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 自闭症谱系障碍（ASD）的普遍筛查已被美国医学会推荐， 儿科，以改善ASD儿童的早期诊断和早期干预。 尽管这一政策得到了广泛的支持，但普遍筛查的最佳方法仍然是未知的， 并且当前的方法可能在几个方面受到限制。首先，新出现的证据突出表明， 自闭症特异性筛查的灵敏度和阳性预测值（即，M-CHAT/F）单独应用于真实的- 世界筛选实践。在18个月和24个月时进行筛查的固定时间表，沿着， 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 这也导致了整体的低检测率。其次，目前的做法忽视了重要的 普遍筛查对诊断过程的下游影响， 有限的诊断服务和长时间的诊断等待时间也严重影响了诊断年龄。 为了弥合这些差距，将开发一个创新的分析框架，以整合一个大型的现实世界的健康 记录数据集，数据分析和模拟建模，其总体目标是确定更有效的 在实际资源有限的情况下，可以进一步降低诊断年龄的普遍筛查政策。在 特别是，该项目将首先将现有的自闭症特异性筛查工具与相关的临床变量结合起来， 已知的ASD风险因素，以开发一个全面的风险模型，以提高筛查的准确性（目标1）。 然后建立离散事件仿真模型，对从筛查到诊断的链条过程进行仿真 对于任何给定的筛查政策，由转诊风险阈值、筛查年龄范围和 重复筛选的间隔时间。模拟还将明确地对诊断的等待过程进行建模 在有限的服务能力下进行评估（目标2）。基于真实世界的临床参数化和校准 数据，然后将使用模拟模型系统地评估和比较一套丰富的备选方案 筛查政策，这将使政策制定者能够确定最佳的普遍筛查政策， 在诊断服务能力有限的情况下，检测ASD，同时降低诊断年龄（目标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，这直接回应了美国预防服务工作队最近的审查呼吁， “一个更广泛的分析框架，考虑整个过程链。”预期的结果是， 研究将提供第一个同类证据，以评估替代性普遍筛查政策设计， 制定更有效的政策，进一步促进早期诊断。