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This paper is in the following e-collection/theme issue:

Cancer Epidemiology, Cancer Surveillance and Infodemiology (176) Epidemiology (254) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (237) Descriptive Epidemiology and Population Size Estimates (237)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66655, first published .
Doctors in a meeting discussing patient charts in a medical office.

Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study

Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study

Authors of this article:

Zixuan Cui1 Author Orcid Image ;   Chen Suo1, 2, 3 Author Orcid Image ;   Yidan Zhao1 Author Orcid Image ;   Shuo Wang1 Author Orcid Image ;   Ming Zhao1, 2 Author Orcid Image ;   Ruilin Chen1 Author Orcid Image ;   Linyao Lu3, 4 Author Orcid Image ;   Tiejun Zhang1, 2, 3, 5 Author Orcid Image ;   Xingdong Chen3, 4, 5, 6 Author Orcid Image
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Journals

  1. Wang D, Zhang H, Zhang H, Song Z, Doblin S, Lu P. Microbiota and cancer: Elucidating the role of Candida albicans in cancer progression. World Journal of Clinical Oncology 2025;16(6) View
  2. Xu J, Gong J, Han H, Wang Z, Wang W, Wang L, Sui X, Chen G, Jia Y, Qin Y. Co-occurrence patterns of esophageal and stomach cancer across 204 countries and territories: a spatial correspondence and systematic analysis. Frontiers in Oncology 2025;15 View
  3. Ahmadi T, Mohammadi A, Mohammadzadeh Z, Maserat E, Naghashi S. Gastric cancer susceptibility mapping using GIS and machine learning in East Azerbaijan province, Iran. International Journal of Health Geographics 2026;25(1) View
  4. Zheng M, Zhang C, Yang B, Lin C, Zhu C, Chen Y, Wan Q. Spatiotemporal co-occurrence patterns and related risk factors of esophageal and lip and oral cavity cancers among older adults across 203 countries and territories: a machine learning-driven systematic analysis. Annals of Medicine 2026;58(1) View