# Enhancing Early Relational Health to Reduce Disparities in Child Health and Development: Addressing ACEs and Promoting PCEs through an Integrated Evidence-based Intervention in Pediatric Primary Care

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $832,999

## Abstract

SUMMARY
Poverty and racism are linked to disparities in early child development (ECD), including school readiness (pre-
academic skills, self-regulation, social-emotional well-being), which are a core contributor to long-term health
and mental health outcomes. These disparities begin early in life, widen as children age, and persist across the
lifespan. Poverty and racism also increase the likelihood of adverse childhood experiences (ACEs) and
decrease the likelihood of positive childhood experiences (PCEs). Importantly, PCEs are not simply the
absence of ACEs; they are factors that are beneficial for children’s development and may be especially
important in providing a buffer for the impacts of ACEs. Two interrelated frameworks (Stress and Investment)
illustrate how ACEs and PCEs impact ECD through a key common pathway of early relational health (ERH;
parent-child relationship quality, structuring the home environment, responsivity/cognitive stimulation). Thus,
enhancing ERH by both addressing ACEs and promoting PCEs is critical for mitigating disparities in ECD.
Evidence-based preventive interventions (EBPIs) designed to reduce these disparities have increasingly been
located in pediatric primary care due to its population-level reach, frequent visits, trusted relationships, and
potential for low cost. However, few EBPIs have successfully integrated strategies to concurrently and directly
address ACEs and promote PCEs, limiting their capacity to fully address disparities synergistically, particularly
for families with fewer resources. Moreover, knowledge gaps related to dissemination and implementation
(D&I) of integrated EBPIs represent a key barrier to improving implementation effectiveness and impact.
We propose a novel integration of HealthySteps (HS) and Video Interaction Project (VIP), two exemplar,
American Academy of Pediatrics (AAP)-recommended EBPIs. HS provides a stepped-care approach with
universal screening for ACEs and additional support and referrals for families with increased concerns. HS has
been shown to reduce family vulnerabilities and negative relationship quality elements of ERH. However, HS
has limited impact on PCEs, suggesting additional strategies may be needed to improve effectiveness of the
HS stepped-care approach in enhancing ERH. Integrating VIP may address this gap with its focus on video-
recording parents and children interacting with a toy or book provided by the program and real-time, strengths-
based feedback. VIP has impacts on parenting assets and responsivity/cognitive stimulation elements of ERH.
We will test an integrated HS+VIP model in order to: 1) identify best practice strategies for implementing
integrated EBPIs in diverse pediatric care sites; 2) examine effectiveness and implementation outcomes of the
HS+VIP model; and 3) examine health disparities mechanisms underlying these outcomes. This integrated
model has the potential for population-level reductions in disparities in ECD outcomes by targe...

## Key facts

- **NIH application ID:** 10914249
- **Project number:** 5R01MD018597-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Caitlin Ford Canfield
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $832,999
- **Award type:** 5
- **Project period:** 2023-08-25 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10914249

## Citation

> US National Institutes of Health, RePORTER application 10914249, Enhancing Early Relational Health to Reduce Disparities in Child Health and Development: Addressing ACEs and Promoting PCEs through an Integrated Evidence-based Intervention in Pediatric Primary Care (5R01MD018597-02). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10914249. Licensed CC0.

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