Early Autism Spectrum Disorder Research Articles

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

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 Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective.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

Prevalence, incidence, and characteristics of autism spectrum disorder among children in Beijing, China.

It is the first study to explore the prevalence, incidence, and co-occurring conditions of autism spectrum disorder for the preschoolers in China. The prevalence and incidence of autism spectrum disorder has increased in recent decades. Autism spectrum disorder has become an important public concern worldwide. In this study, all hospital confirmed cases had an associated diagnosis (International Classification of Diseases, 10th revision (ICD-10) codes: F84.0, 84.5, F84.9). In total, 4457 children aged 4-6 years were identified as having autism spectrum disorder. In 2021, 1 in 95 children aged 6 years, 1 in 115 children aged 5 years, and 1 in 130 children aged 4 years were estimated to have autism spectrum disorder in Beijing. The incidence was 0.11% in 2019 and increased to 0.18% in 2021. There has been a great emphasis on the importance of early autism spectrum disorder diagnosis in large cities in China.

Maternal Prenatal Cannabis Use and Child Autism Spectrum Disorder

Despite an increase in maternal prenatal cannabis use and associations with adverse neonatal outcomes, research on child neurodevelopmental outcomes is limited. To evaluate the association between maternal cannabis use in early pregnancy and child autism spectrum disorder (ASD). This population-based retrospective birth cohort study included children born between 2011 and 2019 to pregnant Kaiser Permanente Northern California members screened for prenatal cannabis use during pregnancy. Statistical analysis was conducted February 2023 to March 2024. Maternal prenatal cannabis use was assessed at entrance to prenatal care (approximately 8- to 10-weeks' gestation) via self-report and/or positive urine toxicology test. Use frequency was assessed. Child ASD was defined by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes ascertained from the electronic health record. Associations between maternal prenatal cannabis use and child ASD were modeled using Cox proportional hazards regression adjusted for maternal sociodemographic, other substance use and disorders, prenatal care initiation, comorbidities, and clustering among maternal siblings. The study cohort included 178 948 singleton pregnancies among 146 296 unique pregnant individuals, including 48 880 (27.3%) Asian or Pacific Islander, 42 799 (23.9%) Hispanic, 9742 (5.4%) non-Hispanic Black, and 70 733 (39.5%) non-Hispanic White pregnancies. The median (IQR) maternal age at pregnancy onset was 31 (6) years; 8486 (4.7%) screened positive for cannabis use, 7054 (3.9%) via urine toxicology testing and 3662 (2.0%) by self-report. In the total study population, the frequency of self-reported use was monthly or less for 2003 pregnancies (1.1%), weekly for 918 pregnancies (0.5%), daily for 741 pregnancies (0.4%), and unknown for 4824 pregnancies (2.7%). ASD was diagnosed in 3.6% of children. After adjustment for maternal characteristics, maternal prenatal cannabis use was not associated with child ASD (hazard ratio [HR], 1.05; 95% CI, 0.84-1.32). When self-reported frequency of use was assessed, no statistically significant associations were observed after confounder adjustment. No sex-specific associations were documented (males: HR, 1.01; 95% CI, 0.77-1.32; and females: HR, 1.19; 95% CI, 0.77-1.85). In this cohort study, maternal cannabis use assessed in early pregnancy was not associated with child ASD. Additional studies are needed to evaluate different patterns of use throughout pregnancy. Given the known adverse neonatal health effects of maternal prenatal cannabis use, clinicians should follow national guidelines and advise against use.

Investigating brain–gut microbiota dynamics and inflammatory processes in an autistic-like rat model using MRI biomarkers during childhood and adolescence

Autism spectrum disorder (ASD) is characterized by social interaction deficits and repetitive behaviors. Recent research has linked that gut dysbiosis may contribute to ASD-like behaviors. However, the exact developmental time point at which gut microbiota alterations affect brain function and behavior in patients with ASD remains unclear. We hypothesized that ASD-related brain microstructural changes and gut dysbiosis induce metabolic dysregulation and proinflammatory responses, which collectively contribute to the social behavioral deficits observed in early childhood. We used an autistic-like rat model that was generated via prenatal valproic acid exposure. We analyzed brain microstructural changes using diffusion tensor imaging (DTI) and examined microbiota, blood, and fecal samples for inflammation biomarkers. The ASD model rats exhibited significant brain microstructural changes in the anterior cingulate cortex, hippocampus, striatum, and thalamus; reduced microbiota diversity (Prevotellaceae and Peptostreptococcaceae); and altered metabolic signatures. The shift in microbiota diversity and density observed at postnatal day (PND) 35, which is a critical developmental period, underscored the importance of early ASD interventions. We identified a unique metabolic signature in the ASD model, with elevated formate and reduced acetate and butyrate levels, indicating a dysregulation in short-chain fatty acid (SCFA) metabolism. Furthermore, increased astrocytic and microglial activation and elevated proinflammatory cytokines—interleukin-1 beta (IL-1β), interleukin-6 (IL-6), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α)—were observed, indicating immune dysregulation. This study provided insights into the complex interplay between the brain and the gut, and indicated DTI metrics as potential imaging-based biomarkers in ASD, thus emphasizing the need for early childhood interventions.

