Quantitative Checklist For Autism In Toddlers Research Articles

Clinical, socio-demographic, and parental correlates of early autism traits in a community cohort of toddlers

Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample.

Fetal brain growth and infant autistic traits

BackgroundStructural differences exist in the brains of autistic individuals. To date only a few studies have explored the relationship between fetal brain growth and later infant autistic traits, and some have used fetal head circumference (HC) as a proxy for brain development. These findings have been inconsistent. Here we investigate whether fetal subregional brain measurements correlate with autistic traits in toddlers.MethodsA total of 219 singleton pregnancies (104 males and 115 females) were recruited at the Rosie Hospital, Cambridge, UK. 2D ultrasound was performed at 12-, 20- and between 26 and 30 weeks of pregnancy, measuring head circumference (HC), ventricular atrium (VA) and transcerebellar diameter (TCD). A total of 179 infants were followed up at 18–20 months of age and completed the quantitative checklist for autism in toddlers (Q-CHAT) to measure autistic traits.ResultsQ-CHAT scores at 18–20 months of age were positively associated with TCD size at 20 weeks and with HC at 28 weeks, in univariate analyses, and in multiple regression models which controlled for sex, maternal age and birth weight.LimitationsDue to the nature and location of the study, ascertainment bias could also have contributed to the recruitment of volunteer mothers with a higher than typical range of autistic traits and/or with a significant interest in the neurodevelopment of their children.ConclusionPrenatal brain growth is associated with toddler autistic traits and this can be ascertained via ultrasound starting at 20 weeks gestation.

Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Brain dynamic functional connectivity characterises transient connections between brain regions. Features of brain dynamics have been linked to emotion and cognition in adult individuals, and atypical patterns have been associated with neurodevelopmental conditions such as autism. Although reliable functional brain networks have been consistently identified in neonates, little is known about the early development of dynamic functional connectivity. In this study we characterise dynamic functional connectivity with functional magnetic resonance imaging (fMRI) in the first few weeks of postnatal life in term-born (n = 324) and preterm-born (n = 66) individuals. We show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain, characterised by six transient states of neonatal functional connectivity with changing dynamics through the neonatal period. The pattern of dynamic connectivity is atypical in preterm-born infants, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age.

Is the association between mothers’ autistic traits and childhood autistic traits moderated by maternal pre-pregnancy body mass index?

BackgroundPrevious studies showed that there is a positive association between mothers’ and children’s autistic traits. We also tested if this association is more pronounced in mothers with a higher pre-pregnancy body mass index (BMI).MethodThe study was embedded in two cohorts with information available for 4,659 participants from the Generation R and for 179 participants from the Cambridge Ultrasound Siblings and Parents Project (CUSP) cohort. In both cohorts, maternal autistic traits were assessed using the short form of the Autism Spectrum Quotient, and information about maternal height and weight before pregnancy was obtained by questionnaire. Child autistic traits were assessed with the short form of Social Responsiveness Scale in Generation R (M = 13.5 years) and with the Quantitative Checklist for Autism in Toddlers (Q-CHAT) in the CUSP cohort (M = 1.6 years).ResultHigher maternal autistic traits were associated with higher autistic traits in toddlerhood (CUSP cohort; βadjusted = 0.20, p < 0.01), in early childhood (Generation R; βadjusted = 0.19, p < 0.01), and in early adolescence (Generation R; βadjusted = 0.16, p < 0.01). Furthermore, a higher maternal pre-pregnancy BMI was associated with higher child autistic traits, but only in Generation R (βadjusted = 0.03, p < 0.01). There was no significant moderating effect of maternal pre-pregnancy BMI on the association between autistic traits of mothers and children, neither in Generation R nor in CUSP. In addition, child autistic traits scores were significantly higher in mothers who were underweight and in mothers who were overweight compared to mothers with a healthy weight.ConclusionWe confirm the association between maternal and child autistic traits in toddlerhood, early childhood, and early adolescence. Potential interacting neurobiological processes remain to be confirmed.

