title: Power to detect spatial disturbances under different levels of geographic aggregation.
abstract: OBJECTIVE
  Spatio and/or temporal surveillance systems are designed to monitor the ongoing appearance of disease cases in space and time, and to detect potential disturbances in either dimension. Patient addresses are sometimes reported at some level of geographic aggregation, for example by ZIP code or census tract. While this aggregation has the advantage of protecting patient privacy, it also risks compromising statistical efficiency. This paper investigated the variation in power to detect a change in the spatial distribution in the presence of spatial aggregation.
  
  
  METHODS
  The authors generated 400,000 spatial datasets with varying location and spread of simulated spatial disturbances, both on a purely synthetic uniform population, and on a heterogeneous population, representing hospital admissions to three community hospitals in Cape Cod, Massachusetts. The authors evaluated the power of the M-statistic to detect spatial disturbances, comparing the use of exact spatial locations versus twelve different levels of aggregation, where the M-statistic is a comparison of two distributions of interpoint distances between locations.
  
  
  RESULTS
  When the spread of simulated spatial disturbances was contained to a small portion of the study region or affects a large proportion of the population at risk, power was highest when exact locations were reported. If the spatial disturbance was a more modest signal, the best power was attained at an aggregated level.
  
  
  CONCLUSIONS
  The precision at which patients' locations are reported has the potential to affect the power of detection significantly.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA.
authors.forename: Caroline
authors.initials: C
authors.lastname: Jeffery
authors.forename: A
authors.initials: A
authors.lastname: Ozonoff
authors.forename: Laura F
authors.initials: LF
authors.lastname: White
authors.forename: Miriam
authors.initials: M
authors.lastname: Nuño
authors.forename: Marcello
authors.initials: M
authors.lastname: Pagano
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control*; D006801:Humans; D008404:Massachusetts / Q000453:epidemiology; D010343:Patient Admission / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods*; D012111:Residence Characteristics; D013029:Space-Time Clustering