Checklist For Autism In Toddlers Research Articles

Development of a school-age extension of the Modified Checklist for Autism in Toddlers through expert consensus and stakeholder input.

The American Academy of Pediatrics recommends universal screening to identify children at higher likelihood for autism at 18- and 24-month well-child visits. There are many children, however, that are missed during this toddler age who do not get diagnosed until much later in development, delaying access to autism-specific interventions. Currently, brief measures for universal autism screening for school-age children, however, are lacking. In this project, we adapted a commonly used autism screener for toddlers, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F), to be used for school-age children. This measure, called the M-CHAT-School (M-CHAT-S), is a parent- and teacher-report questionnaire to be used to screen for autism in school-age children aged 4 to 8 years of age. M-CHAT-S was developed through feedback from autism experts, as well as interviews with parents and teachers to provide input on the items. Two versions of M-CHAT-S were developed, one for verbally fluent and one for minimally verbal school-age children. M-CHAT-S is a brief measure, with updated items to reflect changes in the way experts think and talk about autism, making it a useful measure to use for autism screening in elementary aged children. The next steps include further testing to ensure that M-CHAT-S performs well in identifying children with increased likelihood of autism, after which it will be made available to parents, educators, and other professionals.

Comparison between the Clancy Behavior Scale and the Modified Checklist for Autism in Toddlers in Taiwan

(1) Background: Precise diagnosis and early intervention are crucial for toddlers with autism spectrum disorder (ASD) to achieve a better prognosis. This study investigated the efficacy of the Clancy Behavior Scale (CBS) and Modified Checklist for Autism in Toddlers (M-CHAT) in detecting ASD among toddlers under 30 months of age. (2) Methods: A total of 215 toddlers (117 with ASD and 98 with development delays) aged between 18 and 29 months participated in this study. All the primary caregivers of these toddlers were recruited to complete the CBS and M-CHAT. (3) Results: The findings indicated that the accuracy of the CBS and M-CHAT was promising, and the short forms of these two instruments performed better than their full versions. The CBS:9 critical items presented a sensitivity of 0.75 and a specificity of 0.74, while the M-CHAT:14 brief items showed a sensitivity of 0.72 and a specificity of 0.85. (4) Conclusions: The diagnostic accuracy of high-risk ASD toddlers improved via the combination of CBS and M-CHAT, particularly when the information gathered from these two instruments were consistent. The findings may provide implications for enhancing the early detection of ASD.

Developmental characteristics and accuracy of autism screening among two-year-old toddlers in the ECHO program.

The Modified Checklist for Autism in Toddlers (M-CHAT) is a common pediatric screening tool with mixed accuracy findings. Prior evidence supports M-CHAT screening for developmental concerns, especially in toddlers born preterm. This study examined M-CHAT accuracy in a large, nationwide sample. 3393 participants from the Environmental influences on Child Health Outcomes (ECHO) program were included. Harmonized M-CHAT (M-CHAT-H) results were compared with parent-reported autism diagnosis and autism-related characteristics to assess accuracy for term and preterm children, together and separately. Generalized estimating equations, clustering for ECHO cohort and controlling for demographic covariates, were used to examine associations between developmental and behavioral characteristics with M-CHAT-H accuracy. Sensitivity of the M-CHAT-H ranged from 36 to 60%; specificity ranged from 88 to 99%. Positive M-CHAT-H was associated with more developmental delays and behavior problems. Children with severe motor delays and more autism-related problems were more likely to have a false-negative M-CHAT-H. Children with fewer behavior problems and fewer autism-related concerns were more likely to have a false-positive screen. The M-CHAT-H accurately detects children at low risk for autism and children at increased risk with moderate accuracy. These findings support use of the M-CHAT-H in assessing autism risk and developmental and behavioral concerns in children. Previous literature regarding accuracy of the Modified Checklist for Autism in Toddlers (M-CHAT) is mixed but this study provides evidence that the M-CHAT performs well in detecting children at low risk for autism and consistently detects children with developmental delays and behavioral problems. The M-CHAT moderately detects children at increased risk for autism and remains a useful screening tool. This study examines M-CHAT accuracy in a large-scale, nationwide sample, examining associations between screening accuracy and developmental outcomes. These findings impact pediatric screening for autism, supporting continued use of the M-CHAT while further elucidating the factors associated with inaccurate screens.

