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Submitted on July 27, 2005
Accepted on November 28, 2005
Affiliation of the authors: 1 Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA; Clinical Decision Making Group, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA; 2 Indiana University School of Medicine, Indianapolis, IN; The Regenstrief Institute, Inc., Indianapolis, IN; 3 Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA; Harvard Medical School, Boston, MA
* To whom correspondence should be addressed.
Objective The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic.
Design Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially-created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density.
Measurements 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured.
Results The anonymization algorithm produced an expected skew of cases which resulted in high values of dataset k-anonymity. De-identification that moves points an average distance of 0.25km lowers the spatial cluster detection sensitivity by less than 4%, and lowers the detection specificity less than 1%.
Conclusion A population-density based Gaussian spatial blurring markedly decreases the ability to identify individuals in a dataset while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data spatial epidemiology and surveillance.
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