Validating Measures and Unpacking Differences in Service Use for Diverse Children with Autism

项目总结 英语水平有限(LEP)儿童的家庭已被系统地排除在自闭症之外 谱系障碍(ASD)研究，但他们可能遇到更大的护理障碍。因此，几乎没有什么是 了解哪些因素与这些差异有关，以及它们在不同人群和 设置。由于缺乏数据，开发以数据为导向的战略来解决儿童与 ASD和LEP可能是一个挑战。因此，我们建议通过进行顺序调查来解决这一关键差距 混合方法研究以了解患者、提供者和结构因素与哪些因素相关 对患有LEP的家庭的护理存在差异。首先，与LEP调查研究专家团队合作， 我们提出了有史以来规模最大的多元化、多语种的全国性调查(n=2730)，调查对象是服务不足的儿童家庭。 使用ASD来验证五种语言(西班牙语、 海地-克里奥尔语、越南语、普通话、英语)，并使用这些衡量标准来了解护理方面的差异 在人群之间和在人群之间。这项调查将嵌入自闭症关怀网络(ACN)，这是一个 由20个不同地理位置的大型医院系统组成的国家网络专注于改善对自闭症的护理。我们 然后，将使用定性访谈来更好地理解调查结果。最后，基于以下方面的最佳实践 在实施科学领域，我们将使用严格的共识方法，将调查结果放在背景和 为数据和测量的使用以及政策决策提供建议。我们的结果将对该领域产生影响 通过验证在未来自闭症干预研究中使用的措施--从治疗试验到政策 主动行动--以及编制数据，作为今后缩小差距的干预目标 努力。我们的目标与NIMH减少ASD治疗差异的战略计划直接一致。

PROJECT SUMMARY Families of children with limited English proficiency (LEP) have been systematically excluded from autism spectrum disorder (ASD) research, yet they likely experience greater barriers to care. As a result, little is known about what factors are associated with these disparities, and how they differ across populations and settings. Because of this lack of data, developing data-driven strategies to address disparities for children with ASD and LEP can be a challenge. Thus, we propose to address this critical gap by conducting a sequential mixed methods study to understand what patient, provider, and structural factors are associated with disparities in care for families with LEP. First, in collaboration with a team of experts in LEP survey research, we propose the largest ever diverse, multi-lingual national survey (n=2730) of families of underserved children with ASD to both validate measures (e.g., parent stress, stigma, discrimination) in five languages (Spanish, Haitian-Creole, Vietnamese, Mandarin, English) and use those measures to understand disparities in care between and across populations. The survey will be embedded within the Autism Cares Network (ACN), a national network of 20 large, geographically diverse hospital systems focused on improving care for ASD. We will then use qualitative interviews to better understand survey findings. Finally, based on best practices from the field of implementation science, we will use rigorous consensus methods to place findings in context and make recommendations for data and measure use, as well as policy decisions. Our results will impact the field by validating measures to be used in future studies of ASD interventions – from treatment trials to policy initiatives – as well as by producing data to be used as future intervention targets in disparities reduction efforts. Our aims align directly with the NIMH’s Strategic Plan to reduce disparities in treatment of ASD.