Association between maternal labour epidural analgesia and autistic traits in offspring

Study objectiveStudies investigating associations between maternal epidural analgesia (MEA) and autism spectrum disorder (ASD) in the offspring are conflicting and lack prospective neurobehavioral follow-up assessments for autistic traits. We aim to prospectively investigate associations between MEA and autistic traits in the offspring. DesignProspective neurobehavioral observational cohort study. SettingSingaporean tertiary healthcare institutions. PatientsParticipants recruited were singleton non-IVF children, >36 weeks gestation, delivered via normal vaginal delivery by mothers >18 years of age, delivered in Singapore from June 2009–September 2010 and followed up over 7 years. InterventionsExposure to maternal epidural analgesia during delivery. MeasurementsThe primary outcome is an abnormal Social Responsiveness Scale (SRS) T score at 7 years (≥60 points). Secondary outcomes include the diagnosis of ASD and abnormal scores for autistic traits assessed via a neurobehavioral battery comprising: CBCL (child behavioural checklist), Q-CHAT (Quantitative Checklist for Autism in Toddlers), and Bayley-III. Multivariable analyses adjusting for maternal and offspring characteristics were performed. Main results704 out of 769 mother-child dyads recruited fulfilled the criteria for analysis. 365/704 mothers received MEA.The incidence of an abnormal SRS score at 7 years in offspring exposed to MEA was 19.9%, and 26.1% in non-exposed offspring (p = 0.154). Multivariable analysis did not demonstrate a significant association between MEA and abnormal SRS scores at 7 years (O.R.0.726, 95% C·I. 0.394–1.34, p = 0.305). After adjustment for maternal and fetal demographics, exposure to MEA was not significantly associated with an abnormal screen in all other tests for autistic traits.The clinical incidence of ASD was 1.76% in children without exposure to MEA, and 2.32% in children with MEA exposure (p = 0.506). ConclusionsMEA is not significantly associated with the development of ASD and autistic traits in offspring, assessed over 7 years. Results should be taken into perspective given our wide confidence intervals and small cohort size.

Postnatal maternal depressive symptoms and behavioural outcomes in term-born and preterm-born toddlers: a longitudinal UK community cohort study

ObjectivesTo examine the association between maternal depressive symptoms in the immediate postnatal period and offspring’s behavioural outcomes in a large cohort of term-born and preterm-born toddlers.Design and participantsData were drawn...

252-OR: Maternal Obesity and Diabetes during Pregnancy and Quantitative Checklist for Autism in Toddlers (QCHAT) Scores in Offspring

In utero exposure to maternal obesity and diabetes has been associated with risk of autism spectrum disorders (ASD) in children. As early identification and support improves long-term outcomes in children with ASD, a 10-point Quantitative Checklist for Autism in Toddlers (QCHAT) screening questionnaire was developed and validated for clinical screening between 18-24 months at well-child visits. This study assessed associations between maternal metabolic exposures and offspring performance on early QCHAT screening. Data included 65699 singletons born in a single healthcare system, with QCHAT administered toddler well-child visits. Demographic and social data, maternal pregnancy obesity (BMI≥30 kg/m2) , pre-existing type 1/type 2 (T1D/T2D) , and gestational diabetes (GDM) status, and child’s QCHAT score were extracted from electronic medical records. Negative binomial regression was used to assess associations between maternal obesity and diabetes and QCHAT scores as a continuous measure, adjusting for covariates. Analyses included and excluded children with ASD diagnoses and were run with and without mutual adjustment for each metabolic condition. In this cohort, 18923 (28.8%) of children were exposed to maternal obesity, 2564 (3.9%) to T1/T2, 1946 (3.0%) to GDM diagnosed at ≤26 weeks, and 3290 (5.0%) GDM diagnosed &amp;gt;26 weeks. Of the total cohort, 1622 (2.5%) were later diagnosed with ASD. Maternal obesity (Incidence Rate Ratio: 1.12, 95% CI: 1.09,1.14) , T1D/T2D (1.13, 1.07-1.20) , and GDM ≤26 weeks (1.12, 1.05-1.19) were associated with one-unit increases in QCHAT scores. No associations were reported for GDM &amp;gt;26 weeks. Similar patterns were observed after excluding children with ASD diagnoses. Mutual adjustment did not change results. These data suggest maternal metabolic conditions during pregnancy are associated with increased social and communication impairment on early autism screening tools, even in typically developing children. Disclosure S.A.Carter: None. J.C.Lin: None. T.Chow: None. M.P.Martinez: None. J.M.Alves: None. R.Feldman: None. C.Qiu: None. K.A.Page: None. A.Xiang: None.

