ArticleOsong public health and research perspectives2015
Gastric and esophageal cancers incidence mapping in golestan province, iran: using bayesian-gibbs sampling.
Article in Osong public health and research perspectives, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 9 citations in OpenAlex.
- Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations.The Journal of international medical research · 2026Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Meat consumption and risk of esophageal and gastric cancer in the Golestan Cohort Study, Iran.International journal of cancer · 2022Article
- Epidemiologic Study of Gastric Cancer in Iran: A Systematic Review.Clinical and experimental gastroenterology · 2020Review
- Iranian high risk regions due to esophageal cancer: spatial analysis of cancer registry data.Gastroenterology and hepatology from bed to bench · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRecent studies of esophageal cancer (EC) and gastric cancer (GC) have been reported to have high incidence rates of these cancers in Golestan Province of Iran. The present study describes the geographical patterns of EC and GC incidence based on cancer registry data and display statistically significant regions within this province.
methodsIn order to map the distribution of upper gastrointestinal cancer, relative risk (RR) were calculated. Therefore, to estimate a more reliable RR, Poisson regression models were used. The adjusted models (adjusted to urban-rural area, sex, and grouped age proportion) were utilized. We considered two-component random effects for each observation, an unstructured (noncorrelated) and a group of "neighbor" (correlated) heterogeneities. We estimated the model parameters using Gibbs sampling and empirical Bayes method. We used EC and GC data that were registered with Golestan Research Center of Gastroenterology and Hepatology in the years 2004-2008.
resultsThe EC and GC maps were drawn for 2004-2008 in the province. Kalaleh and Minoodasht counties have a high RR of EC and GC in the years of study. In almost all years, the areas with a high RR were steady.
conclusionThe EC and GC maps showed significant spatial patterns of risk in Golestan province of Iran. Further study is needed to multivariate clustering and mapping of cancers RRs with considering diet and socioeconomic factors.
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