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.

Role of Primary Care Clinician Concern During Screening for Early Identification of Autism.

The aim of this study was to evaluate the added value of primary care clinician (PCC)-indicated concern during primary care universal standardized screening in early identification of autism. Toddlers were screened for autism during primary care checkups (n = 7,039, aged 14.24-22.43 months) in 2 studies. Parents completed the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up. For each participant, PCCs indicated whether they had autism concerns (optional in 1 study-before or after viewing screening results, required before viewing screen results in the other). Children at high likelihood for autism from screen result and/or PCC concern (n = 615) were invited for a diagnostic evaluation; 283 children attended the evaluation. Rates of PCC-indicated autism concerns were similar whether PCCs were required or encouraged to indicate concerns. High likelihood of autism indication on both screen and PCC concern resulted in the highest positive predictive value for autism and positive predictive value for any developmental disorder, as well as the highest evaluation attendance, with no significant difference between the positive screen-only and PCC concern-only groups. Although the frequency of PCC-indicated autism concern did not differ significantly based on the child's cognitive level, PCCs were more likely to identify children with more obvious autism characteristics compared with more subtle autism characteristics as having autism. The findings support the recommendation of the American Academy of Pediatrics that both screening and surveillance for autism be incorporated into well-child visits. High likelihood of autism on either screen or PCC concern should trigger a referral for an evaluation.

IJCM_333A: Household Air Pollution Exposure and Autism Spectrum Disorder Outcomes in Children: Insights from the HAPIN India Study

Background: The global public health challenge of household air pollution, stemming from conventional cooking practices and the widespread use of solid fuels, is a critical concern. Exposure to indoor air pollutants has been associated with adverse health outcomes, particularly affecting vulnerable populations like pregnant women and young children. The Household Air Pollution and Neurodevelopment (HAPIN) study addresses this issue through a multicentric randomized controlled trial spanning four diverse countries: Guatemala, India, Peru, and Rwanda. Objectives: The study aims to evaluate the prevalence of autistic spectrum disorder among children residing in HAPIN study site of India. Methodology: A cross-sectional assessment for autism spectrum disorders within the HAPIN cohort was done.365 children aged 2 to 3 years were studied to explore the potential link between household air pollution and autism spectrum disorder (ASD). Household air quality, measuring PM2.5, CO, and black carbon, was assessed using established protocols. Neurodevelopmental outcomes, particularly ASD, was screened using the Modified Checklist for Autism in Toddlers (M-CHAT) in children aged 2 to 3 years, and screened positive children were evaluated by a psychiatrist to diagnose autism spectrum disorder using DSM-5 criteria. Results: There is significant association between exposure to air pollutants (PM2.5, CO, and black carbon) and the diagnosis of autism spectrum disorder (ASD), with a prevalence rate of 5.48%. Conclusion: ASD prevalence rate of 5.48%, underscore the global relevance of the impact of household air pollution on neurodevelopment. Urgent interventions are warranted to mitigate the risk of ASD, particularly for vulnerable populations like pregnant women and young children, emphasizing the imperative for cleaner cooking practices and improved indoor air quality.