Symptoms of Autism Spectrum Disorders Measured by the Qualitative Checklist for Autism in Toddlers in a Large Sample of Polish Toddlers.

This study aimed to assess some early symptoms of autism spectrum disorders (ASD) measured by a screening tool developed for an early detection of ASD. We investigated if the early symptoms were associated with toddlers’ age, gender or ASD familial risk status. We used the Polish version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT) to assess 1024 children aged 16 to 36 months. The sample included four groups of participants: typically developing toddlers, toddlers with parent-reported ASD-specific concerns, toddlers at risk for autism due to having an older sibling with ASD, and toddlers with a developmental delay. We found that mean Q-CHAT scores were significantly higher in boys than in girls. We did not find any associations between Q-CHAT scores and age. We observed that toddlers with a familial risk for ASD and those with a developmental delay scored significantly higher than controls. We collated these results with previous studies that used the Q-CHAT and other instruments.

Maternal steroid levels and the autistic traits of the mother and infant

BackgroundPrenatal sex steroids have been associated with autism in several clinical and epidemiological studies. It is unclear how this relates to the autistic traits of the mother and how early this can be detected during pregnancy and postnatal development.MethodsMaternal serum was collected from pregnant women (n = 122) before or during their first ultrasound appointment [mean = 12.7 (SD = 0.7) weeks]. Concentrations of the following were measured via immunoassays: testosterone, estradiol, dehydroepiandrosterone sulphate, progesterone; and sex hormone-binding globulin which was used to compute the free fractions of estradiol (FEI) and testosterone (FTI). Standardised human choriogonadotropin (hCG) and pregnancy-associated plasma protein A (PAPP-A) values were obtained from clinical records corresponding to the same serum samples. Mothers completed the Autism Spectrum Quotient (AQ) and for their infants, the Quantitative Checklist for Autism in Toddlers (Q-CHAT) when the infants were between 18 and 20 months old.ResultsFEI was positively associated with maternal autistic traits in univariate (n = 108, Pearson’s r = 0.22, p = 0.019) and multiple regression models (semipartial r = 0.19, p = 0.048) controlling for maternal age and a diagnosis of PCOS. Maternal estradiol levels significantly interacted with fetal sex in predicting infant Q-CHAT scores, with a positive relationship in males but not females (n = 100, interaction term: semipartial r = 0.23, p = 0.036) after controlling for maternal AQ and other covariates. The opposite was found for standardised hCG values and Q-CHAT scores, with a positive association in females but not in males (n = 151, interaction term: r = −0.25, p = 0.005).LimitationsSample size of this cohort was small, with potential ascertainment bias given elective recruitment. Clinical covariates were controlled in multiple regression models, but additional research is needed to confirm the statistically significant findings in larger cohorts.ConclusionMaternal steroid factors during pregnancy are associated with autistic traits in mothers and their infants.

A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT).

Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.

Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism

ObjectiveThis is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.DesignObservational study.SettingLuton,...

Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

Quantitative Checklist for Autism in Toddlers (Q-CHAT): A psychometric study with Serbian Toddlers

BackgroundThe present study evaluated the underlying structure of the Quantitative Checklist for Autism in Toddlers (Q-CHAT) in a Serbian sample and its measurement properties for community-based screening. MethodData were collected from parents of 220 toddlers aged 15–36 months. The parents completed together the Q-CHAT and the Modified Checklist for Autism in Toddlers - Revised (M-CHAT-R). Confirmatory factor analysis (CFA), internal consistency, convergent and known-groups validity were analyzed, with sensitivity, specificity, and predictive values calculated. ResultsFrom CFA, 17 out of 25 items emerged to represent three underlying factors, namely social communication/interaction, restrictive/repetitive behavior, and language impairment. The Q-CHAT-10 was found to have one-dimensional structure. Cronbach’s α was ≥.81 for Q-CHAT scores. All scores for toddlers with typical development were significantly lower compared to those with ASD. The cut-off points that best-balanced sensitivity/specificity for the total scores, were 37 for the Q-CHAT with all 25 items (96.2/81.9 %), 26 for the Q-CHAT with 17 items (96.2/93.3 %), and 3 (100/93.3 %) for the Q-CHAT-10, with the positive/negative predictive values of .39/.99, .57/1.00, and .67/1.00, respectively. ConclusionsThe Serbian Q-CHAT versions with 10 and 17 items showed sound internal consistency, convergent and know-groups validity, and high levels of sensitivity and specificity for community-based screening. A multidimensional structure with social communication/interaction, restrictive/repetitive behaviors, and language impairment aspects was confirmed. This implies that autistic traits in toddlers could be measured dimensionally with the Q-CHAT, but its structure should be verified in additional studies.

