Cancer incidence in men: a cluster analysis of spatial patterns

Cassetti, Tiziana; Rosa, Francesco La; Rossi, Luca; D'Alò, D; Stracci, Fabrizio

doi:10.1186/1471-2407-8-344

Cited by 16 publications

(11 citation statements)

References 19 publications

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“…Individual data Cancer registries Aballay et al (2012), Absalon and Slesak (2011), Al-Ahmadi and Al-Zahrani (2013), Alvarez et al (2009), Bailony et al (2011), Bambhroliya et al (2012, Bristow et al (2014), Buntinx et al (2003), Cassetti et al (2008), Chen et al (2008b, 2011, Chien et al (2013a), Christian et al (2011), Colak et al (2015, Colonna (2004Colonna ( , 2006, Colonna et al (1990), Cramb et al (2011), Dai and Oyana (2008), David et al (2002), DeChello and Sheehan (2007), DeChello et al (2006), Drapeau et al (1995), Elebead et al (2012), Elferink et al (2012), Fairley et al (2008), Fedewa et al (2009), Ferreira et al (2012, Fortunato et al (2011), Gallagher et al (2010, Garcia Martinez et al (2014), Gbary et al (1995), Goodman et al (2010), Xiao (2011, 2012), Guajardo and Oyana (2009), Samociuk (2003, 2013) Godon et al (1991), Goovaerts (2005Goovaerts ( , 2006aGoovaerts ( , 2006b), Hendryx et al (2010), Hosseintabar Marzoni et al (2015, …”

Section: Data Type Source Articlementioning

confidence: 99%

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Roquette¹,

Paìnho²,

Nunes

2017

Geospat Health

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Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i) analysis of spatial distribution of cancer and/or its temporal evolution; ii) risk factors; iii) development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer's spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations. IntroductionGiven the relevance of spatial epidemiology in health research and the emphasis of cancer among chronic diseases, as well as the growing amount of studies in this area, it is important to know what the literature says about spatial epidemiology of cancer as well as provide its structured description. According to the World Health Organization (WHO), cancer is a leading cause of death in the world (WHO, 2015). It is also the cause of various morbidities and co-morbidities and can be responsible for loss of years of life years as well as loss of years without disability. Considering the aging population, it is predicted that the number of new cases of cancer will increase by more than 12% over the next decade in the European Union (EU) (DGS, 2013). The fight against cancer is a major challenge in public health. This challenge is due in part to the inequalities in terms of incidence, mortality, and survival. Therefore, a multidisciplinary approach is needed (Bastos et al., 2010). Among the various fields that can contribute to the development of knowledge about this disease, spatial epidemiology plays an important role. It can promote the understanding of spatial and temporal distribution patterns, helping to better identify the risk factors that influence them.Three types of approach can be established in spatial epidemiology: i) mapping; ii) geographic correlation; and iii) clustering (Elliott and Wartenberg, 2004). Mapping or map design regarding health and disease situations is the most often mentioned and used of these three approaches. Further, geographic correlation studies have the goal to spatially compare the health with several types of factors such as e...

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Section: Data Type Source Articlementioning

confidence: 99%

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Roquette¹,

Paìnho²,

Nunes

2017

Geospat Health

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“…(22) Other studies reported that about one and half to fourfold increase in prevalence of oral cavity cancer was observed in patients above the age of 50 years compared with those below the age of 50, and it is a disease that affects men in the 5th and 6 th decades of life and is associated with habits of smoking, alcohol consumption, different geographic areas and gender. (4)(5)(6)22,24,(25)(26)(27)(28)(29)(30) The male-to-female ratio of 1.6:1 was another characteristic of oral cavity malignant tumors pattern in this study. In developed countries, the male-to-female ratio is 2.5:1, and in less developed countries it is about 1.5:1.…”

Section: Discussionmentioning

confidence: 51%

“…It mainly affects individuals in the 6 th and 7 th decades of the life with history of smoking and or alcohol consumption. (4)(5)(6) Dietary deficiency of vitamin A and iron have their impact on oral mucosa and have been associated with high risk of oral malignancy. (7) In Jordan, 21% of the population were reported to have vitamin A deficiency, (8) 30% of women were anemic and 55% of these anemic had iron deficiency.…”

Section: Introductionmentioning

confidence: 99%

Malignant Tumors of the Oral Cavity among Jordanians

Fdsrcs¹,

Hammouri²

2014

JRMS

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Objective: To determine the incidence of primary malignant tumors of the oral cavity in Jordan and to compare the incidence rates among Jordanian population with world standard population. Methods: A retrospective study was conducted during the period 2000 and 2007, on 156 cases with histological diagnosis of primary malignant tumors of the oral cavity. The cases were registered and received by active case finding from the maxillofacial departments at the Royal Medical Services Hospitals, Hospitals of the

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“…Concerning the geographic distribution of cancer cases, spatial clustering seems to exist in lung, prostate, bladder, and colon-rectum cancers but this clustering depends mainly on the available data [ 15 – 18 ]. Furthermore, the clustering of cancer in a given area may depend on factors such as the socioeconomic status [ 19 ] and on unknown risk factor common to other diseases [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping

Goungounga

Gaudart

Colonna

et al. 2016

BMC Med Res Methodol

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BackgroundThe reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence.MethodsMoran’s I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only.ResultsThe study found a spatial heterogeneity (p < 0.01) and an autocorrelation for prostate (EBI = 0.02; p = 0.001), lung (EBI = 0.01; p = 0.019) and bladder (EBI = 0.007; p = 0.05) cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes (p < 0.05): four in the Western and one in the Northern parts of the study area (standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively). In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods (RR >1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6).ConclusionsIn spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0228-x) contains supplementary material, which is available to authorized users.

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Cancer incidence in men: a cluster analysis of spatial patterns

Cited by 16 publications

References 19 publications

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Malignant Tumors of the Oral Cavity among Jordanians

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping

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