# Improving Inclusivity of Alzheimer’s Disease and Related Dementias Research for Asian Americans and Latinx through Nationally Representative Hybrid Sampling.

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $979,196

## Abstract

ABSTRACT
Racial/ethnic minorities are projected to be a major portion of the aging U.S. population. Two of the strategic
research directions of the National Institute on Aging (NIA) are: 1) improving understanding of Alzheimer’s
Disease and Related Dementias (ADRD) and 2) understanding health disparities to improve the health of
diverse older adult populations as evidenced by NOT-AG-21-033. While achieving these goals for racial/ethnic
disparities requires data, national aging research data are absent beyond (mostly White) Latinx, non-Latinx
Whites, and non-Latinx Blacks. Collecting research data under the traditional sampling framework is resource
intensive and prohibitively so for granular minority groups. In response to NOT-AG-21-033 that addresses this
gap, this study aims to improve inclusivity in ADRD research data by introducing Hybrid Sampling (HybS) for
building a nationally representative panel of middle-age and older adults with an oversample of seven granular
minority groups (Afro Latinx, non-Afro Latinx, Chinese, Asian Indians, Filipinos, Koreans and Vietnamese)
through the push-to-Web method. As an extension of address-based sampling (ABS) and respondent driven
sampling (RDS), HybS starts from a probability sample of seeds and exploits existing social networks for
participant recruitment through chain-referrals, capturing those who, otherwise, are difficult to reach, while
maintaining the probability sampling principles. To do so, we apply the push-to-Web method that also offers an
option of participating over phone to lower the costs and the constraints associated with the time, geography
and interview language. Racial/ethnic minorities are particularly well suited for the push-to-Web HybS, as they
are known to form tight in-group social networks and to access the Web at a high level. For managing such a
panel survey, we will also develop a sample management system and make it publicly available.
 Capitalizing on the connectedness of participants, this study will measure social networks from multiple
angles and examine the role of various social networks on ADRD risks and racial/ethnic disparities within. This
study will collect data using the same methods across racial/ethnic groups, which will eliminate methodological
confounders in examining disparities. Although rare, there are scientifically rigorous and well-established
population-based data about minorities and aging research data. We will triangulate data from these existing
studies with data from the proposed study’s panel through multiple frame estimation in order to improve its
representation properties. By developing a practical data collection framework and providing tools to implement
studies under this framework, outcomes of this study will enable the research community to address the needs
for ADRD data on granular racial/ethnic minorities. Increased inclusivity of research data will inform policy
makers to develop nuanced ADRD prevention and intervention strategies...

## Key facts

- **NIH application ID:** 10798560
- **Project number:** 1R01AG082080-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Sung-Hee Lee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $979,196
- **Award type:** 1
- **Project period:** 2024-04-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798560

## Citation

> US National Institutes of Health, RePORTER application 10798560, Improving Inclusivity of Alzheimer’s Disease and Related Dementias Research for Asian Americans and Latinx through Nationally Representative Hybrid Sampling. (1R01AG082080-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10798560. Licensed CC0.

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