Sex/Gender Differences in Screening for Autism Spectrum Disorder

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Sex/Gender Differences in Screening for Autism

Spectrum Disorder: Implications for Evidence-

Based Assessment

Spencer C. Evans, Andrea D. Boan, Catherine Bradley & Laura A. Carpenter

To cite this article: Spencer C. Evans, Andrea D. Boan, Catherine Bradley & Laura A. Carpenter

(2018): Sex/Gender Differences in Screening for Autism Spectrum Disorder: Implications

for Evidence-Based Assessment, Journal of Clinical Child & Adolescent Psychology, DOI:

10.1080/15374416.2018.1437734

To link to this article: https://doi.org/10.1080/15374416.2018.1437734

Published online: 30 Mar 2018.

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Sex/Gender Differences in Screening for Autism

Spectrum Disorder: Implications for Evidence-Based

Assessment

Spencer C. Evans

Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina

Andrea D. Boan, Catherine Bradley, and Laura A. Carpenter

Department of Pediatrics, Medical University of South Carolina

Autism spectrum disorder (ASD) is diagnosed more often in boys than in girls; however, little is

known about the nature of this sex/gender discrepancy or how it relates to diagnostic assessment

practices. This study examined the performance of the Social Communication Q uestionnaire (SCQ)

in screening for ASD among boys and girls. Data were drawn from the South Carolina Children’s

Educational Surveillance Study, a population-based study of ASD prevalence among children

8–10 years of age. Analyses were conducted using SCQ data from 3,520 children, with direct

assessment data from 272 with elevated SCQ scores. A bifactor model based on the Diagnostic and

Statistical Manual of Mental Disorders’s (5th ed.) two ASD symptom domains ﬁt the data well and

performed slightly better for girls. In the general population sample, girls exhibited fewer social

communication/interaction and restricted-repetitive behavior symptoms than boys. In the direct

assessment sample, however , girls with ASD showed greater impairment in social communica-

tion/interaction than boys with ASD. Items pertaining to social communication/interaction problems

at ages 4–5 were among the most diagnostically efﬁcient overall and particularly for girls. Similarly,

receiver operating characteristic analyses suggested that the SCQ performs adequately among boys

and well among girls. Results support the use of the SCQ in screening for ASD but do not indicate

sex/gender-speciﬁc cutoffs. Girls with ASD may exhibit pronounced intraindividual deﬁcits in

social communication/interaction compared to male peers with ASD and female peers without

ASD. Although more research is needed, careful attention to social communication/interaction

deﬁcits around 4–5 years of age may be especially useful for assessing ASD in girls.

Autism spectrum disorder (ASD) is a neurodevelopmental

disorder deﬁned by pervasive deﬁcits in social communica-

tion and interaction and patterns of restricted, repetitive,

stereotyped behaviors and interests (American Psychiatric

Association [APA], 2013). Beyond these core criteria, how-

ever, there is considerable heterogeneity in the symptom

presentations exhibited by children with ASD, including a

variety of qualitative and quantitative differences (e.g., sever-

ity, language, cognitive ability, co-occurring problems). One

critical factor in understanding ASD symptom variability is

the role of sex/gender

(e.g., Goldman, 2013; Lai, Lombardo,

Auyeung, Chakrabarti, & Baron-Cohen, 2015).

Sex/gender differences in ASD are both over- and under-

acknowledged. On one hand, it has been known for decades

that ASD is more common in boys than girls. Estimates

suggest that the true sex/gender ratio is about 3.3 to 1 (based

on higher quality and population-screening studies; Loomes,

Hull, & Mandy, 2017), whereas the ratio among those

Correspondence should be addressed to Spencer C. Evans, Department

of Psychology, Harvard University, 1036 William James Hall, 33 Kirkland

Street, Cambridge, MA 02138. E-mail: [email protected]

Color versions of one or more of the ﬁgures in the article can be found

online at www.tandfonline.com/hcap.

Following Lai et al. (2015), we use both terms (sex and gender)

together when referring to differences between boys and girls throughout

most of this article to acknowl edge that it is not clear whether bio logical

sex at birth or social gender constructs are the ke y variable; both likely

play a role.

Journal of Clinical Child & Adolescent Psychology, 00(00), 1–15, 2018

ISSN: 1537-4416 print/1537-4424 online

DOI: https://doi.org/10.1080/15374416.2018.1437734

clinically diagnosed is about 4.5 to 1 (based on administrative

records; Christensen et al., 2016). On the other hand, little is

known about the nature of sex/gender differences in symptom

presentations among children with ASD. To the extent that

ASD presents differently in boys versus girls, it is possible that

girls with ASD are underidentiﬁed. Indeed, Loomes et al.’s

(2017) ﬁnding that the magnitude of the sex/gender discre-

pancy is inversely related to study quality suggests that there is

gender-related ascertainment bias in clinical diagnosis.

Complicating matters further, the evidence base pertaining to

ASD—and, by extension, the diagnostic criteria themselves—

historically comes from research among overwhelmingly male

samples (e.g., Edwards, Watkins, Lotﬁzadeh, & Poling, 2012;

Watkins, Zimmermann, & Poling, 2014). Thus, although there

is clearly a need for more research to better understand sex/

gender differences in ASD, it is also important to keep in mind

that existing assessment tools and diagnostic criteria may con-

tain sex/gender bias.

The present study seeks to advance the literature on both

of these fronts: (a) to elucidate the extent and nature of sex/

gender differences in ASD symptom presentations using

comprehensive assessment methods and a population-

based sample and (b) to help understand the extent to

which sex/gender bias may be operating in screening for

ASD using the Social Communication Questionnaire (SCQ;

Rutter & Bailey, 2003). To investigate these questions, we

analyzed data from a large epidemiological sample of

school-age children who were screened and assessed for

ASD. Thus, the present analysis offers a unique lens for

investigating ASD screening and symptom presentation

among boys and girls with and without the diagnosis.

SEX/GENDER DIFFERENCES IN ASD

Evidence has been mixed with respect to sex/gender differ-

ences in core ASD symptoms. Some studies indicate that boys

have greater social and communicative problems compared

with girls (e.g., Beggiato et al., 2017; Head, McGillivray, &

Stokes, 2014; Hiller, Young, & Weber, 2016), whereas others

show the opposite pattern (e.g., Carter et al., 2007; Frazier,

Georgiades, et al., 2014; Hartley & Sikora, 2009), and still

others show no particular differences in this domain (e.g.,

Bölte, Duketis, Poustka, & Holtmann, 2011;Holtmann,

Bölte, & Poustka, 2007; Mandy, Chilvers, et al., 2012;May,

Cornish, & Rinehart, 2016; Reinhardt, Wetherby,

Schatschneider, & Lord, 2015; Szatmari et al., 2012).

Similarly, some studies have found boys with ASD to exhibit

higher levels of repetitive and stereotyped behaviors than girls

(Beggiato et al., 2017; Bölte et al., 2011; Hartley & Sikora,

2009; May et al., 2016;Szatmarietal.,2012), whereas others

have found no differences in this domain (Carter et al., 2007;

Holtmann et al., 2007; Reinhardt et al., 2015). In their meta-

analysis, Van Wijngaarden-Cremers et al. (2014) found that

among individuals with ASD beyond age 6, males show

higher levels of restricted/repetitive behaviors and interests

than females; no signiﬁcant gender differences were found

for social interaction or communication (Van Wijngaarden-

Cremersetal.,20

14). Narrative reviews (e.g., Kirkovski,

Enticott, & Fitzgerald, 2013; Lai et al., 2015) have yielded

similar conclusions.

