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.

项目总结