Understanding Risk Heterogeneity Following Child Maltreatment: An Integrative Data Analysis Approach.

了解虐待儿童后的风险异质性：综合数据分析方法。

• 批准号: 10721233
• 负责人: Justin Russotti
• 金额: $ 11.7万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721233
• 关键词: Address; Age; Big Data; Child; Child Abuse and Neglect; Child Development; Child Welfare; Chronic; Cohort Studies; Collaborations; Data; Data Analyses; Data Pooling; Data Set; Development; Developmental Course; Developmental Process; Dimensions; Economic Burden; Elements; Ethnic Origin; Exposure to; Funding; Goals; Heterogeneity; Incidence; Individual; Intervention; Investments; Knowledge; Learning; Life; Longitudinal Studies; Longitudinal cohort study; Mentorship; Methodology; Methods; Mission; Modeling; National Institute of Child Health and Human Development; Outcome; Pathology; Pathway interactions; Phenotype; Population; Process; Public Health; Quality of life; Race; Reproducibility; Reproducibility of Results; Research; Research Personnel; Research Training; Resources; Risk; Risk Factors; Sampling; Science; Severities; Source; Stretching; Subgroup; Survivors; System; Techniques; Testing; Time; Training; Training Programs; United States National Institutes of Health; Variant; Work; Youth; biological systems; biopsychosocial; career development; cohort; cost; data archive; data harmonization; data sharing; data sharing networks; design; ethnic diversity; ethnic minority; ethnic minority population; experience; health disparity; high risk; improved; innovation; longitudinal design; meetings; minority children; multiple datasets; prospective; psychologic; racial minority; resilience; skill acquisition; skills

PROJECT SUMMARY Child maltreatment (CM) is a broad-ranging risk factor associated with compromised development and maladaptation. Yet, there is vast heterogeneity in the experience of CM and its developmental outcomes. Several of the field’s most pressing developmental questions involve exploring such heterogeneity. However, investigating risk heterogeneity in CM populations requires sensitive longitudinal studies of high-risk, hard-to- reach subjects with adequate power to detect unique subgroups who differ in the experience and consequences of CM—such studies are costly, arduous, and rare. This project aims to address this gap. The overall objective of this project is to apply Integrative Data Analysis (IDA)—a principled set of methodologies and statistical techniques used to conduct simultaneous analysis of raw data pooled from multiple datasets—as a method to address questions about risk heterogeneity that may not be addressed through individual CM studies alone. This project will use IDA to pool data from 7 NIH-funded CM cohorts that used gold-standard methods to examine the development of long-term CM sequelae across biopsychosocial domains. Pooling original data from multiple CM studies stretches the developmental period under observation, generates a more heterogenous sample, and increases statistical power to examine important sources of risk heterogeneity. IDA will yield an integrated sample (N = 2,898) that includes assessment of an array of biopsychosocial processes from ages 4 through 40. The IDA dataset will be used to address three aims: A1) determine how heterogeneity in CM exposure (i.e., variation in types, developmental timing, and chronicity of exposure) differentially influences developmental sequelae; A2) identify heterogeneity in the developmental outcome trajectories of CM survivors and examine which features of CM exposure are associated with specific trajectories; A3) explore how CM exposure and subsequent developmental processes differ based on racial/ethnic heterogeneity. This project is innovative because it will leverage $25 million of NIH investment in CM research to unlock the constraints of isolated studies, creating a pooled source of CM data that is more powerful and diverse than any individual cohort, maximizing the value of complementary efforts in the field. This contribution will be significant because it will help to parse risk heterogeneity in CM survivors, which is necessary to improve the precision of our interventions. Further, this project will create an integrative CM dataset that will be a shared data resource for the field, resulting in exponential contributions that extend beyond this K01. Finally, this proposal will greatly enhance the PI’s career development and enable him to advance toward his long-term goal of becoming an independent investigator who can advance the fields of child development and CM via innovative methods. Training-mentorship will be provided to learn IDA methodologies; gain expertise to study risk heterogeneity; acquire skills in longitudinal data analysis; and gain team science skills.

项目摘要 儿童虐待（CM）是与受损的发展和 适应不良。然而，CM的经验及其发展成果存在巨大的异质性。一些 该领域最紧迫的发展问题涉及探索这种异质性。然而， 调查CM种群中的风险异质性需要对高风险，难以实现的敏感纵向研究 达到具有足够力量的受试者，可以检测出在经验和后果不同的独特子组 CM的研究是昂贵，艰巨而罕见的。该项目旨在解决这一差距。 该项目的总体目的是应用综合数据分析（IDA） - 一组主要方法 以及用于对来自多个数据集汇总的原始数据进行简单分析的统计技术 - 一种解决有关风险异质性问题的方法，该问题可能无法通过单个CM研究来解决 独自的。该项目将使用IDA从7个使用金标准方法的7个NIH资助的CM队列中汇总数据 检查跨生物心理社会领域的长期商业后遗症的发展。汇总原始数据 多个CM研究延伸了观察到的发育期，产生了更异质的 样本，并增加统计能力以检查重要的风险异质性来源。艾达将产生 综合样本（n = 2,898），包括评估4岁的生物心理社会过程 通过40。IDA数据集将用于解决三个目标：A1）确定CM中的异质性如何 暴露（即类型的变化，发育时机和暴露的慢性变化）对影响有所不同 发育后遗症； A2）确定CM表面发展结果轨迹的异质性 并检查CM暴露的哪些特征与特定轨迹相关； A3）探索CM 基于种族/种族异质性，暴露和随后的发展过程不同。 该项目具有创新性，因为它将利用2500万美元的NIH投资在CM研究中解锁 孤立研究的限制，创建了比任何人都更强大和潜水员的汇总CM数据源 个别队列，最大程度地提高该领域的互补工作价值。这项贡献将是重要的 因为它将有助于解析CM存活中的风险异质性，这是提高精度的必要条件 我们的干预措施。此外，该项目将创建一个集成的CM数据集，该数据集将是共享数据资源 对于该领域，导致指数贡献超出了K01。最后，这个建议将大大 增强PI的职业发展，并使他能够朝着自己的长期目标迈向成为一个 可以通过创新方法推进儿童发展领域和CM领域的独立研究人员。 将提供培训训练，以学习IDA方法；获得专业知识来研究风险异质性； 在纵向数据分析中获取技能；并获得团队科学技能。