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

项目摘要/摘要 美国科学院推荐对自闭症谱系障碍(ASD)进行全面筛查 儿科，以改善ASD儿童的早期诊断和便利获得早期干预。 尽管这项政策得到了广泛的支持，但普遍筛查的最佳方法仍然未知， 目前的方法可能在几个方面受到限制。首先，新出现的证据突显了经济低迷 孤独症特异性筛查(即M-Chat/F)单独应用于临床时的敏感性和阳性预测价值 世界各地的筛选实践。固定的18个月和24个月的放映时间表，以及排除 重要的风险指标(例如，性别、早产、家族史、发育迟缓、医疗问题)可能 也是导致总体发现率较低的原因之一。其次，目前的方法忽视了重要的 普遍筛查对诊断过程的下游影响，其中现实世界的资源限制 有限的诊断服务和延长的诊断等待时间也严重影响了确诊年龄。 为了弥合这些差距，将开发一个创新的分析框架，以整合大型真实世界的健康 记录数据集、数据分析和模拟建模，首要目标是确定更有效的 在实际资源有限的情况下，可进一步降低确诊年龄的普遍筛查政策。在……里面 具体地说，这个项目将首先将现有的自闭症特定筛查工具与临床变量相结合 对已知的ASD危险因素建立全面的风险模型，以提高筛查的准确性(目标1)。 然后建立离散事件仿真模型，模拟从筛查到诊断的整个链条过程 对于任何给定的筛查策略，由转诊的风险阈值、筛查的年龄范围和 重复筛选的时间间隔。模拟还将显式地模拟诊断的等待过程 有限服务能力下的评价(目标2)。基于真实临床的参数化和校准 数据，然后将使用模拟模型来系统地评估和比较一组丰富的备选方案 筛查政策，这将使政策制定者能够确定最优的普遍筛查政策，使 鉴于诊断服务能力有限，在降低确诊年龄的同时检测自闭症(目标3)。 这项拟议的研究将为评估自闭症筛查的效果提供一个新的系统框架。 政策，它直接回应了美国预防服务工作组最近的审查，呼吁 “一个更广泛的分析框架，从整体上考虑流程链。”预计由此得出的结论是 这项研究将在评估替代普遍筛查政策设计方面提供首个此类证据，以提供 更有效的政策，进一步促进早期诊断。

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.