# Data-Driven Identification of Costly Multi-Morbidity Groupings and their Progression

> **NIH NIH R15** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2020 · $435,338

## Abstract

The PI proposes a high-impact collaborative research project to identify the most prevalent
multimorbidity combinations; develop an understanding of the typical sequence of disease
progression for each multimorbidity combination; and assess the incremental costs associated
with each progression. The Center for Health Care Strategies calls for developing the means to
identify homogeneous multimorbidity subgroups to more effectively develop targeted
interventions. This work will support the National Institute on Aging 21st Century strategic
directions for research on aging especially goals (E and F) related to understanding health
differences, health disparities and developing strategies for intervention and policy decisions.
Through completing the study's three aims, we will significantly improve scientific knowledge. A
lack of emphasis on multimorbidities can have a significant negative impact on care. Factoring
in multimorbidity should better explain healthcare expenditures. Understanding the typical
disease progression sequence for each multimorbidity combination has the potential to assist
with estimating the future healthcare burden and understand relationships between different
diseases. The process of formulating interventions to treat and prevent combinations of
comorbid diseases can be made simpler, less costly, more reliable and repeatable, more
personalized, and more productive. To our knowledge, no research has attempted to identify all
common multimorbidity patterns in a population of this size using a similar method, nor identified
progression patterns at this scale. Further, greater insight into multimorbidities combinations is
essential to further aging research as a significant proportion of the trend in multimorbidity is
attributable to aging. Additionally, the research project we propose will allow us to expand our
compute cluster, which is accessible online for both masters and Ph.D. students. The research
itself will allow them to improve data science skills learned in the classroom, and help us
matriculate master's students to our Ph.D. program—a trend that has already started.
Furthermore, a significant percentage of our student body is composed of underrepresented
minorities and women. This research will help expose many of them to opportunities they would
not otherwise have.

## Key facts

- **NIH application ID:** 9962881
- **Project number:** 1R15AG067232-01
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Charisse Renee Madlock-Brown
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $435,338
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9962881

## Citation

> US National Institutes of Health, RePORTER application 9962881, Data-Driven Identification of Costly Multi-Morbidity Groupings and their Progression (1R15AG067232-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9962881. Licensed CC0.

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