# Cumulative and synergistic impact of chronic diseases on physical functioning in older adults: development and validation of a novel measure of multimorbidity

> **NIH NIH K23** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $167,400

## Abstract

Project Summary / Abstract
Multimorbidity, the coexistence of multiple chronic conditions, poses a major and growing challenge to aging
adults, their families, and healthcare systems. Most older adults have multiple chronic conditions that interact
to profoundly affect physical functioning and health-related quality of life. Despite this, critical gaps remain in
the measurement of multimorbidity. In particular, there are few practical tools to guide clinicians, researchers,
and policymakers who seek to improve the care for older adults. Current measures for multimorbidity have
used mortality, healthcare cost, and utilization, but have not focused on patient-centered outcomes to quantify
burden of disease. Statistical limitations in traditional methods to measure disease interactions beyond simple
two-way disease interactions have also hampered our epidemiologic understanding of multimorbidity and its
full impact on health and functional outcomes. To bridge these gaps, this proposal aims to 1) Develop and
validate a new multimorbidity index for use in ICD-coded conditions; 2) Refine the multimorbidity index by
incorporating multiple-order disease interactions and determine the incremental value of their inclusion; and 3)
Assess the utility of the multimorbidity index through its association with key functional outcomes including
physical and cognitive performance, basic and instrumental activities of daily living, depression, and mortality.
The proposed studies will use unique data linkages between patient-reported outcomes in the nationally-
representative Health and Retirement Study and Medicare claims to develop and internally validate a
multimorbidity index for International Classification of Diseases (ICD)-coded chronic conditions weighted to
physical functioning. Novel approaches to data shrinkage techniques will be used to select significant
interactions among multiple potential disease combinations associated with physical functioning. The utility of
the multimorbidity index will be assessed through its prospective associations with physical and cognitive
performance, basic and instrumental activities of daily living, and mortality. The successful completion of these
studies will yield a validated multimorbidity measure that captures the impact of coexisting chronic diseases on
physical functioning in older adults relevant for clinical care, research, and policy.
Through this award, the candidate will achieve immediate career goals to gain new specialized skills in using
administrative claims, ICD coded data, and novel statistical approaches to high-dimension data, and achieve a
deeper understanding of measuring key health outcomes in older adults with multimorbidity. The facilities,
sponsoring department, and intellectual resources at the University of Michigan provide an exceptional milieu
for this career development award and Early Stage Investigator. The training and professional development
acquired through this award will contribute ...

## Key facts

- **NIH application ID:** 9925770
- **Project number:** 5K23AG056638-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Melissa Wei
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $167,400
- **Award type:** 5
- **Project period:** 2017-09-15 → 2020-08-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9925770

## Citation

> US National Institutes of Health, RePORTER application 9925770, Cumulative and synergistic impact of chronic diseases on physical functioning in older adults: development and validation of a novel measure of multimorbidity (5K23AG056638-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9925770. Licensed CC0.

---

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