1/2-Testing the impact of early screening on the long-term outcomes of children with ASD

1/2-测试早期筛查对自闭症儿童长期结果的影响

• 批准号: 10543107
• 负责人: Karen L Pierce
• 金额: $ 88.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-05 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10543107
• 关键词: Academy; Achievement; Advisory Committees; Affect; Age; American; Area; Arizona; Biometry; Boston; Brain; California; Child; Childhood; Clinical; Cognition; Cognitive; Communities; Connecticut; Detection; Diagnosis; Early Diagnosis; Early identification; Early treatment; Education; Ensure; Ethnic Origin; Evaluation; Family; Family Characteristics; Funding; Future; Gender; Geographic Locations; Goals; Guidelines; Heterogeneity; Language; Laws; Longitudinal cohort; Machine Learning; Mainstreaming; Massachusetts; Measures; Mental Health; Methods; Mind; Modeling; National Institute of Mental Health; Outcome; Parents; Pathway Analysis; Pediatrics; Pennsylvania; Philadelphia; Policies; Practice Guidelines; Primary Care; Quality of life; Race; Reproducibility; Resources; Sample Size; Sampling; School-Age Population; Schools; Services; Site; Social Behavior; Stress; Symptoms; System; Techniques; Testing; Time; Toddler; United States Preventative Services Task Force; Work; age effect; autism community; autism spectrum disorder; autistic children; checkup examination; cohort; design; early screening; experience; geographic difference; improved; improved outcome; indexing; innovation; machine learning method; predictive modeling; programs; screening; sex; social; success; tool

Autism spectrum disorder (ASD) impacts almost 2% of children born today, yet very little is known regarding how to positively alter the outcomes of affected children. On the one hand, many, including the American Academy of Pediatrics, believe that early universal screening at well baby check‐ups is an important step towards a positive outcome because it can lead to early treatment. In contrast, the United States Preventive Services Task Force (USPSTF) recently failed to endorse early universal ASD screening, noting that the benefits of doing so are poorly understood. What is needed to inform the debate is to examine the outcomes of a large cohort of children detected very early via universal screening at well‐baby checks, and to compare them to children who did not participate in an early detection program. Here we propose to do just that: We will examine the school age outcomes (age 6‐10 years) of an unprecedented sample size of 242 children with ASD detected very early in San Diego and Phoenix (i.e., “west coast cohort”) through our Get SET Early program, which involved screening with the CSBS at well baby check‐ ups (mean age 17.7 months, range 12‐24 months), and immediate referral for comprehensive evaluation and treatment if warranted. They will then be compared to a cohort of 242 ASD community children matched on age, gender, race, ethnicity, and SES who did not participate in our early detection program (Total N = 484). Given the rarity and uniqueness of our cohort, we plan to characterize outcomes not only on traditional measures of cognition, social behavior, etc., but also on outcome on school achievement (e.g., fully mainstreamed and/or lost their diagnosis) and family functioning (do families experience less stress?). Since the national mean age of ASD diagnosis is around 4 years, we expect that children in the community cohort will have later ages of diagnosis and poorer outcomes than those identified early via the Get SET Early program. With scientific rigor and reproducibility in mind, we will proactively test the replication of findings in an independent cohort (N=103) of toddlers screened in Boston, Philadelphia, and New Haven (i.e., “east coast cohort”) through Project Early and a matched community cohort from the same region (Total N=120). Our specific aims are: AIM 1a: we will identify clinically meaningful outcome subtypes of ASD in our west coast cohort using unbiased network clustering approaches. AIM 1b: with this unique longitudinal cohort, we will examine changes in symptom profile, IQ and adaptive functioning between toddler and school ages. AIM 1c: we will evaluate program impact by comparing the outcomes children in our early‐detected to the community cohort. AIM 1d: we will examine how well findings are replicated in our East coast sample. AIM 2: using complimentary regression and machine learning techniques, with our total sample collapsed across both west and east coast cohorts (N=602), we will test our model that earlier age at identification and treatment leads to improved outcomes. To examine other factors relating to a good outcome, moderating variables such as SES and level of treatment participation will also be included in our models. AIM 3: since state context (e.g., policies, guidelines) could also play a role in outcomes, in we plan to collect key state‐level information to place our findings in context.

自闭症谱系障碍 (ASD) 影响着当今出生的近 2% 的儿童，但人们对于如何预防却知之甚少。 积极改变受影响儿童的结果。一方面，包括美国科学院在内的许多机构 儿科认为，在健康婴儿检查中进行早期普遍筛查是迈向积极的重要一步 结果，因为它可以导致早期治疗。相比之下，美国预防服务工作组 (USPSTF) 最近未能认可早期普遍的自闭症谱系障碍 (ASD) 筛查，并指出这样做的好处很差 明白了。为这场辩论提供信息需要的是检查大量检测到的儿童的结果 很早就通过婴儿健康检查进行普遍筛查，并将其与未参加的儿童进行比较 早期检测计划。在这里，我们建议这样做：我们将检查学龄结果（6-10 岁） 年），在圣地亚哥和菲尼克斯（即， “西海岸队列”）通过我们的 Get SET Early 计划进行，其中包括在婴儿健康检查中与 CSBS 进行筛查- ups（平均年龄 17.7 个月，范围 12-24 个月），并立即转诊进行综合评估和 如果有必要进行治疗。然后将他们与年龄匹配的 242 名 ASD 社区儿童进行比较， 未参加我们的早期检测计划的性别、种族、民族和社会经济地位（总 N = 484）。鉴于 由于我们队列的稀有性和独特性，我们计划不仅根据传统的认知指标来描述结果， 社会行为等，而且还取决于学校成绩的结果（例如，完全主流化和/或失去了他们的 诊断）和家庭功能（家庭压力是否减轻？）。自全国 ASD 诊断平均年龄以来 大约 4 岁，我们预计社区队列中的儿童的诊断年龄会较晚，且贫困程度会较低 比通过 Get SET Early 计划早期确定的结果。秉承科学严谨性和可重复性， 我们将在波士顿筛查的独立幼儿队列（N = 103）中主动测试研究结果的重复性， 费城和纽黑文（即“东海岸队列”）通过早期项目和匹配的社区队列 同一区域（总计 N=120）。我们的具体目标是： AIM 1a：我们将确定有临床意义的结果亚型 使用无偏网络聚类方法对我们西海岸队列中的自闭症谱系障碍（ASD）进行研究。 AIM 1b：凭借这种独特的纵向 在队列中，我们将检查幼儿和学龄儿童之间症状特征、智商和适应性功能的变化。 AIM 1c：我们将通过比较早期检测到的儿童的结果来评估计划的影响 社区队列。 AIM 1d：我们将检查研究结果在东海岸样本中的复制情况。目标 2：使用 互补的回归和机器学习技术，我们的总样本在西方和西方都崩溃了 东海岸队列 (N=602)，我们将测试我们的模型，即识别和治疗的年龄越早，效果越好 结果。为了检查与良好结果相关的其他因素，调节变量，例如 SES 和水平 治疗参与也将包含在我们的模型中。目标 3：由于国家背景（例如政策、指导方针）可以 在我们计划收集关键的州级信息以将我们的发现置于背景中时，它也在结果中发挥作用。