Characterizing geospatial access to comprehensive breast cancer services in Northern Tanzania
6 affiliations
- 1Rutgers Global Health Institute
- 2Hologic Inc.
- 3Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health
- 4Rutgers Cancer Institute
- 5Emory University
- 6Bugando Medical Center
Abstract
Background: Tanzania has a high breast cancer mortality burden attributed in part to poor access to comprehensive cancer services. The objective of this study was to characterize geospatial patterns of breast cancer patients presenting to the only tertiary hospital in northern Tanzania that provides comprehensive breast cancer care.
Methods: This geospatial analysis used baseline data from adult breast cancer patients diagnosed at Bugando Medical Center (BMC) in Mwanza, Tanzania, between July 2019 and December 2023. Participants were geocoded to their residential subdistricts as defined by the United States Census Bureau. Forty-five subdistricts in the catchment area were identified as the spatial unit of analysis. Age-standardized observed rates depicted the number of patients diagnosed with breast cancer per subdistrict. Global and local Moran’s I statistics assessed spatial autocorrelation of observed breast cancer rates. Ordinary least squares regression models evaluated community-level factors, including population density, the presence of hospitals, and distance to comprehensive cancer care.
Results: We included 379 women diagnosed with breast cancer from the catchment area, with a median of 3 breast cancer cases per subdistrict (IQR: 0-10). Participant ages ranged from 21–90 years old. The mean age-standardized observed breast cancer rate was 4.2 per 100,000 women (IQR: 0-10.1). Observed breast cancer rates were spatially clustered across the entire catchment area (Moran’s Index= 0.289; p=0.001). Additionally, specific subdistricts where clustering and dispersion existed were identified. Ordinary least squares regression identified community-level factors (low population density; p=0.003, greater distance to BMC; p<0.001) associated with lower breast cancer rates.
Conclusions: We identified spatial clustering of breast cancer rates in the Northern region of Tanzania. Spatial clustering aligned with distance to comprehensive cancer care and population density, suggesting that low observed rates in remote areas may reflect underdiagnosis due to diagnostic availability rather than lower disease burden. Decentralizing diagnostic capacity, particularly in low-rate spatial clusters, may enhance equitable access to care for patients facing geographic barriers and improve breast cancer early detection and outcomes.
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BibTeX
@article{friebelklingner2026characterizing,
title = {{Characterizing geospatial access to comprehensive breast cancer services in Northern Tanzania}},
author = {Tara M. Friebel-Klingner and Anne F. Rositch and Hari S. Iyer and Rachel B. Wagner and Emma Joo and Matogoro Kirahi and Nestory Masalu and Racquel E. Kohler},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1952705},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1952705/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Characterizing geospatial access to comprehensive breast cancer services in Northern Tanzania
AU - Tara M. Friebel-Klingner
AU - Anne F. Rositch
AU - Hari S. Iyer
AU - Rachel B. Wagner
AU - Emma Joo
AU - Matogoro Kirahi
AU - Nestory Masalu
AU - Racquel E. Kohler
PY - 2026
DA - 2026/10/23
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1952705
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1952705/full
AB - Background: Tanzania has a high breast cancer mortality burden attributed in part to poor access to comprehensive cancer services. The objective of this study was to characterize geospatial patterns of breast cancer patients presenting to the only tertiary hospital in northern Tanzania that provides comprehensive breast cancer care. Methods: This geospatial analysis used baseline data from adult breast cancer patients diagnosed at Bugando Medical Center (BMC) in Mwanza, Tanzania, between July 2019 and December 2023. Participants were geocoded to their residential subdistricts as defined by the United States Census Bureau. Forty-five subdistricts in the catchment area were identified as the spatial unit of analysis. Age-standardized observed rates depicted the number of patients diagnosed with breast cancer per subdistrict. Global and local Moran’s I statistics assessed spatial autocorrelation of observed breast cancer rates. Ordinary least squares regression models evaluated community-level factors, including population density, the presence of hospitals, and distance to comprehensive cancer care. Results: We included 379 women diagnosed with breast cancer from the catchment area, with a median of 3 breast cancer cases per subdistrict (IQR: 0-10). Participant ages ranged from 21–90 years old. The mean age-standardized observed breast cancer rate was 4.2 per 100,000 women (IQR: 0-10.1). Observed breast cancer rates were spatially clustered across the entire catchment area (Moran’s Index= 0.289; p=0.001). Additionally, specific subdistricts where clustering and dispersion existed were identified. Ordinary least squares regression identified community-level factors (low population density; p=0.003, greater distance to BMC; p<0.001) associated with lower breast cancer rates. Conclusions: We identified spatial clustering of breast cancer rates in the Northern region of Tanzania. Spatial clustering aligned with distance to comprehensive cancer care and population density, suggesting that low observed rates in remote areas may reflect underdiagnosis due to diagnostic availability rather than lower disease burden. Decentralizing diagnostic capacity, particularly in low-rate spatial clusters, may enhance equitable access to care for patients facing geographic barriers and improve breast cancer early detection and outcomes.
SN - 2234-943X
ER -