# Building Equity Improvement into Quality Improvement in the use of New Glucose-lowering Drugs (GLDs) through Individualized Drug Value Assessment in People with Diabetes

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2022 · $663,931

## Abstract

Project Summary
Since 2007, more than 40 glucose-lowering drugs (GLDs) have been approved by the US Food and Drug
Administration to treat diabetes. These newer GLDs have been proven to have higher cardiorenal benefits than
older classes when applied in people at high risk of cardiovascular and kidney disease. However, the
introduction of these high-cost GLDs has led to significant quality and equity concerns in diabetes care: socially
disadvantaged individuals tend to have limited access to newer GLDs due to barriers related to social
attributes (e.g., income, education), resulting in gaps and disparities in achieving optimal health outcomes.
There is, therefore, an urgent need to improve the quality of care and equity in using newer GLDs among
millions of Americans living with type 2 diabetes (T2D).
Previous studies have found that programs that improve the quality of care by promoting treatment in targeted
clinically high-benefit user groups lead to equity improvement because high-benefit users from socially
disadvantaged subgroups often have larger gaps in care thus benefit more from these programs. However,
critical knowledge gaps exist in identifying the clinically high-benefit users of newer GLDs and designing policy-
level interventions that can adequately motivate patients’ newer GLDs use while having good long-term health
and economic outcomes. Thus, the OBJECTIVE of this proposed project is to identify clinically high-benefit
T2D patient subgroups for newer GLDs and generate empirical economic evidence for designing policy-level
interventions to improve the quality of care and health equity in T2D care.
High-quality comparative effectiveness research (CER) requires the patients to have complete data records
which can track event encounters and treatment exposure with high accuracy. These individuals were often
referred to as “loyal patients.” In this proposed project, we will develop a computable phenotype (CP) for “loyal
patients” using OneFlorida EHRs and cross-network validate the CP using REACHnet EHRs (Aim 1). To
identify clinically high-benefit T2D patient subgroups for newer GLDs, we will conduct comparative
effectiveness and safety analyses of newer GLDs versus guideline-recommended alternatives across patient
subgroups using rigorous causal inference methods and a machine-learning (ML) approach. The high-benefit
T2D patient subgroups will be identified using EHRs of “loyal patients” from OneFlorida and cross-validated in
REACHnet (Aim 2). At last, we will evaluate the impact of potential policy-level interventions for promoting
newer GLDs use in high-benefit users on health, economics, and equity outcomes. Leveraging an advanced
ML algorithm developed by PI, we will also identify the ideal cost-sharing structure at a health-plan level to
maximize drug adherence while reducing the payers' burden.
The proposed research is significant because it will provide solutions for an emergent public health issue in
quality of care and heal...

## Key facts

- **NIH application ID:** 10502997
- **Project number:** 1R01DK133465-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jingchuan Guo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $663,931
- **Award type:** 1
- **Project period:** 2022-07-20 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10502997

## Citation

> US National Institutes of Health, RePORTER application 10502997, Building Equity Improvement into Quality Improvement in the use of New Glucose-lowering Drugs (GLDs) through Individualized Drug Value Assessment in People with Diabetes (1R01DK133465-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10502997. Licensed CC0.

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