# Maximizing the Impact of Preventive Interventions: Identifying Responders and Non-Responders of an Evidence-Based Intervention for Low-Income Families Using a Person-Centered Approach

> **NIH NIH R03** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2024 · $76,731

## Abstract

Project Summary/Abstract
The literature is inconclusive about who does and does not benefit from preventive interventions. Even less is
known about the unique combinations of participant risk factors that impact program effectiveness for low-income
families. These are crucial gaps to address, given that economically disadvantaged families vary in the level and
pattern of adversities they experience; and, these adversities may influence their response to intervention. One
of the few preventive interventions designed specifically to meet the needs of economically disadvantaged
families is Fatherhood, Relationship, and Marriage Education (FRAME), which combines the core elements of
two evidence-based programs: Premarital Relationship Education Program and Families Coping with Economic
Strain. On average, families benefitted from FRAME, but a portion of them did not experience the expected
gains. This project will examine whether certain types or combinations of pre-existing risk factors, such as
economic strain and parental depression, impacted response to FRAME. This information is critical for identifying
program responders and non-responders to inform tailored intervention approaches and maximize the efficacy
and cost-effectiveness of prevention programs for underserved families experiencing various types of adversity.
Using existing data from the FRAME study with low-income families (N = 301 mother-father-child triads), the
long-term goal of this study is to improve the effectiveness of family-based interventions to promote resilience to
adversity and address the Healthy People 2030’s initiative to strengthen the health and well-being of all people.
As a preliminary analysis, we conducted baseline latent class analysis (LCA) and identified four family risk
classes: Job Instability Only (low on all risk factors except job instability; 14%), Economic Stress, Depressed
Parents (high on economic stressors and parental depression; 41%), Extreme Family Dysfunction (high on all
risk factors; 33%), and Mothers At Risk (high mother victimization; 12%). Therefore, the overall objective of this
two-year R03 is to test the effects of these family risk classes on family mental health outcomes and intervention
effectiveness (i.e., families’ response to the FRAME intervention). The specific aims are to: (a) model the effects
of family risk classes on longitudinal family outcomes, (b) identify the effects of family risk classes on families’
engagement and response to intervention, and (c) examine racial and gender differences in family risk classes
and families’ response to intervention. This study will use a novel approach (i.e., latent class analysis and the
BCH method) to understand risk processes and test differential intervention effects. These contributions are
significant because they can inform efforts to maximize the cost-effectiveness of prevention programs for
economically disadvantaged families by ensuring that the most people benefit from the inter...

## Key facts

- **NIH application ID:** 10841009
- **Project number:** 5R03MH127455-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Daniel Kabat Cooper
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $76,731
- **Award type:** 5
- **Project period:** 2023-05-15 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10841009

## Citation

> US National Institutes of Health, RePORTER application 10841009, Maximizing the Impact of Preventive Interventions: Identifying Responders and Non-Responders of an Evidence-Based Intervention for Low-Income Families Using a Person-Centered Approach (5R03MH127455-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10841009. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*