In addition, there are clinically important sex/gender differ-

ences in ASD that are not related to the core diagnostic symp-

toms. Compared to boys, girls with ASD more often go

undiagno sed or are diagnosed at a later age, particularly girls

with less severe ASD symptoms and more intact language and

cognitive skills (Begeer et al., 2013;Giarellietal.,2010;

Rutherford et al., 2016). Girls with ASD may also be better

able to compensate for symptoms despite having persistent core

deﬁcits associated with ASD (Livingston & Happé, 2017),

which might contribute to greater social “ca mouﬂage” (Hull

et al., 2017). For example, some evidence suggests that girls

with ASD perform better on measures of nonverbal communica-

tion, which may mask their symptoms (R ynkiewicz et al., 2016).

Despite this compensation, research examining peer relation-

ships found that boys and girls with ASD exhibit more simila-

rities with one another than with their same-gender, typically

developing peers; however girls with ASD appear to face more

social, friendship, and language demands than boys with ASD

(Dean et al., 2014). More broadly, girls can exhibit patterns of

restricted interests and repetitive behaviors and social and com-

municative problems which might seem more socially accepta-

ble than the patterns seen in boys with ASD (Lai et al., 2015).

This could help explain why girls with ASD often have more

severe behavioral, emotional, and cognitive problems compared

to boys with ASD (e.g., Frazier, Georg iades, et al., 2014;

Holtmann et al., 2007;Horiuchietal.,2014; Stacy et al.,

2014), and even compared to girls at risk for ASD who are not

ultimately diagnosed (Dworzynski, Ronald, Bolton, & Happé,

2012). That is, perhaps girls must exhibit more severe symp-

toms, impairment, or co-occurring problems to receive a diag-

nosis of ASD.

One possible explanation for these sex/gender differences is

the “extreme male brain theory” of ASD (Baron-Cohen,

2002). After reviewing the evidence for behavioral sex/gender

differences, Baron-Cohen concluded that on average, males

exhibited weaknesses at empathizing and strengths at system-

atizing compared to females. Thus, ASD could be a disorder of

the extreme male brain, characterized by low levels of

empathizing traits (e.g., social-emotional understanding, prag-

matic language, friendship development and maintenance) and

high levels of systematizing traits (e.g., attention to detail,

preference for rule-based systems and facts, preoccupation

with cause-and-effect systems, and islets of ability; Baron-

Cohen, 2002; Baron-Cohen, Knickmeyer, & Belmonte,

2005). This theory has garnered some support by way of

between-group behavioral differences (e.g., Stauder, Cornet,

& Ponds, 2011; Tan et al., 2015) and evidence linking mascu-

linization and ASD traits to fetal testosterone exposure (e.g.,

Auyeung et al., 2009; Baron-Cohen et al., 2011). However,

EVANS, BOAN, BRADLEY, CARPENTER

this account has also been criticized for being too biologically

reductive and neglecting gender socialization processes (e.g.,

Buchen, 2011; Krahn & Fenton, 2012). Some evidence sug-

gests that ASD is a gender-deﬁant disorder rather than a

disorder of masculinization (Bejerot et al., 2012), and other

research suggests that normative sex differences in typically

developing populations are absent in children with ASD (Park

et al., 2012). Further research is needed to clarify these mixed

ﬁndings.

RELEVANCE TO SCREENING

Much of the research just summarized has focused on children

who have already received the diagnosis, sometimes with a non-

ASD comparison group. Although such studies provide insight

into clinical populations, they do relatively little to improve the

assessment of boys and girls whose ASD diagnostic status is

unknown. This is a major gap in the literature. W ithout addres-

sing the nosological and diagnostic challenges pertaining to sex/

gender considerations, any research on ASD based on existing

assessment practices is subject to the underlying problem of not

knowing how ASD should be deﬁned and diagnosed in males

compared to females (Lai et al., 2015). Thus, there is a need for

rigorous population-based assessment research with attention to

sex/gender. It is possible that systematic sex/gender differenc es

could arise at any step in the assessment pipeline—from elicit-

ing concerns about ASD to the results of diagnostic evaluations.

Screening measures are particularly key for understanding sex/

gender differences in symptom presentation and for addressing

any systematic problems related to which children get referred

for ASD evaluations. Improved interpretation of screening mea-

sures may lead to earlier identiﬁcationforchildreninneedof

services. The SCQ (Rutter & Bailey, 2003) is one of the most

widely researched and recommended parent-report screening

measures for ASD in youth (Norris & Lecavalier, 2010;

Ozonoff, Goodlin-Jones, & Solomon, 2005). Although previous

research has investigated the general diagnostic utility of SCQ

and similar measures for screening for ASD (e.g., receiver

operating characteristic [ROC] and sensitivity/speciﬁcity;

Barnard-Brak et al., 2016; Chandler et al., 2007; Duvekot,

Van Der Ende, Verhulst, & Greaves-Lord, 2015; Eaves,

W ingert, Ho, & Mickelson, 2006; Ung et al., 2016), there has

been little attention to sex/gender differences. The notable

exception is that some authors have found evidence for little

to no measurement invariance in the SCQ (Wei, Chesnut,

Barnard-Brak, & Richman, 2015) or similar screening measures

(Frazier , Ratliff, et al., 2014; Frazier & Hardan, 2017).

Although the SCQ demonstrates excellent psychometrics

among school-age children (Chesnut, Wei, Barnard-Brak, &

Richman, 2017; Norris & Lecavalier, 2010), its clinical and

research utility is limited by its lack of subscales, yielding

only a single total score. In developing the SCQ (Berument,

Rutter, Lord, Pickles, & Bailey, 1999; Rutter & Bailey,

2003), the authors pulled items from the three Autism

Diagnostic Interview–Revised domains (Lord, Rutter, &

Le Couteur, 1994),

offering one possible subscale structure.

Then they estimated a three-factor exploratory principal

components analysis from their clinical sample of 200,

offering a different possible structure. Neither of these mod-

els has been validated for clinical or research purposes.

Others (Wei et al., 2015) have subsequently adopted the

SCQ’s exploratory model or developed their own (e.g.,

Gau et al., 2011). However, the most compelling and

copious eviden ce from a variety of ASD measures (e.g.,

Frazier & Hardan, 2017; Frazier, Youngstrom, Kubu,

Sinclair, & Rezai, 2008; Frazier et al., 2012; Mandy,

Charman, et al., 2012) supports the two-domain framework

that was codiﬁed in the Diagnostic and Statistical Manual

of Mental Disorders (5th ed.; DSM-5;APA,2013). For this

reason, and to optimize the usefulness of our results, we

examine a two-domain bifactor model of the SCQ.

THE PRESENT STUDY

In sum, the literature documents a large sex/gender discre-

pancy in ASD diagnoses and symptoms, with mixed evidence

and explanations as to why. The present study investigates the

extent and nature of sex/gender differences in ASD symptoms

among a large epidemiological sample of school-age children

and how these differences affect the SCQ in screening for

ASD. Speciﬁcally, we examine (a) the prevalence of ASD

markers in school-age children, overall and by sex/gender;

(b) differences in SCQ results related to sex/gender and ASD

diagnostic status, and their interaction; (c) the diagnostic efﬁ-

ciency of the SCQ in screening for ASD in boys and girls; and

(d) whether different clinical cutoffs should be considered for

boys and girls. Based on previous research, it was hypothe-

sized that, among those with and without ASD diagnoses, boys

would show higher ASD symptoms overall and particularly in

restricted/repetitive interests and behaviors. It was expected

that this would lead to sex/gender-driven measurement pro-

blems, potentially detrimentally affecting the identiﬁcation of

girls’ ASD symptoms. Because diagnostic status was used as

our criterion, this study could not examine sex/gender bias in

the diagnostic construct but rather focused on the performance

of the SCQ. Results may help advance assessment practices

and knowledge related to sex/gender differences in ASD or in

the performance of the SCQ.

