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Privacy protection versus cluster detection in spatial epidemiology.
Karen L Olson1, Shaun J Grannis, Kenneth D Mandl
1Children'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
American Journal of Public Health
|October 5, 2006
Summary
Using precise patient addresses for spatial cluster detection improves accuracy, especially when outbreaks cross administrative boundaries. However, this must be balanced with privacy concerns in public health data exchange.
Area of Science:
- Public Health
- Epidemiology
- Geographic Information Systems (GIS)
Background:
- Patient data privacy is a concern, with aggregated data potentially preserving confidentiality.
- Varying address precision impacts the ability to detect spatial disease clusters.
Purpose of the Study:
- To investigate how different levels of address precision affect the detection of spatial disease clusters.
- To compare the performance of spatial cluster detection using exact locations versus aggregated regional data.
Main Methods:
- Simulated disease outbreaks by adding clustered emergency department visits to real surveillance data.
- Employed a spatial scan statistic to identify clusters.
- Evaluated cluster detection rate and accuracy based on address precision.
Main Results:
- Exact locations led to more identified clusters and higher detection accuracy.
- Clusters identified using exact locations more accurately represented simulated outbreaks.
- These improvements were most notable when simulated clusters spanned multiple administrative boundaries (zip codes, census tracts).
Conclusions:
- Spatial cluster detection algorithms perform better with precise address data compared to aggregated regional data.
- Precise addresses enhance detection, particularly for clusters crossing administrative boundaries.
- Balancing improved detection performance with patient privacy is crucial for public health data exchange policies.
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