Autism Spectrum Disorder Screening Research Articles

Increased observation of predictable visual stimuli in children with potential autism spectrum disorder

Children with autism spectrum disorder (ASD) often exhibit social communication impairments and restricted, repetitive behaviors (RRB). Previous studies have shown that children with ASD prefer observing repetitive movements over random movements, reflecting RRB symptoms, but the developmental timeline of this preference remains unclear. New evidence suggests that children with ASD may develop predictive processing abilities for repeated behaviors, providing insight into how they recognize and respond to predictable patterns. This study employed a preferential-looking paradigm to examine whether children with potential ASD demonstrated longer observation durations for predictable movements compared to typically developing (TD) children. Participants were presented with pairs of stimuli featuring predictable and unpredictable movements, which they freely observed side-by-side. Results showed that children with potential ASD spent significantly more time observing predictable movements, particularly during the latter part of the stimulus presentation. These findings suggest that a gradual increase in attention to predictable movements may reflect difficulties in learning cause-and-effect relationships between movement trajectories and the anticipation of complete shapes. This study highlights the potential utility of predictable movement stimuli as a behavioral marker for early ASD screening. It underscores the essential need for further research into predictive processing in children with ASD.

Knowledge, Behaviours, and Attitudes of Family Physicians and Residents About Autism Spectrum Disorder in Turkey: A Descriptive Cross-Sectional Study.

Autism Spectrum Disorder (ASD) is a growing social health issue. Primary healthcare institutions are where children most commonly seek help, and family physicians in primary care play a crucial role in early ASD detection. This study aims to assess the knowledge level, behaviours, and attitudes of family physicians and family medicine residents across Turkey, identify factors affecting their knowledge, and raise awareness among physicians. This descriptive cross-sectional study was conducted between March and June 2022, with the participation of family medicine specialists, residents, and practicing physicians in Turkey. A 50-question survey was administered face-to-face and digitally. The survey comprised 31 questions on sociodemographic information, factors influencing knowledge, attitudes and behaviours about ASD. The second part included a 19-question "Knowledge about childhood autism among health workers" (KCAHW) questionnaire. The study included 392 physicians with a mean age of 32.8 ± 7.9; 60.7% were women. Among the participants, 50.3% were family medicine residents, 21.7% were family medicine specialists, 17.0% were family physicians and 11.0% were contracted family medicine residents. The physicians' mean KCAHW score was 14.1 ± 2.6. Screening for ASD and following ASD were significantly associated with the perception of knowledge level (p = 0.004; p = 0.028, respectively). A statistically significant relationship was found between paediatric rotation, education about ASD, patient follow-ups and the KCAHW scores. Moreover, 98.2% of physicians believed children with ASD should receive special education, while 79.6% thought ASD carried a social stigma, and 52% believed an ASD diagnosis could lead to discrimination. The study found that participants lacked knowledge about comorbidities with ASD, atypical presentations of ASD, and guiding individuals with ASD. However, having a paediatric rotation, receiving ASD training and following up on children with ASD were associated with increased knowledge levels. Screening for and following up on children with ASD also boosted physicians' self-confidence.

Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.

Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy. This study aims to employ machine learning algorithms to identify key indicators from the 20-item Modified Checklist for Autism in Toddlers, revised (M-CHAT-R) combining with ASD-related sociodemographic and environmental factors, to distinguish ASD from typically developing children. Data from our prior validation study of the Chinese M-CHAT-R (August 2016-March 2017, n = 6,049 toddlers) were reviewed. We extracted the 20-item M-CHAT-R data and integrated 17 sociodemographic and environmental risk factors associated with ASD development to strengthen M-CHAT-R's machine learning screening. Five feature selection methods were used to extract subsets from the original set. Six machine learning algorithms were applied to identify the optimal subset distinguishing clinically diagnosed ASD toddlers from typically developing toddlers. Nine features were grouped into three subsets: subset 1 contained unanimously recommended items (A1 [Follows point], A3 [Pretend play], A9 [Brings objects to show], A10 [Response to name] and A16 [Gazing following]). Subset 2 added two items (A17 [Gaining parent's attention] and A18 [Understands what is said]), and subset 3 included two more items (A8 [Interest in other children] and child's age). The top-performing algorithm resulted in a seven-item classifier of subset 2 with 92.5 % sensitivity, 90.1 % specificity, and 10.0 % positive predictive value. Machine learning classifiers effectively differentiate ASD toddlers from typically developing toddlers using a reduced M-CHAT-R item set. This highlights the clinical significance of machine learning-optimized models for ASD screening in primary health care centers and broader applications.

