Early Screening Of Autism Spectrum Disorder Research Articles

Validating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder.

Early identification of Autism Spectrum Disorder is very important, especially in low and middle-income countries, where access to resources is often limited. The Child Behavior Checklist 1.5-5 is a tool that has been used to help identify children with autism spectrum disorder through specific behavior patterns. However, its effectiveness in low- and middle-income country settings has not been thoroughly studied. This research focused on evaluating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder among Brazilian children. The study involved 1292 children aged 3-5 years from the general population and 70 children with autism spectrum disorder aged 1-5 years. Using advanced statistical methods, the study tested how well the Child Behavior Checklist identified children with autism spectrum disorder and how reliable it was in this context. The findings showed that the Child Behavior Checklist 1.5-5 performed well in identifying autism spectrum disorder, with high reliability and consistency in the results. Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective. In summary, the Child Behavior Checklist 1.5-5 proves to be a reliable and valid tool for early autism spectrum disorder screening in Brazilian children. This can help ensure that more children in low- and middle-income country settings are identified early and receive the necessary support and interventions to help them thrive. Future research should continue to test this tool in different contexts to confirm its usefulness across various populations.

A machine learning model based on CHAT-23 for early screening of autism in Chinese children.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the mental, emotional, and social development of children. Early screening for ASD typically involves the use of a series of questionnaires. With answers to these questionnaires, healthcare professionals can identify whether a child is at risk for developing ASD and refer them for further evaluation and diagnosis. CHAT-23 is an effective and widely used screening test in China for the early screening of ASD, which contains 23 different kinds of questions. We have collected clinical data from Wuxi, China. All the questions of CHAT-23 are regarded as different kinds of features for building machine learning models. We introduce machine learning methods into ASD screening, using the Max-Relevance and Min-Redundancy (mRMR) feature selection method to analyze the most important questions among all 23 from the collected CHAT-23 questionnaires. Seven mainstream supervised machine learning models were built and experiments were conducted. Among the seven supervised machine learning models evaluated, the best-performing model achieved a sensitivity of 0.909 and a specificity of 0.922 when the number of features was reduced to 9. This demonstrates the model's ability to accurately identify children for ASD with high precision, even with a more concise set of features. Our study focuses on the health of Chinese children, introducing machine learning methods to provide more accurate and effective early screening tests for autism. This approach not only enhances the early detection of ASD but also helps in refining the CHAT-23 questionnaire by identifying the most relevant questions for the diagnosis process.

Association between blood mitochondrial DNA copy number and mental disorders: A bidirectional two-sample mendelian randomization study

BackgroundMitochondria is essential for cellular energy production, oxidative stress, and apoptosis. Mitochondrial DNA (mtDNA) encodes essential proteins for mitochondrial function. Although several studies have explored the association between changes in mtDNA copy number (mtDNA-CN) and risk of mental disorders, the results remain debated. This study used a bidirectional two-sample Mendelian randomization (MR) analysis to examine the genetic causality between mtDNA-CN and mental disorders. MethodsGenome-wide association study (GWAS) data for mtDNA-CN were sourced from UK biobank, involving 383,476 European cases. GWAS data for seven mental disorders—attention deficit/hyperactivity disorder, autism spectrum disorder (ASD), schizophrenia, bipolar disorder, major depressive disorder, anxiety, and obsessive-compulsive disorder—were primarily obtained from the Psychiatric Genomics Consortium. Causal associations were assessed using inverse variance weighting, with sensitivity analyses via the weighted median and MR-Egger methods. Reverse MR considered the seven mental disorders as exposures. All analyses were replicated with additional mtDNA-CN GWAS data from 465,809 individuals in the Heart and Ageing Research in Genomic Epidemiology consortium and the UK Biobank. ResultsForward MR observed a 27 % decrease in the risk of ASD per standard deviation increase in genetically determined blood mtDNA-CN (OR = 0.73, 95%CI: 0.58–0.92, p = 0.002), with no causal effects on other disorders. Additionally, reverse MR did not indicate a causal association between any of the mental disorders and mtDNA-CN. Validation analyses corroborated these findings, indicating their robustness. ConclusionsOur study supports the potential causal association between mtDNA-CN and the risk of ASD, suggesting that mtDNA-CN could serve as a promising biomarker for early screening of ASD.

