# Identifying predictors of racial disparity in treatment and mortality among patients diagnosed with breast cancer in South Carolina and geospatial investigation of breast cancer patient navigation

> **NIH NIH K00** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2020 · $95,121

## Abstract

Delay in treatment initiation contributes to higher mortality among Black women despite lower incidence of
breast cancer (BrCa) among Black compared to White women. Survival studies show that a delay of two
months has been linked with a less favorable survival among BrCa cases. The use of Adjuvant Hormonal
Therapy (AHT) has been shown to improve both short- and long-term survival among Hormone Receptor
Positive (HR+) BrCa patients worldwide as it reduces BrCa mortality and reoccurrence. The advantages of
AHT notwithstanding, 10-30% of eligible BrCa patients never start treatment with AHT and many who start
AHT never complete the treatment leading to reoccurrence and increased mortality. This lack of initial uptake
and adherence to AHT is worse among Blacks compared to Whites. Effective reduction of disparities in
treatment delays and mortality among racial minorities will require the identification of the mechanisms by
which disparities occur particularly, studying neighborhood-level factors that have been shown to affect the
odds of receipt of BrCa treatments among Black women. For this application, I propose to assess racial
disparities in BrCa treatment and mortality in South Carolina (SC) utilizing data that was derived from all
female BrCa cases over eight years from the SC Central Cancer Registry linked with administrative medical
and pharmacy claims data for both publicly insured and privately insured BrCa patients. I will assess the
complex interplay between 1) geographic factors, 2) racial disparities 3) Geographical Information System
(GIS) mapping, 4) survival methods and 5) multi-level models to identify predictors of treatment delays and
mortality that can be intervened upon. GIS methods have not been utilized to specifically identify individual-
and neighborhood-level characteristics that may contribute to BrCa mortality among blacks within the context
of multilevel survival modelling; this has the potential to allow for the application of evidence-based approaches
to reduce disparities. This project has 3 aims: 1) to assess racial disparities in treatment delays and the
utilization of AHT among patients diagnosed with breast cancer (Pre-Doctoral and completed); 2) to identify
predictors of dissimilarity in breast cancer related survival by health regions among Black women in SC
utilizing multilevel survival models and GIS methodologies (Pre-Doctoral and yet to be completed); 3) to
assess if neighborhood-level factors modify the effect of patient navigation on uptake of initial recommended
breast cancer care and time to diagnostic resolution among minority populations (Post-Doctoral direction). The
F99 phase of this fellowship award will provide the necessary training and mentoring to further my knowledge
and skill in 1) disparities, 2) survival, 3) multi-level, 4) GIS, and 5) geospatial analyses. The K00 phase will
expose me to 1) interventional analyses such as cancer patient navigation, 2) Randomized Controlled Trials
and 3) enha...

## Key facts

- **NIH application ID:** 9978735
- **Project number:** 5K00CA222722-04
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Oluwole Adeyemi Babatunde
- **Activity code:** K00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $95,121
- **Award type:** 5
- **Project period:** 2019-07-15 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9978735

## Citation

> US National Institutes of Health, RePORTER application 9978735, Identifying predictors of racial disparity in treatment and mortality among patients diagnosed with breast cancer in South Carolina and geospatial investigation of breast cancer patient navigation (5K00CA222722-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9978735. Licensed CC0.

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