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

> **NIH NIH K01** · UNIVERSITY OF ROCHESTER · 2024 · $115,103

## Abstract

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 e...

## Key facts

- **NIH application ID:** 10931483
- **Project number:** 5K01HD112516-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Justin Russotti
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $115,103
- **Award type:** 5
- **Project period:** 2023-09-19 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931483

## Citation

> US National Institutes of Health, RePORTER application 10931483, Understanding Risk Heterogeneity Following Child Maltreatment: An Integrative Data Analysis Approach. (5K01HD112516-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10931483. Licensed CC0.

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