METHODS

Participants

Data were drawn from the South Carolina Children’s

Educational Surveillance Study (SUCCESS), a population-

SCREENING FOR ASD IN BOYS AND GIRLS 3

based study of ASD prevalence among school-age children.

The study design and methodology has been detailed else-

where (see Carpenter et al., 2016). The present analyses and

descriptive statistics are based on all available data for

children whose parent provided consent and fully completed

the English version of the SCQ (n = 3,520). The target

population consisted of all children born in 2004 living in

a three-county catchment area in coastal South Carolina.

Participants were 8 to 10 years of age at the time of the

initial screening. Those who were invited for a direct assess-

ment were slightly older by the time their evaluation

occurred (M = 10.3 years; SD = 0.5; range = 8.8–11.4).

Procedures (detailed next) were designed to obtain as

large and representative a sample as possible, and prelimin-

ary results suggest a reason able degree of representativeness

was achieved. In the population-screening sample, racial/

ethnic backgrounds were as follows (roughly similar to

census estimates): 61% non-Hispanic White, 27% non-

Hispanic Black, 6% Hispanic, 3% other, and 3% multiracial.

In the direct assessment sample, racial/ethnic backgro und

proportions were as follows: 44% non-Hispanic White, 37%

non-Hispanic Black, 13% Hispanic, 1% other, and 4% mul-

tiracial. Compared to girls, greater proportions of boys fell

in the clinical range (SCQ ≥ 15) and in the at-risk range (8 ≥

SCQ < 15; see Table 1) during the screening, rendering

them more likely to be eligible for a direct assessmen t

(35% of boys vs. 24% of girls). Of these, boys (29%)

were more likely to be invited to and ultimately complete

an assessment compared to girls (22%). Thus, the gender

ratio shifted from census-estimated 51% male in the popula-

tion to 49% male in the screening sample to 65% male in

the direct assessment sample. Sociodemographic variables

were not used to adjust the population screening procedures,

direct assessment sampling, clinical assessment, or analyses.

Procedures

All procedures were approved by the researchers’ institu-

tional review board. As described by Carpenter et al. (2016),

a multiphase sampling design was used. Procedures were

designed to maximize participation rates from the entire

population, including special education students. Extensive

efforts were taken to ensure that the sample was as repre-

sentative as possible, including steps to boost participation

among studen ts from ethnic minority and lower socioeco-

nomic backgrounds. Recruitment and sampling procedures

were developed based on the literature and in partnership

with schools and organizations in the three-county catch-

ment area. Ultimately, 123 of 127 public and private schools

agreed to participate. Within a 2-month period, families of

eligible children received via their school an introductory

letter; packet with cover letter, waiver, and SCQ; and up to

two reminders. Parents were allowed to complete an online

or paper version of the SCQ or to decline. Incentives for

responding were provided for students, parents, and

teachers.

After co mpleting the SCQ, a subset of participants was

identiﬁed and invited for an in-person ASD assessment

TABLE 1

Descriptive Statistics for SCQ by Sex/Gender, SCQ Risk Level, and Diagnostic Group

Sex/Gender Comparisons

Full Sample Boys Girls

Population Sample (N, n) n = 3,520 n = 1,731 n = 1,789

SCQ Total Score

M 5.97 6.77 5.19

MSE 0.09 0.14 0.11

SD 5.41 5.91 4.76

Mdn 4.5 5 4

Mode 2 3 1

Range 0–36 0–36 0–34

Skewness 1.71 1.62 1.68

Kurtosis 3.88 3.23 4.03

SCQ Risk Groups (%)

At Risk (SCQ ≥ 15) 7.1 9.4 4.9

Subthreshold (8 ≤ SCQ ≤ 14) 22.4 25.7 19.2

Low Risk (SCQ ≤ 7) 70.5 64.9 75.9

Direct Assessment Sample n = 272 n = 177 n =95

SCQ Total Scores by ASD Groups

ASD+, M (SD) 20.98 (6.86) 20.45 (7.03) 24.29 (4.75)

ASD–, M (SD) 13.09 (4.66) 13.38 (4.92) 12.65 (4.24)

Distribution of ASD Groups (%)

ASD+ 18.8 24.9 7.4

ASD– 81.3 75.1 92.6

Note: SCQ = Social Communication Questionnaire; ASD = Autism Spectrum Disorder.

4 EVANS, BOAN, BRADLEY, CARPENTER

based on their SCQ scores. Given questions regarding the

optimal cutoff value for the SCQ (Eaves et al., 2006; Norris

& Lecavalier, 2010), all those in the “at-risk” range (SCQ ≥

15; 100% invited; 44% completed; n = 112) and a randomly

selected portion of those in the “subthreshold” range (8 ≤

SCQ ≤ 14; 69% invited; 20% completed; n = 160) were

invited for a direct assessment. This included a separa te

informed consent and a comprehensive ASD diagnostic

assessment (measures described next). These direct assess-

ments were completed by doctoral-level psychologists with

appropriate training and expertise in ASD evaluation .

Participants’ ASD diagnostic status was determined based

on the integration of all assessment data. Examiners were

not blinded to SCQ scores, but neither these nor sex/gender

status were considerations for diagnostic decision making.

All cases were reviewed by the team on a weekly basis, with

diagnostic ambiguity resolved by consensus. Examiners’

interrater reliability was 100% for case status.

From the census-estimated population of 8,780 children,

4,185 survey responses were recorded, of which 3,698

(42%) were usable data.

The present analyses are based

on data with complete responses on the English SCQ

(excluding Spanish and partial SCQs), resulting in a ﬁnal

analytic sample of 3,520 (40% of population), including 272

who ultimately completed a direct assessment.

Measures

Screening

The SCQ Lifetime Form was used to screen for ASD in the

full sample. The SCQ is a brief, standardized checklist of 40

items pertaining to symptoms of ASD, including problems with

communication and reciprocal social interaction, and restricted,

repetitive, and stereotyped behaviors (Rutter & Bailey, 2003).

All items are in a yes/no format; some ask if the child has ever

exhibited the behavior, whereas others focus speci ﬁcally on the

period of 4–5 years of age when symptoms of ASD may

become more apparent. Items assess both atypical and typical

behaviors, the latter being reverse coded. Possible scores range

from 0 to 39, with higher scores indicating greater likelihood of

ASD. To minimize the possibility of parents recognizing ques-

tions as pertaining to ASD symptoms, the project was promoted

as a study of child social development and the SCQ was licensed

by the publisher to be presented as a “SUCCESS

Questionnaire”; no changes were made to the SCQ instructions

or items. The English Lifetime SCQ has demonstrated ample

evidence of validity and reliability, including good speciﬁcity

and sensitivity (Chandler et al., 2007; Chesnut et al., 2017). The

SCQ had good internal consistency in the present study

(Cronbach’s α = .82). As previously noted, the SCQ does not

have validated subscales. For the present analyses, three of the

coauthors (two doctoral-level psychologists and one predoctoral

psychology intern) with expertise in ASD evaluation divided the

SCQ items into two subdomains mapping onto DSM-5 ASD

criteria: (a) social communication and interaction (SCI) deﬁcits

(25 items; e.g., spontaneously used gestures, smiles back, talks

to be friendly) and (b) restricted and repetitive behavior (RRB;

12 items, e.g., unusual special interests, odd mannerisms, repe-

titive language). Two items pertaining to self-injurious behavior

(SCQ Item 17) and solitary make-believe play (SCQ Item 35),

which are included in the total score, were not included in

subdomain scores because there was no direct correspondence

with DSM-5. This bifactor model was tested and supported via

conﬁrmatory factor analysis (see Results).

