served communities.
Acknowledgements
Portions of this study were presented at the 2013 annual
International Meeting for Autism Research.
Funding
This research was funded in part by a grant from the NJ Governor’s
Council for Medical Research and Treatment of Autism, NJ State
Department of Health.
References
American Psychiatric Association (APA) (1994) Diagnostic
and Statistical Manual of Mental Disorders. 4th ed. Rev.
Washington, DC: APA.
Baghdadli A, Picot MC, Pascal C, et al. (2003) Relationship
between age of recognition of first disturbances and severity
in young children with autism. European Journal of Child
Adolescent Psychiatry 12(3): 122–127.
Bethell C, Reuland C, Schor E, et al. (2011) Rates of parent-
centered developmental screening: disparities and links to
service access. Pediatrics 128: 146–155.
Branson D, Vigil DC and Bingham A (2008) Community child-
care providers’ role in the early detection of autism spectrum
disorders. Early Childhood Education Journal 35: 523–530.
CDC (2014) Prevalence of autism spectrum disorders—autism and
developmental disabilities monitoring network, 11 sites, United
States, 2010. MMWR Surveillance Summaries 63(SS02): 1–21.
Chandler S, Charman T, Baird G, et al. (2007) Validation of
the social communication questionnaire in a population.
Journal of the American Academy of Child and Adolescent
Psychiatry 46(10): 1324–1332.
Constantino JN, Lavesser PD, Zhang Y, et al. (2007) Rapid quan-
titative assessment of autistic social impairment by class-
room teachers. Journal of the American Academy of Child
and Adolescent Psychiatry 46(12): 1668–1676.
Corsello C (2005) Early intervention in autism. Infants and
Young Children 18(2): 74–85.
Dawson G, Rogers S, Munson J, et al. (2010) Randomized, con-
trolled trial of an intervention for toddlers with autism: the
Early Start Denver Model. Pediatrics 125(1): e17–e23.
Dereu M, Raymaekers R, Warreyn P, et al. (2012) Can child care
workers contribute to the early detection of autism spec-
trum disorders? A comparison between screening instru-
ments with child care workers versus parents as informants.
Journal of Autism and Developmental Disorders 42(5):
781–796.
DeVincent C, Gadow KD, Strong G, et al. (2008) Screening
for autism spectrum disorder with the Early Childhood
Inventory-4. Journal of Developmental and Behavioral
Pediatrics 29(1): 1–10.
Dosreis S, Weiner CL, Johnson L, et al. (2006) Autism spec-
trum disorder: screening and management practices among
general pediatric providers. Journal of Developmental and
Behavioral Pediatrics 27(2): S88–S94.
DurkinMS,MaennerMJ,MeaneyFJ,etal.(2010)Socioeconomic
inequality in the prevalence of autism spectrum disorder:
evidence from a US cross-sectional study. PLoS ONE 5(7):
e11551.
Eaves L, Wingert H and Ho HH (2006) Screening for autism:
agreement with diagnosis. Autism 10: 229–242.
Fouad MN, Partridge E, Dignan M, et al. (2010) Targeted inter-
vention strategies to increase and maintain mammography
utilization among African American women. American
Journal of Public Health 100(12): 2526–2531.
Fountain C, King MD and Bearman PS (2010) Age of diagno-
sis for autism: individual and community factors across
10 birth cohorts. Journal of Epidemiology and Community
Health 65: 503–510.
Goin RP and Myers BJ (2004) Characteristics of infantile autism:
moving toward earlier detection. Focus on Autism and
Other Developmental Disabilities 19(1): 5–12.
Harris SL and Handleman JS (2000) Age and IQ at intake as pre-
dictors of placement for young children with autism: a four-
to six-year follow-up. Journal of Autism and Developmental
Disorders 30(2): 137–142.
Hepburn SL, DiGuiseppi C, Rosenberg S, et al. (2008) Use of a
teacher nomination strategy to screen for autism spectrum
disorders in general education classrooms: a pilot study.
373–382.
Jacobson JW and Mulick JA (2000) System and cost research
issues in treatments for people with autistic disorders.
585–593.
Jacobson JW, Mulick A and Green G (1998) Cost-benefit
estimates for early intervention for young children with
autism—general model and single state case. Behavioral
Interventions 13: 201–226.
Jarbrink K and Knapp M (2001) The economic impact of autism
in Britain. Autism 5(1): 7–22.
Johnson CP and Myers SM (2007) Identification and evalua-
tion of children with autism spectrum disorders. Pediatrics
120(5): 1183–1215.
Kleinman JM, Robins DL, Ventola PE, et al. (2008) The modified
checklist for autism in toddlers: a follow-up study investigat-
ing the early detection of autism spectrum disorders. Journal
of Autism and Developmental Disorders 38: 827–839.
Liptak GS, Benzoni LB, Mruzek DW, et al. (2008) Disparities in
diagnosis and access to health services for children with autism:
data from the National Survey of Children’s Health. Journal of
Developmental and Behavioral Pediatrics 29(3): 152–160.

