Improving Universal Screening and Modeling the Effects on Referral and Diagnosis for Autism Spectrum Disorder

改善普遍筛查并建模对自闭症谱系障碍转诊和诊断的影响

• 批准号: 10527839
• 负责人: Qiushi Chen
• 金额: $ 26.43万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527839
• 关键词: 2 year old; 5 year old; Academy; Adaptive Behaviors; Address; Advisory Committees; Affect; Age; American; Autism Diagnosis; Awareness; Big Data Methods; Birth; Caring; Child; Childhood; Chronic Disease; Clinical; Clinical Data; Clinical Informatics; Clinical Services; Data; Data Analytics; Data Set; Detection; Developmental Delay Disorders; Diagnosis; Diagnostic; Diagnostic Services; Early Diagnosis; Early Intervention; Electronic Health Record; Ensure; Equilibrium; Evaluation; Event; Exclusion; Family; Family history of; Foundations; Frequencies; Future; Goals; Healthcare Systems; Language; Link; Malignant Neoplasms; Medical; Modeling; Outcome; Pathway interactions; Patients; Pediatric Hospitals; Pediatrics; Philadelphia; Policies; Policy Maker; Population; Predictive Value; Preventive service; Process; Recording of previous events; Research; Resources; Risk; Risk Factors; Schedule; Screening Result; Screening procedure; Services; Site; Specific qualifier value; Standardization; System; Testing; Time; United States; Wait Time; Waiting Lists; Well Child Visits; World Health; autism spectrum disorder; autistic children; base; design; disorder risk; health care service utilization; health record; improved; innovation; models and simulation; novel; novel strategies; operation; premature; primary care setting; screening; screening policy; sex; simulation; social skills; tool; uptake

PROJECT SUMMARY/ABSTRACT Universal screening for autism spectrum disorder (ASD) has been recommended by the American Academy of Pediatrics in order to improve early diagnosis and facilitate access to early intervention for children with ASD. Despite the widespread support for this policy, the optimal approach for universal screening remains unknown, and the current approach may be limited in several ways. First, emerging evidence has highlighted the low sensitivity and positive predictive value of autism-specific screening (i.e., M-CHAT/F) alone when applied in real- world screening practice. The fixed schedule of screenings at 18 and 24 months, along with the exclusion of important risk indicators (e.g., sex, prematurity, family history, developmental delays, medical concerns) may also contribute to the overall low detection rate. Second, the current approach has overlooked the important downstream effects of universal screening on the diagnostic process, where real-world resource constraints of limited diagnostic services and the prolonged waiting time for diagnosis also critically affect the age at diagnosis. To bridge these gaps, an innovative analytic framework will be developed to integrate a large real-world health record dataset, data analytics and simulation modeling, with the overarching goal of identifying more effective universal screening policies that could further lower the age at diagnosis under practical resource constraints. In particular, this project will first incorporate an existing autism-specific screening tool with clinical variables related to known ASD risk factors to develop a comprehensive risk model for improving the screening accuracy (Aim 1). Then a discrete-event simulation model will be built to simulate the chain process from screening to diagnosis for any given screening policy, which is specified by risk threshold for referral, age range for screening, and interval for repeated screening. The simulation will also explicitly model the waiting process for the diagnostic evaluation under a limited-service capacity (Aim 2). Parameterized and calibrated based on the real-world clinical data, the simulation model will then be used to systematically evaluate and compare a rich set of alternative screening policies, which will allow policy makers to identify the optimal universal screening policy that maximizes the detection of ASD while lowering the age at diagnosis given the limited diagnostic service capacity (Aim 3). This proposed study will present a novel systemic framework for evaluating the effects of autism screening policies, which directly responds to the United States Preventive Services Task Force’s recent review calling for “a broader analytic framework that considers the process chain in its entirety.” The findings anticipated from this study will provide first-of-its-kind evidence in evaluating alternative universal screening policy designs to inform more effective policies to further facilitate early diagnosis.

项目摘要/摘要 美国学院推荐了自闭症谱系障碍（ASD）的普遍筛查 儿科以改善早期诊断并促进ASD儿童的早期干预。 尽管对该政策提供了广泛的支持，但通用筛查的最佳方法仍然未知， 当前的方法可能会以多种方式受到限制。首先，新兴的证据强调了低 自闭症特异性筛查的敏感性和正预测价值（即，M-Chat/f）仅应用于实地 世界筛查实践。 18和24个月的固定筛查时间表以及排除 重要的风险指标（例如，性，早产，家族史，发展延迟，医疗问题）可能 也有助于总体低检测率。其次，当前的方法忽略了重要的 通用筛查对诊断过程的下游影响，现实世界中的资源约束 有限的诊断服务和延长的等待时间的诊断时间也严重影响诊断年龄。 为了弥合这些差距，将开发一个创新的分析框架，以整合大型现实世界的健康 记录数据集，数据分析和仿真建模，其总体目标是确定更有效的 在实际资源限制下，可以进一步降低诊断年龄的普遍筛查政策。在 特别是，该项目将首先结合具有与临床变量相关的现有自闭症特异性筛查工具 已知的ASD风险因素开发一种综合风险模型，以提高筛查准确性（AIM 1）。 然后，将建立一个离散事件模拟模型，以模拟从筛选到诊断的链过程 对于任何给定的筛查政策，该政策由转介的风险阈值指定，筛选年龄范围和 重复筛选的间隔。模拟还将明确对诊断的等待过程进行建模 在有限服务能力下进行评估（AIM 2）。基于现实世界临床的参数化和校准 然后，数据，仿真模型将用于系统地评估和比较一组丰富的替代方案 筛选政策，这将使政策制定者能够确定最大化的最佳通用筛选政策 鉴于诊断能力有限，在降低诊断年龄的同时检测ASD（AIM 3）。 这项拟议的研究将提出一个新的系统框架，以评估自闭症筛查的影响 政策，直接回应美国预防服务工作队最近呼吁的政策 “一个更广泛的分析框架，全面考虑了过程链。”从中预期的发现 研究将为评估替代性通用筛查政策设计时提供首要证据以告知 更有效的政策，以进一步促进早期诊断。