HLA-A, -B, -C and -DRB1 Association with Autism Spectrum Disorder Risk: A Sex-Related Analysis in Italian ASD Children and Their Siblings.

Autism Spectrum disorders (ASD) are diagnosed more often in males than in females, by a ratio of about 3:1; this is likely to be due to a difference in risk burden between the sexes and/or to "compensatory skills" in females, that may delay the diagnosis of ASD. Identifying specific risk factors for ASD in females may be important in facilitating early diagnosis. We investigated whether HLA- class I: -A, -B, -C and class II -DRB1 alleles, which have been suggested to play a role in the development of ASD, can be considered as sex-related risk/protective markers towards the ASD. We performed HLA allele genotyping in 178 Italian children with ASD, 94 healthy siblings, and their parents. HLA allele distribution was compared between children with ASD, sex-matched healthy siblings, and a cohort of healthy controls (HC) enrolled in the Italian bone marrow donor registry. Allele transmission from parents to children with ASD and their siblings was also assessed. Our findings suggest that HLA-A*02, B*38, and C*12 alleles are more frequently carried by females with ASD compared to both HC and healthy female siblings, indicating these alleles as potential risk factors for ASD in females. Conversely, the HLA-A*03 allele was more commonly transmitted to healthy female siblings, suggesting it might have a protective effect. Additionally, the HLA-B*44 allele was found to be more prevalent in boys with ASD, indicating it is a potential risk factor for male patients. This is the first Italian study of sex-related HLA association with ASD. If confirmed, these results could facilitate early ASD diagnosis in female patients, allowing earlier interventions, which are crucial in the management of neurodevelopmental disorders.

Early detection of autism spectrum disorder via deep-learning application of fMRI and machine learning for ASD children identifications

This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics – accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32% – and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model’s hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention. And, this study addresses the critical need for early detection and intervention in autism spectrum disorder (ASD) using machine learning. Specific therapies are needed for ASD, a neurodevelopmental disease that affects social interaction and communication. To find trends in ASD, our research uses a variety of early childhood screening tests as training sets for machine learning algorithms. The methodology that has been suggested utilizes methods of machine learning to compute the ASD spectrum, considering its many expressions. By using multidisciplinary methods and sophisticated screening instruments, we want to create an accurate system for early ASD detection. Algorithmic transparency, data protection, and ethical considerations are essential. This study seeks to build precise instruments for early ASD detection by promoting collaboration between specialists in neurodevelopment, psychology, and machine learning. A robust instrument that enhances the knowledge of medical practitioners is machine learning. Results show how innovation may transform early interventions and help people on the autistic spectrum achieve enhanced results.

Association between exposure to antibiotics during pregnancy or early infancy and risk of autism spectrum disorder, intellectual disorder, language disorder, and epilepsy in children: population based cohort study

ObjectiveTo evaluate the association between antibiotic use during pregnancy or early infancy and the risk of neurodevelopmental disorders in children.DesignNationwide population based cohort study and sibling analysis.SettingKorea’s National Health Insurance...

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

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

Exploring the Neurogenetic Landscape of Autism Spectrum Disorder: The Role of Brain-Derived Neurotrophic Factor (BDNF) Gene in the Complex Web of Neurodevelopment

