Understanding Risk Heterogeneity Following Child Maltreatment: An Integrative Data Analysis Approach.
Understanding Risk Heterogeneity Following Child Maltreatment: An Integrative Data Analysis Approach.
批准号:
10721233
负责人:
Justin Russotti
金额:
$11.7万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-19 至 2028-08-31
关键词:
AddressAgeBig DataChildChild Abuse and NeglectChild DevelopmentChild WelfareChronicCohort StudiesCollaborationsDataData AnalysesData PoolingData SetDevelopmentDevelopmental CourseDevelopmental ProcessDimensionsEconomic BurdenElementsEthnic OriginExposure toFundingGoalsHeterogeneityIncidenceIndividualInterventionInvestmentsKnowledgeLearningLifeLongitudinal StudiesLongitudinal cohort studyMentorshipMethodologyMethodsMissionModelingNational Institute of Child Health and Human DevelopmentOutcomePathologyPathway interactionsPhenotypePopulationProcessPublic HealthQuality of lifeRaceReproducibilityReproducibility of ResultsResearchResearch PersonnelResearch TrainingResourcesRiskRisk FactorsSamplingScienceSeveritiesSourceStretchingSubgroupSurvivorsSystemTechniquesTestingTimeTrainingTraining ProgramsUnited States National Institutes of HealthVariantWorkYouthbiological systemsbiopsychosocialcareer developmentcohortcostdata archivedata harmonizationdata sharingdata sharing networksdesignethnic diversityethnic minorityethnic minority populationexperiencehealth disparityhigh riskimprovedinnovationlongitudinal designmeetingsminority childrenmultiple datasetsprospectivepsychologicracial minorityresilienceskill acquisitionskills
中文摘要
项目总结
英文摘要
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.
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