Privacy protection versus cluster detection in spatial epidemiology.
OBJECTIVE
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.
Olson
Karen L
KL
Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Children's Hospital Boston, Boston, Mass 02215, USA. karen.olson@childrens.harvard.edu
Grannis
Shaun J
SJ
Mandl
Kenneth D
KD
D000465:Algorithms; D019352:Censuses; D016000:Cluster Analysis; D003198:Computer Simulation; D003219:Confidentiality; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D005843:Geography / Q000145:classification; D006801:Humans; D011177:Postal Service / Q000145:classification; D040442:Public Health Informatics / Q000379:methods* / Q000592:standards; D018571:Sentinel Surveillance