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with varied manifestations including altered metabolic profiles. Recent research highlights the potential role of Brain-Derived Neurotrophic Factor (BDNF), a hormone derived from adipose tissue, in the pathology of ASD. BDNF levels are reportedly correlated with the severity of autism symptoms, suggesting a link between metabolic dysfunction and the disorder. Objective: This study aims to deepen the understanding of BDNF levels in individuals with ASD and to explore the implications for diagnosis, treatment, and management of the disorder. It seeks to elucidate the relationship between low BDNF levels and increased autism severity, and to identify potential therapeutic interventions targeting BDNF dysregulation. Methods: EDTA blood samples (5ml each) from 140 participants (108 with ASD and 32 healthy controls) were collected across various hospitals in Karachi. Plasma serum levels of BDNF were analyzed following a six-month follow-up. A comprehensive statistical analysis was performed, focusing on the relationship between BDNF levels and autism severity, and evaluating diagnostic and therapeutic implications of BDNF dysregulation. Results: Analysis revealed that lower BDNF serum levels were consistently associated with higher autism severity. Specifically, 60-70% of participants with ASD scored below 8 on the Mini-Mental State Examination (MMSE), indicating significant cognitive impairment. The statistical significance of these findings was confirmed with p-values <0.05. Conclusion: The study confirms that low BDNF levels are significantly associated with greater severity of autism symptoms, underscoring the importance of metabolic factors in the pathogenesis of ASD. BDNF stands out as a potential biomarker for early ASD diagnosis and personalized treatment strategies. Future Prospects: Future research should aim to delineate the mechanistic relationship between BDNF dysregulation and ASD pathology. Longitudinal studies are needed to assess the long-term effects of BDNF modulation on ASD outcomes and to validate its effectiveness as both a diagnostic and prognostic marker.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Facial Classification for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a mental condition that affects people’s learning, communication, and expression in their daily lives. ASD usually makes it difficult to socialize and communicate with others, and also sometimes shows repetition of certain behaviors. ASD can be a cause of intellectual disability. ASD is a big challenge in neural development, specially in children. It is very important that it is identified at an early stage for timely guidance and intervention. This research identifies the application of deep learning and vision transformer (ViT) models for classification of facial images of autistic and non-autistic children. ViT models are powerful deep learning models used for image classification tasks. This model applies transformer architectures to analyze input image patches and connect the information to achieve global-level information. By employing these techniques, this study aims to contribute toward early ASD detection. ViT models are showing good results in identifying facial features associated with ASD, leading toward early diagnostics. Results show the ViT model’s capability in distinguishing the faces of autistic and non-autistic children.

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

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

Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization.

The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation in early ASD diagnosis. Our focus is on improving model performance across diverse data sources. Utilizing the Kaggle ASD and YTUIA datasets, we meticulously analyze domain variations and assess transfer learning and active learning methodologies. Two state-of-the-art convolutional neural networks, Xception and ResNet50V2, pretrained on distinct datasets, demonstrate noteworthy accuracies of 95% on Kaggle ASD and 96% on YTUIA, respectively. However, combining datasets results in a modest decline in average accuracy, underscoring the necessity for effective domain adaptation techniques. We employ uncertainty-based active learning to address this, which significantly mitigates the accuracy drop. Xception and ResNet50V2 achieve 80% and 79% accuracy when pretrained on Kaggle ASD and applying active learning on YTUIA, respectively. Our findings highlight the efficacy of uncertainty-based active learning for domain adaptation, showcasing its potential to enhance accuracy and reduce annotation needs in early ASD diagnosis. This study contributes to the growing body of literature on ASD diagnosis methodologies. Future research should delve deeper into refining active learning strategies, ultimately paving the way for more robust and efficient ASD detection tools across diverse datasets.

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

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

Age at autism spectrum disorder diagnosis and its association with child and family characteristics in a tertiary care hospital in Malaysia.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Despite the absence of a cure, early diagnosis and intensive early intervention can improve the outcomes. However, little is known about the median age at ASD diagnosis in Malaysia or the child/family characteristics associated with early diagnosis. Therefore, this study aimed to determine the median age at ASD diagnosis among Malaysian children presenting to the country's largest public tertiary neurodevelopmental center and to investigate the possible demographic, child, and family characteristics associated with an early age at diagnosis. Data were collected between February 2017 and February 2019 from a database maintained by the child development unit of the country's largest publicly funded tertiary hospital, containing data from an ethnically diverse population. Among Malaysian children attending the clinic, the median age at ASD diagnosis was 48 months. Early autism diagnosis (<36 months of age) was associated with increased severity of social communication and interaction impairments, coexisting intellectual impairment, children from high socioeconomic status families, and children who receive joint care from their families and a maid or babysitter. The study findings highlight the socioeconomic inequalities in the country, a lack of parental awareness of early ASD signs, and the presence of cultural influences on the age at diagnosis of ASD.

White matter development and language abilities during infancy in autism spectrum disorder.