Cross-Cultural Generalizability of the First Year Inventory for Early ASD Screening in China.

The First Year Inventory (FYI) is a parent report screening measure, aimed at identifying the risk of autism spectrum disorder (ASD) in 12-month-old infants. This study aimed to investigate the utility of FYI within the Chinese community and develop a short version, encompassing both a low-risk sample and a high-risk sample comprising infants with older siblings diagnosed with ASD. Parents of 53 high-risk (HR) infants and 519 low-risk (LR) infants, aged 11 to 13 months, were recruited. After comparing response distributions across Chinese and American samples, a new factorial structure was developed according to the factor analyses. The construct validity and internal consistency of the two FYI versions were examined. The implementation of FYI in the HR sample was also assessed. Noteworthy disparities in response distribution were observed between the Chinese and American samples. Both FYI 2.0 and the FYI short version demonstrated moderate construct validity and internal consistency, with the FYI short version exhibiting better predictive ability in the HR sample. Significant lower risk scores was observed in the HR sample compared to the LR sample. These findings substantiate the applicability and validity of the Chinese short version of FYI. Future research should include follow-up assessments with the Chinese sample to evaluate cutoff scores, considering the cutoff between sensitivity and specificity and the sample?s characteristics.

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset.

Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening. Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model. WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001). This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps.

Integrating smartphone applications into screening and identifying autism spectrum disorder (ASD) represents a promising and innovative frontier within healthcare. This forward-looking paper examines the current landscape of ASD screening apps, shedding light on their potential advantages and addressing and navigating significant challenges. One of the most compelling aspects of these apps lies in their potential to democratize access to ASD screening, effectively breaking down geographical barriers. By using the widespread availability of smartphones, these apps make it possible for individuals, caregivers, and healthcare providers to engage in early ASD screening from virtually anywhere. This accessibility is especially crucial in underserved areas or regions with limited access to specialized healthcare services. Moreover, these apps offer a degree of objectivity that traditional screening methods may need help to match. By relying on data-driven algorithms and machine learning, they can provide a more impartial assessment of a child's behavior, minimizing the potential for subjective bias. This objectivity, combined with the ability to monitor and assess a child's development over time, empowers caregivers with valuable insights into their child's progress. However, as with any technological advancement in healthcare, integrating smartphone apps for ASD screening is not without its share of ethical and privacy considerations. Ensuring informed consent is obtained, especially when collecting data from children, is complex and critical. Striking the right balance between collecting necessary data and protecting an individual's privacy requires careful thought and transparent communication. Additionally, the "digital divide" represents a challenge that needs to be acknowledged and addressed. Not all individuals and families have equal access to smartphones or the technological literacy required to use these apps effectively. This disparity in access must be considered when developing and implementing app-based screening solutions to prevent exacerbating existing healthcare inequalities. Nevertheless, the future of ASD screening apps holds significant promise. Advancements in technology, including integrating advanced sensors, wearables, augmented reality, and machine learning, can further enhance the accuracy and depth of screening. Interdisciplinary collaboration between researchers, developers, clinicians, and educators is crucial to ensure that these apps are effective, culturally sensitive, and user-friendly. Furthermore, integrating these apps into broader healthcare systems, including electronic health records and telehealth platforms, can streamline the screening process and enable a more seamless transition from screening to diagnosis and intervention.

