Autism In Toddlers Research Articles

Turkish Validation of the Rapid Interactive Screening Test for Autism in Toddlers.

It is important to diagnose autism spectrum disorder at an early age and to start an early intervention program without delay. In this study, we aimed to validate the Rapid Interactive Screening Test for Autism in Toddlers (RITA-T) in a group of Turkish children and found that the RITA-T which has been shown to be a valid and reliable screening test for 18- to 36-month-old children in studies conducted in different countries, is also valid in Turkish children. Similar to previous studies, our results showed that the RITA-T has good sensitivity and specificity in distinguishing children with autism spectrum disorder. We think that our study will contribute to the timely initiation of early intervention programs for many children with autism by enabling a valid test to be used in screening programs.

Two-Year Autism Risk Screening and 3-Year Developmental Outcomes in Very Preterm Infants

Use of the Modified Checklist for Autism in Toddlers, Revised With Follow-Up, a 2-stage parent-report autism risk screening tool, has been questioned due to reports of poor sensitivity and specificity. How this measure captures developmental delays for very preterm infants may provide support for continued use in pediatric care settings. To determine whether autism risk screening with the 2-stage parent-report autism risk screening tool at age 2 years is associated with behavioral and developmental outcomes at age 3 in very preterm infants. Neonatal Neurobehavior and Outcomes for Very Preterm Infants was a longitudinal, multisite cohort study. Enrollment occurred April 2014 to June 2016, and analyses were conducted from November 2022 to May 2023. Data were collected across 9 university-affiliated neonatal intensive care units (NICUs). Inclusion criteria were infants born less than 30 weeks' gestational age, a parent who could read and speak English and/or Spanish, and residence within 3 hours of the NICU and follow-up clinic. Prematurity and use of the 2-stage parent-report autism risk screening tool at age 2 years. Outcomes include cognitive, language, motor composites on Bayley Scales for Infant and Toddler Development, third edition (Bayley-III) and internalizing, externalizing, total problems, and pervasive developmental disorder (PDD) subscale on the Child Behavior Checklist (CBCL) at age 3 years. Generalized estimating equations tested associations between the 2-stage parent-report autism risk screening tool and outcomes, adjusting for covariates. A total of 467 children (mean [SD] gestational age, 27.1 [1.8] weeks; 243 male [52%]) were screened with the 2-stage parent-report autism risk screening tool at age 2 years, and outcome data at age 3 years were included in analyses. Mean (SD) maternal age at birth was 29 (6) years. A total of 51 children (10.9%) screened positive on the 2-stage parent-report autism risk screening tool at age 2 years. Children with positive screening results were more likely to have Bayley-III composites of 84 or less on cognitive (adjusted odds ratio [aOR], 4.03; 95% CI, 1.65-9.81), language (aOR, 5.38; 95% CI, 2.43-11.93), and motor (aOR, 4.74; 95% CI, 2.19-10.25) composites and more likely to have CBCL scores of 64 or higher on internalizing (aOR, 4.83; 95% CI, 1.88-12.44), externalizing (aOR, 2.69; 95% CI, 1.09-6.61), and PDD (aOR, 3.77; 95% CI, 1.72-8.28) scales. Results suggest that the 2-stage parent-report autism risk screening tool administered at age 2 years was a meaningful screen for developmental delays in very preterm infants, with serious delays detected at age 3 years.

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.

Study of Machine Learning Models to Detect Autism Spectrum Disorder

ASD is a spectrum disorder, some people with ASD may have mild symptoms and can live independently, while others may need more support and assistance in their daily lives. Some of the risk factors for ASD include having an older parent, being born prematurely, having a sibling with ASD, or being exposed to certain infections or toxins during pregnancy some of the common types of treatments include behavioural therapy, occupational therapy, physical therapy, and passionate about their interests. Such as the Modified Checklist for Autism in Toddlers, observational tools, such as the Autism Diagnostic Observation Schedule, and developmental assessments, such as the Mullen Scales of Early Learning. This study refers to some Machine Learning (ML) based applications that are able to detect Autism among individuals. This comprehensive review explores the innovative integration of machine learning (ML) models in the detection and diagnosis of Autism Spectrum Disorder (ASD). It begins by highlighting the complexities and diagnostic challenges of ASD, noting the limitations of traditional assessment methods. The review then delves into the realm of artificial intelligence (AI), discussing how AI, particularly ML and deep learning techniques, are revolutionizing the approach to ASD detection. It covers various ML models, including supervised, unsupervised, and reinforcement learning, and their application using behavioural, genetic, and neuroimaging data. A significant focus is given to the use of Logistic Regression and Hybrid Autism Screening Models in predicting ASD. The review also examines the efficacy and performance of supervised and deep learning models in ASD detection, evaluating their accuracy and precision. By providing a detailed analysis of the current state of AI in healthcare, specifically for ASD, this review underscores the potential of ML models in offering more accurate, accessible, and efficient diagnosis methods for ASD