Association of Growth During Infancy with Neurodevelopment and Obesity in Children Born Very Preterm: The Environmental Influences on Child Health Outcomes Cohort

To evaluate associations between change in weight z-score after neonatal intensive care unit (NICU) discharge and neurodevelopmental outcomes and obesity at 12-48 months of age among individuals born very preterm. This secondary analysis used data from infants born very preterm participating in the Environmental influences on Child Health Outcomes cohort (n=1400). Growth during infancy was calculated as change in weight z-score between NICU discharge and follow-up at a mean of 27 months of age. Very low weight gain was defined as a change in weight z-score <-1.67; very high weight gain was a change in weight z-score >1.67. Neurodevelopmental outcomes included the Bayley Scales of Infant and Toddler Development, Child Behavior Checklist 1.5-5 years, and Modified Checklist for Autism in Toddlers. Multivariable linear regression was used to estimate associations between increase in weight z-score and neurodevelopmental outcomes. Very low weight gain between NICU discharge and follow-up (experienced by 6.4% of participants) was associated with lower scores on cognitive (adjusted mean difference: -4.26; 95% confidence interval: -8.55, -0.04) and language (adjusted mean difference: -4.80; 95% confidence interval: -9.70, -0.11) assessments. Very high weight gain (experienced by 13.6% of participants) was associated with an increased obesity risk (adjusted relative risk: 6.20; 95% confidence interval: 3.99, 9.66) but not with neurodevelopmental outcomes. Very high weight gain in the first 12-48 months after NICU discharge was associated with a higher risk of obesity at follow-up; very low weight gain was associated with lower scores on cognitive and language assessments.

A Direct Comparison of Three Screening Methods for Autism Spectrum Disorder in a High-Likelihood Sibling Population.

Targeted screening of children at increased likelihood of autism is recommended. However, autism screening tools are usually validated for use mainly in low-likelihood populations. This study compared the accuracy of the Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F), the ASDetect app, and the Social Attention and Communication Surveillance, Revised (SACS-R). Siblings of autistic children underwent autism screening at 12, 18 and 30months old. At each visit, caregivers completed the M-CHAT-R/F and ASDetect while trained nurses tested the siblings using the SACS-R. At 36 to 48months, the siblings underwent an Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) assessment. 189 siblings were screened, 141 completed the study, and 32 were confirmed to have autism. Although not validated for use at 12months, the M-CHAT-R/F had the best sensitivity among the three tools for this age group, suggesting that early signs are already apparent to caregivers. The M-CHAT-R/F had overall better sensitivity (0.72-0.83) across all age groups, but with overall lower specificity (0.55-0.77). The SACS-R and ASDetect had better positive predictive values at 18 and 30months (0.60-0.68), while the M-CHAT-R/F was 0.43-0.48. Negative predictive values were generally high across all three tools across all age groups (0.78-0.93). Targeted screening of children at high likelihood of autism yielded a detection rate of 22.7% and should therefore be implemented routinely to facilitate early detection and intervention. The performance of autism screening tools should be examined in higher-likelihood populations for targeted screening of these children.

Screening preterm-born infants for autistic traits may help to identify social communication difficulties at five years of age.

This study compared neurodevelopmental screening questionnaires completed when preterm-born children reached 2 years of corrected age with social communication skills at 5.5 years of age. Eligible subjects were born in 2011 at 24-34 weeks of gestation, participated in a French population-based epidemiological study and were free of motor and sensory impairment at 2 years of corrected age. The Ages and Stages Questionnaire (ASQ) and the Modified Checklist for Autism in Toddlers (M-CHAT) were used at 2 years and the Social Communication Questionnaire (SCQ) at 5.5 years of age. We focused on 2119 children. At 2 years of corrected age, the M-CHAT showed autistic traits in 20.7%, 18.5% and 18.2% of the children born at 24-26, 27-31 and 32-34 weeks of gestation, respectively (p = 0.7). At 5.5 years of age, 12.6%, 12.7% and 9.6% risked social communication difficulties, with an SCQ score ≥90th percentile (p = 0.2). A positive M-CHAT score at 2 years was associated with higher risks of social communication difficulties at 5.5 years of age (odds ratio 3.46, 95% confidence interval 2.04-5.86, p < 0.001). Stratifying ASQ scores produced similar results. Using parental neurodevelopmental screening questionnaires for preterm-born children helped to identify the risk of later social communication difficulties.

