# Microsimulation Modeling to Compare the Effectiveness and Cost-Effectiveness of Nondrug Interventions to Manage Clinical Symptoms in Racially/Ethnically Diverse Persons with Dementia

> **NIH NIH R01** · BROWN UNIVERSITY · 2021 · $398,857

## Abstract

Project Summary
Alzheimer’s Disease and Related Disorders (ADRD) affects >5 million Americans, disproportionately impacts
minority populations, and has significant economic consequences. Two clinical features in particular: functional
decline and behavioral symptoms, are associated with more caregiving and Medicare/Medicaid/family
expenditures when compared to cognitive decline alone. To help individuals with ADRD remain at home with
quality of life, it is vital to provide family caregivers, the largest providers of ADRD care, with effective support.
Unlike drugs (e.g., antipsychotics), nondrug ADRD interventions are not associated with adverse events and
are recommended as first-line treatments. Systematic reviews conclude that nondrug dyadic interventions that
engage the person with ADRD and provide caregivers skills effectively maintain or slow functional decline
and/or reduce ADRD-related behaviors. It is unclear as to the effects of these proven programs on outcomes of
relevance to families and policymakers (e.g., time spent caregiving) throughout the disease trajectory and
whether effects differ by race/ethnicity. Racial/ethnic differences in the use of nursing homes, ability to pay for
health care, and family structures may impact population effectiveness of interventions. In response to PAR-
18-331, we propose to use innovative methods in simulation modeling to extend findings from completed
randomized controlled trials (RCTs) on select dyadic interventions. Specifically, we will use our published
ADRD microsimulation model (ADRD-MM) to infer the effect of proven dyadic interventions on outcomes of
hours caregiving, days in a nursing home, costs, and the person with ADRD’s and their caregiver’s quality-
adjusted life-years. We will also evaluate outcomes by race/ethnicity. Our simulation model used data from the
National Alzheimer’s Coordinating Center, the Health and Retirement Study, and Medicare to simulate an
incident ADRD case’s decline in function, behavior, and cognition and associated family and policy outcomes.
For our proposed study, we have identified eight proven dyadic interventions that meet our inclusion criteria of
1) being tested in RCTs in US community-dwelling persons with ADRD and/or their caregivers and 2) having
outcome publications that provided effect sizes and sufficient data to estimate implementation cost. Using the
ADRD-MM, our specific aims are to: 1) Determine the effects by race (African American, Asian, and White)
and ethnicity (Hispanic) of identified nondrug ADRD dyadic interventions on family hours caregiving, days in a
nursing home, costs to families/Medicaid/Medicare, and quality-adjusted life-years of the person with ADRD
and their caregivers; 2) compare effectiveness, cost-effectiveness, and affordability by race (African American,
Asian, and White) and ethnicity (Hispanic) of the identified ADRD interventions; and 3) determine which of the
identified ADRD interventions should be tested in additional...

## Key facts

- **NIH application ID:** 10218006
- **Project number:** 5R01AG060871-03
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Eric Jutkowitz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $398,857
- **Award type:** 5
- **Project period:** 2019-07-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218006

## Citation

> US National Institutes of Health, RePORTER application 10218006, Microsimulation Modeling to Compare the Effectiveness and Cost-Effectiveness of Nondrug Interventions to Manage Clinical Symptoms in Racially/Ethnically Diverse Persons with Dementia (5R01AG060871-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10218006. Licensed CC0.

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