Neurodevelopmental Outcomes of Children Born to Women Who Traveled to a Zika Virus-Endemic Region During Pregnancy

Abstract Background Zika virus (ZIKV) exposure during pregnancy causes a wide range of congenital malformations and developmental deficits, but we do not know whether potential ZIKV exposure during pregnancy without a formal ZIKV diagnosis results in worse developmental outcomes. Pregnant women without symptoms are not recommended to be tested for ZIKV infection per CDC guidelines. However, ZIKV infections can be asymptomatic, which means that pregnant women may have unknowingly been infected with ZIKV during travel to an endemic region. Our study aims to determine whether neurodevelopmental outcomes of children born to women with a travel history to a ZIKV-endemic region at the height of the pandemic are worse than children without a maternal travel history. Methods We identified women who presented to a maternal-fetal medicine clinic within the University of Wisconsin Hospitals and Clinics system in Madison, WI, between 2015-2018 and recently traveled to a ZIKV-endemic region (n=175) and conducted a retrospective chart review. Children with no maternal travel history to a ZIKV-endemic region were identified by state registry data and matched based on month of birth and gestational age (n=189). Developmental outcomes were defined from regular screening tools obtained during well child visits, the Ages and Stages Questionnaire (ASQ-3), and the Modified Checklist for Autism in Toddlers (MCHAT). We compared the number of “No Concern”, “Borderline” and “Concern” ASQ scores between groups using a generalized linear mixed effects model with a logit link function. The number of Low, Medium or High MCHAT risk scores at 18 and 24 months were compared between groups using Fisher’s exact test. Birth weight, maternal age and maternal race were included as covariates. All reported p-values are two-sided and P&amp;lt;0.05 was used to define statistical significance. Results Cases had a significantly higher rate (13.9%) of borderline/concern scores in the ASQ screening tool compared to controls (7.7%) at 16-22 months (p=0.0288), which appeared to be driven by differences in the personal-social domain where more cases (16.1%) had borderline/concern scores than the controls (5.5%, p=0.0348). There were no differences seen at 9-15 months or at 23-36 months. We also found that cases had higher rates of moderate-high risk scores (10.3%) on the MCHAT compared to controls (2.2%) at 18 months (p=0.0240), but this difference wasn’t statistically significant at 24 months. There were no differences in birth weight, maternal age and gestational age between groups. Very few of the mothers (18.3%) reported 1 or more signs or symptoms consistent with a ZIKV infection, and of the 105 women with ZIKV testing, 103 were negative. Conclusions Our findings indicate that children born to mothers who traveled to a ZIKV-endemic region during pregnancy, regardless of the maternal ZIKV testing result, had worse developmental screening scores around 18 months of age. This suggests that children with maternal travel history to a ZIKA-endemic region should be regularly monitored and receive early formal developmental testing as needed.

Cognitive and Emotional Well-Being of Preschool Children Before and During the COVID-19 Pandemic