A novel telehealth tool using a snack activity to identify autism spectrum disorder

BackgroundThe COVID-19 pandemic has caused an unprecedented need for accessible health care services and significantly accelerated the development processes of telehealth tools for autism spectrum disorder (ASD) early screening and diagnosis. This study aimed to examine the feasibility and utility of a time-efficient telehealth tool combining a structured snack time assessment activity and a novel behaviour coding scheme for identifying ASD.MethodsA total of 134 1–6-year-old individuals with ASD (age in months: mean = 51.3, SD = 13.1) and 134 age- and sex-matched typically developing individuals (TD) (age in months: mean = 54, SD = 9.44) completed a 1-min snack time interaction assessment with examiners. The recorded videos were then coded by trained coders for 17 ASD-related behaviours; the beginning and end points and the form and function of each behaviour were recorded, which took 10–15 min. Coded details were transformed into 62 indicators representing the count, duration, rate, and proportion of those behaviours.ResultsTwenty indicators with good reliability were selected for group difference, univariate and multivariate analyses. Fifteen behaviour indicators differed significantly between the ASD and TD groups and remained significant after Bonferroni correction, including the children’s response to the examiner’s initiation, eye gaze, pointing, facial expressions, vocalization and verbalization, and giving behaviours. Five indicators were included in the final prediction model: total counts of eye gaze, counts of standard pointing divided by the total counts of pointing, counts of appropriate facial expressions, counts of socially oriented vocalizations and verbalizations divided by the total counts of vocalizations and verbalizations, and counts of children using giving behaviours to respond to the examiner's initiations divided by the total counts of the examiner's initiation of snack requisitions. The ROC curve revealed a good prediction performance with an area under the curve (AUC) of 0.955, a sensitivity of 92.5% and a specificity of 84.3%.ConclusionOur results suggest that the snack activity-based ASD telehealth approach shows promise in primary health care settings for early ASD screening.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Combining multi-omics approaches to prioritize the variant-regulated functional long non-coding RNAs in autism spectrum disorder.

Rising evidence has indicated that long non-coding RNA (lncRNA) may play an essential role in the development of autism spectrum disorder (ASD). However, identifying the lncRNAs associated with ASD and the risk loci on them remains a major challenge. This study aims to identify potential causative variants and explore the related mechanisms. By leveraging differential expression analysis, WGCNA analysis and cis-expression quantitative analysis, our study mined functional SNPs with the regulated long non-coding RNA genes in brain tissues. We recruited 611 ASD children and 645 healthy children in the case-control study. Total 68 different expressed lncRNAs were validated by calculating the brain tissue-specific expression using RNA-seq data. By the WGCNA method, 9 functional lncRNAs classified as e-lncRNA were found to interact with 976 ASD risk genes. Furthermore, we mined functional SNPs regulated long non-coding RNAs in brain tissues. We analyzed the association between candidate SNPs and ASD risks in Chinese children, which showed BDNF-AS rs1565228 allele G to C reduced the risk of ASD (OR = 0.81, 95%CI: 0.66-0.98). Further bioinformatics analysis showed that the variant rs1565228 C>G with the low binding affinity of transcription factor SRF caused the decreased expression of lncRNA BDNF-AS. Our study revealed that rs2295412 in the non-coding sequence of the lncRNA gene region was significantly associated with the risk of ASD. These findings suggested that the SNPs in the non-coding region of lncRNA may play important roles in the genetic susceptibility of ASD, which may facilitate the early screening of ASD.

A multimodal discrimination method for the response to name behavior of autistic children based on human pose tracking and head pose estimation

The incidence rate of autism spectrum disorder (ASD) has been rising in the past decades, and has reached 1 ∼ 2%. Clinical ASD screening requires long testing time, complicated doctor training and high cost. Therefore, an objective and accurate automated method is urgently needed for large-scale early screening of autistic children. The lack of response to one’s own name is one of the early warning signals of ASD. Response to name is included in various ASD early screening and diagnostic assessments. In this paper, we propose an algorithm that combines automatic name detection with human pose tracking and head pose estimation in computer vision. The final prediction is determined by the combination of shoulder rotation angle, head pose information and duration. The experimental data comes from the process of clinical screening for ASD, which has less laboratory control, more realistic child response, and a flexible number of accompanying parents. Thirty subjects including 29 children with ASD and 1 typical developing (TD) child participated in the experiment. The experimental results show that the consistency between our proposed method and clinical diagnosis achieves 93.3%, which indicates our method can play an effective auxiliary role in the early screening of ASD.

