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RESEARCH AND PRACTICE |
Karen L. Olson and Kenneth D. Mandl are with the Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Childrens Hospital Boston, Boston, Mass; the Division of Emergency Medicine, Childrens Hospital Boston; and the Department of Pediatrics, Harvard Medical School, Boston. Shaun J. Grannis is with Regenstrief Institute Inc and the Indiana University School of Medicine, Indianapolis.
Correspondence: Requests for reprints should be sent to Karen L. Olson, PhD, Informatics Program, Childrens Hospital Boston, 1 Autumn St, Box 721, Boston, MA 02215 (e-mail: karen.olson{at}childrens.harvard.edu).
Objectives. Patient data that includes precise locations can reveal patients identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases.
Methods. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy.
Results. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts.
Conclusions. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.
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A. J. McMurry, C. A. Gilbert, B. Y. Reis, H. C. Chueh, I. S. Kohane, and K. D. Mandl A Self-scaling, Distributed Information Architecture for Public Health, Research, and Clinical Care J. Am. Med. Inform. Assoc., July 1, 2007; 14(4): 527 - 533. [Abstract] [Full Text] [PDF] |
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S. C. Wieland, J. S. Brownstein, B. Berger, and K. D. Mandl Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes PNAS, May 29, 2007; 104(22): 9404 - 9409. [Abstract] [Full Text] [PDF] |
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