Spatial variation in prostate cancer survival in the Northern and Yorkshire region of England using Bayesian relative survival smoothing

Fairley, Lesley; Forman, David; West, Robert; Manda, Samuel

doi:10.1038/sj.bjc.6604757

Cited by 25 publications

(33 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In cluster analysis the most used methods were spatial scan statistics, and Moran's I (both global and local) Factor studies Methods such as hierarchical modelling, multilevel modelling, logistic regression or geographical weighted regression were mentioned in studies of factors or in environmental context analysis°S values Fairley et al, 2008;Goovaerts, 2006b). This problem, called the small numbers problem (Goovaerts, 2005;Shi, 2009), is further enhanced when the diseases investigated are rare (Thompson et al, 2007) and/or the populations of the geographical units under analysis are small (Goovaerts, 2006a;Short et al, 2002).…”

Section: Methods Appliedmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

Roquette¹,

Paìnho²,

Nunes

2017

Geospat Health

View full text Add to dashboard Cite

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...

show abstract

Section: Methods Appliedmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…It is a measure of the deaths, which occur over and above those that would be expected for a given population. Such a Bayesian relative survival model (Appendix Part E, Box 8) has been used by Fairley et al (2008) and Cramb et al (2011a). See Appendix Part A for the statistical models for incidence and relative survival.…”

Section: Bayesian Spatial Statistical Modelsmentioning

confidence: 99%

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

et al. 2016

View full text Add to dashboard Cite

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

show abstract

“…The Bayesian spatial survival model adopted for this analysis assumed the hazards were constant within pre-specified followup time intervals, and was based on the model described by Fairley et al (Fairley et al, 2008),…”

Section: Statistical Modelmentioning

confidence: 99%

“…Most analyses have focused on cause-specific survival (Henry et al, 2009;Huang et al, 2007;Wan et al, 2012). We chose to instead use Bayesian hierarchical methods to model relative survival (Fairley et al, 2008;Saez et al, 2012), where cancer patient mortality is compared against mortality in the population of similar age, sex and time period. Our focus was on comparing survival up to 5-years after diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Spatial inequalities in colorectal and breast cancer survival: Premature deaths and associated factors

et al. 2012

View full text Add to dashboard Cite

This study examines the influence of cancer stage, distance to treatment facilities and area disadvantage on breast and colorectal cancer spatial survival inequalities. We also estimate the number of premature deaths after adjusting for cancer stage to quantify the impact of spatial survival inequalities. Population-based descriptive study of residents aged <90 years in Queensland, Australia diagnosed with primary invasive breast (25,202 females) or colorectal (14,690 males, 11,700 females) cancers during 1996-2007. Bayesian hierarchical models explored relative survival inequalities across 478 regions. Cancer stage and disadvantage explained the spatial inequalities in breast cancer survival, however spatial inequalities in colorectal cancer survival persisted after adjustment. Of the 6,019 colorectal cancer deaths within 5 years of diagnosis, 470 (8%) were associated with spatial inequalities in non-diagnostic factors, i.e. factors beyond cancer stage at diagnosis. For breast cancers, of 2,412 deaths, 170 (7%) were related to spatial inequalities in non-diagnostic factors. Quantifying premature deaths can increase incentive for action to reduce these spatial inequalities.

show abstract

Spatial variation in prostate cancer survival in the Northern and Yorkshire region of England using Bayesian relative survival smoothing

Cited by 25 publications

References 38 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

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Spatial inequalities in colorectal and breast cancer survival: Premature deaths and associated factors

Contact Info

Product

Resources

About