Direct Assessment

Consistent with recommendations (e.g., Ozonoff et al.,

2005), multiple instruments and methods were used in the

diagnostic evaluation. First, a structured ASD diagnostic inter-

view was administered to a primary caregiver. This interview

was developed for the SUCCESS study to assess current and

lifetime symptoms of ASD using an integrated set of criteria

that is compatible with DSM-IV (APA, 1994) and DSM-5.

Second, the Autism Diagnostic Observation Schedule, second

edition (ADOS-2; Lord, Luyster, Gotham, & Guthrie, 2012)

was administered. The ADOS-2 is a semistructured, standar-

dized test, commonly considered a “gold standard” instrument

in ASD assessment. The ADOS-2 facilitates direct observation

of ASD-related behaviors across several developmentally

appropriate tasks and items, yielding a total score representing

the likelihood of ASD and the severity of symptoms. The

ADOS-2 and its predecessors have substantial evidence for

validity, reliability, and utility in assessing ASD (Gotham et al.,

2008;Lordetal.,2012, 2000; Molloy, Murray, Akers,

Mitchell, & Manning-Courtney, 2011). Standard ADOS-2

procedures were followed, with modules determined by the

child’s expressive language abilities (96% were Module 3).

Finally, a variety of additional measures were administered

sessing broadband (e.g., CBCL/TRF) and narrowband (e.g.,

Social Responsiveness Scale, 2nd ed.) symptoms, adaptive

(Vineland-2) and cognitive functioning (e.g., Kaufman Brief

Intelligence Test, 2nd ed.), language (Children’s

Communication Checklist, 2nd ed.), medical and educational

history, and demographics (see Carpenter et al., 2016). The

DSM-5 ASD diagnoses were determined using clinical best-

estimate procedures incorporating all available data, with pri-

mary consideration to the diagnostic interview and ADOS-2

results (Carpenter et al., 2016).

Analytic Plan

Descriptive statistics of SCQ scores and items were inspected

overall and by sex/gender and diagnostic subgroups. Group

differences in SCQ scores and item endorsements were

The other 487 screeners were excluded due to duplicate submissions,

not living in the surveillance area in 2012, declined participation, or

insufﬁcient number of SCQ item responses for scoring.

SCREENING FOR ASD IN BOYS AND GIRLS

estimated using t tests (Cohen’s d effect sizes), analyses of

variance (ANOVAs; partial η

effect sizes), and chi-square

tests (Cramér’s V effect sizes). Hypothesized SCQ factor

structures were assessed via conﬁrmatory factor analysis

(CFA), with model ﬁt evaluated through collective considera-

tion of the root mean square error of approximation (RMSEA),

and conﬁrmatory ﬁt index (CFI) and Tucker–Lewis ﬁtindex

(TLI). Following recent recommendations (Kline, 2016;Little,

2013), ﬁt indices were interpreted collectively as continuous

measures with approximate thresholds (rather than strict cut-

offs) for adequate model ﬁt as follows: CFI/TLI ≥ .90 and

RMSEA ≤ .08. CFAs were estimated in Mplus Version 7

(Muthén & Muthén, 2012) using weighted least squares. All

other analyses were conducted in SPSS Version 24 (IBM,

2016).

The diagnostic utility of SCQ scores and items were

examined through ROC analyses and diagnostic efﬁciency

statistics as follows: sensitivity (proportion of those with

ASD with positive test result

out of all those with positive

result), speciﬁcity (proportion of those without ASD with

negative test result out of all those with negative result),

positive predictive value (PPV; likelihood of ASD diagnosis

given a positive test result), negative predictive values

(NPV; likelihood of no ASD diagnosis given negative test

result), and diagnostic likelihood ratios (DLRs; calculated as

[sensitivity]/[1 – speciﬁcity]). Clinically, DLR values repre-

sent the most concise estimate of diagnostic probability.

DLRs around 1 indicate no change in the probability of

the diagnosis, whereas higher DLRs represent increases in

the probability of the diagnosis (e.g., DLRs 2, 5, 10 corre-

spond to 15%, 30%, and 45% increases, respectively), and

DLRs below 1 represent decreasing probability. These esti-

mates should be interpreted according to the pretest and

posttest probabilities of the population being considered

(e.g., the probability of ASD diagnosis in a clinical setting

vs. in the general population) (Youngstrom, 2013). Finally,

ROC analyses were also utilized to consider diagnostic

efﬁciency of the SCQ among boys and girls. Complex

sampling weights were considered but were not used

because they had little inﬂuence on other analyses, suggest-

ing that the data are sufﬁciently representative for none-

pidemiological analyses.

RESULTS

Conﬁrmatory Factor Analysis

The single-factor CFA model (i.e., original SCQ scoring,

with all 39 items loading onto a single construct) ﬁt the data

poorly, χ

(702) = 17064.53, p < .001, RMSEA = 0.081,

90% conﬁdence interval (CI) [0.080, 0.082], CFI = 0.711,

TLI = .695. By contrast, a bifactor model (i.e., with the

single-factor plus subdomain factors of SCI and RRB)

showed acceptable ﬁt, χ

(665) = 6056.07, p < .001,

RMSEA = 0.048, 90% CI [0.047, 0.049], CFI = 0.905,

TLI = 0.894. The weighted least squares–adjusted Δχ

test

was signiﬁcant, Δχ

(37) = 4305.26, p < .001, conﬁrming

that the bifactor model ﬁt the data better than the single-

factor model, and supporting the use of the two subdomain

scores and the total score in subsequent analyses. Factorial

invariance by sex/gender could not be examined due to

nonconvergence of multiple-group models. Thus, the bifac-

tor model was estimated separately by sex/g ender, showing

adequate ﬁt for both boys, χ

(665) = 3442.52, p < .001,

RMSEA = 0.049, 90% CI [0.048, 0.051], CFI = 0.905,

TLI = 0.894, and girls, χ

(665) = 2595.03, p < .001,

RMSEA = 0.040, 90% CI [0.039, 0.042], CFI = 0.924,

TLI = 0.916. This model appears to show better ﬁt for

girls than for boys (e.g., nonoverlapping RMSEA CIs).

SCQ Results: Population Screening Sample

Descriptive statistics for SCQ scores are presented in

Table 1 and item endorsement frequencies in Figure 1.As

shown, markers of ASD symptoms were commonly

endorsed in the general population. More than 50% of

boys and girls had four or more SCQ items endorsed, and

about one fourth of the items were endorsed by at least 20%

of the sample. Boys had higher SCQ scores than girls, t

(3518) = 8.77, p < .001, Cohen’s d = 0.29, and greater

variability in the distribution of scores. A similar pattern

was observed for the SCQ risk subgroups: 35.1% of boys

fell in the elevated ranges (“at-risk” or “subthreshold”),

compared to only 24.1% of girls, χ

(2,

N = 3,520) = 56.48, p < .001, Cramér’s V = 0.13. Boys

also had higher scores than girls for both the RRB domain, t

(3518) = 24.75, p < .001 (boys M = 2.52, SD = 2.83; girls

M = 2.00, SD = 2.58; Cohen’s d = 0.19) and for the SCI

domain, t(3518) = 57.23, p < .001 (boys M = 3.97,

SD = 4.01; girls M = 3.04, SD = 3.20; Cohen’s d = 0.26).