Commentary: Taking stock and moving forward - the need to consider the influence of loss to follow-up in autism screening research.

This commentary highlights the limitations of many existing population-based studies examining the utility of the Modified Checklist for Autism in Toddlers, Revised/Follow-Up (M-CHAT-R/F) in screening for autism. We expound on three major factors: (a) the limited number of screen-negative children who undergo diagnostic evaluations, (b) the substantial number of children who screen positive and were subsequently lost to follow-up (i.e. without further diagnostic evaluations), and (c) the sizeable number of children who did not complete the full two-stage screening process as intended. Each of these factors can lead to erroneous estimates of the psychometric properties, specifically, the sensitivity, specificity, and negative predictive value. Hence, we emphasize the need for future studies to increase the number of children who screen negative and receive a diagnostic evaluation and ensure that these children are selected at random without a higher likelihood for the presence of autism. It is also imperative that concrete steps are taken to minimize the number of screen-positive children who are lost to follow-up both within and after the screening process. Both of these will play a major role in ensuring more robust results from empirical research that can guide the clinical implementation of the M-CHAT-R/F.

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.

Autism and neurodevelopmental disability risks in children with tracheostomies and ventilators.

Infants who survive prematurity and other critical illnesses and require continued invasive mechanical ventilation (IMV) postdischarge (at home) are at high risk of developmental delays and disabilities. Studies of extremely preterm cohorts (<28-week gestation) demonstrate rates of 25% for intellectual disability (ID) and 7% for autism spectrum disorder (ASD). Rates of ASD and ID in children with IMV are unknown. This study aimed to determine neurodevelopmental disability risk in a cohort of children with postdischarge IMV. A consecutive series of children with IMV were assessed 1 month, 6 months, and 1 year after discharge. Cognitive, social, and communicative domains were assessed by a Developmental and Behavioral Pediatrician using (1) clinical adaptive test/clinical linguistic and auditory milestone scale (CAT/CLAMS) of the capute scales; (2) pediatric evaluation of disability inventory computer adaptive test (PEDI-CAT); and (3) modified checklist for autism in toddlers, revised (MCHAT-R). Red flag signs and symptoms of ASD using DSM-V criteria were noted. Longitudinal testing was reviewed. Expert consensus impressions of evolving ASD and/or ID were determined. Eighteen children were followed for 1 year; at 1 year, the median age (range) was 23 (17-42) months. Children were 44% male, 33% non-Hispanic White, 39% non-Hispanic Black, and 28% Hispanic. Fifteen (83%) children were prematurity survivors. Median (range) developmental quotients (DQs): full-scale DQ 59 (11-86), CAT DQ 66.5 (8-96), and CLAMS DQ 49.5 (13-100). Twelve (67%) children were highly suspicious for ASD and/or evolving ID. This cohort of children with at-home IMV demonstrates a higher risk of ASD and ID than prior premature cohorts. Larger investigations with longer follow-up are needed.

