This grant focuses on advancing healthcare research for older adults from disadvantaged populations, aiming to improve guidelines, shared decision-making, and care coordination.
Funder: National Institutes of Health
Due Dates: June 5, 2025 (New) | July 5, 2025 (Renewal/Resubmission/Revision) | September 7, 2025 (AIDS) | October 5, 2025 (New) | November 5, 2025 (Renewal/Resubmission/Revision) | January 7, 2026 (AIDS) | Additional standard NIH R01 due dates through January 7, 2028
Funding Amounts: No budget cap; budgets must reflect actual needs. Project period up to 5 years.
Summary: Supports innovative, multi-level healthcare research to improve screening, care guidelines, shared decision-making, and care coordination for older adults from populations experiencing health disparities.
Key Information: Clinical trials are optional; projects must focus on NIH-designated health disparity populations aged 65+ in the U.S. or territories.
This opportunity from the National Institutes of Health (NIH) funds research to advance the science and implementation of innovative, multi-level healthcare approaches for older adults (age 65+) from populations that experience health disparities. The initiative aims to:
Projects should address one or more NIH-designated health disparity populations and consider multi-level determinants of health (e.g., patient, clinician, system, community). Research may be conducted in outpatient, inpatient, long-term care, home-based, or emergency care settings, and may include partnerships with agencies providing home and community-based services.
Both clinical and non-clinical outcomes are of interest, including intermediary outcomes such as trust, self-efficacy, autonomy, empowerment, safety, and resilience. The initiative encourages a range of research methodologies, including descriptive studies, interventions (multi-component, multi-sectoral, or multi-level), clinical trials (including cluster-randomized and pragmatic trials), quasi-experimental studies, natural experiments, quality improvement, mixed methods, and simulation modeling.