The Criterion Validity of the First Year Inventory and the Quantitative-CHecklist for Autism in Toddlers: A Longitudinal Study.

Pediatric surveillance through screening procedures is needed to detect warning signs of risk for Autism Spectrum Disorder under 24 months of age and to promote early diagnosis and treatment. The main purpose of this study is to extend the literature regarding the psychometric properties of two screening tools, the First Year Inventory (FYI) and the Quantitative-CHecklist for Autism in Toddler (Q-CHAT), testing their criterion validity. They were administered during a three-wave approach involving the general population. At T1, 657 children were tested with the FYI and 36 of them were found to be at risk. At T2, 545 were tested with the Q-CHAT and 29 of them were found to be at risk. At T3, 12 out of the 36 children with a high score on the FYI and 11 out of the 29 children with a high score on the Q-CHAT were compared to 15 typically developing children. The criterion validity was tested considering the severity of the autistic symptoms, emotional/behavioral problems, and limited global functioning as criteria. Accuracy parameters were also calculated. Furthermore, we investigated which dimension of each questionnaire better predicted the aforementioned criterion. The results corroborated the hypotheses and confirmed the criterion validity of FYI and Q-CHAT.

Minor neurological signs and behavioural function at age 2 years in neonatal hypoxic ischaemic encephalopathy (HIE).

Neurodevelopmental follow-up in Neonatal Hypoxic Ischaemic Encephalopathy (HIE) typically focusses on major neuromotor (cerebral palsy, CP) and severe cognitive impairment. Outcomes in those without major neuromotor impairment are less well explored. To examine behavioural, cognitive and neurological outcomes after neonatal HIE, in a clinical cohort of children without CP, at age 2 years. Clinical routine outcome data from children admitted to a tertiary centre with neonatal HIE for hypothermia treatment between 05/08/09-30/05/2016. Children were assessed for neuromotor status - particularly minor neurological signs (MNS), with Bayley Scales of Infant and Toddler Development III (Bayley III) or Ages and Stages Questionnaire-3 (ASQ), Child Behavior Checklist 1.5-5 (CBCL), Quantitative Checklist for Autism in Toddlers (Q-CHAT). Of 107 children, 75.5% had normal neurology, 12.1% CP, 12.1% MNS. Children with CP were excluded from analyses. For those without CP, Bayley-III scores were in the average range for the majority; mild cognitive delay observed in 5%, 4.2% language, 1.3% motor development; severe delay in 1.3% for cognitive, 4.2% for language. More than in the normative population scored in clinical ranges for CBCL externalising, sleep, and other problems. No significant difference was seen for Q-CHAT. Children with MNS were significantly more likely to have impaired Bayley-III scores, parent-reported internalising, sleep, and other problems. In this clinical cohort, the majority of children had favourable outcome at 2 years. However, children with MNS were at risk for cognitive and behavioural difficulties and will benefit from enhanced clinical follow-up and support.

A Two-Stage Screening Approach with I-TC and Q-CHAT to Identify Toddlers at Risk for Autism Spectrum Disorder within the Italian Public Health System.