Figure 1 presents the frequency of item endorsement by

sex/gender. The most frequently endorsed items overall

included not using gestures at 4– 5 years of age, using odd

or repetitive speech, not spontaneously copying others at

4–5, getting pronouns mixed up, and making socially inap-

propriate questions or statements. The least frequently

endorsed items included not responding positively to other

children at 4–5 years of age, not showing things to parents

at 4–5, not wanting parents to join in her or his enjoyment at

4–5, showing limited range of facial expressions at 4–5, and

not able to have a to-and-fro conversation. After adjusting

for multiple comparisons (family-wise Bonferroni approach;

all ps < .0013), about half of the items showed signiﬁcantly

pronounced gender differences; in all cases, boys were rated

as having higher symptom counts than girls. As shown in

Positive test result = SCQ score falling on or above the cutoff value.

In standard usage, the SCQ cutoff is 15. We adopt this cutoff value as a

default, but also consider alternative cutoff values where speciﬁed.

6 EVANS, BOAN, BRADLEY, CARPENTER

Figure 1, items with the greatest sex/gender differences

include interest in parts of toys, unusually intense interests,

not having a best friend, odd mannerisms such as hand

ﬂapping, odd/repetitive language, and a variety of social

deﬁcits at 4–5 years of age (e.g., make-believe/imaginative

games, spontaneously copying/joining others).

SCQ Results: Direct Assessment Sample

Among those who completed an in-person diagnostic evalua-

tion, there were signiﬁcant differences in the frequencies with

which boys and girls were diagnosed with ASD, χ

(1,

N = 272) = 12.41, p < .001, Cramér’s V = 0.21, with one

fourth (24.9%) of boys assessed receiving the diagnosis

compared to only 7.4% of girls assessed. A 2 × 2 ANOVA

revealed a signiﬁcant difference in SCQ scores between those

with and without a diagnosis of ASD, F(1, 268) = 72.46,

p < .001, partial η

= .213. Although there was no main effect

for sex/gender (p = .160) after ASD diagnosis was included

in the model, there was a signiﬁcant interaction between sex/

gender and diagnostic status, F(1, 268) = 4.32, p =.039,

partial η

= .016. On average, girls diagnosed with ASD had

FIGURE 1 Prevalence of autism spectrum disorder markers in school-age children by sex/gender in the general population. Note:4–5=4–5 years of age.

*Signiﬁcant difference between boys and girls after family-wise Bonferoni adjustment (ps < .0013).

SCREENING FOR ASD IN BOYS AND GIRLS

SCQ scores approximately 4 points higher than boys diag-

nosed with ASD, whereas boys and girls without the diag-

nosis did not differ in their SCQ scores. (See Table 1 for all

descriptive statistics concerning total SCQ scores.)

Next, ANOVAs were reestimated for the RRB and SCI

symptom domains. In the RRB model, there was only a

main effect for ASD diagnosis in the expected direction, F

(1, 268) = 6.22, p = .013, partial η

= .023, with diagnosed

children showing higher levels of RRB (M = 7.18,

SD = 3.17) compared to nondiagnosed children (M = 5.17,

SD = 3.11), with no main effect or interaction for sex/gender

(ps > .4). In the SCI model, however, there was a signiﬁcant

interaction between diagnostic status and gender, F(1,

268) = 7.67, p = .006, partial η

= .028, such that girls

with ASD had SCI scores that were more than 4 point s

higher (M = 16.71, SD = 3.90) than boys with ASD

(M = 12.23, SD = 5.41), whereas the pattern ran in the

opposite direction for nondiagnosed girls (M = 6.93,

SD = 4.17) and boys (M = 7.74, SD = 4.31). There was

also a signiﬁcant main effect for diagnostic status, F(1,

268) = 55.57, p < .001, partial η

= .172, with those with

ASD showing greater SCI scores than those without. There

was a marginal main effect for gender, F(1, 268) = 3.69,

p = .056, partial η

= .014, with boys showing slightly

higher levels of SCI overall (M = 8.86, SD = 4.99) com-

pared to girls (M = 7.65, SD = 4.86).

Diagnostic Efﬁciency and Clinical Cutoffs

The sensitivity, speciﬁcity, PPVs, NPVs, and DLRs are

present overall and by sex/gender at the item level in

Table 2 and for the total SCQ scores (various cutoffs) in

Table 3. At the item level, DLRs were relatively higher (> 2)

for items related to spontaneous showing, shari ng, initiation

of joint attention, shared enjoyment, cooperative, imagina-

tive, and spontaneous play, positive social response, not

smiling back, interest in same-age peers, comforting par-

ents, limited range of facial expressions, and odd manner-

isms such as hand ﬂapping. Regarding sex/gender

differences, DLRs were higher for girls compared to boys

on items relating to seeking shared enjoyment, pointing,

nodding and shaking yes and no, sharing and show ing,

playing make-believe games, talking to be friendly, having

a to-and-fro conversation, playing cooperative games with

children, not looking when parent spoke, having little inter-

est in same-age peers, and not spontaneously copying

others. Only a few items showed greater DLRs for boys

compared to girls, including odd mannerisms such as hand

ﬂapping, and whole-body movem ents (e.g., spinning, boun-

cing). Notably, for both boys and girls the most diagnosti-

cally efﬁcient items were those pertaining to social-

communication/interaction behaviors at 4–5 years of age.

As shown in Table 3, at the existing clinical cutoff of 15,

the SCQ demonstrated good sensitivity and NPV, moderate

speciﬁcity and DLR, and poor PPV. These lower values

were driven by low PPV for both boys and girls (reﬂecting

the high proportion of non-ASD cases above the cutoff) and

low speciﬁcity particularly for boys (reﬂecting the high

proportion of ASD cases below the cutoff). Similar patterns

can be seen when alternative cutoffs are considered, with

higher thresholds leading to better speciﬁcity, NPVs, and

DLRs and poorer sensitivity and PPVs, and vice versa for

lower thresholds. Figure 2 presents the ROC curves and the

distribution of SCQ scores by sex/gender and diagnostic

status. The SCQ showed better sensitivity and speciﬁcity

for the identiﬁcation of girls with ASD (area under the curve

[AUC] = .977). Still, results showed a good AUC for boys

(.791) and for the overall sample (.824). Al though it is clear

that the SCQ performed differently in boys and girls in this

sample, it is difﬁcult to discern speciﬁc cutoffs based on the

observed data due to the truncated range and qualitative

symptom differences as noted above. Stri ctly speaking, the

optimal trade-off between sensitivity and speciﬁcity (i.e.,

maximizing the AUC) falls between 15 (boys) and 19

(girls). However, a visual inspection of Figure 2 indicates

that using cutoffs this high would have resulted in 10 boys

(but zero girls) with ASD going unidentiﬁed in the directly

assessed sample. To the extent that screening measures

should prioritize sensitivity, these results do not provide

compelling evidence for developing gender-speciﬁc cutoffs

or changing the overall clinical cutoff.