Autism detection for toddlers from behavioural indicators using classification techniques

Autism spectrum disorder (ASD) is a condition associated with impairments in communication, social, and repetitive behaviour; the degree of impairment varies between individuals with ASD. Since ASD has a substantial impact on the individual, caregivers, and family members due to the social and economic costs involved, early ASD screening becomes fundamental to enable faster access to healthcare resources. One of the important studied groups in ASD research is toddlers – detecting autistic traits at an early stage can help physicians develop treatment plans. This paper aims to improve the detection rate of ASD screening for toddlers using a data driven approach by identifying the impactful feature set related to ASD, and then processing these features using classification algorithms to accurately screen for ASD. To achieve the aim, a data driven framework consisting of feature selection and classification algorithms is proposed, and then implemented on a real dataset related to the ASD screening of toddlers. Empirical evaluations on the ASD screening dataset using different classification methods reveal that when support vector machine (SVM) or Naïve Bayes are integrated with the proposed framework good predictive models are constructed for toddler ASD screening. These predictive models can be adopted by different medical staff and caregivers to replace scoring functions of conventional screening methods.

Closing the Diagnostic Gap: Early Autism Spectrum Disorder Screening for Every Child.

Closing the Diagnostic Gap: Early Autism Spectrum Disorder Screening for Every Child.

A Nine-Item Red Flag Sign Card for Identification of Autism Spectrum Disorder among Toddlers Aged 12 to 18 Months.

There is a dearth of validated red flags measures for early identification of autism spectrum disorder (ASD) among toddlers. Hence, a new screening measure was developed. Item generation was done through literature search. Content validity (CVI) assessment was done. Criterion validity was done using Diagnostic and Statistical Manual 5 (DSM-5) as reference standard, data were collected from the case records of children with ASD diagnosis at 2 y, and evaluated for developmental milestones between 12 and 18 mo in a tertiary care setting. Item reduction of the measure from 18 to 9 was done. The area under the curve in the receiver operating characteristic (ROC)curve for new measure was 0.81 (95% CI = 0.73, 0.87); z = 7.874; p < 0.001 against DSM-5 score of ≥ 2 in the new measure; achieved sensitivity of 93.42% (95% CI = 85.3, 97.8) and specificity of 60% (95% CI = 45.9, 73.0). Thus, new validated red flag sign card (Concern-9) can be used effectively for early screening and identification of ASD among children aged 12-18 mo.

Early diagnosis of autism in the community is associated with marked improvement in social symptoms within 1-2 years.

It is widely believed that early diagnosis and treatment of autism spectrum disorder is essential for better outcome. This is demonstrated by the American Academy of Pediatrics recommendation to screen all 1.5–2.5-year-old toddlers for autism spectrum disorder. However, multiple longitudinal studies of children diagnosed with autism spectrum disorder at 1.5–6 years of age and treated in community settings have not reported any associations between earlier diagnosis and improved outcome in core symptoms. Here, we quantified Longitudinal changes in core autism spectrum disorder symptoms of 131 children diagnosed at 1.2–5 years of age using the Autism Diagnostic Observation Schedule–Second Edition Calibrated Severity Scores over a 1-2 year period. We examined the prevalence and magnitude of Calibrated Severity Scores changes across children who were diagnosed at different ages. The results revealed that age of diagnosis was significantly correlated with poorer outcome (r(129) = 0.41, p < 0.001). Approximately 65% of the children diagnosed before 2.5 years of age exhibited improvements in Autism Diagnostic Observation Schedule–Second Edition Calibrated Severity Scores (⩾2 points) in contrast to only 23% of the children diagnosed after this age. Changes in younger children were driven by improvements in social symptoms despite deterioration in restricted and repetitive behaviors. These findings reveal that autism spectrum disorder diagnosis before the age of 2.5 is associated with considerable improvement in social symptoms. We suggest that greater brain plasticity and behavioral flexibility enable younger children to benefit more from autism spectrum disorder interventions even in community settings with heterogeneous services. This motivates further prioritization of early autism spectrum disorder screening as recommended by American Academy of Pediatrics guidelines.Lay abstractIt is widely believed that early diagnosis and treatment of autism spectrum disorder is essential for better outcome. This is demonstrated by the American Academy of Pediatrics recommendation to screen all 1.5–2.5-year-old toddlers for autism spectrum disorder. However, multiple longitudinal studies of children diagnosed with autism spectrum disorder at 1.5–6 years of age and treated in community settings have not reported any associations between earlier diagnosis and improved outcome in core autism spectrum disorder symptoms. In this study, we measured changes in core autism spectrum disorder symptoms over a 1–2-year period in 131 children diagnosed with autism spectrum disorder at 1.2–5 years of age, and treated in the community. The results revealed that children who were diagnosed before 2.5 years of age were three times more likely to exhibit considerable improvements in social autism spectrum disorder symptoms in comparison to children diagnosed at later ages. These results highlight the importance of early diagnosis and treatment of autism spectrum disorder even in community settings with heterogeneous services. In addition, these results motivate further prioritization of universal screening for autism spectrum disorder before 2.5 years of age.