Early Screening for Autism Spectrum Disorder in Toddlers Aged 16-30 Months Using M-CHAT-R/F: A Cross-Sectional Study

Early Screening for Autism Spectrum Disorder in Toddlers Aged 16-30 Months Using M-CHAT-R/F: A Cross-Sectional Study

Characteristics and outcomes of the screening program of autism spectrum disorder in Primary Healthcare Centers in Bahrain

Abstract: BACKGROUND: Autism spectrum disorder (ASD) is a chronic neurodevelopmental condition that requires early diagnosis and intervention for the improvement of the patient’s skills and functioning. Several guidelines, therefore, recommend the screening of all children for ASD. This study determined the outcomes of ASD screening program in Primary Healthcare Centers (PHCs) in Bahrain. MATERIALS AND METHODS: A cross-sectional study was conducted in 12 of the 27 PHCs in Bahrain. The Modified Checklist for Autism in Toddlers-Revised (MCHAT-R) was completed for children who attended the selected centers. Findings from the checklists and the outcome of positive screening cases were recorded. MCHAT-R scores of <3 were indicative of a low risk for ASD. Children with moderate and high MCHAT-R risk were referred for psychiatric assessment. SPSS version 25.0 was used for data analysis; mean and standard deviation were calculated for continuous variables, and categorical variables presented as frequency and percentage. Chi-square test or Fisher’s exact test, as appropriate, used to test for statistical significance. RESULTS: A total of 3729 MCHAT-R checklists of children with a mean age of 2 years were included. Most children were Bahraini (78.0%), half of whom were males (51.3%). While most of the children (98.8%) had a low-risk MCHAT-R score, 1.2% had a moderate-high risk MCHAT score (12.33/1000). Of the cohort, 19 cases (0.51%) had ASD, 5.10/1000 children. Nearly two-thirds of children with confirmed ASD were males ( 63.2%), and one-third were females (36.8%). In addition, 14 cases (41.2%) of those who had moderate-high MCHAT refused to follow the subsequent diagnostic protocol. CONCLUSION: This study revealed a low prevalence of positive MCHAT-R screening and confirmed ASD cases. The vast majority of children who were positive on screening had a confirmed diagnosis of ASD. Some cases with positive MCHAT results failed to follow up for diagnosis. Therefore, improving parental and community awareness regarding ASD is important for a better outcome.

Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm

Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm

Early Autism Spectrum Disorder Screening in Toddlers: A Comprehensive Stacked Machine Learning Approach

Early Autism Spectrum Disorder Screening in Toddlers: A Comprehensive Stacked Machine Learning Approach

Autism Spectrum Disorder (ASD) or early electronic screen exposure – 67 month-old case study

