# Predicting and Reducing Future Health Disparities for U.S. Adults with Diabetes

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $402,573

## Abstract

PROJECT SUMMARY
Disparities in diabetes and its complications have persisted, despite increased public health focus on reducing
them. Compared to whites living with diabetes, blacks have a two-fold increased risk of stroke, amputation, and
end-stage renal disease (ESRD); Hispanics and Asians have a 20 to 50% higher risk for eye problems, and at
least an 80% higher risk of ESRD. Many interventions have been developed to reduce diabetes health
disparities; however, despite accumulated evidence, adoption of interventions to eliminate disparities has
been slow across healthcare organizations and payers. A critical barrier to developing and adopting policies to
eliminate diabetes health disparities is the lack of long-term data on the economic and clinical impact of these
interventions. Simulation models are mathematical representations of the complex relationships between
predictors (e.g., hemoglobin A1c) and outcomes (e.g., ESRD) among specific populations. Diabetes simulation
models have been used for over a decade to describe the cost-effectiveness of clinical guidelines, clinical
interventions, and new drugs and devices. However, they have been rarely used to study the disparate impact
between populations, i.e. health disparities. Without such a model, it is difficult to compare the relative value of
different clinical and public health interventions to reduce health disparities and difficult for policymakers to
decide where to allocate resources. The reason that such a model does not exist is that the majority of current
diabetes simulation models rely on data from the UKPDS and other sources, which did not have large
populations of Hispanic and/or Asian patients. The major challenge to developing a more useful multi-ethnic
diabetes simulation model is the need to have comprehensive data across all domains of healthcare utilization,
including clinically-measured risk factors (e.g., blood pressure and laboratory results) and race/ethnicity data.
We will use data from Kaiser Permanente Northern California, a multi-ethnic, socioeconomically diverse
population with diabetes (n~192,000) (16% Latino, 11% African American, 7% South Asian, 4% Chinese; 13%
difficulty with English) to develop a mathematical model of the relationships between patient risk factors and
outcomes using Kaiser data, and then to input national data and published data into the model in order to
forecast the long-term implications of efforts to reduce diabetes health disparities. Our specific aims are to 1)
develop a simulation model of diabetes outcomes for white, African American, Latino, South Asian, and
Chinese populations; 2) forecast the impact of past changes in risk factor control on future diabetes health
disparities; and 3) determine the cost-effectiveness of diabetes health disparities interventions.

## Key facts

- **NIH application ID:** 10144853
- **Project number:** 5R01MD013420-04
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Neda Laiteerapong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $402,573
- **Award type:** 5
- **Project period:** 2018-09-19 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10144853

## Citation

> US National Institutes of Health, RePORTER application 10144853, Predicting and Reducing Future Health Disparities for U.S. Adults with Diabetes (5R01MD013420-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10144853. Licensed CC0.

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