IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i7p1505-d158315.html
   My bibliography  Save this article

Waterborne Disease Outbreak Detection: A Simulation-Based Study

Author

Listed:
  • Damien Mouly

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

  • Sarah Goria

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

  • Michael Mounié

    (Unité D’évaluation Médico-Economique, Université Paul Sabatier, CHU 31059 Toulouse, France)

  • Pascal Beaudeau

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

  • Catherine Galey

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

  • Anne Gallay

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

  • Christian Ducrot

    (Institut National de la Recherche Agronomique, UR346-Unité d’Épidémiologie Animale, 63 122 Saint Genès Champanelle, France)

  • Yann Le Strat

    (Santé Publique France, the French National Public Health Agency, 94 410 Saint-Maurice, France)

Abstract

Waterborne disease outbreaks (WBDOs) remain a public health issue in developed countries, but to date the surveillance of WBDOs in France, mainly based on the voluntary reporting of clusters of acute gastrointestinal infections (AGIs) by general practitioners to health authorities, is characterized by low sensitivity. In this context, a detection algorithm using health insurance data and based on a space–time method was developed to improve WBDO detection. The objective of the present simulation-based study was to evaluate the performance of this algorithm for WBDO detection using health insurance data. The daily baseline counts of acute gastrointestinal infections were simulated. Two thousand simulated WBDO signals were then superimposed on the baseline data. Sensitivity (Se) and positive predictive value (PPV) were both used to evaluate the detection algorithm. Multivariate regression was also performed to identify the factors associated with WBDO detection. Almost three-quarters of the simulated WBDOs were detected (Se = 73.0%). More than 9 out of 10 detected signals corresponded to a WBDO (PPV = 90.5%). The probability of detecting a WBDO increased with the outbreak size. These results underline the value of using the detection algorithm for the implementation of a national surveillance system for WBDOs in France.

Suggested Citation

  • Damien Mouly & Sarah Goria & Michael Mounié & Pascal Beaudeau & Catherine Galey & Anne Gallay & Christian Ducrot & Yann Le Strat, 2018. "Waterborne Disease Outbreak Detection: A Simulation-Based Study," IJERPH, MDPI, vol. 15(7), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1505-:d:158315
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/7/1505/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/7/1505/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Patrick Levallois & Cristina M. Villanueva, 2019. "Drinking Water Quality and Human Health: An Editorial," IJERPH, MDPI, vol. 16(4), pages 1-4, February.
    2. Frederic Bounoure & Damien Mouly & Pascal Beaudeau & Malek Bentayeb & Julie Chesneau & Gabrielle Jones & Mohamed Skiba & Malika Lahiani-Skiba & Catherine Galey, 2020. "Syndromic Surveillance of Acute Gastroenteritis Using the French Health Insurance Database: Discriminatory Algorithm and Drug Prescription Practices Evaluations," IJERPH, MDPI, vol. 17(12), pages 1-11, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1505-:d:158315. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.