DISCUSSION

This study investigated sex/gender differences in ASD

symptoms in a large sample of school-age children assessed

for ASD. By applying rigorous assessment methods to a

population-based sample, this design helps advance the

literature beyond descriptions of sex/gender differences

among diagnosed populations, toward useful clinical and

research recommendations for diagnostic assessment. Our

results coalesce around one interesting conclusion: Unlike

their typically developing peers, girls with ASD have higher

SCQ scores overall, speciﬁcally greater social communica-

tion problems, compared to boys with ASD. This pattern is

only partially consistent with our hypotheses and may help

explain other aspects of our results, as discussed next.

In the population sample, boys received higher SCQ

scores than girls both overall and in the SCI and RRB

domains, a ﬁnding that is roughly co nsistent with prior

research (e.g., Van Wijngaarden-Cremers et al., 2014).

Among those with ASD, however, girls showed higher

SCI

scores than boys. This is consistent with the notion

that girls may need to exhibit more severe difﬁculties to

receive an ASD diagnosis—a pattern that has been found on

a variety of variables in previous studies (e.g., Dworzynski

et al., 2012; Frazier, Georgiades, et al., 2014 ; Holtmann

et al., 2007; Horiuchi et al., 2014; Russell, Steer, &

Golding, 2011; Stacy et al., 2014). Our results suggest

EVANS, BOAN, BRADLEY, CARPENTER

that, among those likely to be referred for assessment, there

might be little or no sex/gender differences in levels of

RRB, whether overall or in terms of an interaction with

diagnostic status. Thus, despite the robust sex/gender differ-

ences in RRB in the population, these particular behaviors

do not appear to be associated with sex/gender differences

or differentially contribute to an ASD diagnosis for boys

more than girls or vice versa. Over all, these results are in

line with Park et al.’s(2012) ﬁnding that normative sex/

gender differences may be absent in children with ASD.

Item-level analyses offer further insight into these ﬁnd-

ings, with some of the least frequently endorsed items

tending to be most useful for screening. Speciﬁcally, items

pertaining to children’s spontaneous interaction and socially

TABLE 2

Diagnostic Efﬁciency of SCQ Items for Identifying ASD in Boys and Girls Sorted by Full Sample DLR (Highest to Lowest)

Full Sample Boys Girls

Item

Symptom

Domain Sens Spec PPV NPV DLR Sens Spec PPV NPV DLR Sens Spec PPV NPV DLR

DLR

Diff

33 At 4–5, Range of Facial Expression SCI .45 .85 .40 .87 2.93 .45 .86 .51 .83 3.18 .43 .83 .17 .95 2.51 0.67

40 At 4–5, Group Play SCI .76 .73 .39 .93 2.82 .73 .72 .46 .89 2.61 1.00 .74 .23 1.00 3.83 −1.22

37 At 4–5, Response to Peers SCI .59 .79 .39 .89 2.77 .59 .80 .50 .86 3.02 .57 .76 .16 .96 2.39 0.63

30 At 4–5, Seeking Shared Enjoyment SCI .41 .85 .39 .86 2.76 .36 .83 .41 .80 2.10 .71 .89 .33 .98 6.29 −4.19

31 At 4–5, Offering Comfort SCI .57 .78 .37 .89 2.56 .55 .78 .45 .84 2.50 .71 .77 .20 .97 3.14 −0.64

27 At 4–5, Social Smiling SCI .53 .78 .36 .88 2.39 .52 .76 .42 .83 2.17 .57 .81 .19 .96 2.96 −0.79

15 Hand or Finger Mannerisms RRB .55 .76 .35 .88 2.29 .57 .77 .45 .84 2.44 .43 .75 .12 .94 1.71 0.73

29 At 4–5, Offering to Share SCI .61 .73 .34 .89 2.24 .57 .71 .39 .83 1.94 .86 .76 .22 .99 3.59 −1.65

36 At 4–5, Interest in Peers SCI .69 .67 .33 .90 2.11 .66 .65 .38 .85 1.87 .86 .72 .19 .98 3.02 −1.15

34 At 4–5, Imitative Social Play SCI .75 .64 .32 .92 2.06 .75 .58 .37 .88 1.78 .71 .73 .17 .97 2.62 −0.84

28 At 4–5, Showing and Directing

Attention

SCI .35 .82 .32 .85 2.00 .30 .84 .38 .78 1.87 .71 .80 .22 .97 3.49 −1.62

20 At 4–5, Social Chat SCI .57 .71 .32 .88 1.99 .50 .72 .37 .81 1.80 1.00 .70 .21 1.00 3.38 −1.58

26 At 4–5, Eye Gaze SCI .75 .62 .31 .91 1.98 .73 .62 .39 .87 1.90 .86 .64 .16 .98 2.36 −0.46

35 At 4–5, Imaginative Play — .63 .68 .31 .89 1.98 .59 .65 .36 .83 1.67 .86 .74 .21 .98 3.28 −1.61

38 At 4–5, Attention to Voice SCI .57 .70 .31 .88 1.90 .52 .70 .37 .82 1.74 .86 .70 .19 .98 2.90 −1.16

17 Self-Injury — .33 .82 .30 .84 1.84 .34 .81 .38 .79 1.81 .29 .83 .12 .94 1.68 0.13

16 Complex Body Mannerisms RRB .49 .72 .29 .86 1.78 .52 .74 .40 .83 2.04 .29 .69 .07 .92 .93 1.11

14 Unusual Sensory Interests RRB .61 .66 .29 .88 1.77 .59 .67 .37 .83 1.79 .71 .64 .14 .97 1.96 −0.17

39 At 4–5, Imaginative Play With Peers SCI .73 .59 .29 .90 1.76 .70 .55 .34 .85 1.56 .86 .65 .16 .98 2.43 −0.87

18 Unusual Attachment to Objects RRB .45 .74 .28 .85 1.72 .41 .77 .38 .80 1.81 .71 .68 .15 .97 2.24 −0.43

24 At 4–5, Nodding Head Yes SCI .41 .75 .28 .85 1.65 .36 .74 .31 .78 1.38 .71 .77 .20 .97 3.14 −1.76

11 Unusual Preoccupations RRB .69 .58 .27 .89 1.63 .68 .56 .34 .84 1.54 .71 .61 .13 .96 1.85 −0.31

Unusually Intense Interests RRB .82 .48 .27 .92 1.57 .86 .44 .34 .91 1.55 .57 .52 .09 .94 1.20 0.35

