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）可以降低儿童的年龄。 诊断，允许更多的时间进行关键的ASD特异性早期干预（EI），这大大改善了结果。 此外，普遍筛查减轻了代表性不足的儿童在ASD检测方面的差异 少数民族和经济上处于不利地位的背景。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。然而，目前的文献 表明ASD筛查的保真度较低，包括（1）与普遍筛查相反， 选择哪些儿童进行筛查，（2）仅在一个月而不是18个月和24个月时对儿童进行筛查- 儿童访问，和（3）不转介儿童进行评估和EI时，积极的屏幕表明ASD的风险。 目前缺乏关于预测筛查保真度和诊断年龄的因素的知识， 这对于解决ASD延迟检测的公共卫生挑战至关重要。当前项目使用 拟试验设计，以评估与高保真筛查和ASD诊断年龄相关的因素， 至少250名儿科提供者的多样化样本和至少27，000份电子健康记录 幼儿18个月随访。因素包括儿童的社会人口统计学（性别、种族、民族、保险 状态）;儿科提供者的信念和态度，性别和实践年数;以及实践因素，包括 规模，资源，位置（城市，郊区和农村），以及他们是否隶属于学术机构 医学中心第一个具体目标是调查高保真筛查的预测因素，主要是 对筛查有ASD风险的儿童进行评估和EI的转介结果，以及次要保真度 在18个月和24个月访视时，普遍（与选择性）筛查和重复筛查的结果（与 单一时间点）。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。前两个目标将使用 random forests是一种有监督的机器学习算法，它不仅评估哪些因素可以预测 结果，但其相对重要性。第三个目标使用混合方法来识别潜在的可修改内容 与早期发现自闭症相关初级保健的医疗保健提供者和实践因素， 定量调查和与供应商的半结构化访谈。该项目有助于公众健康 和自闭症科学推进整个生命过程中的公平战略（阶段）中心的目标， 调查可改变的健康决定因素、卫生服务的不公平现象和缓解机会， 特别是在种族/族裔多样化和经济上处于不利地位的儿童中，以及保健服务的影响 通过探索影响高保真ASD筛查的因素， 为所有儿童，特别是得不到充分服务的儿童提供早期诊断， ASD患者的寿命。研究结果将为未来的试验提供信息，这些试验可以在社区中扩大规模。