Mapping Cancer Disease Using Geographical Information System (GIS) in Gezira State-Sudan

Elebead, F. M.; Hamid, Amna Ahmed; Hilmi, H. S. M.; Galal, Hossam

doi:10.1007/s10900-011-9517-9

Cited by 18 publications

(14 citation statements)

References 5 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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“…In Sudan, breast cancer is a neglected disease [8,9]. Breast and Cervical Cancer account for about 50% of all cancers in Sudanese women [10].…”

Section: Discussionmentioning

confidence: 99%

Clinicopathological profile of female Sudanese patients with locally advanced breast cancer

Ahmed

2014

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“…Geographic information systems (GIS), including mapping and geo-spatial analysis, are powerful tools used often for the investigation of infectious disease (7–9) and less frequently for the study of non-transmissible conditions (10, 11). To date, these methodologies and types of analysis are relatively new in mental health research, with a few exceptions (12–16).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

Ruíz-Grosso

Miranda

Gilman

et al. 2016

Annals of Epidemiology

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Purpose To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru. Methods Presence of major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale (CES-D). Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RR) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms. Results Data from 7946 participants, 28.3% male, mean age 39.4 (SD: 13.9) years, was analyzed. The prevalence of depressive symptoms was 17.0% (95% CI: 16.2%–17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR=1.82; p=0.003 and secondary: RR=2.83; p=0.004 and RR=5.92; p=0.01), and two clusters with significantly low prevalence (primary: RR=0.23; p=0.016 and secondary: RR=0; p=0.035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones. Conclusions Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.

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Mapping Cancer Disease Using Geographical Information System (GIS) in Gezira State-Sudan

Cited by 18 publications

References 5 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

Clinicopathological profile of female Sudanese patients with locally advanced breast cancer

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

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