Pediatrician and parental evaluation of child neurodevelopment at 2 years of age

BackgroundThe early identification of infants with a risk for neurodevelopmental disorders in the first few years of life is essential for better developmental outcomes. Screenings should be carried out by combining the family pediatricians’ and parents’ perspectives, the two fundamental sources of information on children’s health. The present study has three aims: (a) to test the feasibility of parent-report instruments to detect warning signs in their children’s development; (b) to ascertain whether there is an agreement between the family pediatricians’ (FP) clinical judgments of warning signs and the parental perceptions; (c) to determine whether there is a link between parents’ distress and child development.MethodsWithin the NASCITA birth cohort, in addition to the family pediatrician’s clinical evaluation with routine tools, the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) was completed by parents to assess the child’s language, social skills, behavior, and sensory areas. Parents were also asked to complete the Parenting Stress Index, Short Form (PSI-SF) to verify the magnitude of stress in the parent-child system. Univariate and multivariate analyses were performed to evaluate the association between child and parental characteristics and the presence of warning signs.ResultsThe follow-up assessment was completed for 435 infants: 69 (15.8%) presented warning signs: 43 in the pediatrician’s assessment and 36 in the M-CHAT-R (10 in both). A total of 16 children (14 with warning signs) received a diagnosis after a specialist evaluation. Being male (OR 2.46, 95%CI: 1.23–4.91) and having sleep disorders (OR 2.43, 95% CI 1.17–5.04) was associated with a greater likelihood of warning signs in the multivariate analysis, while reading aloud was a protective factor (not exposed versus exposed (OR = 3.14; 95% CI 1.60–6.17). For 73 children (18.4%), at least one parent tested positive for PSI-SF. An increased prevalence of parental distress was observed in children with warning signs (OR 2.36, 95% CI 1.27–4.37).ConclusionsIntegrating physician and parental perspectives during well-child visits and in clinical practice appears feasible and can improve the identification of children at risk of developmental disorders.

The Early Social Cognition Inventory - Autism Version: psychometric properties for young children with autism spectrum disorder

In this study, we examined the psychometric properties of the Early Social Cognition Inventory (ESCI) in children with autism spectrum disorder (ASD). The study included 76 typically developing children (TDC) (mean age= 39.28 months) and 81 participants (mean age= 36.07 months) with clinical diagnoses of ASD. The parents completed both the ESCI and Modified checklist for Autism in Toddlers (M-CHAT) checklists. Our exploratory factor analysis of the ESCI in children with autism revealed two factors (joint attention and understanding beliefs and emotions) with 17 items. We named this 17-item scale the Early Social Cognition Inventory - Autism Version (ESCI-AV). Regarding convergent validity, we found a strong correlation between the ESCI-AV and the M-CHAT (rho = 0.786, p < .001. The discriminant validity of the ESCI-AV was determined by comparing ESCI-AV scores of children with ASD and children with TDC using an independent t-test. The results revealed that TDC scored significantly higher than children with ASD on the ESCI-AV (t = −11.61, df= 155, p < 0.001), thus indicating good discriminant validity of the ESCI-AV. In addition, reliability was assessed using Kuder-Richardson 20 (KR20) and Guttman spilt-half coefficients. Both KR20 (0.85) and Guttman’s split-half (0.88) were high, indicating good reliability and internal consistency of the ESCI-AV. This study indicates that the ESCI-AV measures social cognition in children with ASD under the age of 5 years in a reliable and valid way.

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.

Referral patterns and outcomes of children who failed Modified Checklist for Autism in Toddlers screening during routine health screening at maternal and child health clinics in the northeast district of Penang: A retrospective cohort study.

Autism spectrum disorder (ASD) is a developmental disability that causes significant social, communication and behavioural challenges. The Modified Checklist for Autism in Toddlers (M-CHAT) is a parent-administered screening questionnaire for ASD used at 18 and 36 months of age. This study aimed to determine the outcomes of children who failed M-CHAT screening during routine health screening at maternal and child health clinics in northeast district, Penang and the prevalence of ASD among those with a final diagnosis. This retrospective cohort study was conducted at 12 maternal and child health clinics. All children who failed M-CHAT screening at 18 and 36 months from January 2017 to December 2021 and received a final diagnosis before 31 March 2022 were recruited. All information required was recorded in a data collection form and analysed using SPSS. Multiple logistic regression was performed to assess the association between the factors and ASD status. Eighty-two children failed M-CHAT screening. Fifty children did not receive a final diagnosis. Among 32 children who received a final diagnosis, 25 were diagnosed with ASD (78.1%). Among the children who underwent M-CHAT screening, the odds of having ASD increased by a factor of 1.2 for every 1-unit increase in age at final diagnosis. In northeast district, Penang, more than half of children who fail M-CHAT screening have no final diagnosis. The specificity of the M-CHAT is 78.1% among children with a final diagnosis of ASD. The age at final diagnosis is positively associated with the diagnosis of ASD.

