Developing a Framework to Advance Cultural Sensitivity and Accommodate Behavioral Variability in Toddler Autism Screening: A Mixed-Methods Cross-Cultural Approach to Improve Autism Screening Accuracy

项目摘要 自闭症谱系障碍（ASD）是一种普遍的终身疾病，在生命早期表现出来， 患有ASD的儿童面临重大的社会沟通挑战1 -3和经济需求1， 家庭获得早期的ASD特异性服务可以大大改善患有ASD的儿童的长期结果。 ASD 4 -9，并依赖于及时检测的条件。然而，ASD筛查的预测准确性 远低于筛查工具的推荐标准10，15，21，对于种族和少数民族甚至更低 与白色儿童相比，儿童10岁，31-38岁。筛查工具通常依赖于父母的报告， 缺乏预测准确性，因为它们不能充分说明内部和之间异质性 文化群体。已经注意到基于培养的父母对早期ASD表达的感知的变化 在组29，46-51之间，ASD筛查中使用的一些社交行为表现显著 differently不同between种族and ethnic种族groups 31，32，36-38.此外，现有的现象学异质性 通过临床医生观察到，在前驱期的社会沟通出现的发展中， individuals44，53-58 within cultural groups.然而，目前的筛选采用的方法不足以 捕捉这种现有的异质性，这种异质性并没有被精确地描述为 前瞻性观察和报告的照顾者。因此，开发新的家长报告仍然很困难 筛选器旨在以知情的方式解释发展轨迹的异质性。 这项建议的主要目标是制定和评估一个框架， 和高度预测性的父母报告筛查措施，以提高未来ASD的公平性和准确性 用于不同家庭的筛选器。这将通过以下方式实现：i）开发对文化敏感的ASD 筛选项目; ii）通过生态瞬时评估（EMA）在数据收集中使用这些项目;以及iii） 采用机器学习（ML）开发原型算法，最大限度地提高和之间的ASD预测 在不同的文化群体中。EMA在接近目标的时间段内提供实时背景监测 动力学60 -62.因此，EMA是捕捉发育轨迹异质性的理想选择， 社会交往行为随着时间的推移，观察和报告的照顾者。ML开发预测 算法，可以有区别地衡量和联合收割机ASD指标的基础上，组的隶属关系，个人 差异，和时间特异性，是算法开发的最佳方法， 发育异质性76，但在ASD筛查工作中未得到充分利用77。通过拟议的研究 和培训计划，申请人将发展所需的技能和专业知识，作出实质性的贡献 作为一名独立的临床科学家参与ASD研究

PROJECT SUMMARY Autism Spectrum Disorder (ASD) is a prevalent, lifelong condition that manifests early in life and is associated with significant social communication challenges1–3 and economic needs1 for children with ASD and their families. Access to early, ASD-specific services can profoundly improve long-term outcomes for children with ASD4–9 and depends on timely detection of the condition. However, the predictive accuracy of ASD screening is well below recommended standards for screening tools10,15,21 and is even lower for racial and ethnic minority children10,31–38 when compared to White children. Screening tools, which typically rely on parental report, may lack predictive accuracy because they do not sufficiently account for heterogeneity between and within cultural groups. Variation in culture-based parental perception of early ASD expressions has been noted between groups29,46–51, and some social communication behaviors used in ASD screening perform significantly differently between racial and ethnic groups31,32,36–38. Additionally, existing phenomenological heterogeneity has been observed via clinicians in the development of social communication emergence across prodromal individuals44, 53–58 within cultural groups. However, current screeners employ methods that do not sufficiently capture this existing heterogeneity, and this heterogeneity has not been precisely characterized as prospectively observed and reported by caregivers. Therefore, it remains difficult to develop new parent-report screeners designed to account for heterogeneity in developmental trajectories in an informed way. The primary objective of this proposal is to develop and assess a framework for developing culturally-sensitive and highly predictive parental-report screening measures to improve the equity and accuracy of future ASD screeners for use with diverse families. This will be achieved by i) developing culturally-sensitive ASD screening items; ii) using these items in data collection via ecological momentary assessment (EMA); and iii) employing Machine Learning (ML) to develop prototype algorithms that maximize ASD prediction between and within different cultural groups. EMA provides real-time in-context monitoring at time-periods proximal to target dynamics60–62. EMA is therefore an ideal choice to capture the heterogeneity in developmental trajectories of social-communication behaviors over time as observed and reported by caregivers. ML develops prediction algorithms that can differentially weigh and combine ASD indicators based on group affiliation, individual differences, and temporal specificity and is an optimal approach for algorithm development in the presence of developmental heterogeneity76 but is underutilized in ASD screening efforts77. Through the proposed research and training plan, the applicant will develop the skills and expertise needed to make a substantive contribution to ASD research as an independent clinical scientist.