21 At 4–5, Spontaneous Imitation SCI .59 .62 .26 .87 1.53 .57 .56 .30 .80 1.30 .71 .69 .16 .97 2.33 −1.03

19 Friends SCI .43 .71 .26 .84 1.49 .41 .69 .31 .78 1.33 .57 .74 .15 .96 2.19 −0.86

10 Use of Other’s Hand SCI .35 .76 .25 .84 1.47 .32 .77 .32 .77 1.41 .57 .74 .15 .96 2.19 −0.78

8 Compulsions or Rituals RRB .76 .47 .25 .90 1.44 .80 .46 .33 .87 1.47 .57 .49 .08 .93 1.12 0.35

22 At 4–5, Pointing to Express Interest SCI .51 .65 .25 .85 1.44 .43 .62 .27 .77 1.13 1.00 .69 .21 1.00 3.26 −2.13

2 Conversation SCI .16 .89 .23 .84 1.43 .15 .87 .26 .77 1.18 .20 .92 .13 .95 2.43 −1.25

7 Verbal Rituals RRB .68 .52 .22 .89 1.41 .67 .56 .31 .85 1.52 .80 .45 .08 .97 1.45 0.07

25 At 4–5, Shaking Head No SCI .39 .71 .24 .84 1.38 .34 .68 .26 .76 1.08 .71 .76 .19 .97 2.99 −1.91

12 Interest in Parts of Toy RRB .57 .58 .24 .85 1.37 .61 .56 .31 .81 1.38 .29 .63 .06 .92 .76 0.62

6 Idiosyncratic Language RRB .59 .53 .20 .86 1.25 .56 .50 .25 .79 1.13 .80 .56 .10 .98 1.84 −0.71

9 Inappropriate Facial Expression SCI .35 .71 .22 .83 1.22 .36 .73 .31 .78 1.34 .29 .68 .07 .92 .90 0.44

3 Stereotyped Utterances RRB .77 .33 .19 .88 1.15 .77 .35 .26 .83 1.18 .80 .29 .06 .96 1.13 0.05

23 At 4–5, Gestures to Request SCI .63 .41 .20 .83 1.06 .64 .36 .25 .75 1.00 .57 .48 .08 .93 1.09 −0.09

4 Inappropriate Questions or

Statements

SCI .59 .40 .17 .83 .99 .59 .43 .24 .78 1.04 .60 .36 .05 .94 .94 0.10

5 Pronoun Reversal RRB .55 .41 .16 .82 .93 .54 .48 .24 .78 1.03 .60 .32 .05 .93 .88 0.15

32 At 4–5, Quality of Social Overtures SCI .22 .69 .14 .79 .69 .20 .67 .17 .72 .62 .29 .72 .07 .93 1.01 −0.39

Note: SCQ = Social Communication Questionnaire; ASD = autism spectrum disorder; DLR = diagnostic likelihood ratio; Sens = sensitivity;

Spec = speciﬁcity; PPV = positive predictive value; NPV = negative predictive value; 4–5=4–5 years of age; SCI = social communication and interaction;

RRB = restricted and repetitive behavior.

SCREENING FOR ASD IN BOYS AND GIRLS

oriented behaviors at ages 4–5 appear to be among the most

diagnostically efﬁcient (showing good sensitivity and spe-

ciﬁcity) for informing a diagnosis of ASD in all children,

and particularly for girls. Persistent SCI deﬁcits appear to be

a relatively sensitive and speciﬁc marker for differentiating

girls with ASD from their typically developing female

peers. Of interest, hallmark RRB features of ASD were

generally not among the most diagnostically efﬁcient

items; only odd mannerisms such as hand ﬂapping showed

good diagnostic efﬁciency for boys and girls.

The bifactor SCQ model ﬁt the data better than the

single-factor model, and slightly better for girls than for

boys. Although research examining measurement invariance

is limited, studies have found similar item functioning and

measurement invariance in the SCQ and other screen ing

measures (Frazier & Hardan, 2017; Wei et al., 2015).

Direct comparisons of the SCQ and SRS-2 might be parti-

cularly useful. Previous research suggests that age is a key

factor affecting the performance of ASD symptom scales

and screening measures; different patterns of results have

been found, and other measures might perform better among

children roughly 6 years of age and younger (Barnard-Brak

et al., 2016; Van Wijngaarden-Cremers et al., 2014).

However, in the present sample of 8- to 10-year-old chil-

dren, results supported the SCQ’s bifactor structure and two-

domain conceptualization of ASD put forth in DSM-5

(APA, 2013; Frazier et al., 2012; Mandy, Charman, et al.,

2012). This suggests that there may be utility in further

validation and clinical/research use of these two subscales

derived from the SCQ. Although the SCQ items have pre-

viously been subdivided according to a variety of different

exploratory (Berument et al., 1999; Gau et al., 2011) and

conceptual/conﬁrmatory (Rutter & Bailey, 2003; Wei et al.,

2015) approaches, these results add to a body of evidence

suggesting that ASD symptoms can be differentiated into a

general domain with two subdomains, with implications for

research and clinical assessment. For example, the SCQ

could be reﬁned not only as a screening tool but also as

secondary instrument to help support or rule out ASD

symptom domains for diagnosis. Due to the different num-

ber of items, however (25 for SCI vs. 12 for RRB), results

should be interpreted with caution, and future work may be

needed to render these scales more comparable.

ROC results show that the SCQ performs adequat ely as a

diagnostic instrument for boys and girls, especially for girls.

These ﬁndings are consistent with the factor analysis, sub-

domain, and item-level sex/gender differences just

described. These results do not provide a compelling reason

to alter the existing cutoff for boys or girls. However,

caution in both directions is warranted: For boys and girls

alike, scores falling in the “at-risk” range (≥ 15) are more

likely to be false positives than true positives (probability of

true positive = 43% and 21%, respectively); and among

boys, scores falling below this cutoff (in the “subthreshold”

range) still had a 10% probability of diagnosis. Thus, the

SCQ should be interpreted cautiously and probabi listically.

Clinically, an SCQ score of 15 or higher is associated with a

small but clinically signiﬁcant increase in the probability of

ASD. As scores increase beyond 15, the probability of ASD

increases proportionately, particularly for girls. Scores

between 11 and 15 may increase the probability enough to

warrant careful clinical judgment (e.g., Corsello et al., 2007;

TABLE 3

Diagnostic Efﬁciency Estimates at Varying SCQ Total Score Cutoff Values

Sex/Gender Comparisons

Full Sample Boys Girls

SCQ

Cutoff

Value Sens Spec PPV NPV DLR Sens Spec PPV NPV DLR Sens Spec PPV NPV DLR

9 .98 .14 .21 .97 1.13 .98 .09 .26 .92 1.07 1.00 .21 .09 1.00 1.26

10 .96 .26 .23 .97 1.30 .96 .24 .29 .94 1.26 1.00 .30 .10 1.00 1.42

11 .92 .38 .26 .95 1.49 .91 .37 .32 .92 1.44 1.00 .40 .12 1.00 1.66

12 .84 .48 .27 .93 1.62 .82 .46 .33 .88 1.51 1.00 .51 .14 1.00 2.04

13 .80 .54 .29 .92 1.74 .77 .52 .35 .87 1.61 1.00 .57 .16 1.00 2.31

14 .80 .61 .32 .93 2.07 .77 .61 .40 .89 1.98 1.00 .61 .17 1.00 2.59

15 .80 .68 .37 .94 2.50 .77 .66 .43 .90 2.29 1.00 .71 .21 1.00 3.39

16 .76 .73 .39 .93 2.82 .73 .72 .46 .89 2.62 1.00 .74 .23 1.00 3.83

17 .75 .78 .44 .93 3.43 .71 .77 .51 .89 3.12 1.00 .80 .28 1.00 4.88

18 .73 .81 .46 .93 3.73 .68 .80 .53 .88 3.36 1.00 .82 .30 1.00 5.49

19 .67 .87 .55 .92 5.26 .61 .86 .59 .87 4.29 1.00 .90 .44 1.00 9.80

20 .61 .91 .61 .91 6.72 .57 .90 .66 .86 5.81 .86 .92 .46 .99 1.78

21 .57 .94 .67 .90 8.98 .52 .92 .70 .85 6.95 .86 .95 .60 .99 18.86

Note: Estimates for the standard Social Communication Questionnaire (SCQ) cutoff value are in bold. Sens = sensitivity; Spec = speciﬁcity; PPV = positive

predictive value; NPV = negative predictive value; DLR = diagnostic likelihood ratio.

