Strategies to Facilitate Early Detection of Autism in Primary Care

促进初级保健中自闭症早期发现的策略

• 批准号: 10523863
• 负责人: Diana L Robins
• 金额: $ 70.4万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-06 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10523863
• 关键词: Academic Medical Centers; Academy; Address; Age; American; Asian; Attitude; Belief; Black race; Child; Child Care; Childhood; Collection; Communities; Detection; Diagnosis; Early Diagnosis; Early Intervention; Economically Deprived Population; Effectiveness; Electronic Health Record; Enrollment; Ethnic Origin; Evaluation; Evidence based practice; Future; Health; Health Personnel; Health Services; Healthcare Systems; Hispanic; Insurance Coverage; Interview; Investigation; Knowledge; Life Cycle Stages; Literature; Location; Longevity; Machine Learning; Methods; Not Hispanic or Latino; Outcome; Pediatrics; Predictive Factor; Primary Health Care; Provider; Public Health; Race; Randomized Controlled Trials; Readiness; Research Design; Resources; Risk; Rural; Sample Size; Sampling; Science; Socioeconomic Status; Structure; Surveys; Testing; Time; Toddler; Underrepresented Minority; Visit; Well Child Visits; autism spectrum disorder; autistic children; base; disadvantaged background; ethnic diversity; ethnic minority; future implementation; health care delivery; health inequalities; implementation fidelity; implementation trial; improved; improved outcome; individuals with autism spectrum disorder; interest; machine learning algorithm; multi-racial; outcome prediction; peer; practice factors; primary outcome; provider factors; racial and ethnic; random forest; scale up; screening; sex; sociodemographics; socioeconomics; suburb; supervised learning; trial design

Screening for autism spectrum disorder (ASD) during well-child pediatric check-ups reduces the age of diagnosis, allowing more time for critical ASD-specific early intervention (EI), which greatly improves outcomes. Furthermore, universal screening mitigates disparities in ASD detection for children who are underrepresented minorities and from economically disadvantaged backgrounds. The American Academy of Pediatrics (AAP) recommends autism-specific screening at 18 and 24 months for all children. However, current literature suggests low fidelity of ASD screening, including (1) Inconsistent use of universal screening as opposed to selecting which children to screen, (2) Screening children at only one instead of both 18 and 24 month well- child visits, and (3) Not referring children for an evaluation and EI when positive screens indicate risk for ASD. There is currently lack of knowledge regarding factors that predict screening fidelity and age of diagnosis, critical in order to address the public health challenge of delayed detection of ASD. The current project uses a pseudo-trial design to assess factors that relate to high-fidelity screening and to age of ASD diagnosis in a diverse sample of at least 250 pediatric providers and electronic health records from a minimum of 27,000 toddlers seen for 18 month visits. Factors include child sociodemographics (sex, race, ethnicity, insurance status); pediatric providers’ beliefs and attitudes, sex, and years in practice; and practice factors, including size, resources, location (urban, suburban, and rural), and whether or not they are affiliated with an academic medical center. The first specific aim investigates predictors of high-fidelity screening, with the primary outcomes of referrals for evaluation and EI for children who screen at risk for ASD, and secondary fidelity outcomes of universal (vs. selective) screening and repeat screening at both 18- and 24-month visits (vs. single timepoint). The second aim investigates predictors of age of diagnosis in children with autism including child, provider, and practice factors as well as fidelity of screening. The first two aims will be addressed using random forests, a supervised machine learning algorithm that assesses not only which factors predict outcomes, but their relative importance. The third aim uses mixed methods to identify potentially modifiable healthcare provider and practice factors that relate to early detection of autism in primary care, through quantitative surveys and semi-structured interviews with providers. This Project contributes to the Public Health and Autism Science advancing Equitable Strategies across the life course (PHASES) Center’s aims of investigating modifiable health determinants, inequities in health services and opportunities for mitigation, especially in racially/ethnically diverse and economically disadvantaged children, and impact of health services delivery on subsequent health outcomes, through exploration of factors impacting high-fidelity ASD screening and early diagnosis for all children, particularly underserved children, improving long-term outcomes across the lifespan for individuals with ASD. Findings will inform future trials that can be scaled up in the community.

筛查自闭症谱系障碍（ASD）在儿童小儿检查期间 诊断，为特定于ASD的特定早期干预（EI）提供了更多时间，从而大大改善了结果。 此外，普遍筛选减轻了代表性不足的儿童的ASD检测中的分布 少数群体和经济弱势背景。美国儿科学会（AAP） 建议所有儿童在18和24个月时进行自闭症特定筛查。但是，目前的文献 提示ASD筛查的低忠诚度，包括（1）使用通用筛选不一致而不是 选择要筛选哪些孩子，（2）仅以18和24个月的筛查儿童筛选 儿童就诊，（3）当正屏幕表明ASD的风险时，没有转介儿童进行评估和EI。 目前缺乏关于预测筛查忠诚度和诊断年龄的因素的知识， 为了应对延迟发现ASD的公共卫生挑战至关重要。当前项目使用 伪试验设计，以评估与高保真筛查和与ASD诊断年龄有关的因素 至少27,000的小儿提供者和电子健康记录的各种样本 蹒跚学步的幼儿访问了18个月。因素包括儿童社交人口统计学（性，种族，种族，保险） 地位）;小儿提供者的信念和参加，性行为和实践中的岁月；和实践因素，包括 大小，资源，位置（城市，郊区和粗糙），以及它们是否与学术有关联 医疗中心。第一个特定目的研究了高保真筛查的预测指标，主要目的是 评估的转介结果和EI的EI，筛查有ASD风险的儿童和次要保真度 在18个月和24个月的访问中，通用（与选择性）筛查和重复筛选的结果（VS。 单个时间点）。第二个目标调查了自闭症儿童诊断年龄的预测指标，包括 儿童，提供者和实践因素以及筛查的保真度。前两个目标将使用 随机森林，一种监督的机器学习算法，不仅评估了哪些因素预测 结果，但它们相对重要。第三个目标使用混合方法来识别潜在的修改 通过 定量调查和与提供者的半结构化访谈。该项目有助于公共卫生 自闭症科学推进了整个生活课程（阶段）中心的公平策略的目标 调查可修改的健康决定者，卫生服务的不平等和缓解机会， 尤其是在大致/种族多样化和经济不利的儿童中，以及卫生服务的影响 通过探索影响高保真性ASD筛查的因素，以随后的健康结果交付 以及所有儿童，尤其是服务不足的儿童的早期诊断，改善了整个整个过程 ASD患者的寿命。调查结果将为未来的试验提供信息，这些试验可以在社区中扩展。