# Child Welfare Data S.M.A.R.T

> **NIH NIH P50** · WASHINGTON UNIVERSITY · 2022 · $355,112

## Abstract

ABSTRACT
Child Maltreatment (CM) is a major threat to children's health and development with lifetime (0-18) prevalence
of investigation, substantiation and foster care placement in the US being about 37%, 12% and 6%,
respectively. While not all CM is detected, children with CM reports have markedly worse outcomes in virtually
all domains, extending into adulthood. Such a signal provides an opportunity for preventive intervention, but
this requires advances in detection for targeting of scarce preventive resources and identifying key points of
intervention to guide system response once CM occurs. This project takes advantage of recent advances in
computer hardware and software, analysis and state data availability to achieve several aims. Child Welfare
DATA SMART (Specification, Management, Analysis, Replication and Transfer) is a five-year, multi-state
project that represents the first attempt to create standardized, replicable programming with linked longitudinal
administrative data to address several priority areas of the Capstone RFA (HD-18-012) related to identifying
CM risk, child welfare response, and longer term outcomes associated with CM. First, we develop similar
longitudinal cross-sector datasets (Common Data Set – Source: CDS-S) in five states (AK, CA, MD, MI, NC)
with very different populations and policy structures. This will provide identical variable and data structures to
allow replication of data management and analysis across sites. The second aim involves answering three sets
of critical research questions. (1) Prediction cluster: We will explore the potential of Predictive Risk Modeling
(PRM) to inform screening for CM risk at the population level as well as CM risk following a hotline. We will
evaluate how well PRM's trained in one state or relative to a particular outcome function in other states or with
other outcomes such as child injury. The data linkage required to build the PRMs provide the opportunity to
address questions in two additional areas: (2) Rare and Understudied Cluster: We will look at rare and
understudied populations, types of maltreatment and outcomes to better understand their predictors,
occurrence and associated outcomes. (3) Race and Culture: We will look at potential areas of bias in CPS
reporting, substantiation and placement relative to race, class and visibility. Programming and methods will be
publicly “transferred” as a national resource in Year 5. Consistent with the mission of the Center for Innovation
in Child Maltreatment Policy, Research, and Training (CICM), this project will advance science related to the
use of linked administrative data for screening and targeting of services and develop a set of creative
dissemination and training products to prevent CM and promote healthy development of victims of CM.

## Key facts

- **NIH application ID:** 10475108
- **Project number:** 5P50HD096719-05
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** F. Brett Drake
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $355,112
- **Award type:** 5
- **Project period:** 2018-09-20 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10475108

## Citation

> US National Institutes of Health, RePORTER application 10475108, Child Welfare Data S.M.A.R.T (5P50HD096719-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10475108. Licensed CC0.

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