Concerns about screen addiction have become increasingly prevalent, particularly with regard to its potential impact on children's development. This study examines the possible connection between Autism Spectrum Disorder (ASD) symptoms and early exposure to electronic screens. It focuses on a case involving a 67-month-old child who was heavily exposed to screens during the COVID-19 lockdown. The child spent an average of 5 hours per day in front of screens, which included television and mobile devices. Observations indicated delays in language development, poor social skills, and an anxious emotional state. The family initially associated these symptoms with ASD and sought professional consultation. However, it was observed that the child exhibited behaviors such as restlessness, avoidance of social interaction, and a preference for screen time over fulfilling basic needs, such as eating. These behaviors were particularly evident when the child was not engaged with digital devices, manifesting in sadness, frustration, and anger. The case highlights how early and excessive screen exposure can lead to developmental and psychological challenges that mimic ASD-like behaviors. Excessive screen time in children has been associated with attention disorders, sleep disturbances, language delays, and emotional instability. In this case, the family did not impose restrictions on screen usage during the lockdown period, which contributed to the child’s increased demand for digital media. The therapeutic intervention focused on reducing screen time and encouraging non-digital activities, including outdoor play and social interactions with peers. As a result, improvements were noted in the child’s anxiety, social engagement, and language development. In conclusion, this case underscores the importance of distinguishing between ASD and the effects of excessive screen exposure, as the symptoms of both conditions can overlap. Reducing screen time and promoting parent-child interactions are essential in mitigating these developmental issues.

New eye tracking metrics system: the value in early diagnosis of autism spectrum disorder.

Eye tracking (ET) is emerging as a promising early and objective screening method for autism spectrum disorders (ASD), but it requires more reliable metrics with enhanced sensitivity and specificity for clinical use. This study introduces a suite of novel ET metrics: Area of Interest (AOI) Switch Counts (ASC), Favorable AOI Shifts (FAS) along self-determined pathways, and AOI Vacancy Counts (AVC), applied to toddlers and preschoolers diagnosed with ASD. The correlation between these new ET metrics and Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) scores via linear regression and sensitivity and specificity of the cut-off scores were assessed to predict diagnosis. Our findings indicate significantly lower FAS and ASC and higher AVC (P<0.05) in children with ASD compared to their non-ASD counterparts within this high-risk cohort; the significance was not seen in total fixation time neither pupil size (p > 0.05). Furthermore, FAS was negatively correlated with ADOS-2 total scores and social affect (SA) subscale (p < 0.05). Among these new ET metrics, AVC yielded the best sensitivity 88-100% and specificity 80-88% with cut off score 0.305-0.306, followed by FAS and ASC to separate ASD from non-ASD for diagnosis. This study confirms the utility of innovative ET metrics-FAS, AVC, and ASC-which exhibit markedly improved sensitivity and specificity, enhancing ASD screening and diagnostic processes.

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) is a neuro developmental condition that significantly impacts the daily lives of those affected. While complete eradication remains challenging, early interventions can help alleviate its severity. This study presents a novel framework for assessing various Machine Learning (ML) techniques to detect ASD early. The framework incorporates four Feature Scaling (FS) methods—Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Subsequently, the scaled datasets are subjected to classification using eight ML algorithms: Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). Experiments are conducted on four established ASD datasets categorizing individuals by age groups—Toddlers, Adolescents, Children, and Adults. By evaluating classification outcomes through diverse statistical metrics such as Accuracy, Receiver Operating Characteristic (ROC) curve, F1-score, Precision, Recall, Mathews Correlation Coefficient (MCC), Kappa score, and Log loss, optimal classification methods and FS techniques are determined for each dataset. Results show AB achieving the highest accuracy of 99.25% for Toddlers and 97.95% for Children, while LDA achieves 97.12% for Adolescents and 99.03% for Adults. Notably, using normalizer FS for Toddlers and Children, and QT FS for Adolescents and Adults yield the best accuracies. Furthermore, ASD risk factors are quantified, and attribute importance is ranked employing four Feature Selection Techniques (FSTs)—Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These comprehensive evaluations underscore the significance of fine-tuning ML methodologies for accurate ASD prediction across different age groups. The detailed analysis of feature importance presented herein can aid healthcare practitioners in ASD screening, offering promising advancements compared to existing detection approaches.

Sex-specific DNA methylation signatures of autism spectrum disorder in newborn blood.

