ArticlePreventing chronic disease2024
Geospatial Hot Spots and Cold Spots in US Cancer Disparities and Associated Risk Factors, 2004-2008 to 2014-2018.
Article in Preventing chronic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Spatial Variations and Gender Differences in the Associations Between Lung Cancer Mortality Rate and Contextual Factors: A County-Level Cross-Sectional Ecological Analysis.Journal of prevention (2022) · 2026Article
- Shared Decision-Making for Prostate Cancer Screening in Racial and Ethnic Minority Populations: A Systematic Review.Journal of racial and ethnic health disparities · 2026Article
- Neighborhood geographic disparities in colorectal, prostate, breast, and lung cancer risk in Alabama.Journal of cancer survivorship : research and practice · 2026Article
- Medicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer.JAMA network open · 2026Article
- [Pathology data in spatial epidemiology (REDPath) : A web-based application for oncology and service planning].Pathologie (Heidelberg, Germany) · 2026Article
- Older people's preferences for elderly care mode in Northwest China: a multilevel analysis stratified by socioeconomic status.BMC geriatrics · 2026Article
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Authors and funding
5 authors.
Funding
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Abstract
Introduction: Despite declining cancer death rates in the US, cancer remains the second deadliest disease and disparities persist. Although research has focused on identifying risk factors for cancer deaths and associated disparities, few studies have examined how these relationships vary over time and space. The primary objective of this study was to identify cancer mortality hot spots and cold spots - areas where cancer death rates decreased less than or more than neighboring areas over time. A secondary objective was to identify risk factors of cancer mortality hot spots and cold spots. Methods: We analyzed county-level cancer death rates from 2004 through 2008 and 2014 through 2018, exploring disparities in changes over time for socioeconomic and demographic variables. We used hot spot analysis to identify areas with larger decreases (cold spots) and smaller decreases (hot spots) in cancer death rates and random forest machine learning analysis to assess the relative importance of risk factors associated with hot spots and cold spots. We mapped spatial clustering areas. Results: Geospatial analysis showed hot spots predominantly in the Plains states and Midwest and cold spots in the Southeast, Northeast, 2 Mountain West states (Utah and Idaho), and a portion of Texas. Factors with the strongest influence on hot spots and cold spots were unemployment, preventable hospital stays, mammography screening, and high school education. Conclusion: Geospatial disparities in changes in cancer death rates point out the critical role of access to care, socioeconomic position, and health behaviors in persistent cancer mortality disparities. Study results provide insights for interventions and policies that focus on addressing health care access and social determinants of health.
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