The association between COVID-19 social disruption and young children's development is largely unknown. To examine associations of pandemic exposure with neurocognitive and socioemotional development at 24 and 54 months of age. This cross-sectional study evaluated associations between pandemic exposure vs nonexposure and developmental outcomes with covariate adjustment using data from the Ontario Birth Study collected between February 2018 and June 2022. Eligible participants were children aged 24 and 54 months. Data were analyzed from June to November 2022. COVID-19 pandemic exposure defined as assessment after March 11, 2020. Neurodevelopmental assessment using the ASQ-3 (Ages and Stages Questionnaire, Third Edition) and MCHAT-R (Modified Checklist for Autism in Toddlers, Revised) at 24 months of age, and neurocognitive and socioemotional assessment using the National Institutes of Health Toolbox at 54 months of age. A total of 718 children at age 24 months (mean [SD] age, 25.6 [1.7] months; 342 female [47.6%]; 461 White [64.2%]) and 703 at age 54 months (mean [SD] age, 55.4 [2.6] months; 331 female [47.1%]; 487 White [69.3%]) were included. At 24 months of age, 460 participants (232 female [50.4%]) were assessed during the pandemic (March 17, 2020, to May 17, 2022) and 258 (110 female [42.6%]) were assessed prepandemic (April 17, 2018, to March 10, 2020). At 54 months of age, 286 participants (129 female [45.1%]) were assessed from March 14, 2020, to June 6, 2022, and 417 (202 female [48.4%]) were assessed from February 8, 2018, to March 10, 2020. At 24 months of age, pandemic-exposed children had reduced risk of problem-solving difficulties using cutoff scores (odds ratio [OR], 0.33; 95% CI, 0.18-0.62; P = .005) and higher problem-solving (B, 3.93; 95% CI, 2.48 to 5.38; P < .001) compared with nonexposed children. In contrast, pandemic-exposed children had greater risk for personal-social difficulties using cutoff scores (OR, 1.67; 95% CI, 1.09-2.56; P = .02) and continuous scores (B, -1.70; 95% CI, -3.21 to -0.20; P = .02) compared with nonexposed children. At 54 months of age, pandemic-exposed children had higher receptive vocabulary (B, 3.16; 95% CI, 0.13 to 6.19; P = .04), visual memory (B, 5.95; 95% CI, 1.11 to 10.79; P = .02), and overall cognitive performance (B, 3.89; 95% CI, 0.73 to 7.04; P = .02) compared with nonexposed children, with no differences in socioemotional development. This cross-sectional study found both positive and negative associations between pandemic exposure and preschool children's cognitive and emotional well-being within a relatively socioeconomically advantaged sample.

2 Infant Imitation: Detecting Risk in the First Year with PediaTrac™

Objective:Imitation has pervasive associations with social and communicative development. However, few methods have been developed to measure this construct in typically developing infants, and even less is available for at-risk populations, such as infants born preterm. Autism spectrum disorder (ASD), a particular risk of premature birth, is associated with atypical imitation and social communication. Although imitation emerges in infancy, most current screening and diagnostic tools for ASD cannot be utilized prior to 12 months. The present study aimed to develop and validate a caregiver-report measure of infant imitation, characterize imitation profiles at 4, 6, and 9 months in term and preterm infants, and explore the relationship between imitation and scores on an ASD screening questionnaire at 18 months.Participants and Methods:Participants (N = 571) were recruited from a larger multi-site study of PediaTrac™ v3.0, a web-based tool for monitoring and tracking infant development, and were surveyed longitudinally at birth, 2, 4, 6, 9, 12, 15, and 18 months. Participants completed the online PediaTrac™ survey and several reliable and validated questionnaires via pen-and-paper format. For the purposes of this study, only the Ages and Stages Questionnaire (3rd ed.; ASQ-3), Communication and Symbolic Behavior Scales-Developmental Profile (CSBS-DP), Brief Infant Sleep Questionnaire (BISQ), and the Modified Checklist for Autism in Toddlers - Revised with Follow-Up (M-CHAT-R/F) were examined. The following hypotheses were tested: (1) proposed imitation items will represent a unitary latent construct, for which convergent and discriminant validity will be demonstrated, (2) there will be measurement invariance between term status groups at each assessment period, (3) preterm infants will obtain lower caregiver-reported imitation scores compared to term infants, and (4) imitation abilities at the assessment period with the most robust imitation factor will predict M-CHAT-R/F scores at 18 months.Results:Distinct imitation factors at 4, 6, and 9 months were modeled with confirmatory and exploratory factor analyses. Relationships between the factors and established measures of infant communication (CSBS; ASQ) and sleep (BISQ) revealed convergent and discriminant validity, respectively. Strict measurement invariance was demonstrated for the 4- and 9-month factors, and metric invariance for the 6-month measure. Full term infants scored higher on imitation at 9 months, though variance in this outcome was related to term status differences in sensorimotor skills. Lastly, the 9-month imitation factor, coupled with 6-month sensorimotor skills, predicted 18-month ASD risk over and above gestational age.Conclusions:This study provides support for the assessment of infant imitation, utilizing imitation to detect risk in preterm infants, and extending the age of identification for ASD risk into the first year. PediaTrac™ imitation, in combination with the PediaTrac™ sensorimotor domain, may be useful in detecting developmental risk, and specifically risk for ASD, within the first year, leading to earlier initiation of intervention. Further, with its minimal completion time and ease of dissemination through digital platforms, this measure can expand access to care and improve long-term outcomes for children and families.

