Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers
- URL: http://arxiv.org/abs/2606.01217v1
- Date: Sun, 31 May 2026 13:10:41 GMT
- Title: Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers
- Abstract summary: White Europeans are 81% increased risk of ASD and Middle Easterners are at 79% reduced risk of ASD compared to Asians.<n> neonatal jaundice is a significant predictor of ASD, while male children are at much higher risk of ASD compared to female children.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior. This study examines the relationship between ethnicity and ASD traits, along with behavioural scores, sex and neonatal jaundice across three ethnic groups: White Europeans, Asians, and Middle Eastern individuals. We perform a logistic regression and show that ethnicity has a significant effect on incidence of ASD. White Europeans are 81% increased risk of ASD and Middle Easterners are at 79\% reduced risk of ASD compared to Asians. We also confirm earlier studied which show that neonatal jaundice is a significant predictor of ASD, while male children are at much higher risk of ASD compared to female children. These results suggest the need for diagnostic frameworks and interventions that account for ethnic in the presentation and assessment of ASD traits
Related papers
- A multimodal large language model for evidence-based autism spectrum disorder screening [43.813514045885704]
ASDchat is a large language model designed for evidence-based autism screening.<n>The model was trained and evaluated on a dataset of 1,035 participants from 27 sites in China.
arXiv Detail & Related papers (2026-09-15T00:34:21Z) - An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes [36.54657524599473]
We investigate racial and ethnic disparities in intensive care unit (ICU) outcome between majority and minority groups in Australia and the U.S.<n>Minority patients are younger at admission, have worse chronic health, are more likely to be admitted for urgent and non-elective reasons, and have higher illness severity.<n>We find a protective direct effect of belonging to a minority group, with minority patients showing improved survival compared to their majority counterparts, with all other variables kept equal.
arXiv Detail & Related papers (2025-01-09T12:48:15Z) - Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder [2.3001245059699014]
We introduce a novel dataset, Hugging Rain Man, which includes facial action units (AUs) manually annotated by FACS experts for both children with ASD and typical development (TD)
The dataset comprises a rich collection of posed and spontaneous facial expressions, totaling approximately 130,000 frames, along with 22 AUs, 10 Action Descriptors (ADs) and atypicality ratings.
arXiv Detail & Related papers (2024-11-21T02:51:52Z) - Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data [2.521920073279046]
Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood.
Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive.
We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems.
arXiv Detail & Related papers (2024-11-13T03:38:56Z) - Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework [6.785167067600156]
This work proposes a novel Parent-Child Dyads Block-Play (PCB) protocol to identify behavioral patterns distinguishing ASD from typically developing toddlers.
We have compiled a substantial video dataset, featuring 40 ASD and 89 TD toddlers engaged in block play with parents.
This dataset exceeds previous efforts on both the scale of participants and the length of individual sessions.
arXiv Detail & Related papers (2024-08-29T21:53:01Z) - Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study [41.94295877935867]
Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities.
Within an intersectional framework, patient SES, race, and sex were found to have significant interactions.
Increased SES is associated with a higher probability of obtaining a schizophrenia diagnosis in Black Americans.
Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans.
arXiv Detail & Related papers (2024-05-30T19:10:35Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - Screening Autism Spectrum Disorder in childrens using Deep Learning
Approach : Evaluating the classification model of YOLOv8 by comparing with
other models [0.0]
We propose a practical solution for ASD screening using facial images using YoloV8 model.
Our model achieved a remarkable 89.64% accuracy in classification and an F1-score of 0.89.
arXiv Detail & Related papers (2023-06-25T18:02:01Z) - Evaluate underdiagnosis and overdiagnosis bias of deep learning model on
primary open-angle glaucoma diagnosis in under-served patient populations [64.91773761529183]
Primary open-angle glaucoma (POAG) is the leading cause of blindness in the United States.
Deep learning has been widely used to detect POAG using fundus images.
Human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models.
arXiv Detail & Related papers (2023-01-26T18:53:09Z) - A Two-stage Multi-modal Affect Analysis Framework for Children with
Autism Spectrum Disorder [3.029434408969759]
We present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states in real-world play therapy scenarios.
This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.
arXiv Detail & Related papers (2021-06-17T01:28:53Z) - Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD.
Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
arXiv Detail & Related papers (2020-08-21T20:22:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.