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Privacy protection versus cluster detection in spatial epidemiology

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  • Olson, K.L.
  • Grannis, S.J.
  • Mandl, K.D.

Abstract

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.

Suggested Citation

  • Olson, K.L. & Grannis, S.J. & Mandl, K.D., 2006. "Privacy protection versus cluster detection in spatial epidemiology," American Journal of Public Health, American Public Health Association, vol. 96(11), pages 2002-2008.
  • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2005.069526_6
    DOI: 10.2105/AJPH.2005.069526
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    Cited by:

    1. Brandon W Higgs & Mojdeh Mohtashemi & Jennifer Grinsdale & L Masae Kawamura, 2007. "Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless: Trade-Offs Between Spatial Resolution and Temporal Scale," PLOS ONE, Public Library of Science, vol. 2(12), pages 1-9, December.
    2. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    3. Jeffery Caroline & Ozonoff Al & White Laura Forsberg & Pagano Marcello, 2013. "Distance-Based Mapping of Disease Risk," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 265-290, May.

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