# Using Geographically Targeted and Community-Based Methods to Identify Factors Associated with Micro-Level Disparities in Diabetic Outcomes and Enhance Monitoring of Glycemic Control Among Black Men

> **NIH NIH K23** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $197,661

## Abstract

Project Summary / Abstract
 This mentored patient-oriented research career development award (K23) will support Dr. David Lee’s
training and research to use geographically targeted and community-based methods to identify factors
associated with micro-level disparities in diabetic outcomes and enhance monitoring of glycemic control among
Black men. Though the prevalence of diabetes is increasing nationally, current diabetes surveillance methods
are unable to identify hot spots of poor diabetic outcomes at a community level. However, studies based on a
novel geographic method of diabetes surveillance have found that the increase in diabetes burden has been
focused in specific communities, especially among Black neighborhoods. For Black adults, poor diabetes
control has been associated with fewer primary care visits, less frequent HbA1c testing, and higher rates of
emergency department use and hospitalizations, especially among diabetic Black men. Given this infrequent
interaction with a usual source of healthcare, community-based settings may provide the advantages of pre-
existing trust and engagement to optimize health outcomes for high-risk populations living in neighborhoods
that are hot spots of diabetic complications. Thus, the specific aims of this proposal are 1) to use geospatial
and quantitative methods to identify which micro-contextual factors account for local disparities in diabetic
outcomes among Black communities, 2) to use geographically-targeted qualitative interviews to identify
neighborhood-level factors that explain poor diabetic outcomes in certain Black communities, and 3) to perform
community-based HbA1c testing and diabetes self-care surveys among Black men living in neighborhoods with
a high prevalence of diabetic complications. This community-based research will leverage existing partnerships
within a network of local Black-owned barbershops in New York City. Barbershops have become increasingly
effective sites for promoting health and measuring health outcomes among Black men, a population which has
high rates of mortality and morbidity from diabetes. The results of this research will inform future R-series
applications to expand this approach to other high-risk subgroups in Black neighborhoods and other racial and
ethnic communities with extremely poor diabetic outcomes. Dr. Lee’s training goals closely parallel his
research aims and will further enhance his understanding of: 1) advanced quantitative analysis, 2) qualitative
and mixed methods, 3) social and behavioral science, and 4) diabetes education and management. The
proposed research and training will be conducted at the New York University School of Medicine and leverage
resources of the other professional schools at NYU, which offer outstanding opportunities for collaboration,
learning, and multidisciplinary research. This environment, in addition to his research and training plan will
provide Dr. Lee with a strong foundation from which he can accelerate his car...

## Key facts

- **NIH application ID:** 9931224
- **Project number:** 5K23DK110316-05
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** David C Lee
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $197,661
- **Award type:** 5
- **Project period:** 2016-08-15 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9931224

## Citation

> US National Institutes of Health, RePORTER application 9931224, Using Geographically Targeted and Community-Based Methods to Identify Factors Associated with Micro-Level Disparities in Diabetic Outcomes and Enhance Monitoring of Glycemic Control Among Black Men (5K23DK110316-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9931224. Licensed CC0.

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