Quantitative Checklist For Autism 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.

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) class data using ML classifiers. In our work, we have used different ASD data sets of all age groups (toddlers, adults, children, and adolescents) to classify ASD and TD cases. We implemented One-Hot encoding to translate categorical data into numerical data during preprocessing. We then used kNN Imputer with MinMaxScaler feature transformation to handle missing values and data normalization. ASD and TD class data is classified using Support vector machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural network classifiers. RF gives the best performance in terms of the accuracy of 100% with different training and testing data split for all four types of data sets and has no over-fitting issue. We have also examined our results with already published work, including recent methods like Deep Neural Network (DNN) and Convolution Neural Network (CNN). Even using complex architectures like DNN and CNN, our proposed methods provide the best results with low-complexity models. In contrast, existing methods have shown accuracy upto 98% with log-loss upto 15%. Our proposed methodology demonstrates the improved generalization for real-time ASD detection during clinical trials.

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.

Maternal obesity and diabetes during pregnancy and early autism screening score at well-child visits in standard clinical practice.

Early intervention and treatment can help reduce disability in children diagnosed with autism spectrum disorder. Screening for autism spectrum disorder in young children identifies those at increased likelihood of diagnosis who may need further support. Previous research has reported that exposure to maternal obesity and diabetes during pregnancy is associated with higher likelihood of autism spectrum disorder diagnosis in children. However, little is known about whether these maternal conditions are associated with how very young children score on autism spectrum disorder screening tools. This study examined associations between exposure to maternal obesity and diabetes during pregnancy and offspring scores on the Quantitative Checklist for Autism in Toddlers, an autism spectrum disorder screening questionnaire administered between 18-24 months at well-child visits. A higher score on the Quantitative Checklist for Autism in Toddlers suggests a higher likelihood of autism spectrum disorder; children with scores 3 or greater are referred to developmental pediatricians for evaluation. Our study found that children of mothers with obesity or diabetes during pregnancy had higher scores than children whose mothers did not have these conditions. Associations with maternal obesity and gestational diabetes diagnosed at or before 26 weeks of pregnancy were also present in children who did not have later autism spectrum disorder diagnoses, suggesting that exposure to these conditions during early pregnancy may be associated with a broad range of social and behavioral abilities. Identifying associations between maternal health conditions and early Quantitative Checklist for Autism in Toddlers screening scores could influence future screening and provision of support for children of mothers with these conditions.

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...

Are There Bidirectional Influences Between Screen Time Exposure and Social Behavioral Traits in Young Children?

Screen time in early childhood has been associated with children's prosocial and behavioral skills; however, the directionality of this relationship is unclear. We aimed to determine the direction of the relationship between screen time, social skills, and nonsocial behavioral traits in young children. This was a population-based, prospective cohort study with data across 5 time points. We examined the reciprocal relationships between caregiver-reported children's screen time at 12, 18, 24, 36, and 54 months and social behaviors collected using the Infant-Toddler Social-Emotional Assessment at 12 months; the Quantitative Checklist for Autism at 18, 24, and 36 months; and the Social Responsiveness Scale at 54 months. Cross-lagged path models were used for analysis. A multiple imputation data set and complete data from 229 participants were included in the analyses. Screen time at 12, 18, and 36 months predicted nonsocial behavioral traits at 54 months. Cross-lagged path models showed a clear direction from increased screen time at earlier time points to both poorer social skills and atypical behaviors at later time points (Akaike information criterion 18936.55, Bayesian information criterion 19210.73, root mean square error of approximation 0.037, and comparative fit index 0.943). Social skills or behavioral traits at a younger age did not predict later screen time at any of the time points. Screen time in early childhood has lagged influences on social skills and nonsocial behaviors; the reverse relationship is not found. Close monitoring of social behaviors may be warranted in the setting of excessive screen time during early childhood.

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.

