Waterborne Disease Outbreak Detection: A Simulation-Based Study

Damien Mouly, Sarah Goria, Michael Mounié, Pascal Beaudeau, Catherine Galey, Anne Gallay, Christian Ducrot and Yann Le Strat
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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

IJERPH, 2018, vol. 15, issue 7, 1-15

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.

Keywords: waterborne disease outbreak; simulation study; health insurance data; space–time detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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