Investigation of Leukaemia Clusters by Use of a Geographical Analysis Machine

Openshaw, S.; Charlton, Martin; Craft, A W; Birch, Jillian M

doi:10.1016/s0140-6736(88)90352-2

Cited by 162 publications

(76 citation statements)

References 6 publications

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“…There is a variety of real phenomena that form these patterns in domains such as epidemiology, criminology, transportation safety, ecology and environmental science, business, medicine, and urban planning. In epidemiology, for example, hotspot detection on disease datasets reveals regions that report high concentrations of incidents, potentially indicating outbreaks [16,17]. Geospatial hotspot detection should incorporate domain-specific theories to better understand the actual phenomena.…”

Section: Foundations Of Hotspot Detectionmentioning

confidence: 99%

Transdisciplinary Foundations of Geospatial Data Science

Xie

Eftelioglu

Ali

et al. 2017

IJGI

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Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

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Section: Foundations Of Hotspot Detectionmentioning

confidence: 99%

Transdisciplinary Foundations of Geospatial Data Science

Xie

Eftelioglu

Ali

et al. 2017

IJGI

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“…One such study used data from 1978-1982 regarding 592 cases of leukemia and the population data for each of the 790 national census tracts in a region consisting of eight counties of New York in the United States (Alexander et al, 1992). This study is a widely known case of test method application proposed for the testing for spatial clustering (Openshaw et al, 1988;Waller et al, 1992). The northeastern United States is known for its high mortality due to breast cancer.…”

Section: Breast Cancer Smr and Sir In Kanagawa Above Smr Below Sirmentioning

confidence: 99%

Breast Cancer Clustering in Kanagawa, Japan: A Geographic Analysis

Katayama

Yokoyama²,

Yako-Suketomo³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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Background: The purpose of the present study was to determine geographic clustering of breast cancer incidence in Kanagawa Prefecture, using cancer registry data. The study also aimed at examining the association Materials and Methods: Incidence data were collected (e.g., city, special ward, town, and village) in Kanagawa by an indirect method using Kanagawa female population data. Spatial scan statistics were used to detect any area of elevated risk as a cluster for breast cancer deaths and/ regions. Results:incidence in southeastern area of Kanagawa Prefecture. It was suggested that the cluster region was related to the tendency to marry later. This study methodology will be helpful in the analysis of geographical disparities in cancer deaths and incidence.

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“…Other investigations of childhood leukemia have generated scientific and media interest, such as the cluster near a nuclear power plant in Sellafield, England. 30,31 An exceptionally large cluster of childhood leukemia occurred in Churchill County (Fallon), Nevada from 1997 to 2001. Eleven cases of leukemia were identified over a five-year period among children in a community of 26,000 people.…”

Section: Why Are Investigations Of Community Cancer Clusters Difficult?mentioning

confidence: 99%

Understanding Cancer Clusters

Thun

Sinks

2004

CA: A Cancer Journal for Clinicians

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Each year, state and local health departments respond to more than 1,000 inquiries about suspected cancer clusters. Three quarters of these reports involve situations that are clearly not clusters and can be resolved by telephone. For the remainder, follow-up is needed, first to confirm the number of persons affected, their age, type of cancer, dates of diagnosis, and other factors, and then to compare cancer incidence in the affected population with background rates in state tumor registries. In approximately 5% to 15% of the reported situations, formal statistical testing confirms that the number of observed cases exceeds the number expected in a specific area, given the age, sex, and size of the affected population. Even in these instances, however, chance remains a plausible explanation for many clusters, and further epidemiologic investigation almost never identifies the underlying cause of disease with confidence. The few exceptions have involved clusters of extremely rare cancers occurring in well-defined occupational or medical settings, generally involving intense and sustained exposure to an unusual chemical, occupation, infection, or drug. This article discusses the resources and scientific tools currently available to investigate cancer clusters. It also provides a framework for understanding cancer clusters and a realistic appraisal of what cluster investigations can and cannot provide in the context of community expectations. (CA Cancer J Clin 2004;54: 273-280.)

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Investigation of Leukaemia Clusters by Use of a Geographical Analysis Machine

Cited by 162 publications

References 6 publications

Transdisciplinary Foundations of Geospatial Data Science

Transdisciplinary Foundations of Geospatial Data Science

Breast Cancer Clustering in Kanagawa, Japan: A Geographic Analysis

Understanding Cancer Clusters

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