Towards equitable early identification of autism spectrum disorders in females

实现女性自闭症谱系障碍的公平早期识别

• 批准号: 10722011
• 负责人: Catherine Ann Burrows
• 金额: $ 12.67万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-11 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722011
• 关键词: Age; Algorithms; Assessment tool; Brain; Child; Clinical; Cohort Studies; Communities; Computer Models; Data; Data Collection; Decision Trees; Detection; Development; Diagnosis; Diagnostic; Dimensions; Early Intervention; Early identification; Environment; Equity; Evaluation; Factor Analysis; Family; Female; Funding; Future; General Population; Goals; Heterogeneity; Infrastructure; Intervention; Interview; Knowledge; Life; Longevity; Machine Learning; Measurement; Measures; Medical; Mental disorders; Mentored Clinical Scientist Development Program; Mentors; Mentorship; Methodology; Methods; Minnesota; Modeling; National Institute of Mental Health; Outcome; Parents; Perception; Performance; Phase; Positioning Attribute; Primary Care; Provider; Psychologist; Quality of life; Questionnaires; Registries; Reporting; Research; Risk; Sampling; Screening procedure; Sex Differences; Signs and Symptoms; Site; Strategic Planning; Subgroup; Symptoms; Testing; Toddler; Training; Training Support; Universities; Visit; Waiting Lists; Work; autism spectrum disorder; autistic children; career development; clinical translation; clinically actionable; clinically relevant; cohort; community based participatory research; disorder risk; dissemination science; early childhood; early screening; gender equity; gradient boosting; implementation science; improved; information gathering; lens; male; novel; pediatric department; pediatrician; precision medicine; professor; random forest; recruit; repetitive behavior; screening; service intervention; sex; skills; social communication; training opportunity; trait; translational research program

PROJECT SUMMARY/ABSTRACT Screening tools for autism spectrum disorder (ASD) show poor predictive performance in practice, particularly for females, which may arise due to sex-related measurement bias of screening questionnaires, and lack of precision in capturing the variability in early symptom profiles of ASD. Computational approaches to characterize heterogeneity and assess and account for sex-related measurement bias in early ASD symptoms may identify ASD risk profiles that can be clinically actionable in practice. The candidate's long-term goals are to enhance goals quality of life for children with ASD and their families by lowering the age of diagnosis, especially in females missed by traditional screening methods. The research and training described in this K23 application will build on the candidate's existing expertise, adding conceptual and methodological skills needed to develop and implement a novel screening approach that will more precisely identify ASD risk in a community-based sample. Aim 1 evaluates the extent of sex-based measurement bias in measures shown to capture clinically-relevant variability in early ASD traits in a sample of 3,000 children between 17-25 months recruited from a community research registry. Aim 2 applies computational approaches to model dimensional variability in early ASD symptoms and identify subgroups of risk in the same sample that are hypothesized to vary on clinical outcomes at 36 months. Aim 3 takes a dissemination and implementation (D&I) science lens to assess parent and provider views on screening practices to identify facilitators and barriers to change via qualitative interviews (Pediatrician N=20; Parent N=40). This project is in line with NIMH Strategic Plan Goal 2 to “examining mental illness trajectories across the lifespan.” The candidate is a clinical psychologist and Assistant Professor at the University of Minnesota, with expertise in characterizing sex differences in early ASD trajectories. The proposed K23 application will provide the candidate with the training needed to develop new knowledge and skills in conducting community-based screening for ASD, computational modeling of heterogeneity, and dissemination and implementation science. Mentors Dr. Damien Fair, Jed Elison, and Timothy Beebe possess the expertise and mentoring skills to support these training and scientific aims. This will position the candidate to build an independent clinical-translational research program focused on improving the precision of early screening for ASD to enable precision medicine for early ASD concerns that are equitable by sex. Training will occur in an exceptional scientific environment in the Department of Pediatrics at the University of Minnesota and the newly established Masonic Institute of the Developing Brain.

项目摘要/摘要 自闭症谱系障碍（ASD）的筛查工具在实践中表现出较差的预测性能，特别是 对于女性，这可能是由于筛选问卷的性别相关测量偏倚以及缺乏 准确捕捉ASD早期症状特征的变异性。计算方法 描述异质性并评估和解释早期ASD症状中的性别相关测量偏倚 可以识别ASD的风险概况，可以在临床实践中采取行动。候选人的长期目标是 通过降低诊断年龄来提高ASD儿童及其家庭的生活质量， 特别是在传统筛查方法遗漏的女性中。本K23中描述的研究和培训 申请将建立在候选人现有的专业知识，增加所需的概念和方法的技能 开发和实施一种新的筛查方法，可以更精确地识别ASD风险， 基于社区的样本目标1评估了基于性别的测量偏差的程度， 在17-25个月的3,000名儿童样本中捕获早期ASD特征的临床相关变异性 从社区研究登记处招募的目标2将计算方法应用于模型维 早期ASD症状的变异性，并在同一样本中识别假设为 在36个月时的临床结局不同。目标3采用传播和实施科学透镜， 评估家长和提供者对筛选实践的看法，以确定促进因素和障碍， 定性访谈（儿科医生N=20;父母N=40）。该项目符合NIMH战略计划目标2 到“检查一生中的精神疾病轨迹”候选人是临床心理学家， 明尼苏达大学助理教授，擅长描述早期 ASD轨迹。拟议的K23应用程序将为候选人提供开发所需的培训 进行基于社区的ASD筛查、ASD计算建模的新知识和技能 异质性、传播与实施科学。导师达米恩·费尔博士、杰德·埃利森和 蒂莫西·毕比（Timothy Beebe）拥有支持这些培训和科学目标的专业知识和指导技能。这 将定位候选人建立一个独立的临床转化研究计划，重点是改善 ASD早期筛查的精确性，以实现对早期ASD问题的精确医学， 性别平等。培训将在儿科的特殊科学环境中进行， 明尼苏达大学和新成立的共济会大脑发育研究所。