Syndromic Surveillance of Gastroenteritis using Medication by mikesanye

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									   Syndromic Surveillance of
     Gastroenteritis Using
   Medication Sales in France

Camille Pelat   1,2,   Clément Turbelin 1,2, Pierre-Yves Boëlle   1,2,   Bruno Lambert   3
                            and Alain-Jacques Valleron 1,2


 (1) INSERM UMR-S 707, (2) Université Pierre et Marie Curie, (3) IMS-Health France
Use of drug sales data for surveillance

   Medication sales are a good proxy of the incidence of
    acute illnesses
       influenza-like or gastrointestinal illness outbreaks [1]
       thresholds for surveillance of bioterrorist attacks [2]
       forecast ILI incidences [3]

   Gastroenteritis epidemics
       etiology unclear but medication sales detect outbreaks of bacterial
        and viral origin [4, 5]

   Aim of this work : create an indicator based on
    pharmaceutical data to detect gastroenteritis epidemics
       Validate it at the national level using clinical surveillance data

                                                                             2
    Data
   Clinical data
        Sentinel Network: data available
         on www.sentiweb.fr
        Since 1990: ~ 100 General
         Practitioners report Acute Diarrhea
         cases (WHO definition) each week




    Drug sales
        IMS-Health
        13,000 pharmacies (>50% of all)~ 10,000 by week send data
        Therapeutic classes: EPhMRA ATC
                   (European Pharmaceutical Market Research Association
                   Anatomical Therapeutic Chemical Classification System)
              582 therapeutic classes
        Unit: number of boxes sold each week by therapeutic class
        Since 2000                                                         3
Methods (1/3)
   Data mining approach to identify therapeutic classes
    linked to Acute Diarrhea (AD)

       Hierarchical tree on the time series of the therapeutic classes +
        incidence of AD
            Takes into account the distance of therapeutic classes between
             themselves + with incidence of AD
            Creates clusters of homogeneous time series

       Distance between 2 series: 1-correlation at the best lag


       Identify the cluster that contains incidence of AD
            Therapeutic classes of this cluster are candidates for the detection of
             gastroenteritis epidemics


                                                                                   4
    Methods (2/3)
   Detect epidemics in the time series of the selected
    therapeutic classes
         Principle : historical data used to set detection threshold

         Changes in the mean and the variance of series  method that
          relies on few historical data

         Limited Baseline CUSUM
            Sum of the differences between observed and expected values
            One-sided CUSUM: only positive deviations are searched
            Alert when the sum exceeds a predefined threshold




   Create a unique indicator of epidemics
         Global alert if at least n of the selected classes emit an alert
                                                                             5
Methods (3/3)
   Evaluation
       Gold standard: alerts published by the Sentinel Network, relying
        on the incidence of acute diarrhea
          Epidemic weeks: weeks defined as epidemic by the gold
            standard + the 2 preceding weeks
            An epidemic was detected if an alert was emitted for at least
             one of the epidemic weeks

       Metric [6] :
          Sensitivity: # detected epidemics / # epidemics


            Specificity: # of non-epidemic weeks without alert / # of non-
             epidemic weeks
            Timeliness: detection time – time of the gold standard alert

                                                                              6
    Selection of the therapeutic classes (1/2)
                                                                     Clus 1
   Hierarchical tree :                                              Clus 2
        therapeutic                                                  Clus 3
          classes
             +                                                       Clus 4
       AD incidence
                                                                       etc
                                      To provide                       …
                 Tree is cut at the
                  distance 0.55        distincts
                                       clusters




          Distance
       between series                              Names of series    7
Selection of the therapeutic classes (2/2)




                                                                       * p<0.001
   8 classes in the same cluster than AD incidence

   All medically linked to gastroenteritis

   Best correlation when lag is 0 except for the gastroprokinetics: they are 1
    week late over AD incidence

   Best correlation for motility inhibitors: 0.78                                 8
Time series of the selected classes (1/2)


                                    Therapeutic
                                      Class


                                     Rescaled
                                       Acute
                                      Diarrhea
                                     Incidence




                                          9
Time series of the selected classes (2/2)


                                    Therapeutic
                                      Class


                                     Rescaled
                                       Acute
                                      Diarrhea
                                     Incidence




                                         10
CUSUM on the selected classes
                                                               *            *




                                              * At a fixed specificity of 0.95


   At a fixed specificity of 0.95, intestinal anti-infective antidiarrheals
    have the best performances
      Sensitivity is 1
      Timeliness is in average 1 week before the gold standard


                                                                                 11
Alerts of the global indicator
                                      *                 *                *




                                          * At a fixed specificity of 0.95

   At a fixed specificity of 0.95, the detection rule that optimizes the
    sensitivity and the timeliness is
         « emit a global alert if at least 5 classes emit an alert »

        Sensitivity is 1
        Timeliness is 1 week before the gold standard
                                                                             12
Discussion
   8 medically pertinent classes selected by data mining
       Many papers: expert advice
       Antidiarrheals and antiemetics: used by other papers
       Plain antispasmodics and anticholinergics, gastroprokinetics: new



   Simultaneous monitoring vs single monitoring ?
       Global indicator: same performance than best therapeutic class
        (A07A, intestinal anti-infective antidiarrheals )
       Monitor a global indicator composed of 8 classes: more robustness
        against future unexpected changes in one series
            Due to other cause than an epidemic (commercial, legal, etc…)



                                                                             13
Conclusion
   Efficient data source for detecting gastroenteritis
    epidemics
       Good sensitivity, specificity, timeliness
       Massive sample of pharmacies


   Indicator validated at the national level
       Operational application: additional data source for Sentinel Network
          More confidence when emitting alert



   Perspective: regional level
       Use indicator to detect outbreaks where few Sentinel GP



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Thank you




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References

[1] Das, et al. (2005). MMWR Morb Mortal Wkly Rep 54 Suppl: 41-6.
[2] Goldenberg, et al. (2002). Proc Natl Acad Sci U S A 99(8): 5237-40.
[3] Vergu, et al. (2006). Emerg Infect Dis 12(3): 416-21.
[4] Edge, et al. (2004). Can J Public Health 95(6): 446-50.
[5] Edge, et al. (2006). Can J Infect Dis Med Microbiol 17(4): 235-41.
[6] Kleinman, K. P. and A. M. Abrams (2006). Stat Methods Med Res
    15(5): 445-64.




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