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Figure S1 Summary of the keywords provided by authors of the original articles included in the review. from A Review of the Application of Spatial Survival Methods in Cancer Research: Trends, Modeling, and Visualization Techniques

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posted on 2023-08-01, 08:20 authored by Habtamu Mellie Bizuayehu, Jessica K. Cameron, Paramita Dasgupta, Peter D. Baade

Supplementary Figure S1 shows summary of keywords extracted from articles

Funding

Sylvia and Charles Viertel Charitable Foundation (SCVCF)

University of Queensland (UQ)

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ARTICLE ABSTRACT

Spatial modeling of cancer survival is an important tool for identifying geographic disparities and providing an evidence base for resource allocation. Many different approaches have attempted to understand how survival varies geographically. This is the first scoping review to describe different methods and visualization techniques and to assess temporal trends in publications. The review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline using PubMed and Web of Science databases. Two authors independently screened articles. Articles were eligible for review if they measured cancer survival outcomes in small geographical areas by using spatial regression and/or mapping. Thirty-two articles were included, and the number increased over time. Most articles have been conducted in high-income countries using cancer registry databases. Eight different methods of modeling spatial survival were identified, and there were seven different ways of visualizing the results. Increasing the use of spatial modeling through enhanced data availability and knowledge sharing could help inform and motivate efforts to improve cancer outcomes and reduce excess deaths due to geographical inequalities. Efforts to improve the coverage and completeness of population-based cancer registries should continue to be a priority, in addition to encouraging the open sharing of relevant statistical programming syntax and international collaborations.

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    Cancer Epidemiology, Biomarkers & Prevention

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