Biomedical subjects

Gerard Rushton

Publications and source records attributed to Gerard Rushton.

5 recordsLinked to original sources

PubMed2007

Modeling the probability distribution of positional errors incurred by residential address geocoding.

BACKGROUND: The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated geocoding and E911 geocoding. RESULTS: Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched geocoding errors; outliers occurred for the other two types of geocoding errors also but were much smaller. E911 geocoding was more accurate (median error length = 44 m) than 100%-matched automated geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated geocoding and E911 geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. CONCLUSION: Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.

Censuses↗

PubMed2006

Geocoding in cancer research: a review.

There is now widespread agreement that geographic identifiers (geocodes) should be assigned to cancer records, but little agreement on their form and how they should be assigned, reported, and used. This paper reviews geocoding practice in relation to major purposes and discusses methods to improve the accuracy of geocoded cancer data. Differences in geocoding methods and materials introduce errors of commission and omission into geocoded data. A common source of error comes from the practice of using digital boundary files of dubious quality to place addresses into areas of interest. Geocoded data are linked to demographic, environmental, and health services data, and each data type has unique accuracy considerations. In health services applications, the accuracy of distances computed from geocodes can differ markedly. Privacy and confidentiality issues are important in the use and release of geocoded cancer data. When masking methods are used for disclosure limitation purposes, statistical methods must be adjusted for the locational uncertainty of geocoded data. We conclude that selection of one particular type of geographic area as the geocode may unnecessarily constrain future work. Therefore, the longitude and latitude of each case is the superior basic geocode; all other geocodes of interest can be constructed from this basic identifier.

Biomedical Research↗

PubMed2005

Using software agents to preserve individual health data confidentiality in micro-scale geographical analyses.

Confidentiality constraints often preclude the release of disaggregate data about individuals, which limits the types and accuracy of the results of geographical health analyses that could be done. Access to individually geocoded (disaggregate) data often involves lengthy and cumbersome procedures through review boards and committees for approval (and sometimes is not possible). Moreover, current data confidentiality-preserving solutions compatible with fine-level spatial analyses either lack flexibility or yield less than optimal results (because of confidentiality-preserving changes they introduce to disaggregate data), or both. In this paper, we present a simulation case study to illustrate how some analyses cannot be (or will suffer if) done on aggregate data. We then quickly review some existing data confidentiality-preserving techniques, and move on to explore a solution based on software agents with the potential of providing flexible, controlled (software-only) access to unmodified confidential disaggregate data and returning only results that do not expose any person-identifiable details. The solution is thus appropriate for micro-scale geographical analyses where no person-identifiable details are required in the final results (i.e., only aggregate results are needed). Our proposed software agent technique also enables post-coordinated analyses to be designed and carried out on the confidential database(s), as needed, compared to a more conventional solution based on the Web Services model that would only support a rigid, pre-coordinated (pre-determined) and rather limited set of analyses. The paper also provides an exploratory discussion of mobility, security, and trust issues associated with software agents, as well as possible directions/solutions to address these issues, including the use of virtual organizations. Successful partnerships between stakeholder organizations, proper collaboration agreements, clear policies, and unambiguous interpretations of laws and regulations are also much needed to support and ensure the success of any technological solution.

Computer Security↗

PubMed2004

Analyzing geographic patterns of disease incidence: rates of late-stage colorectal cancer in Iowa.

This study, using geocodes of the locations of residence of newly diagnosed colorectal cancer patients from the Iowa Cancer Registry, computed continuous spatial patterns of late-stage rates of colorectal cancer in Iowa. Variations in rates in intrahospital service regions were as great as interhospital service regions, shown by analysis of variance tests. Some of the spatial variations observed could be explained, using a general linear regression model on individual-level data, by spatial variations in attributes of individuals and their relationships to health resources. We show how this source of variation can be removed from the original map leaving a new map showing the remaining variation in late-stage rate not explained by these relationships. We argue that it would be more appropriate to organize prevention and control activities targeted at the areas with higher than expected late-stage rates shown on this map. The originality of this approach is in the integration of geocoded data from a cancer registry with methods of spatial analysis that provide considerable geographic detail in the cancer rate while controlling for rate stabilization and reliability due to the small number problem.

Colorectal Neoplasms↗

PubMed2002

Public health, GIS, and spatial analytic tools.

We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of populations. This review focuses on these advances.

Cluster Analysis↗