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Bayesian Methods for Monitoring Public Health Surveillance Data Owen Devine Division of STD Prevention National Center for HIV, STD and TB Prevention Centers.

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Presentation on theme: "Bayesian Methods for Monitoring Public Health Surveillance Data Owen Devine Division of STD Prevention National Center for HIV, STD and TB Prevention Centers."— Presentation transcript:

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

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

3 Observed rates can be “unstable” estimates of the true underling risk Num. OfRate per County1998 Pop.Events/100000 Rich 1793 1 56 Davis229393 128 56 Rich 1793 2 112 Mapping Surveillance Data

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

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 Rate  2 2 < Rate  10 Rate > 10 2001 P&S Syphilis Rates in North Carolina Observed Bayesian Smooth Population Weighted Average

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

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

10 An Approach to Bayesian Aberration Detection in Temporally Referenced Health Surveillance Data 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.

11 Bayesian Aberration Detection For Health Surveillance Data StabilizationLack of Portability Etiologic EvaluationLack of Transparency Intuitive Models Bayesian Approach Pros Cons

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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