# Adding a Module for Alzheimer/Dementia Outcomes to a Microsimulation Model of Obesity/Diet/Physical Activity Interventions.

> **NIH NIH R01** · RAND CORPORATION · 2020 · $488,963

## Abstract

Abstract/Project Summary
Proposed Supplement:
We will add a new module on Alzheimer's Disease and Related Dementias (ADRD) to the
microsimulation model developed in the parent grant. Our current project studies how
interventions on risk factors like obesity, diet, and physical activity affect incidence and prevalence
of chronic conditions like hypertension, diabetes, or stroke. These health conditions in turn affect
health and long-term care needs, quality of life, disability, and mortality. So far, our model does
not include any measures related to ADRD. However, many of the conditions and pathways
modeled in the parent grant have also been implicated in ADRD. The supplement will add ADRD
as an additional outcome dimension. Adding ADRD necessitates some restructuring and
incorporating other data, but is a natural extension using the same architecture as the original
project. Our specific aims for the supplement are to:
 1. Incorporate cognition and ADRD measures from the Health and Retirement Survey
 2. Validate the new ADRD modules and assess statistical uncertainty
 3. Expand our visualization tool for ADRD outcomes
 4. Simulate population outcomes related to ADRD and predict burden of ADRD with and
 without interventions targeting diet, physical activity, or obesity.
Innovations of this supplement will be: 1) the first microsimulation model to link population
interventions in diet/physical activity/obesity to ADRD outcomes; 2) the first microsimulation
model on ADRD that assesses statistical uncertainty for statistical inference beyond point
estimates.
Aims of Current parent grant:
The prevalence of obesity has increased dramatically over the last three decades, as has its
corresponding burden of disease. Much of that burden is preventable. There is no shortage of
suggestions how to address the obesity epidemic – the gap is how to assess the likely long-run
effects of intervention. As the ultimate outcomes of interest (prevention of chronic illness at the
population level) will not manifest for many years following an intervention, mathematical models
like the one we develop in this project are needed to integrate the information from multiple
sources. Without good simulation models, it is difficult – if not impossible – to make predictions of
socially important outcomes of prevention efforts, most of which are many years in the future and
beyond the scope of clinical trials. The specific aims are:
(1) Develop, test and validate a dynamic microsimulation modeling platform that captures
individual and social dynamics of diet/nutrition, physical activity, and BMI;
(2) Use the platform to predict health, social and economic outcomes for the coming decades;
(3) Assess the outcomes consequences of health policies in multiple areas (food taxes and
subsidies, labeling laws, bariatric surgery) at reducing social harms;
(4) Provide an open version to researchers and policy makers to conduct their own calculations.

## Key facts

- **NIH application ID:** 10122794
- **Project number:** 3R01HD087257-05S1
- **Recipient organization:** RAND CORPORATION
- **Principal Investigator:** ROLAND STURM
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $488,963
- **Award type:** 3
- **Project period:** 2016-05-06 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10122794

## Citation

> US National Institutes of Health, RePORTER application 10122794, Adding a Module for Alzheimer/Dementia Outcomes to a Microsimulation Model of Obesity/Diet/Physical Activity Interventions. (3R01HD087257-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10122794. Licensed CC0.

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