10 EVANS, BOAN, BRADLEY, CARPENTER

Eaves et al., 2006). Overall, at the recommended threshold

of 15, the sensitivity and NPV were good, the speciﬁcity

and DLR were moderate, and the PPV poor. Thus, for both

boys and girls, a positive value (above the cutoff) does not

necessarily predict a diagnosis of ASD; indeed, a majority

of cases above this cutoff were false-positives. For boys in

particular, ASD cases were common among those in the

subthreshold range. Again, if the full range of possible SCQ

scores were represented in the data, these values might

differ. As a screening instrument within the broader assess-

ment context, false negatives might be more clinically detri-

mental than false positives given that the latter only

indicates the need for further assessment.

We interpret our sex/gender-discrepant ﬁndings as evi-

dence for qualitative rather than quantitative differences in

ASD symptom presentations between boys and girls. The

pattern of sex/gender symptom differences observed among

those with ASD (SCI, boys < girls; RRB, boys = girls) is

qualitatively distinct from the pattern observed in the general

population (SCI, boys > girls; RRB, boys > girls). These

results differ from the conclusions of a recent meta-analysis

by Hull, Mandy, and Petrides (2016), which found no differ-

ence in RRB, and equivocal evidence for social impairment

(Hull et al., 2016). In general, our ﬁndings are broadly con-

sistent with the well-established sex/gender differences in

ASD prevalence (e.g., Christensen et al., 2016;Loomes

et al., 2017) but do not align neatly with existing theories

in the literature that attempt to explain this discrepancy. For

example, if the extreme male brain theory (Baron-Cohen,

2002) were supported, we might expect similar patterns of

male–female levels of SCI (related to empathizing) and RRB

(related to systematizing) in those with ASD as in those in the

full population sample; this was not the case. Similarly, we

did not ﬁnd evidence for a female camouﬂage effect (e.g.,

Hull et al., 2017; Livingston & Happé, 2017; Rynkiewicz

et al., 2016) insofar as parents did not rate girls diagnosed

with ASD as possessing compensatory social skills (i.e.,

lower SCI scores), which might obfuscate their symptoms.

It is possible that camouﬂage may still exist in settings that

parents typically do not actively observe, such as educational

FIGURE 2 Receiver operating characteristic curves and frequency distributions of Social Communication Questionnaire (SCQ) scores by sex/gender and

diagnosis. Note. AUC = area under the curve; ASD = Autism Spectrum Disorder.

SCREENING FOR ASD IN BOYS AND GIRLS

settings. Notably, the pattern of results shown in Figure 2

suggests it may be more difﬁcult to differentiate ASD versus

non-ASD status in boys than in girls.

Strengths, Limitations, and Implications

One strength of the present study is that comprehensive,

multimethod assessment practices were used, which mini-

mizes the possibility of results being inﬂuenced by bias

or error. In other words, if the present gender-discrepant

results reﬂect assessment error, then it is likely an under-

lying problem in ASD diagnostic criteria and sassessment

tools in general rather than the particulars of the present

study. Herein li es the dilemma arti culate d by Lai et al.

(2015): Because our existing conceptualizations and

instruments (including SCQ, ADOS-2, and DSM-5 cri-

teria) are derived from predominately male ASD samples,

there remains a challenging problem of “the chicken and

the egg.” That is, to the extent that ASD symptoms truly

manifest differently in boys compared to girls, studies

such as this one are not able to ascertain this difference.

Broader research is needed to understand the qualitative

nature of SCI and RRB among typically and atypically

developing girls.

This does not, however, rule out the possibility of

informant bias affecting SCQ scores, and this should be

considered in interpreting these results. Parents’ percep-

tions of SCI deﬁcits in boys and girls are likely inﬂu-

enced by sex/gender expectations relative to typically

developing same-sex peers. This is consistent with pre-

vious research suggesting that the social relationships

between boys and girls with ASD are more similar than

relationships with their same-gendered peers (Dean et al.,

2014). Sim ilarly, bias ma y be operating in items pertain-

ingto4–5 years of age, as these rely on parents’ recall of

behaviors occurring several years ago. The possibility of

response bias might be illustrated in the rates at which

different items were endorsed. Approximately half of

children were rated as not using gestures at 4–5years

of age, suggesting that parents may be misinterpreting

this item. This may be a l imitat ion of the yes/no format

of the SCQ, which some parents might struggle with. For

example, Frazier et al. (2010) found that 5.1% of unaf-

fected siblings were rated by their caregivers with SCQ

score of 15 or higher. Thus, results of single items should

be interpreted cautiously.

Additional limitations should be noted. First, using SCQ

scores as the basis for direct assessment sampling results in

an artiﬁcially truncated range and distribution of SCQ

scores, which woul d not be seen if all participants received

all measures. Thus, there may be children with ASD with

scores in the low-risk range (SCQ < 8) who were missed,

whereas those in the at-risk range (SCQ ≥ 15) were more

likely to be invited and assessed than those in the subthres-

hold range (8 ≤ SCQ ≤ 14). Second, despite our large

overall sample size, our direct assessment sample was rela-

tively small in terms of gender-by-diagnosis subgroups,

with 177 boys (only 25% of whom had ASD) and 95 girls

(only 7% of whom had ASD). A related consequence is that

the completion of in-person assessments may have been

higher due to greater levels of parental concern; indeed,

the response rate was higher among the at-risk group

(44%) compared to the subthreshold group (29%).

Although these data are considered to be a representative

sample from a weighted epidemiological study, the present

results should not be generalized to the entire population in

an epidemiological manner (e.g., complex survey weights

were not used in analyses). Rather, these ﬁndings can be

interpreted simply as results of screening and assessment

analyses conducted among the observed data with its limita-

tions as just noted. However, these limitations are also

reﬂective of a larger strength of this study—representative

sampling of a population of more than 8,000 children, with

nearly 50% participation and inclusion of subthreshold chil-

dren so as to not miss more mildly affected cases.

Third, t his study did not use a well-validated, pub-

lished diagnostic interview. Rather, the evaluations used

an unpublished structured diagnostic interview designed

tomapontobothDSM-IV and

DSM-5 criteria,

assessing

lifetime and present symptoms within a reasonable

administration time. Lastly, a larger and more pernicious

problem is the possibility of sex/gender bias in the

diagnosis of ASD itself. The p resent study (and m uch

of ASD research) relies upon diagn ostic criteria that

have dev eloped over the y ears f rom re search largely

among boys with ASD. The present study ut ilized a

rigorous assessment protocol t o ascertain the diagnosi s;

however, to the extent that the ASD construct is gender

biased, the se res ults cann ot she d light on the natu re of

that bias a nd only highlight the ne ed for broader

research.

These ﬁndings have several implications for clinical

and research assessment practices. First, elevated scores

on the SCQ should be taken seriously regardless of sex/

gender. It may be the case that clinically some girls with

ASD are overlooked due to their perceived strengths in

certain domains. These are important questions for a

diagnostic evaluation. During the screening phase, how-

ever, a high score should not be overridden based on

other perceived strengths. In addition, responses to spe-

ciﬁc items should be interpreted according to their devel-

opmental and social context. Careful attention might be

given to items addressing social-communication and

interaction behaviors at 4–5 years of age. In particular,

girls with A SD may exhibit pronounced intraindividual

SCI deﬁcits compared to both their male peers with ASD

and their female peers without ASD. Finally, positive

results on screening measures should not be interpreted

as indicating a diagnosis but only a need for a more

comprehensive evaluation.

EVANS, BOAN, BRADLEY, CARPENTER

FUNDING

This work was supported by Autism Speaks (7793, 8408)

and the National Institutes of Health Clinical and

Translational Science Award Program (UL1TR001450).

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