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental conditions currently diagnosed by behavioral assessment in childhood, although neuropathology begins during gestation. A poorly understood male bias for ASD diagnosis is thought to be due to both biological sex differences and cultural biases against female diagnosis of ASD. Identification of molecular biomarkers of ASD likelihood in newborns would provide more objective screening and early intervention. Epigenetic dysregulation has been reported in multiple tissues from newborns who are later diagnosed with ASD, but this is the first study to investigate sex-specific DNA methylation signatures for ASD in newborn blood, an accessible and widely banked tissue. DNA methylation was assayed from ASD and typically developing (TD) newborn blood (discovery set n = 196, replication set n = 90) using whole genome bisulfite sequencing (WGBS). Sex-stratified differentially methylated regions (DMRs) were assessed for replication, comparisons by sex, overlaps with DMRs from other tissues, and enrichment for biological processes and SFARI ASD-risk genes. We found that newborn blood ASD DMRs from both sexes significantly replicated in an independent cohort and were enriched for hypomethylation in ASD compared to TD samples, as well as location in promoters, CpG islands and CpG shores. Comparing females and males, we found that most DMRs with sex differences amongst TD individuals were also found in ASD individuals, plus many additional DMRs with sex differences that were only found in those with ASD. Newborn blood DMRs from females were enriched for the X chromosome and both sexes showed significant overlap with DMRs from umbilical cord blood and placenta but not post-mortem cortex. DMRs from all tissues were enriched for neurodevelopmental processes (females) and SFARI ASD-risk genes (females and males). This study is primarily limited by sample sizes, particularly amongst females. Overall, we found a highly replicated sex-specific DNA methylation signature of ASD in newborn blood that showed support for the female protective effect and convergence with epigenetic and genetic signatures of ASD in newborns. These results demonstrate the utility of newborn blood in ASD screening and emphasizes the importance of sex-stratification in future studies.

Investigating the Presence of Autistic Traits and Prevalence of Autism Spectrum Disorder Symptoms in Anorexia Nervosa: A Systematic Review and Meta-Analysis.

The present meta-analysis aims to assess whether individuals with anorexia nervosa (AN) demonstrate elevated autistic traits, to explore potential associations between autistic traits and eating disorder symptoms; as well as to estimate the prevalence of a positive screen for autism spectrum disorder (ASD) assessed via Autism Diagnostic Observation Schedule (ADOS), in AN. A systematic literature search was conducted in PsycINFO, MEDLINE, and Web of Science in August 2023 and later updated in April 2024 to identify relevant studies. Twenty-two studies with 1172 AN patients and 2747 healthy controls (HCs) met the inclusion criteria. There was a significant difference between AN and HC groups in autistic traits (g = 0.88, CI = 0.65-1.12), and a significant but modest correlation was found between autistic traits and severity of eating disorder symptoms (r = 0.28, CI = 0.11-0.44). Proportion meta-analysis indicated that 29% (CI = 0.19-0.38) of children and adults scored above the cut-off for ASD. The current findings suggest that AN frequently overlaps with both autistic traits and autistic symptomatology. Therefore, it is essential to evaluate autism and autistic traits in individuals with AN to tailor individualized treatment plans.

Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies

Autism spectrum disorder (ASD) is defined by the deficits of social relating, language, object use and understanding, intelligence and learning, and verbal and nonverbal communication. Most of the individuals with ASD have genetic conditions; however, early identification and intervention reduce the use of health services and other diagnostic procedures. The varied nature of ASD is widely acknowledged, with each affected individual displaying distinct traits. The variability among autistic children underscores the challenge of identifying effective teaching strategies, as what works for one child may not be suitable for another. In this study, we merge two ASD screening datasets focusing on toddlers. We employ three feature engineering techniques to extract significant features from the dataset to enhance model performance. This study presents an innovative two-phase method where initially, we employ diverse machine learning models, such as a combination of logistic regression and support vector machine classifiers. The focus of the second phase is on identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. The main goal of this study is to develop personalized educational strategies for individuals with ASD. This will be achieved by employing machine learning techniques to enhance precision and better meet their unique needs. Experimental results achieve a classification accuracy of 94% in ASD identification using Chi-square extracted features. Concerning the choice of the best teaching approach for ASD children, the proposed approach shows 99.29% accuracy. Performance comparison with existing studies shows the superior performance of the proposed LR-SVM ensemble coupled with Chi-square features. In conclusion, the proposed approach provides a two-phase strategy for identifying ASD children and offering a suitable teaching strategy with respect to the severity of the ASD, thereby potentially contributing to the development of tailored solutions for children with varying needs.