Autism screening at 18\xa0months of age: a comparison of the Q-CHAT-10 and M-CHAT screeners

BackgroundAutism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months.MethodsThis is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old.Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines.ResultsWhile the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10.LimitationsMany parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages.ConclusionsLimitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age.

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,...

Q-CHAT-NAO: A robotic approach to autism screening in toddlers

The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human–robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction.

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.

Severity of nausea and vomiting in pregnancy and early childhood neurobehavioural outcomes: The Growing Up in Singapore Towards Healthy Outcomes study

BackgroundNausea and vomiting of pregnancy (NVP) affects 50 to 80 per cent of women. The existing literature has examined NVP from the perspective of the mother, and relatively less is known about offspring outcomes.ObjectivesTo study the relationships of NVP with social‐emotional, behavioural, and cognitive outcomes of the offspring in a multi‐ethnic Asian cohort.MethodsIn the Growing Up in Singapore Towards Healthy Outcomes prospective mother‐offspring cohort study, mothers responded to a structured NVP questionnaire at 26‐28 weeks’ gestation (n = 1172) and participants with severe NVP were confirmed using medical records. Children underwent multiple neurodevelopmental assessments throughout childhood. We conducted multivariable regressions with post‐estimation predictive margins to understand the associations of NVP with offspring neurobehavioural outcomes, which included 1‐year Infant‐Toddler Social and Emotional Assessment, 1.5‐year Quantitative Checklist for Autism in Toddlers, 2‐year Bayley Scales of Infant and Toddler Development, 2‐ and 4‐year Child Behavior Checklist, and 4.5‐year Kaufman Brief Intelligence Test. Analyses were adjusted for household income, birth variables, maternal mental health, and other relevant medical variables. Cohen's d effect sizes were calculated using standardised mean differences (μd).ResultsMothers were categorised into no (n = 296, 25.3%), mild‐moderate (n = 686, 58.5%), and severe NVP (n = 190, 16.2%), of whom 67 (5.7%) required admission. Compared to children of mothers who had no or mild‐moderate NVP, children with exposure to severe NVP exhibited more externalising behaviours (μd 2.0, 95% CI 0.3, 3.6; Cohen's d = 0.33) and social communication difficulties before 2 years (μd 4.1, 95% Cl 0.1, 8.0; Cohen's d = 0.38), both externalising (μd 1.5, 95% CI 0.4, 2.6; Cohen's d = 0.43) and internalising behaviours at 2 years (μd 1.2, 95% CI 0.1, 2.2; Cohen's d = 0.35), and only internalising behaviours after 2 years (μd 1.1, 95% CI 0.4, 2.0; Cohen's d = 0.37).ConclusionsSevere NVP is highly prevalent in this Asian cohort and may be adversely associated with multiple offspring neurobehavioural outcomes.

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.

Systematic Review of Level 1 and Level 2 Screening Tools for Autism Spectrum Disorders in Toddlers.

The present study provides a systematic review of level 1 and level 2 screening tools for the early detection of autism under 24 months of age and an evaluation of the psychometric and measurement properties of their studies. Methods: Seven databases (e.g., Scopus, EBSCOhost Research Database) were screened and experts in the autism spectrum disorders (ASD) field were questioned; Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and Consensus-based Standard for the selection of health Measurement INstruments (COSMIN) checklist were applied. Results: the study included 52 papers and 16 measures; most of them were questionnaires, and the Modified-CHecklist for Autism in Toddler (M-CHAT) was the most extensively tested. The measures’ strengths (analytical evaluation of methodological quality according to COSMIN) and limitations (in term of Negative Predictive Value, Positive Predictive Value, sensitivity, and specificity) were described; the quality of the studies, assessed with the application of the COSMIN checklist, highlighted the necessity of further validation studies for all the measures. According to COSMIN results, the M-CHAT, First Years Inventory (FYI), and Quantitative-CHecklist for Autism in Toddler (Q-CHAT) seem to be promising measures that may be applied systematically by health professionals in the future.

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.