Analyzing postdisaster surveillance data: The effect of the statistical method

Charles Di Maggio, David Abramson, Sandro Galea

Research output: Contribution to journalArticlepeer-review


Data from existing administrative databases and ongoing surveys or surveillance methods may prove indispensable after mass traumas as a way of providing information that may be useful to emergency planners and practitioners. The analytic approach, however, may affect exposure prevalence estimates and measures of association. We compare Bayesian hierarchical modeling methods to standard survey analytic techniques for survey data collected in the aftermath of a terrorist attack. Estimates for the prevalence of exposure to the terrorist attacks of September 11, 2001, varied by the method chosen. Bayesian hierarchical modeling returned the lowest estimate for exposure prevalence with a credible interval spanning nearly 3 times the range of the confidence intervals (Cls) associated with both unadjusted and survey procedures. Bayesian hierarchical modeling also returned a smaller point estimate for measures of association, although in this instance the credible interval was tighter than that obtained through survey procedures. Bayesian approaches allow a consideration of preexisting assumptions about survey data, and may offer potential advantages, particularly in the uncertain environment of postterrorism and disaster settings. Additional comparative analyses of existing data are necessary to guide our ability to use these techniques in future incidents. (Disaster Med Public Health Preparedness. 2008;2:119-126).

Original languageEnglish (US)
Pages (from-to)119-126
Number of pages8
JournalDisaster medicine and public health preparedness
Issue number2
StatePublished - Jun 2008


  • Bayesian
  • Data
  • Surveillance
  • Survey

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


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