10 Autism
Lord C, Rutter M and Le Couteur A (1994) Autism Diagnostic
Interview-Revised: a revised version of a diagnostic inter-
view for caregivers of individuals with possible perva-
sive developmental disorders. Journal of Autism and
Developmental Disorders 24(5): 659–685.
Lord C, Rutter M, DiLavore PC, et al. (1999) Autism Diagnostic
Observation Schedule-WPS (ADOS-WPS). Los Angeles,
CA: Western Psychological Services.
Mandell DS, Ittenbach RF, Levy SE, et al. (2007) Disparities in
diagnoses received prior to a diagnosis of autism spectrum
disorder. Journal of Autism and Developmental Disorders
37(9): 1795–1802.
Mandell DS, Listerud J, Levy SE, et al. (2002) Race differences
in the age at diagnosis among Medicaid-eligible children
with autism. Journal of the American Academy of Child and
Adolescent Psychiatry 41(12): 1447–1453.
Mandell DS, Wiggins LD, Carpenter LA, et al. (2009) Racial/
ethnic disparities in the identification of children with
autism spectrum disorders. American Journal of Public
Health 99(3): 493–498.
Moore V and Goodson S (2003) How well does early diagnosis
of autism stand the test of time? Follow-up study of children
assessed for autism at age 2 and development of an early
diagnostic service. Autism 7(1): 47–63.
Mullen EM (1995) Mullen: Scales of Early Learning. AGS ed.
Circle Pines, MN: American Guideline Service, Inc.
Robins DL, Fein D, Barton ML, et al. (2001) The Modified
Checklist for Autism in Toddlers: an initial study investi-
gating the early detection of autism and pervasive devel-
opmental disorders. Journal of Autism and Developmental
Disorders 31(2): 131–144.
Rutter M, Bailey A and Lord C (2003) Social Communication
Questionnaire. Los Angeles, CA: Western Psychological
Services.
Sallows GO and Graupner TD (2005) Intensive behavioral
treatment for children with autism: four-year outcome and
predictors. American Journal of Mental Retardation 110(6):
417–438.
Sices L (2007) Developmental screening in primary care: the
effectiveness of current practice and recommendations
for improvement. The Commonwealth Fund. Available at:
http://www.commonwealthfund.org/usr_doc/1082_Sices_
developmental_screening_primary_care.pdf
Snow AV and Lecavalier L (2008) Sensitivity and specificity
of the Modified Checklist for Autism in Toddlers and the
Social Communication Questionnaire in preschoolers sus-
pected of having pervasive developmental disorders. Autism
12(6): 627–644.
Szatmari P, Bryson SE, Boyle MH, et al. (2003) Predictors of
outcome among high functioning children with autism
and Asperger syndrome. Journal of Child Psychology and
Psychiatry, and Allied Disciplines 44(4): 520–528.
Volkmar F, Chawarska K and Klin A (2005) Autism in infancy
and early childhood. Annual Review of Psychology 56:
315–336.
Volkmar FR, Lord C, Bailey A, et al. (2004) Autism and
pervasive developmental disorders. Journal of Child
Psychology and Psychiatry, and Allied Disciplines 45(1):
135–170.
Wong V, Hu L, Lee W, et al. (2004) A modified screening tool
for autism (Checklist for Autism in Toddlers [CHAT 23])
for Chinese children. Pediatrics 114: e166–e176.
Yeargin-Allsopp M, Rice C, Karapurkar T, et al. (2003)
Prevalence of autism in a US metropolitan area. Journal of
the American Medical Association 289(1): 49–55.
Zuckerman KE, Mattox K, Donelan K, et al. (2013) Pediatrician
Identification of Latino children at risk for autism spectrum
disorder. Pediatrics 132: 445–453.
Zuckerman KE, Sinche B, Cobian M, et al. (2014)
Conceptualization of autism in the Latino community and its
relationship with early diagnosis. Journal of Developmental
and Behavioral Pediatrics 35: 522–533.

final published online 5-20-15

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (10)

Similar to final published online 5-20-15

Similar to final published online 5-20-15 (20)

final published online 5-20-15