Validity and feasibility of using the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) in primary care clinics in Singapore.

Systematic screening for autism in early childhood has been suggested to improve eventual outcomes by facilitating earlier diagnosis and access to intervention. However, clinical implementation of screening has to take into account effectiveness and feasibility of use within a healthcare setting for accurate diagnosis of autism. In Singapore, autism screening using a structured screening tool is not currently employed as a part of routine well-child visits for children in primary care clinics. In this study, 5336 children (aged 17-20 months) were screened for autism using the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) during their 18-month well-child visit in seven primary care clinics. Screening and follow-up interviews were administered by nursing staff at each clinic. Children screened positive and a portion of those screened negative then underwent diagnostic assessments to determine whether they met the diagnostic criteria for autism. In total, 113 (2.1%) were screened positive, of which 54 (1.0%) met the criteria for autism. Children who screened positive and received a diagnosis accessed autism-specific intervention at an average age of 22 months. Nurses and physicians rated the acceptability and practicality of the M-CHAT-R/F highly. Therefore, the M-CHAT-R/F questionnaire was an effective and feasible tool for autism screening among 18-month-old children in this study. Future studies will be designed to determine the optimal age of screening and role of repeated screening in Singapore, as well as to better understand any potential improved outcomes nationwide compared with pre-implementation of autism screening.

Pediatric Mental Disorders and Related Screening Practices: Qualitative Insights from Early Childhood Educators

ObjectiveThe purpose of this research is to qualitatively identify perceived concerns about mental disorders among children and related screening practices for mental illness among early childcare and education providers. DesignQualitative research design with a phenomenological approach was used. Sample53 early childcare and education providers from Nebraska participated in semi-structured interviews focused on mental disorders and related screening practices among children. MethodsThe recorded interviews were transcribed, coded, and systematically analyzed through thematic content analysis. ResultsThe analysis identified two themes concerning pediatric mental health disorders and screening practices. The first theme, mental health concerns, includes three categories: perceived level of mental disorder (n=39; 74 %), most common (attention-deficit disorder [n=28; 53.8 %] and anxiety [n=13; 25.6 %]), and least common (mood disorders [n=5; 10.2 %] and substance abuse [n=8; 15.4 %]). The second theme, screening practices, reflects the use of screening tools and their implication to screen mental disorders and suggested one category: screening measure usage (Ages and Stages and the Modified Checklist for Autism in Toddlers). ConclusionAmong the 53 participants, 39 or 74 % of them stated their concern about pediatric mental health disorders in their organization, with some of the most commonly reported disorders including attention-deficit/hyperactivity disorder (ADHD) (53.8 %), anxiety (25.6 %), depression (17.9 %), and substance use (15.4 %). Participants were also concerned about the lack of standard screening instruments for mental health issues among children of ages 0 to 5. There is a need for early childcare organizations, healthcare providers, and educational organizations to collaborate and implement a coordinated approach to improving Nebraska's pediatric mental health.

Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM

The incidence of Autism Spectrum Disorder (ASD) among children, attributed to genetics and environmental factors, has been increasing daily. ASD is a non-curable neurodevelopmental disorder that affects children’s communication, behavior, social interaction, and learning skills. While machine learning has been employed for ASD detection in children, existing ASD frameworks offer limited services to monitor and improve the health of ASD patients. This paper presents a complex and efficient ASD framework with comprehensive services to enhance the results of existing ASD frameworks. Our proposed approach is the Federated Learning-enabled CNN-LSTM (FCNN-LSTM) scheme, designed for ASD detection in children using multimodal datasets. The ASD framework is built in a distributed computing environment where different ASD laboratories are connected to the central hospital. The FCNN-LSTM scheme enables local laboratories to train and validate different datasets, including Ages and Stages Questionnaires (ASQ), Facial Communication and Symbolic Behavior Scales (CSBS) Dataset, Parents Evaluate Developmental Status (PEDS), Modified Checklist for Autism in Toddlers (M-CHAT), and Screening Tool for Autism in Toddlers and Children (STAT) datasets, on different computing laboratories. To ensure the security of patient data, we have implemented a security mechanism based on advanced standard encryption (AES) within the federated learning environment. This mechanism allows all laboratories to offload and download data securely. We integrate all trained datasets into the aggregated nodes and make the final decision for ASD patients based on the decision process tree. Additionally, we have designed various Internet of Things (IoT) applications to improve the efficiency of ASD patients and achieve more optimal learning results. Simulation results demonstrate that our proposed framework achieves an ASD detection accuracy of approximately 99% compared to all existing ASD frameworks.