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.

Predicting language, cognition, and social skills in individuals with Autism spectrum disorder using the Arabic validation of Gilliam Autism Rating scale-Third Edition

The Gilliam Autism Rating Scale-Third Edition (GARS-3) has emerged as a pivotal instrument in the diagnostic assessment of Autism Spectrum Disorder (ASD), with its utility extending across linguistic and cultural boundaries. This study endeavors to adapt and validate the GARS-3 for the Arabic-speaking population, addressing the paucity of culturally congruent diagnostic tools in the Arab world. The primary objective is to validate the Arabic version of GARS-3 (A-GARS-3) to assess children with and without ASD, ensuring the scale’s reliability and validity within the target population. A methodical cross-sectional study design was employed, encompassing a sample of 178 participants from both educational and clinical settings in Saudi Arabia. The process involved direct translation, expert panel review, back-translation, and pilot testing to ensure semantic equivalence and cultural relevance. Data were collected using the A-GARS-3, and analyses included Exploratory and Confirmatory Factor Analysis (EFA and CFA), internal consistency reliability measures, and a One-Way Analysis of Variance (ANOVA) to establish concurrent validity. The A-GARS-3 demonstrated high internal consistency (Cronbach’s alpha = 0.971; McDonald’s omega = 0.972) and strong construct validity, with factor loadings and model fit indices substantiating the factor structure. Predictive validity was confirmed through significant correlations between the Autism Index and the subscales, particularly in the domains of social interaction and communication. The A-GARS-3 is a psychometrically sound instrument that offers reliable assessment for ASD within the Arab cultural context. The positive validation outcomes indicate that the scale is an effective, culturally adapted tool for the localized diagnosis of ASD. The validated scale has significant implications for enhancing ASD screening and diagnostic practices in Arabic-speaking regions, potentially improving early diagnosis and intervention strategies.

Evaluating ASD in children through automatic analysis of paintings

Evaluating ASD in children through automatic analysis of paintings

Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).

Gilliam Autism Rating Scale-Third Edition (GARS-3) serves as an effective screening tool for autism spectrum disorder (ASD) that is based on the latest and authoritative diagnostic criteria, however, there is a deficiency in adaptive research in China. We aimed to revise the Chinese version of GARS-3 (CV-GARS-3) and evaluate its psychometric characteristics, providing a theoretical basis for the improvement of ASD screening tools in China. This study developed CV-GARS-3 through translation and cultural adaptation of GARS-3. 362 ASD individuals, 126 typical development individuals, and 103 individuals with other disorders were recruited to analyze the psychometric characteristics of CV-GARS-3. The results showed that exploratory structural equation model demonstrated satisfactory goodness-of-fit. Within the non-verbal ASD samples, all items loaded on anticipated factors. Regarding verbal ASD samples, 3 items exhibited considerable cross-loadings and were categorized under unexpected factors. Meanwhile, acceptable criterion validity was reflected in the four subscales (r = 0.71) and the six subscales (r = 0.74). Satisfactory reliability was observed in the four subscales (Cronbach's α = 0.96, inter-rater consistency = 0.86, test-retest consistency = 0.87) and the six subscales (Cronbach's α = 0.94, inter-rater consistency = 0.81, test-retest consistency = 0.81). In addition, receiver operating characteristic analysis demonstrated that both the four subscales (sensitivity = 89%, specificity = 85%, accuracy = 88%) and the six subscales (sensitivity = 86%, specificity = 88%, accuracy = 86%) had outstanding screening effects. Therefore, the results suggested that the CV-GARS-3 is considered as a useful tool for the screening and auxiliary diagnosis of ASD. Notably, the expression of scale should be further improved to adapt the context of Chinese culture and achieve more precise diagnostic results.

