Geocoding in Cancer Research

Rushton, Gérard; Armstrong, Marc P.; Gittler, Josephine; Greene, Barry R.; Pavlik, Claire; West, Michele; Zimmerman, Dale L.

doi:10.1016/j.amepre.2005.09.011

Cited by 160 publications

(173 citation statements)

References 58 publications

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“…Because geocoded residential locations have primarily been used as proxy for retrospective exposures in early life, subsequent exposure classification and disease risk estimates depend (in part) on the quality of geocoding with respect to the historical residence. Common problems and solutions in the geocoding steps of health data have been well documented (Rushton et al, 2006;Goldberg et al, 2008). However, the potential bias due to the incompleteness of geocoding, especially of historical records, has not been fully evaluated in the research of early-life factors.…”

Section: Discussionmentioning

confidence: 99%

“…There are several sources of uncertainty in spatial and environmental epidemiology studies of early life factors, and the geocoding process could reveal many possible sources of error, including positional error and difference (Krieger et al, 2001;Rushton et al, 2006). Similarly, geocoding failure of the historical residence may cause geographic bias in spatial analysis of the relations between exposures during early life and disease risk.…”

Section: Discussionmentioning

confidence: 99%

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Assessing bias associated with geocoding of historical residence in epidemiology research

et al. 2013

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Abstract. The use of geocoded historical residence as proxy for retrospective assessment of exposure in early life is increasing in epidemiological studies of chronic health outcomes. Dealing with historical residence poses challenges, primarily due to higher uncertainties associated with data collection and processing. A possible source of bias is connected with the exclusion of subjects, who cannot, for various reasons, be geocoded. We evaluated the potential bias that may arise due to incomplete geocoding, using birth residence data collected as part of a population-based case-control study of breast cancer in western New York state. We found that geocoded and non-geocoded populations did not differ in the distribution of most risk factors compared, and that the geocoding status did not modify the spatial patterns of the study populations. However, the results emphasize the need for epidemiological studies to consider the potential biases that may be introduced by geocoding of historical residence when investigating retrospectively chronic disease and early-life exposure.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessing bias associated with geocoding of historical residence in epidemiology research

et al. 2013

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“…In the nearest-neighbor masking method, only the distance between an individual record and the nearest record(s) of interest is reported, which is often sufficient for clustering analyses (Rushton, 2006). Furthermore, certain cluster analyses (e.g., Cuzick-Edwards statistic) also require attribute knowledge of the nearest neighbor (Leitner and Curtis, 2006).…”

Section: Geographic Perturbation Methodsmentioning

confidence: 99%

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Clifton¹,

Gehrke²

2014

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“…Various studies have been undertaken that used GIS technology and spatial data analysis in evaluating health data (Levine et al, 2009;Rushton et al, 2006). Spatial analysis is a useful tool to explore health data, map and identify patterns, generate new hypothesis, and provide evidences about existing hypothesis (Boscoe et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Investigating Geographic Distribution of Colorectal Cancer Cases: An Example from Penang State, Malaysia

Samat¹,

Shattar²,

Sulaiman³

et al. 2013

ASS

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The map has widely been used to depict and disseminate information on the spatial distribution of various diseases, including cancer cases. Increasingly, population-based cancer data need to incorporate spatial information that enables various spatial and proximity analyses to be conducted whereby results can be graphically presented. Yet, disease maps as a communication form remain largely unexamined. This is probably due to the confidentiality of the disease cases and the cost of incorporating the spatial component in the database. In the state of Penang, Malaysia, although Penang Cancer Registry (PCR) collects and collates data of all cancer cases diagnosed in the state as well as cancer cases diagnosed elsewhere whose home address is given as a Penang address, geographical location is not included. Mapping of cases using information from PCR will give a fairly complete picture of spatial distribution of cancer cases from Penang State and clustering of cases can be readily evaluated. This study demonstrates the application of spatial analysis methods and GIS in mapping and understanding the spatial distribution of Colorectal cancer cases in Penang State. The cases were mapped to identify spatial clustering of cancer cases and measure distance from existing health facilities. This study finds that spatial information is pertinent to be included as part of the database kept by Cancer Registry since this information can effectively be used for communications with and education of the public, as well as for planning health care delivery.

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Geocoding in Cancer Research

Cited by 160 publications

References 58 publications

Assessing bias associated with geocoding of historical residence in epidemiology research

Assessing bias associated with geocoding of historical residence in epidemiology research

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Investigating Geographic Distribution of Colorectal Cancer Cases: An Example from Penang State, Malaysia

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