Early detection of neurodevelopmental disorders in African children living in informal settlements in Nairobi

BackgroundChildren in low-income and middle-income countries (LMICs) are at a substantially increased risk of delayed physical, emotional and sociocognitive outcomes, with consequential neurodevelopmental disorders. Evidence based, cost-effective and culturally appropriate...

Screening tools interventions and strategies for early identification and management of autism spectrum disorders

Autism spectrum disorder is a neurodevelopmental disorder marked by social communication difficulties, restricted interests, and repetitive activities. Both genetic and environmental variables impacting the growing brain influence it. Around the world, 1 in 100 children is identified with autism spectrum disorder. Estimates of prevalence have increased over time and varied significantly inside and across sociodemographic groups. Autism spectrum disorder is a prevalent neurodevelopmental condition that significantly shortens life expectancy. Early diagnosis on a community level is a critical public health concern since mounting evidence shows that early diagnosis makes functional gains and has better outcomes. In healthcare settings, autism spectrum disorder screening tools can be utilized to precisely confirm the diagnosis of autism. The American Academy of Pediatrics recommends screening for ASDs between ages 18 and 24 months, although recent research indicates that abnormal behaviours may be visible in certain children even earlier. the checklist for autism in toddlers, the modified checklist for autism in toddlers, the psychological development questionnaire-1, and the autism behaviour checklist are some of the commonly used screening tools in practice for the diagnosis of autism spectrum disorder. The majority of interventions are non-pharmacological and depend on behavioural modification strategies. Pharmacological therapy should only be used to treat target symptoms that are resistant to behavioural treatment. Although there is no cure, prompt and suitable intervention can dramatically raise the quality of life for children. The purpose of this research is to review the screening tools, interventions, and strategies for early identification and management of autism spectrum disorder.

A Birth Cohort Follow-Up Study on Congenital Zika Virus Infection in Vietnam.

We assessed the development, sensory status, and brain structure of children with congenital Zika virus (ZIKV) infection (CZI) at two years and preschool age. CZI was defined as either ZIKV RNA detection or positive ZIKV IgM and neutralization test in the cord or neonatal blood. Twelve children with CZI born in 2017-2018 in Vietnam, including one with Down syndrome, were assessed at 23-25.5 months of age, using Ages and Stages Questionnaire (ASQ-3), ASQ:Social-Emotional (ASQ:SE-2), Modified Checklist for Autism in Toddlers, automated auditory brainstem response (AABR), and Spot Vision Screener (SVS). They underwent brain CT and MRI. They had detailed ophthalmological examinations, ASQ-3, and ASQ:SE-2 at 51-62 months of age. None had birthweight or head circumference z-score < -3 except for the one with Down syndrome. All tests passed AABR (n = 10). No ophthalmological problems were detected by SVS (n = 10) and detailed examinations (n = 6), except for a girl's astigmatism. Communication and problem-solving domains in a boy at 24 months, gross-motor area in a boy, and gross-motor and fine-motor areas in another boy at 59-61 months were in the referral zone. Brain CT (n = 8) and MRI (n = 6) revealed no abnormalities in the cerebrum, cerebellum, or brainstem other than cerebellar hypoplasia with Down syndrome. The CZI children were almost age-appropriately developed with no brain or eye abnormalities. Careful and longer follow-up is necessary for children with CZI.

Neurodevelopmental and behavioral outcomes of very preterm infants: latent profile analysis in the Environmental influences on Child Health Outcomes (ECHO) Program.

