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.

在健康儿童儿科检查期间筛查自闭症谱系障碍 (ASD) 可降低儿童年龄 诊断，从而为关键的 ASD 特异性早期干预 (EI) 留出更多时间，从而大大改善结果。 此外，普遍筛查可以减少代表性不足的儿童自闭症谱系障碍检测方面的差异 少数族裔和经济弱势群体。美国儿科学会 (AAP)。 建议在 18 个月和 24 个月时对所有儿童进行自闭症特异性筛查。 表明 ASD 筛查的保真度较低，包括 (1) 普遍筛查的使用不一致，而不是普遍筛查 选择要筛查的儿童，(2) 仅对 1 个月大的儿童进行筛查，而不是同时对 18 个月和 24 个月大的儿童进行筛查 (3) 当阳性筛查表明存在 ASD 风险时，不转介儿童进行评估和 EI。 目前缺乏关于预测筛查保真度和诊断年龄的因素的知识， 对于解决自闭症谱系障碍 (ASD) 延迟检测的公共卫生挑战至关重要。 伪试验设计，用于评估与高保真筛查和 ASD 诊断年龄相关的因素 至少 250 个儿科提供者的不同样本和至少 27,000 个电子健康记录 18 个月就诊的幼儿。因素包括儿童社会人口统计特征（性别、种族、民族、保险）。 状况）；儿科服务提供者的信念和态度、性别以及实践年限和实践因素； 规模、资源、位置（城市、郊区和农村）以及是否隶属于学术机构 第一个具体目标是调查高保真筛查的预测因素，其中主要目标是 对有 ASD 风险的儿童进行评估和 EI 转介的结果以及二次忠诚度 普遍（相对于选择性）筛查和 18 个月和 24 个月就诊时重复筛查（相对于选择性）的结果 第二个目标是调查自闭症儿童诊断年龄的预测因素，包括 儿童、提供者和实践因素以及筛查的保真度将通过使用来解决。 随机森林，一种机器学习算法，仅评估哪些预测因素不受监督 第三个目标使用混合方法来确定潜在的可修改的结果。 与初级保健中自闭症早期发现相关的医疗保健提供者和实践因素，通过 调查和对提供者的半结构化访谈该项目有助于公共卫生。 和自闭症科学促进整个生命历程（PHASES）的公平策略中心的目标 可改变的健康决定因素、卫生服务的不平等和缓解机会， 特别是对于种族/族裔多样化和经济弱势的儿童，以及卫生服务的影响 通过探索影响高保真 ASD 筛查的因素，实现后续健康结果 对所有儿童，特别是服务不足的儿童进行早期诊断，改善整个国家的长期结果 研究结果将为未来在社区中扩大规模的试验提供信息。