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Bayesian Methods for Monitoring Public Health Surveillance Data

Explore the application of Bayesian methods for monitoring and detecting aberrations in public health surveillance data, focusing on spatially and temporally referenced information. Learn about Bayesian smoothing techniques to estimate underlying disease risks, advantages and disadvantages, and Bayesian aberration detection models. Understand the evaluation of etiologic models and decision-making processes using Bayesian decision theory.

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Bayesian Methods for Monitoring Public Health Surveillance Data

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  1. Bayesian Methods for Monitoring Public Health Surveillance Data October 17, 2002 Owen Devine Division of STD Prevention National Center for HIV, STD and TB Prevention Centers for Disease Control and Prevention

  2. Spatially referenced data • Temporally referenced data Focus on detection of aberrations in public health surveillance data

  3. Mapping Surveillance Data Observed rates can be “unstable” estimates of the true underling risk Num. Of Rate per County 1998 Pop. Events /100000 Rich 1793 1 56 Davis 229393 128 56 Rich 1793 2 112

  4. = Observed number of cases in area i = Parameters describing prior uncertainty about true risk Hyper-prior Bayesian Smoothing = Underlying true risk of disease in area i Prior Likelihood

  5. Bayesian Smoothing Updated (Posterior) distribution of Fully Bayesian : Empirical Bayes :

  6. Advantages: • Stabilization of observed risk measures in areas with small populations • Evaluation of etiologic models • Two stage model is intuitive for observed measures of health disease burden • Disadvantages: • Analytic and computational resources may not be available to utilize these methods in local health departments • Over-smoothing Bayesian Smoothing for Detecting Spatial Aberrations

  7. 2001 P&S Syphilis Rates in North Carolina Observed Rate  2 2 < Rate  10 Rate > 10 Bayesian Smooth Population Weighted Average Bayesian Smoothing for Detecting Spatial Aberrations

  8. Crashes Model : Prior : Likelihood : Month An Approach to Bayesian Aberration Detection in Temporally Referenced Health Surveillance Data

  9. Crashes Month An Approach to Bayesian Aberration Detection in Temporally Referenced Health Surveillance Data Posterior :

  10. Advantages: • Successive updating fits nicely with temporal surveillance • Evaluation of etiologic models • Disadvantages: • Analytic and computational resources may not be available to utilize these methods in local health departments • Model for may differ between outcomes, locations, etc. An Approach to Bayesian Aberration Detection in Temporally Referenced Health Surveillance Data

  11. Bayesian Aberration Detection For Health Surveillance Data Bayesian Approach Pros Cons Stabilization Lack of Portability Etiologic Evaluation Lack of Transparency Intuitive Models

  12. Bayesian Decision Making Suppose some rule, , leads to a decision, for example Sound alarm Do not sound alarm Let be the loss due to making an incorrect decision, then choose to minimize the posterior risk, , where

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