title: Influence of spatial resolution on space-time disease cluster detection.
abstract: BACKGROUND
  Utilizing highly precise spatial resolutions within disease outbreak detection, such as the patients' address, is most desirable as this provides the actual residential location of the infected individual(s). However, this level of precision is not always readily available or only available for purchase, and when utilized, increases the risk of exposing protected health information. Aggregating data to less precise scales (e.g., ZIP code or county centroids) may mitigate this risk but at the expense of potentially masking smaller isolated high risk areas.
  
  
  METHODS
  To experimentally examine the effect of spatial data resolution on space-time cluster detection, we extracted administrative medical claims data for 122500 viral lung episodes occurring during 2007-2010 in Tennessee. We generated 10000 spatial datasets with varying cluster location, size and intensity at the address-level. To represent spatial data aggregation (i.e., reduced resolution), we then created 10000 corresponding datasets both at the ZIP code and county level for a total of 30000 datasets. Using the space-time permutation scan statistic and the SaTScan™ cluster software, we evaluated statistical power, sensitivity and positive predictive values of outbreak detection when using exact address locations compared to ZIP code and county level aggregations.
  
  
  RESULTS
  The power to detect disease outbreaks did not largely diminish when using spatially aggregated data compared to more precise address information. However, aggregations negatively impacted the ability to more accurately determine the exact spatial location of the outbreak, particularly in smaller clusters (<800 km²).
  
  
  CONCLUSIONS
  Spatial aggregations do not necessitate a loss of power or sensitivity; rather, the relationship is more complex and involves simultaneously considering relative risk within the cluster and cluster size. The likelihood of spatially over-estimating outbreaks by including geographical areas outside the actual disease cluster increases with aggregated data.
authors.affiliation: Department of Medical Informatics, BlueCross BlueShield of Tennessee, Chattanooga, Tennessee, United States of America. stephen_jones@bcbst.com
authors.forename: Stephen G
authors.initials: SG
authors.lastname: Jones
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
mesh_terms: D054598:Blue Cross Blue Shield Insurance Plans / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003141:Communicable Diseases / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D008171:Lung Diseases / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods; D015203:Reproducibility of Results; D012141:Respiratory Tract Infections / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D062206:Spatial Analysis; D013714:Tennessee / Q000453:epidemiology; D013997:Time Factors; D014777:Virus Diseases / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology