ArticleInternational journal of environmental research and public health2018
Waterborne Disease Outbreak Detection: A Simulation-Based Study.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review.BMC infectious diseases · 2021Pooled it
- Power Law for Estimating Underdetection of Foodborne Disease Outbreaks, United States.Emerging infectious diseases · 2023Article
- Implementation of a national waterborne disease outbreak surveillance system: overview and preliminary results, France, 2010 to 2019.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2021Article
- Syndromic Surveillance of Acute Gastroenteritis Using the French Health Insurance Database: Discriminatory Algorithm and Drug Prescription Practices Evaluations.International journal of environmental research and public health · 2020Article
- Drinking Water Quality and Human Health: An Editorial.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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