White matter (WM) fiber tract differences are present in autism spectrum disorder (ASD) and could be important markers of behavior. One of the earliest phenotypic differences in ASD are language atypicalities. Although language has been linked to WM in typical development, no work has evaluated this association in early ASD. Participants came from the Infant Brain Imaging Study and included 321 infant siblings of children with ASD at high likelihood (HL) for developing ASD; 70 HL infants were later diagnosed with ASD (HL-ASD), and 251 HL infants were not diagnosed with ASD (HL-Neg). A control sample of 140 low likelihood infants not diagnosed with ASD (LL-Neg) were also included. Infants contributed expressive language, receptive language, and diffusion tensor imaging data at 6-, 12-, and 24 months. Mixed effects regression models were conducted to evaluate associations between WM and language trajectories. Trajectories of microstructural changes in the right arcuate fasciculus were associated with expressive language development. HL-ASD infants demonstrated a different developmental pattern compared to the HL-Neg and LL-Neg groups, wherein the HL-ASD group exhibited a positive association between WM fractional anisotropy and language whereas HL-Neg and LL-Neg groups showed weak or no association. No other fiber tracts demonstrated significant associations with language. In conclusion, results indicated arcuate fasciculus WM is linked to language in early toddlerhood for autistic toddlers, with the strongest associations emerging around 24 months. To our knowledge, this is the first study to evaluate associations between language and WM development during the pre-symptomatic period in ASD.

Psychological Assessment of Autism Spectrum Disorders using Machine Learning

This research aims to advance the field of autism spectrum disorder (ASD) assessment by proposing a machine learning-based approach tailored for school and community settings. Leveraging a diverse dataset encompassing behavioral, physiological, and neuroimaging data, we apply advanced machine learning algorithms to develop predictive models for early ASD detection. Our study integrates expert interviews to validate the clinical utility of these models and explores ethical considerations surrounding data privacy and bias. Preliminary results show promising accuracy in ASD identification. This research contributes to a more accessible and objective ASD assessment, with implications for early intervention and inclusive support, particularly in educational and community contexts.

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Validation of preparedness and willingness on early detection of autism spectrum disorder among healthcare providers in Jordan

In recent years, there have been significant advancements in the field of early detection and intervention for children with developmental and behavioural disorders, with significant in the context of Autism Spectrum Disorder (ASD). It highlights the advantages of early detection, such as reduced long-term costs and positive effects on a child's development. However, it also acknowledges the prevalent barriers to early ASD diagnosis and intervention. This study aims to develop and validate questionnaires on Preparedness and Willingness in early ASD detection among Al-Mafraq City, Jordan, healthcare providers. The research in the Al-Mafraq region of Jordan employed a cross-sectional research design. The study first involved the development of a questionnaire, which underwent rigorous content validation by a panel of experts well-versed in ASD. Face validity was assessed utilizing the Face Validity Index (FVI). A random sample of 130 healthcare providers from one hospital answered the draft questionnaires online for the EFA analysis recommended. It resulted in satisfactory items' factor loadings (r&gt;0.60) and internal reliability (Cronbach's alpha &gt;0.8). Followed by CFA from a random sample of another 202 healthcare providers from another hospital and healthcare centres in Al-Mafraq showed that the instrument effectively met the validation criteria, demonstrating its reliability and validity in assessing healthcare providers' Preparedness and Willingness for early ASD detection. Notably, the participants in the study exhibit differing degrees of Preparedness and Willingness. In conclusion, the study underscores the critical standing of developing a comprehensive instrument to evaluate the limitations associated with early ASD detection among healthcare providers.

Key personal factors and moderation effect of belief on shaping the willingness for early ASD detection in Jordan: A SEM Analysis

Background: Autism Spectrum Disorder (ASD) prevalence increases healthcare utilisation, underscoring healthcare providers' vital role in early detection. This growing prominence underscores the pivotal roles of healthcare providers in the early detection of ASD. Understanding the influence of their factors, Knowledge, Awareness, Experience, Preparedness, and Beliefs impact providers' Willingness for early ASD detection. This study explores these factors comprehensively, contributing to a nuanced understanding and assessing the effect of Beliefs on healthcare providers' Willingness for early ASD detection, examining their influence on Awareness, Knowledge, Experience, and perceived Preparedness. Methodology: In the Al-Mafraq region of Jordan, this study surveyed 202 participants from various healthcare facilities using structural equation modelling (SEM) to analyse the connection between Beliefs and healthcare providers' Awareness, Knowledge, Experience, perceived Preparedness, and Willingness for early ASD detection. Result: Belief moderated the path from Preparedness towards Willingness. This means that the higher the value of Belief, the greater the effect of healthcare providers' Preparedness for early ASD detection. Conclusion: The study uncovers significant associations between healthcare providers' Knowledge, Experience, and perceived Preparedness and their Willingness for early ASD detection. Belief emerges as a pivotal moderator, emphasising its influential role in shaping the connection between Preparedness and Willingness.