Symptom trajectories in the first 18 months and autism risk in a prospective high-risk cohort.

Although early autism spectrum disorder (ASD) detection strategies tend to focus on differences at a point in time, behavioral symptom trajectories may also be informative. Developmental trajectories of early signs of ASD were examined in younger siblings of children diagnosed with ASD (n = 499) and infants with no family history of ASD (n = 177). Participants were assessed using the Autism Observation Scale for Infants (AOSI) from 6 to 18 months. Diagnostic outcomes were determined at age 3 years blind to previous assessments. Semiparametric group-based modeling using AOSI scores identified three distinct trajectories: Group 1 ('Low', n = 435, 64.3%) was characterized by a low level and stable evolution of ASD signs, group 2 ('Intermediate', n = 180, 26.6%) had intermediate and stable levels, and group 3 ('Inclining', n = 61, 9.3%) had higher and progressively elevated levels of ASD signs. Among younger siblings, ASD rates at age 3 varied by trajectory of early signs and were highest in the Inclining group, membership in which was highly specific (94.5%) but poorly sensitive (28.5%) to ASD. Children with ASD assigned to the inclining trajectory had more severe symptoms at age 3, but developmental and adaptive functioning did not differ by trajectory membership. These prospective data emphasize variable early-onset patterns and the importance of a multipronged approach to early surveillance and screening for ASD.

Early screening of autism spectrum disorder using cry features

The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.

Screening Early Children With Autism Spectrum Disorder via Response-to-Name Protocol

Incidence of children with autism spectrum disorder (ASD) has increased with an average rate of 1% worldwide. Clinical ASD screening, especially for children screening is a laborious and skilled task; however, there is no objective and effective method automating ASD children screening. Analyzing children ASD characteristics in predefined motion behavior protocols is attempted to provide automatic solutions to children ASD screening. A novel protocol, response to name (RTN), is proposed in this article for ASD clinical validation and diagnosis. The RTN method is jointly designed with clinical partners, and novel gaze estimation is developed for validating ASD characteristic behavior. Seventeen subjects including ten adults and seven children (five ASD subjects and two healthy subjects) have participated the experiment. The experiment results show that the proposed RTN system achieves an average classification score of 92.7% fully demonstrating that the principle of motion protocol based ASD screening has the potential to have early ASD screening automated.