The profile of autism characteristics in Fetal Alcohol Spectrum Disorder

Living with undiagnosed fetal alcohol spectrum disorder (FASD) or autism spectrum disorder (ASD) can result in adverse secondary life outcomes. Despite an identified overlap between these two conditions in the literature, the profile of autistic traits in FASD is widely under-researched. This study investigated this profile by conducting an exploratory factor analysis on SCQ data (the Social Communication Questionnaire; an ASD screening tool) from individuals with FASD (n=178) and compared SCQ total and domain scores between and within three equal sized (n=30), age-comparable groups (FASD+ASD, FASD-only, and idiopathic ASD). Results indicate that FASD is highly heterogeneous, with an atypical ASD profile. This has implications for diagnosis and intervention.

Data Mining Technique for Diagnosing Autism Spectrum Disorder

Early detection of autistic symptoms can help lower overall medical expenses, which is beneficial given that autism is a developmental disease that is associated with high medical costs. To assess whether or not a kid may have autism spectrum disorder (ASD), screening for ASD involves asking the child's parents, caregivers, and other members of the child's immediate family a series of questions. The current methods for screening for autism, such as the autistic quotient (AQ), might require a significant number of questions in addition to careful question design, which can make an autism examination more time-consuming. The effectiveness and reliability of the test could be improved, for example, by employing data mining strategies. It could be possible to create a system that can foretell ASD at an early stage and give patients, caregivers, and medical professionals dependable and precise findings on the probable need for expert diagnostic services. This research aims to develop a reliable model for estimating the likelihood of an individual being diagnosed with autism spectrum disorder between the ages of 4 and 17. To identify varying degrees of autism, one such model was constructed by utilizing the stochastic gradient descent (SGD) algorithm. Mining data is typically understood to be a decision-making process that enables more effective utilization of available resources in terms of overall performance. The results showed that the suggested prediction model, which used the stochastic gradient descent (SGD) algorithm, could find ASD with an average error of 0.03% and an accuracy of up to 94.5%.

Suicide risk with ASD traits or ADHD traits in non-clinical young adults

The impact of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) traits on suicidal thoughts and behaviors (STBs) remains inadequately explored. Hence, the present study aims to delve deeper into these intricate issues, contributing to a better understanding of these complex challenges among Chinese youth. The data of 90,882 participants was collected from 63 Chinese universities and colleges in Jilin, China. We aim to explore the risks of STBs on ASD, ADHD, and co-occurrence of ASD + ADHD by using the Autism Spectrum Quotient-10 items (AQ-10) and Adult ADHD Self Report Scale (ASRS). Propensity score matching (PSM) was implemented to balance demographical variables across the four groups. Binary logistic regression was then conducted to further analyze and compare suicide risk across the four groups, also accounting for sex differences. Suicide risks were significantly higher in the ADHD (OR = 1.24, p < 0.001) and ASD + ADHD(OR = 1.49, p < 0.001) traits group. However, there was no significant difference in suicide risk between the ASD traits group (OR = 1.00, p = 1.00) and the control group. The risk of suicide was significantly higher for females than for males across all four groups (p < 0.001). These results indicate the importance of screening for ASD + ADHD traits in young adults, especially females due to its higher risk of STBs. Further studies are warranted to delve deeper into this issue, emphasizing the necessity of formal diagnostic tests and directing focused attention toward addressing these complexities.