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Advancing Health Equity—Addressing Cancer Disparities

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1559

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Guest Editor
Department of Public Health, School of Health & Applied Human Sciences, University of North Carolina Wilmington, Wilmington, NC 28403, USA
Interests: racial health disparities; social support; cancer prevention
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer disparities are unequal cancer incidence, mortality, and outcomes among different populations, which remain a pressing public health issue. These disparities are often rooted in systemic factors such as socioeconomic status, race, ethnicity, and geographic location. To address these inequities, a multifaceted approach is required. This includes the following:

  • Social Determinants of Health: Examining and addressing factors like poverty, education, housing, and access to healthcare that significantly impact cancer disparities.
  • Patient–Provider Communication: Ensuring that patients feel reassured to express their health concerns and are fully aware of their health risks within a safe space.
  • Community-Based Interventions: Implementing culturally relevant programs and services that meet the specific needs of underserved communities.
  • Policy and Systems Changes: Advocating for policies that promote health equity and health inequality, such as expanding access to affordable healthcare.
  • Research and Data: Conducting rigorous research and interventions centered on understanding the root causes of cancer disparities.
  • Collaboration and Partnerships: Fostering partnerships between healthcare providers, researchers, policymakers, community leaders, and community members to create a unified effort in reducing cancer disparities.

By utilizing these techniques for addressing cancer health disparities, a comprehensive approach can be utilized to understand existing cancer disparities and achieve a more equitable healthcare system for all.

Dr. Shaila Strayhorn-Carter
Guest Editor

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Keywords

  • social determinates
  • community engagement
  • cancer disparities
  • patient–provider communication
  • policy change

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Published Papers (1 paper)

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Research

20 pages, 1325 KiB  
Article
A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health
by Claire J. Han, Xia Ning, Christin E. Burd, Fode Tounkara, Matthew F. Kalady, Anne M. Noonan and Diane Von Ah
Int. J. Environ. Res. Public Health 2024, 21(12), 1694; https://doi.org/10.3390/ijerph21121694 - 19 Dec 2024
Viewed by 1150
Abstract
Background: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily function, and mortality. GI health screening is crucial for preventing and managing this distress. However, accurate classification methods for GI health remain unexplored. We [...] Read more.
Background: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily function, and mortality. GI health screening is crucial for preventing and managing this distress. However, accurate classification methods for GI health remain unexplored. We aimed to develop machine learning (ML) models to classify GI health status (better vs. worse) by incorporating biological aging and social determinants of health (SDOH) indicators in cancer survivors. Methods: We included 645 adult cancer survivors from the 1999–2002 NHANES survey. Using training and test datasets, we employed six ML models to classify GI health conditions (better vs. worse). These models incorporated leukocyte telomere length (TL), SDOH, and demographic/clinical data. Results: Among the ML models, the random forest (RF) performed the best, achieving a high area under the curve (AUC = 0.98) in the training dataset. The gradient boosting machine (GBM) demonstrated excellent classification performance with a high AUC (0.80) in the test dataset. TL, several socio-economic factors, cancer risk behaviors (including lifestyle choices), and inflammatory markers were associated with GI health. The most significant input features for better GI health in our ML models were longer TL and an annual household income above the poverty level, followed by routine physical activity, low white blood cell counts, and food security. Conclusions: Our findings provide valuable insights into classifying and identifying risk factors related to GI health, including biological aging and SDOH indicators. To enhance model predictability, further longitudinal studies and external clinical validations are necessary. Full article
(This article belongs to the Special Issue Advancing Health Equity—Addressing Cancer Disparities)
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