Commentary: Some water in the data desert: the Cancer Intervention and Surveillance Modeling Network’s capacity to guide mitigation of cancer health disparities

Author:

Winn Robert A1,Tossas Katherine Y2,Doubeni Chyke3

Affiliation:

1. Massey Comprehensive Cancer Center, Virginia Commonwealth University , Richmond, VA, USA

2. Department of Health Behavior & Policy, School of Population Health, Virginia Commonwealth University , Richmond, VA, USA

3. Department of Family and Community Medicine, Wexner Medical Center, Arthur G. James Cancer Hospital, and Richard J. Solove Research Institute, The Ohio State University , Columbus, OH, USA

Abstract

Abstract Despite significant progress in cancer research and treatment, a persistent knowledge gap exists in understanding and addressing cancer care disparities, particularly among populations that are marginalized. This knowledge deficit has led to a “data divide,” where certain groups lack adequate representation in cancer-related data, hindering their access to personalized and data-driven cancer care. This divide disproportionately affects marginalized and minoritized communities such as the U.S. Black population. We explore the concept of “data deserts,” wherein entire populations, often based on race, ethnicity, gender, disability, or geography, lack comprehensive and high-quality health data. Several factors contribute to data deserts, including underrepresentation in clinical trials, poor data quality, and limited access to digital technologies, particularly in rural and lower-socioeconomic communities.The consequences of data divides and data deserts are far-reaching, impeding equitable access to precision medicine and perpetuating health disparities. To bridge this divide, we highlight the role of the Cancer Intervention and Surveillance Modeling Network (CISNET), which employs population simulation modeling to quantify cancer care disparities, particularly among the U.S. Black population. We emphasize the importance of collecting quality data from various sources to improve model accuracy. CISNET's collaborative approach, utilizing multiple independent models, offers consistent results and identifies gaps in knowledge. It demonstrates the impact of systemic racism on cancer incidence and mortality, paving the way for evidence-based policies and interventions to eliminate health disparities. We suggest the potential use of voting districts/precincts as a unit of aggregation for future CISNET modeling, enabling targeted interventions and informed policy decisions.

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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