Development and Validity of Behavior Development Screening for Toddlers-Questionnaire/Parents(BeDevel-Q/P)

본 연구는 9~47개월 영유아를 대상으로 자폐스펙트럼장애 위험군을 선별하기 위한 부모 보고용 설문 검사인 걸음마기 아동 행동 발달 선별 척도-부모용 설문지(BeDevel-Q/P:Behavior Development Screening for Toddlers-Questionnaire/ Parents)를 개발하고 타당화하였다. 국내외 문헌 연구와 국내 자폐스펙트럼장애 영유아 및 부모 대상 자료를 토대로 예비 문항 505개를 개발하였다. 자폐스펙트럼장애 관련 다학제 전문가의 내용타당도 검증, 영유아 자폐스펙트럼장애 자녀를 둔 부모 대상 안면타당도 검증과 예비 조사를 거쳐 51개 예비 문항을 추출하였다. 이후 예비 조사(총 289명, 이 중 일반발달 196명, 자폐스펙트럼장애 43명, 발달지연 51명)와 본 조사(총 2,273명, 이 중 일반발달 1,918명, 자폐스펙트럼장애 221명, 발달지연 134명)를 실시하였다. 그 결과 월령대가 9∼11개월, 12∼23개월, 24∼35개월, 36∼47개월로 구분되고, 월령별로 10∼28개 문항이 개발되었으며, 3개의 하위요인이 도출되었다. BeDevel-Q/P와 M-CHAT, Q-CHAT-10, ADOS-2, K-CARS, CBCL 1.5-5의 전반적 발달문제 척도, BITSEA의 자폐증 선별에 민감한 문항, K-DST의 사회성 영역과의 상관분석 및 자폐스펙트럼장애 집단, 발달지연 집단, 일반발달 집단간 차이 분석을 활용하여 BeDevel-Q/P의 타당도를 검증하였고, 본 척도는 받아들일 만한 수준(acceptable level)의 타당도를 보였다. BeDevel-Q/P의 민감도(78.5∼84.0%), 특이도(81.0∼82.0%), 문항 내적 일치도(Cronbach’s α=.85∼.88), 검사-재검사신뢰도(r=.712∼.980)에서도 높은 수준의 결과를 나타냈다. 끝으로 본 연구의 의의와 제한점 및 후속 연구 방향을 기술하였다.This study developed and validified the Behavior Development Screening for Toddlers-Questionnaire/ Parents (BeDevel-Q/P), an autism spectrum disorder (ASD) screening instrument. This assessment serves the purpose of parental reports for early screening of ASD on infants aged 9-47 months. 565 preliminary questionnaires were generated based on prior studies of ASD infants and parents. After Context Validity tests of interdisciplinary ASD experts, Face Validity tests of parents with ASD children, and preliminary research, 51 questionnaires were created. Pilot tests were conducted on a total of 289 people (N = 196 typical development, N = 43 ASD, N = 51 developmentally delayed). A total of 2273 participants were tested N = 1918 typical development, N = 221 ASD, N = 134 developmentally delayed). The results showed a line between age ranges of 9-11 months, 12-23 months, 24-35 months and 36-47 months. Each age ranges consisted of 10-28 questionnaires and 3 subfactors. BeDevel-Q/P validity was tested by the general developmental criterion of BeDevel-Q/P, M-CHAT, Q-CHAT-10, ADOS-2, K-CARS and CBCL 1.5-5 DSM pervasive developmental problems. Autism items from BITSEA were also employed and correlation analysis was performed on sociability items from K-DST, and general differentiation factors between ASD, developmental delayed, and normal groups were also considered. Results showed a reasonable level of acceptability. BeDevel-Q/P showed high sensitivity (78.5-84.0%), specificity (81.0-82.0%), high internal consistency (Cronbach’s α=0.85-88) as well as high test-retest reliability (r=0.712-980). Lastly, the significance and limitations of this study and directions for future research is suggested.

The Feasibility and Validity of Autism Spectrum Disorder Screening Instrument: Behavior Development Screening for Toddlers (BeDevel)-A Pilot Study.