Very preterm infants are at high risk for neurodevelopmental impairments. We used a child-centered approach (latent profile analysis [LPA]) to describe 2-year neurobehavioral profiles for very preterm infants based on cognitive, motor, and behavioral outcomes. We hypothesized that distinct outcome profiles would differ in the severity and co-occurrence of neurodevelopmental and behavioral impairment. We studied children born <33 weeks' gestation from the Environmental influences on Child Health Outcomes Program with at least one neurobehavioral assessment at age 2 (Bayley Scales of Infant and Toddler Development, Child Behavior Checklist, Modified Checklist for Autism in Toddlers, cerebral palsy diagnosis). We applied LPA to identify subgroups of children with different patterns of outcomes. In 2036 children (52% male; 48% female), we found four distinct neurobehavioral profiles. Most children (~85%) were categorized into one of two profiles characterized by no/mild neurodevelopmental delay and a low prevalence of behavioral problems. Fewer children (~15%) fell into one of two profiles characterized by severe neurodevelopmental impairments. One profile consisted of children (5%) with co-occurring neurodevelopmental impairment and behavioral problems. Child-centered approaches provide a comprehensive, parsimonious description of neurodevelopment following preterm birth and can be useful for clinical and research purposes. Most research on outcomes for children born very preterm have reported rates of impairment in single domains. Child-centered approaches describe profiles of children with unique combinations of cognitive, motor, and behavioral strengths and weaknesses. We capitalized on data from the nationwide Environmental influences on Child Health Outcomes Program to examine these profiles in a large sample of children born <33 weeks gestational age. We found four distinct neurobehavioral profiles consisting of different combinations of cognitive, motor, and behavioral characteristics. This information could aid in the development of clinical interventions that target different profiles of children with unique developmental needs.

Association of Risk Factors with At-Risk Cases of Autism

AbstractA neurological condition known as autism spectrum disorder (ASD) first manifests before the age of 3. Numerous factors may have an impact on the development of autism; however, its precise causes are yet unknown. The research aims to identify certain risk factors that associate with children at a higher risk of developing autism. This study is a descriptive analysis of 80 children who were identified as being at risk for autism and meeting the requirements of the Modified Checklist for Autism in Toddlers at the community health center. A semistructured proforma was used to collect the baseline data from the mother and the child. Through the use of a thoughtfully created checklist, each child enrolled in the study had a thorough history completed, including the suspected prenatal, intranatal, neonatal, and infant risk factors with the help of their mothers. Descriptive statistics with frequency percentages were adopted for the demographic data of the participants. To find the association between possible risk factors and at-risk cases of ASD, an independent t-test was used. Fisher's exact test and Pearson's chi-squared test (p = 0.05, confidence interval = 95%) were used to find how the risks (odds ratios) of the factors studied affect the incidence of ASD. It was discovered that prenatal risk factors, such as pregnancy-related infections and medication use, were statistically significantly linked to an increased risk for autism. Significant intranatal risk factors for autism risk included a breech presentation, cesarean section, forced labor, and resuscitation at birth. Similar to this, the neonatal risk factors included maternal hemorrhage, Rh or ABO incompatibility, birth injuries, and feeding problems. Poor socioeconomic status and seizures were the baby risk variables, both of which were statistically significant and linked to an increased chance of autism. The results of the study support an array of perinatal, intranatal, neonatal, and infant risk factors linked to the likelihood of developing autism.

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.

Spatiotemporal patterns of spontaneous movement in neonates are significantly linked to risk of autism spectrum disorders at 18 months old

Infants make spontaneous movements from the prenatal period. Several studies indicate that an atypical pattern of body motion during infancy could be utilized as an early biomarker of autism spectrum disorders (ASD). However, to date, little is known about whether the body motion pattern in neonates is associated with ASD risk. The present study sought to clarify this point by examining, in a longitudinal design, the link between features of spontaneous movement at about two days after birth and ASD risk evaluated using the Modified Checklist for Autism in Toddlers by their caregivers at 18 months old. The body movement features were quantified by a recently developed markerless system of infant body motion analysis. Logistic regression analysis revealed that ASD risk at 18 months old is associated with the pattern of spontaneous movement at the neonatal stage. Further, logistic regression based on body movement features during sleep shows better performance in classifying high- and low-risk infants than during the awake state. These findings raise the possibility that early signs of ASD risk may emerge at a developmental stage far earlier than previously thought.

Early Diagnosis of Autism Spectrum Disorder: A Review and Analysis of the Risks and Benefits.

