ArticleInternational health2021
Spatial distribution of esophageal cancer mortality in China: a machine learning approach.
Article in International health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 11 citations in OpenAlex.
- Nasojejunal tube, jejunostomy tube, and fine needle catheter jejunostomy effects after esophagectomy.Frontiers in surgery · 2026Article
- Integrating migration into cancer geography: spatial clustering of five digestive cancer burden in China.Frontiers in public health · 2026Article
- Beyond numbers: Public health insurance and oesophageal cancer mortality risk.World journal of gastrointestinal oncology · 2025Article
- A novel mitochondria-related gene signature in esophageal carcinoma: prognostic, immune, and therapeutic features.Functional & integrative genomics · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Social Support and Spiritual Well-Being of Patients With Esophageal Cancer Aged Over 50 Years: The Mediating Role of Rumination.Frontiers in psychiatry · 2022Article
- Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors.Frontiers in public health · 2022Article
- Familial Esophageal Cancer in Taihang Mountain, China: An Era of Personalized Medicine Based on Family and Population Perspective.Cell transplantationReview
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention.
methodsEC mortality surveillance data covering 964 surveyed counties in China in 2014 and three classes of auxiliary data, including physical condition, living habits and living environment data, were collected. Genetic programming (GP), a hierarchical Bayesian model and sandwich estimation were used to estimate the spatial distribution of female EC mortality. Finally, we evaluated the accuracy of the three mapping methods.
resultsThe results show that compared with the root square mean error (RMSE) of the hierarchical Bayesian model at 6.546 and the sandwich estimation at 7.611, the RMSE of GP is the lowest at 5.894. According to the distribution estimated by GP, the mortality of female EC was low in some regions of Northeast China, Northwest China and southern China; in some regions downstream of the Yellow River Basin, north of the Yangtze River in the Yangtze River Basin and in Southwest China, the mortality rate was relatively high.
conclusionsThis paper provides an accurate map of female EC mortality in China. A series of targeted preventive measures can be proposed based on the spatial disparities displayed on the map.
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