# Strengthening perinatal healthcare utilization and quality of care for Indigenous and low socioeconomic status women through systems change: integrating person, provider, and policy perspectives.

> **NIH NIH U54** · AVERA MCKENNAN · 2024 · $516,959

## Abstract

PROJECT SUMMARY
The goal of this project is to identify high-efficacy and actionable strategies to improve perinatal healthcare utilization
and perinatal healthcare quality (perinatal care utilization and quality; PCUQ) and reduce maternal morbidity (MM) rates
and disparities experienced by for American Indian (AI) and low socioeconomic status women within a Northern Plains
area. High rates of MM and disproportionate burden of MM experienced by marginalized groups has resulted in a wide
range of recommendations to improve maternal health. Many recommendations focus on reducing barriers and
enhancing facilitators (B/F) that will increase utilization of perinatal care or will improve the quality of perinatal care
provided. However, there is little understanding of which B/F have the strongest impact on PCUQ and are therefore the
most effective targets to improve PCUQ across the spectrum of person/patient, provider/health system and policy
levels. This understanding is critical for actions that will reduce MM, as the association between PCUQ and MM is still
unclear. Recommendations also rarely account the unique contexts of both cultural strengths as facilitators and
systemic/structural racism as barriers experienced by AI communities, or contexts that impact the feasibility and success
of implementing specific changes. To address these issues, this project involves building a community-based system
dynamics simulation model that will detail the PCUQ B/F on person/patient, provider/health system and policy levels
driving low PCUQ rates and disparities for AI and low socioeconomic status women (Aim 1). Models will be developed
using a community-based participatory approach in collaboration with stakeholders who have personal and professional
experience in the multiple components of PCUQ. This model will be simulated, and community-informed hypotheses will
be tested to identify which B/F should be targeted as the best strategies for improving PCUQ and reducing disparity.
Simulation models will be created in part with survey data from 380 women (65% AI) who live within the target
communities and have had recent pregnancy and/or childbirth experience. This data will also be used to estimate path
models that will evaluate PCUQ B/F as indirect effects on MM (Aim 2). Finally, we will develop qualitative community-
based system dynamics models detailing the process of mechanisms that drive decision making and implementing
actions for changes to perinatal healthcare practice and policy and the role of research during these processes (Aim 3).
Models will be developed in collaboration with a sample of health system and policy decisionmakers and stakeholders,
and with a sample of grassroots advocates for AI health and health equity to further evaluate equity or inequity within
decision making and change processes. Key mechanisms for change and equity will be identified through network-based
analysis of the qualitative model. The three studies detailed within th...

## Key facts

- **NIH application ID:** 10908742
- **Project number:** 5U54HD113179-02
- **Recipient organization:** AVERA MCKENNAN
- **Principal Investigator:** Arielle R. Deutsch
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $516,959
- **Award type:** 5
- **Project period:** 2023-08-17 → 2030-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10908742

## Citation

> US National Institutes of Health, RePORTER application 10908742, Strengthening perinatal healthcare utilization and quality of care for Indigenous and low socioeconomic status women through systems change: integrating person, provider, and policy perspectives. (5U54HD113179-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10908742. Licensed CC0.

---

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