Although early screening is critical for individuals with autism spectrum disorder (ASD) in order to receive early intervention and improve function later in life, screening is often delayed. Limitations of existing screening instruments, and the need for a culturally appropriate early screening tool in Korean children, led us to develop Behavior Development Screening for Toddlers (BeDevel). The BeDevel assessment consists of two parts: BeDevel-Interview, a structured interview measure for parents/primary caregivers; and BeDevel-Play, a play-based semi-structured observational measure in children. To examine the feasibility and validity of BeDevel, 155 children (N = 75 ASD, N = 55 typical development, N = 25 developmentally delayed) aged 18-42 months (M = 31.54 months, SD = 7.60) were examined through parent-reported screening questionnaires, BeDevel, and standard diagnostic assessments. When BeDevel items were analyzed using Cohen's kappa statistics, most items in BeDevel-Interview and all items in BeDevel-Play were reasonably consistent with diagnoses. We identified primary items, which were significantly interacted with actual diagnosis in the chi-squared test (P < 0.05, range = 0.000-0.032). Using cutoff numbers of items determined using the receiver operating characteristics curve, BeDevel showed satisfactory levels of sensitivity (83.33%-100%), specificity (81.25%-100%), positive predictive values (80.65%-100%), and negative predictive values (83.87%-100%), as well as high internal consistency (Cronbach's α = 0.866-959). The agreement between BeDevel and most other screening/diagnostic instruments was moderate (k = 0.419-1.000). These results suggest that BeDevel can be a useful instrument for early screening of ASD. Autism Res 2019, 12: 1112-1128. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Although early screening is critical for individuals with autism spectrum disorder (ASD) in order to receive early intervention and improve function later in life, screening is often delayed. Limitations of existing screening instruments and the need for a culturally appropriate early screening tool in Korean children led us to develop Behavior Development Screening for Toddlers (BeDevel). The BeDevel assessment consists of two parts: BeDevel-Interview, a structured interview measure for parents/primary caregivers; and BeDevel-Play, a play-based, semi-structured observational measure in children. In order to test the feasibility and validity of BeDevel, we analyzed preliminary data of total 155 children aged 18-42 months, examined through parent-reported screening questionnaires, BeDevel, and standard diagnostic assessments. When individual items were analyzed, responses of all BeDevel-Interview items and of most BeDevel-Play items well matched actual diagnoses, and we identified primary items, which were particularly useful in differentiating between the ASD group and the non-ASD group. With the optimal screening criteria determined, the BeDevel was able to identify individuals with a diagnosis of ASD and those without it, all at satisfactory levels. Lastly, BeDevel items were closely related as a set, and the BeDevel screening results were reasonably consistent with the results of most other screening/diagnostic instruments. These results suggest that BeDevel can be a useful instrument for early screening of ASD.

Sensitivity and specificity of early screening for autism.

Early identification and diagnosis is beneficial for children with autism spectrum disorder (ASD). Universal early screening is recommended by many experts, but disputed because evidence is limited, and sensitivity and specificity in general populations are largely unknown. To estimate the sensitivity and specificity of early population-based screening for ASDs. The study was based on the Norwegian Mother and Child Cohort Study. The 36-month cohort questionnaire included the Social Communication Questionnaire (SCQ), a 40-item screening instrument for ASD. A total of 58 520 mothers (58%) responded to the questionnaire. By the end of follow-up on 31 December 2015, 385 (0.7%) individuals with ASD had been identified among the responders' children. The distributions of SCQ scores in those with ASD and other children had large degrees of overlap. With the cut-off of 15 recommended in the SCQ manual, screening sensitivity was 20% (95% CI 16-24) for ASD overall. For children with ASD who had not developed phrase speech at 36 months, sensitivity was 46% (95% CI 35-57%), whereas it was 13% (95% CI 9-17) for children with ASD with phrase speech. Screening specificity was 99% (95% CI 99-99). With the currently recommended cut-off of 11, sensitivity increased to 42% for ASD overall (95% CI 37-47), 69% (95% CI 58-79) for ASD without phrase speech and 34% (95% CI 29-40) for ASD with phrase speech. Specificity was then reduced to 89% (95% CI 89-90). Early ASD screening with a parent checklist had low sensitivity. It identified mainly individuals with ASD with significant developmental delay and captured very few children with ASD with cognitive skills in the normal range. Increasing sensitivity was not possible without severely compromising specificity. C.L. receives royalty for the Social Communication Questionnaire, which she has co-authored.