Standardized screening programs ensure that children are monitored for early signs of autism spectrum disorder (ASD) in order to promote earlier diagnosis and intervention. The aim of this study is to identify early signs of atypical development consistent with ASD or other developmental disorders in a population of 224 low-risk toddlers through a two-stage screening approach applied at 12 and 18 months of age. We adopted two screening tools combined: 1. the Communication and Symbolic Behavior Scales Developmental Profile (CSBS DP) Infant–Toddler Checklist (I-TC) and 2. The Quantitative Checklist for Autism in Toddlers (Q-CHAT). We assessed their sensitivity and specificity related to the diagnostic outcome at 36 months. The results showed that autistic signs can be detected as early as the first year even through a few questions extrapolated from both screeners and that our model could be used as a screening procedure in the Italian public health system.

Early Screening of Autism among 18 to 24 months Old Toddlers Using the Quantitative Checklist for Autism in Toddlers (Q-CHAT).

The aim of the present study was to screen the toddlers who are suspected to be autistic in their well-child visits at age 18 to 24 months via the Quantitative-Checklist for Autism in Toddlers (Q-CHAT). After the screening, the screen-positive cases were clinically assessed by a child psychiatrist and a child developmental psychologist. The total sample included 2467 toddlers; the mean total score of Q-CHAT was 30.64 (SD: 9.133). According to the clinical examinations, 6 cases met the criteria for autism (equal to 5.8% of screen-positive cases and 0.25% of the total sample), and 18 cases had a high probability of autism or other neurodevelopmental disorders. This study showed that the toddlers who were at risk of autism could be screened in PHC. Also, Q-CHAT could be used as a screening tool in Iran.

Validation of the Quantitative Checklist for Autism in Toddlers in an Italian Clinical Sample of Young Children With Autism and Other Developmental Disorders.

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers. Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children. Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained. Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills. Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay.

Psychometric properties, factor structure and cross-cultural validity of the quantitative CHecklist for autism in toddlers (Q-CHAT) in an Italian community setting

BackgroundIn the past decade, several screening instruments have been developed to detect toddlers at risk for autism, both in clinical and unselected samples.We aimed to assess the validity and cross-cultural stability of the Quantitative CHecklist for Autism in Toddlers (Q−CHAT), a quantitative and normally distributed measure of autistic traits, during routine paediatric developmental surveillance in a large Italian community of toddlers. MethodsA group of n = 2400 unselected Italian toddlers were screened by paediatricians during routine well−CHild visits between 18 and 24 months,in three Italian regions, representative of the North, Centre and South of Italy. The psychometric properties and factor structure of the Q−CHAT were explored and compared with published literature from Asia and the UK. ResultsThe total Q−CHAT scores in our sample confirmed a normal distribution. Mean Q−CHAT scores across regions were consistent with those reported in UK studies and likewise, a gender effect was found, with boys scoring higher than girls.We also found a remarkable consistency with the three-factor structure of the Asian Q−CHAT. Internal consistency was acceptable for both the Q−CHAT total score and the three factors. ConclusionsOur results confirm the dimensional distribution of autistic traits and related symptoms in unselected populations across different cultures. The Q−CHAT is a valid and reliable dimensional screening instrument to be used within a routine paediatric setting during well-child assessments.The main normative data and factor structure replicated in our sample

Reliability and Validity of the Korean Translation of Quantitative Checklist for Autism in Toddlers: A Preliminary Study.

Objectives:We aimed to assess the test-retest reliability, internal consistency, and validity of the Korean version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT).Methods:The Korean version of the Q-CHAT and the Korean version of the Child Behavior Checklist (CBCL) 1.5-5 were completed by parents of 24 toddlers and preschoolers with autism spectrum disorder (ASD) and 80 unselected toddlers and preschoolers. Parents of the ASD group also completed the Social Communication Questionnaire (SCQ), and Childhood Autism Rating Scale (CARS) scores were obtained from medical records.Results:The ASD group scored higher on the Q-CHAT than the unselected group. The Cronbach’s alpha coefficient of the Q-CHAT was 0.658, and test-retest reliability was calculated to be 0.836. The estimated area under the curve was 0.793. The total scores of the Q-CHAT in the ASD group demonstrated significant positive correlations with findings regarding pervasive development problems in the CBCL, SCQ, and CARS. A total score of 33.5 may be a useful cutoff point to use when identifying toddlers at risk of ASD.Conclusion:The Korean version of the Q-CHAT has good reliability and validity and can be used as a screening tool in order to identify toddlers and preschool children at risk of ASD.