Screening of children attending well baby clinic for risk of autism using modified checklist for autism in toddlers in a pediatric tertiary care Centre in Chennai

Background: Autism spectrum disorder is a common neurodevelopmental disorder with core deficits in social communication, interaction, and restrictive/repetitive behavior. Early intervention provides opportunity for good functional outcome. The study aimed to screen children attending well baby clinic for risk of autism using modified checklist for autism in toddlers (MCHAT) questionnaire. Methods: This descriptive cross-sectional study was conducted on 302 children attending the pediatric well baby clinic. The children aged 16-30 months who met the inclusion criteria were screened for risk of autism using MCHAT. Results: 20 out of 302 children failed the MCHAT screening, 6.6% of the children were found to be at risk of autism. Boys: girls’ ratio was 2:1. Advanced parental age and higher socioeconomic status were found to have significant association with the risk of autism. 70% of children in the at-risk group were first born, 70% of children who failed the MCHAT screen were from a nuclear family. Conclusions: All toddlers attending the well-baby clinic should be screened for autism by pediatricians. MCHAT is a simple easy to use validated tool to screen for autism in a busy pediatric OPD. Timely referral for comprehensive assessment and intervention can help to improve the outcome in children with autism.

Early Prediction of Autistic Spectrum Disorder Using Developmental Surveillance Data

With the continuous increase in the prevalence of autistic spectrum disorder (ASD), effective early screening is crucial for initiating timely interventions and improving outcomes. To develop predictive models for ASD using routinely collected developmental surveillance data and to assess their performance in predicting ASD at different ages and in different clinical scenarios. This retrospective cohort study used nationwide data of developmental assessments conducted between January 1, 2014, and January 17, 2023, with minimal follow-up of 4 years and outcome collection in March 2023. Data were from a national program of approximately 1000 maternal child health clinics that perform routine developmental surveillance of children from birth to 6 years of age, serving 70% of children in Israel. The study included all children who were assessed at the maternal child health clinics (N = 1 187 397). Children were excluded if they were born at a gestational age of 33 weeks or earlier, had no record of gestational age, or were followed up for less than 4 years without an ASD outcome. The data set was partitioned at random into a development set (80% of the children) and a holdout evaluation set (20% of the children), both with the same prevalence of ASD outcome. For each child, demographic and birth-related covariates were extracted, as were per-visit growth measurements, quantified developmental milestone assessments, and referral summary covariates. Only information that was available before the prediction age was used for training and evaluating the models. The main outcome was eligibility for a governmental disabled child allowance due to ASD, according to administrative data of the National Insurance Institute of Israel. The performance of the models that predict the outcome was evaluated and compared with previous work on the Modified Checklist for Autism in Toddlers (M-CHAT). The study included 1 187 397 children (610 588 [51.4%] male). The performance of the ASD prediction models improved with prediction age, with fair accuracy already at 12 months of age. A model that combined longitudinal measures of developmental milestone assessments with a minimal set of demographic variables, which was applied at 18 to 24 months of age, achieved an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 45.1% at a specificity of 95.0%. A model using single-visit assessments achieved an area under the receiver operating characteristic curve of 0.81 and a sensitivity of 41.2% at a specificity of 95.0%. The best performing prediction models surpassed the pooled performance of M-CHAT (sensitivity, 40%; specificity, 95%) reported in studies with a similar design. This cohort study found that ASD can be predicted from routine developmental surveillance data at an accuracy surpassing M-CHAT screening. This tool may be seamlessly integrated in the clinical workflow to improve early identification of children who may benefit from timely interventions.

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.