The epidemic of lung cancer in Tuscany (Italy): A joint analysis of male and female mortality by birth cohort

Biggeri, Annibale; Catelan, Dolores; Dreassi, Emanuela

doi:10.1016/j.sste.2009.07.006

Cited by 18 publications

(21 citation statements)

References 39 publications

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“…The importance of creating maps that accurately describe disease spatial distribution patterns appeared to be a consensual issue (Kulldorff et al, 2006) though the method used to achieve this was not consensual. Some articles intended to define the best method for some type of analysis for some particular datasets by comparing the results of the application of different spatial analysis methods (Bailony et al, 2011;Biggeri et al, 2009;Chen et al, 2008a;Colonna, 2004;Dasgupta et al, 2014;Goovaerts, 2005Goovaerts, , 2006aHegarty et al, 2010;Huang et al, 2008;Kaldor and Clayton, 1989;Kulldorff et al, 2006;Meliker et al, 2009;Sherman et al, 2014;Sloan et al, 2012;Zhou et al, 2008b). Table 5 shows a classification of some of most common spatial issues covered by research papers, as well as methods used to answer them.…”

Section: Applied Methods In Data Analysismentioning

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: Applied Methods In Data Analysismentioning

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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“…Maps of disease information over time can provide insight into how a given disease behaves and help predict the development of this disease and future needs for intervention and medical facilities. Temporal disease mapping can identify trends over time [42][43][44] -when the occurrence increases or decreases in specific areas [30,45]; moves in a certain direction [46,47] -or is a periodically recurring pattern (cycles).…”

Section: Spatial Analysis Of Diseasementioning

confidence: 99%

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

Lyseen

Nøhr²,

Sørensen

et al. 2014

Yearb Med Inform

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SummaryObjectives: The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. Method: The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. Results: A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Conclusion: Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health. KeywordsEpidemiology, geographic information system, public health, cluster analysis, spatial analysis Yearb Med Inform 2014:110-24 http://dx

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“…Due to covariates, the data set counts are spread over 12,064 cells with 2,346 zeros (19% of cells have zeros). The cohort dimension is important when analysing time trend (Dreassi et al, 2005;Catelan et al, 2006;Biggeri et al, 2009). However, the inclusion of the cohort in the analysis will increase the size of the dataset and consequently the number of zero-cells.…”

Section: Data Description and Exploratory Analysismentioning

confidence: 99%

Resources allocation in healthcare for cancer: a case study using generalised additive mixed models

2012

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Abstract. Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, in order to replan the allocation of providers. Ageing of the population has a considerable impact on the use of health resources because aged people require more specialised medical care due notably to cancer. We propose a method useful to monitor changes of cancer incidence in space and time taking into account two age categories, according to healthcar general organisation. We use generalised additive mixed models with a Poisson response, according to the methodology presented in Wood, Generalised additive models: an introduction with R. Chapman and Hall/CRC, 2006. Besides one-dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modelled using scale invariant smoothers. Incidence data collected by a general cancer registry between 1992 and 2007 in a specific area of France is studied. Our best model exhibits a strong increase of the incidence of cancer along time and an obvious spatial pattern for people more than 70 years with a higher incidence in the central band of the region. This is a strong argument for re-allocating resources for old people cancer care in this sub-region.

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The epidemic of lung cancer in Tuscany (Italy): A joint analysis of male and female mortality by birth cohort

Cited by 18 publications

References 39 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

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

Resources allocation in healthcare for cancer: a case study using generalised additive mixed models

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