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Evidence-Based Conceptual Collection of Methods for Spatial Epidemiology and Analysis to Enhance Cancer Surveillance and Public Health

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  • Dimitra Sifaki-Pistolla

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Vasiliki Eirini Chatzea

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Elpiniki Frouzi

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Enkeleint A. Mechili

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Georgia Pistolla

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • George Nikiforidis

    (Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Vassilis Georgoulias

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Christos Lionis

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

  • Nikos Tzanakis

    (Cancer Registry of Crete, School of Medicine, University of Crete, 71003 Heraklion, Greece)

Abstract

(1) Background: Although spatial statistics are often used by cancer epidemiologists, there is not yet an established collection of methods to serve their needs. We aimed to develop an evidence-based cancer-oriented conceptual collection of methods for spatial analysis; (2) Methods: A triangulation of approaches was used; literature review, consensus meetings (expert panel), and testing the selected methods on “training” databases. The literature review was conducted in three databases. This approach guided the development of a collection of methods that was subsequently commented on by the expert panel and tested on “training data” of cancer cases obtained from the Cancer Registry of Crete based on three epidemiological scenarios: (a) low prevalence cancers, (b) high prevalence cancers, (c) cancer and risk factors; (3) Results: The final spatial epidemiology conceptual collection of methods covered: data preparation/testing randomness, data protection, mapping/visualizing, geographic correlation studies, clustering/surveillance, integration of cancer data with socio-economic, clinical and environmental factors. Some of the tests/techniques included in the conceptual collection of methods were: buffer and proximity analysis, exploratory spatial analysis and others. All suggested that statistical models were found to fit well (R2 = 0.72–0.96) in “training data”; Conclusions: The proposed conceptual collection of methods provides public health professionals with a useful methodological framework along with recommendations for assessing diverse research questions of global health.

Suggested Citation

  • Dimitra Sifaki-Pistolla & Vasiliki Eirini Chatzea & Elpiniki Frouzi & Enkeleint A. Mechili & Georgia Pistolla & George Nikiforidis & Vassilis Georgoulias & Christos Lionis & Nikos Tzanakis, 2022. "Evidence-Based Conceptual Collection of Methods for Spatial Epidemiology and Analysis to Enhance Cancer Surveillance and Public Health," IJERPH, MDPI, vol. 19(19), pages 1-11, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12765-:d:934443
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    References listed on IDEAS

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    1. Kulldorff, Martin, 2006. "Tests of Spatial Randomness Adjusted for an Inhomogeneity: A General Framework," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1289-1305, September.
    2. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    3. Min Weng & Jianhua Pi & Bingqing Tan & Shiliang Su & Zhongliang Cai, 2017. "Area Deprivation and Liver Cancer Prevalence in Shenzhen, China: A Spatial Approach Based on Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 317-332, August.
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