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

自闭症谱系障碍（ASD）影响了今天出生的近2%的儿童，但关于如何治疗却知之甚少。 积极改变受影响儿童的结果。一方面，许多人，包括美国科学院， 儿科专家认为，在婴儿健康检查中进行早期普遍筛查是朝着积极的方向迈出的重要一步。 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）最近未能支持早期普遍ASD筛查，并指出这样做的好处很差 明白需要为辩论提供信息的是检查一大批被发现的儿童的结果。 通过在健康婴儿检查中进行普遍筛查，并将其与未参加 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）的一个前所未有的样本量的242名儿童ASD检测非常早期在圣地亚哥和凤凰城（即， “西海岸队列”）通过我们的“尽早设置”计划，其中涉及在婴儿检查时与CSBS进行筛查- UPS（平均年龄17.7个月，范围12-24个月），并立即转诊进行全面评估， 治疗，如有必要。然后将他们与242名年龄匹配的ASD社区儿童进行比较， 性别、种族、民族和SES，未参与我们的早期检测计划（总N = 484）。鉴于 罕见和独特的，我们的队列，我们计划的特点，不仅对传统的认知措施的结果， 社会行为等等，而且还取决于学校成绩的结果（例如，完全纳入主流和/或失去了 诊断）和家庭功能（家庭经历较少的压力？）。由于ASD诊断的全国平均年龄 大约是4岁，我们预计社区队列中的儿童将有更晚的诊断年龄和更差的 比那些通过早期计划确定的结果更早。考虑到科学的严谨性和可重复性， 我们将在波士顿筛选的一个独立的幼儿队列（N=103）中主动测试结果的重复性， 费城和纽黑文（即，“东海岸队列”）通过早期项目和来自 相同区域（总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在我们的西海岸队列中发现ASD。AIM 1b：通过这种独特的纵向 队列中，我们将检查症状特征，智商和适应功能之间的幼儿和学龄的变化。 目标1c：我们将通过比较我们早期发现的儿童与 社区队列。目的1d：我们将研究结果如何在我们的东海岸样本中复制。目标2：使用 免费的回归和机器学习技术，我们的总样本在西部和西部都崩溃了， 东海岸队列（N=602），我们将测试我们的模型，即早期识别和治疗年龄导致改善 结果。为了检验与良好结局相关的其他因素，调节变量，如SES和 参与治疗也将包括在我们的模型中。目标3：由于状态上下文（例如，政策、指导方针）可以 我们计划收集关键的州一级信息，以便将我们的调查结果置于背景之中。

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.