Autism spectrum disorder (ASD) is a neurodevelopmental condition made up of enduring challenges in social communication and interaction and the presence of repetitive and restricted behavior patterns. Early diagnosis of autism is crucial for timely intervention and improved long-term outcomes. This review aims to explore some of its signs and symptoms, look into some diagnostic tools, and analyze the benefits and risks associated with an early diagnosis of autism. The symptoms of ASD vary from child to child, some of which are: avoidance of eye contact, lack of response to names, excessive fear, and lack of interactive and pretend play. Early identification of these symptoms by caregivers and healthcare providers facilitates the need for diagnosis and appropriate interventions. Some screening and diagnostic tools that have been found to help make the diagnosis are the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F), the Social Communication Questionnaire (SCQ), the Parents' Evaluation of Developmental Status (PEDS), and the Childhood Autism Rating Scale (CARS), amongst others. The benefits of early diagnosis include the opportunity for early intervention, which has been shown to enhance developmental outcomes and improve adaptive skills. Early identification allows for the implementation of specialized interventions tailored to the specific needs of individuals with autism, targeting social communication, language development, and behavioral challenges. Furthermore, early diagnosis enables families to access appropriate support services, educational resources, and community programs, facilitating better coping mechanisms, reducing parental stress, and increasing adult independence. However, early diagnosis of autism also entails certain risks. One significant concern is the potential for labeling and stigmatization, which can impact the child's self-esteem and social interactions. There is a risk of overdiagnosis or misdiagnosis, leading to unnecessary interventions and treatments. Additionally, the diagnostic process can be lengthy, complex, and emotionally challenging for families, requiring comprehensive assessments by multidisciplinary teams. This review highlights the importance of a balanced approach when considering the benefits and risks of early diagnosis. Early identification allows for timely interventions that significantly improve developmental outcomes and quality of life for individuals with autism. To mitigate the risks, it is crucial to ensure accurate and reliable diagnostic procedures, support families throughout the process, and promote societal awareness and acceptance. We also highlighted some future directions in the management of autism, including the use of biomarkers and the use of artificial intelligence and learning for diagnosing ASD.

Validating the Malaysian modified checklist for autism in toddlers, revised with follow-up (M-CHAT-R/F): a cross-cultural adaptation

IntroductionThe Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) is a two-stage parent-reported tool for screening autism spectrum disorder (ASD). Early detection of ASD is highly associated with improved social communication and reduced restricted and repetitive behaviors associated with ASD. However, there is limited availability of ASD screening tools in Malaysia and there are no relevant validation studies published. The process of modifying a screening instrument to align with the cultural and linguistic characteristics of the target population is a crucial component in establishing the instrument's validity.MethodsTherefore, this study translates and culturally adapts the M-CHAT-R/F into Malay and verifies its psychometric properties among the Malaysian population. 500 Malaysian toddlers aged between 18 and 48 months were recruited from different settings. The parents of the toddlers were asked to complete the Malaysian M-CHAT-R/F. The reliability of the screening tool was verified using Cronbach's alpha.ResultsBy comparing the screening outcomes of the Malaysian M-CHAT-R/F and clinical evaluation results, the prevalence of ASD was determined as 6.6% in the sample. High values of sensitivity (96.6%) and specificity (93.2%) and a satisfactory positive predictive value (47.5%) supported the validity of the Malaysian M-CHAT-R/F. Furthermore, the receiver operating characteristic analysis yielded three as the optimal cut-off score of the Malaysian M-CHAT-R/F.DiscussionThese results suggest that the Malaysian M-CHAT-R/F is an effective screening tool reliable for use in clinical practice. Further investigation using a representative sample of the whole country is recommended given the high prevalence rate obtained in the current sample.

National screening for Egyptian children aged 1 year up to 12 years at high risk of Autism and its determinants: a step for determining what ASD surveillance needs

This study aimed to provide a national estimate of the prevalence of the high risk of autism spectrum disorder (ASD) and their determinants. A national screening survey was conducted for 41,640 Egyptian children aged 1 to 12 years in two phases. Tools used were Vineland's Adaptive Behavior Scales, Modified Checklist for Autism in Toddlers, Gilliam Autism Rating scale, and Denver II Developmental screening test. The overall prevalence of children at high risk of ASD was 3.3% (95% CI:3.1%–3.5%). Children living without mothers in homes, suffered from convulsions (AOR = 3.67; 95%CI:2.8–4.8), a history of cyanosis after birth (AOR = 1.87; 95% CI:1.35–2.59) or history of LBW babies (AOR = 1.53; 95% CI:1.23–1.89) carried higher odds of being at high risk of ASD.