Description of two waterborne disease outbreaks in France: a comparative study with data from cohort studies and from health administrative databases

Mouly, Damien; Cauteren, Dieter Van; Vincent, N.; Vaissière, Emmanuelle; Beaudeau, Pascal; Ducrot, Christian; Gallay, Anne

doi:10.1017/s0950268815001673

Cited by 14 publications

(18 citation statements)

References 13 publications

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“…This difference is probably due to several factors, including healthcare-seeking behaviors: in France, the mean consultation rate for AGI is quite low at 32% [ 19 ] and depends on age and pathogen agent. The true distribution of cases during outbreaks is described in a previous article which compared, for two WBDOs, the true distribution of cases identified in cohort studies among the impacted population and the true distribution of medicalized cases identified in the SNIIRAM database [ 10 ]. Results highlighted a good temporal correlation between both data sources.…”

Section: Discussionmentioning

confidence: 99%

“… For each simulation, the variation of incidence ratio (VI), defined as the proportion between the number of outbreak AGI cases and the number of expected cases of AGI (baseline data) during the outbreak period, was randomly selected between 0.5% and 6%. These values were chosen according to what we observed in previous WBDOs [ 10 ]. The outbreak duration was randomly selected between 3 and 28 days in accordance with the observed values in reported WBDOs [ 6 ].…”

Section: Methodsmentioning

confidence: 99%

“… Finally, outbreak cases were distributed over time according to a log-normal distribution [ 15 , 21 ] ( Figure 3 ). The parameters of the log-normal distribution used to shape the time distribution of the outbreak AGI cases were randomly chosen between 0.33 and 0.5 for the median, and fixed at 0.5 for the standard deviation [ 10 , 21 ]. When the selected DZ serviced more than one zip code, daily cases in the AGI outbreak were then distributed according to the proportion of inhabitants serviced by the DZ in each zip code.…”

Section: Methodsmentioning

confidence: 99%

“…Although not all medicalized AGI cases can be specifically attributed to drinking water contamination, the correspondence between AGI cases and water distribution zones (DZs) at a local level provides the opportunity to study the ecological relationship between tap water and infectious gastrointestinal diseases. Medicalized AGI data have already proven relevant for studying the relationship between tap water quality parameters (e.g., turbidity) and the incidence of AGIs [ 8 , 9 ], and also for retrospectively identifying and describing outbreaks of AGIs notified by general practitioners [ 8 , 10 ]. Consequently, using these data to develop a specific automated nationwide system for local WBDO detection offers a promising way forward.…”

Section: Introductionmentioning

confidence: 99%

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Waterborne Disease Outbreak Detection: A Simulation-Based Study

Mouly

Goria

Mounié

et al. 2018

IJERPH

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Waterborne Disease Outbreak Detection: A Simulation-Based Study

Mouly

Goria

Mounié

et al. 2018

IJERPH

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“…A study investigating healthcare-seeking behaviors for AG in the French population has shown that 33.4% of individuals having an AG episode would consult a physician (of which 92.8% would consult a GP), and 96% would buy prescribed medications [9, 11].…”

Section: Introductionmentioning

confidence: 99%

Cross-validation of an algorithm detecting acute gastroenteritis episodes from prescribed drug dispensing data in France: comparison with clinical data reported in a primary care surveillance system, winter seasons 2014/15 to 2016/17

Vilcu

Blanchon

Sabatte

et al. 2019

BMC Med Res Methodol

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Background This study compares an algorithm to detect acute gastroenteritis (AG) episodes from drug dispensing data to the validated data reported in a primary care surveillance system in France. Methods We used drug dispensing data collected in a drugstore database and data collected by primary care physicians involved in a French surveillance network, from season 2014/15 to 2016/17. We used an adapted version of an AG discrimination algorithm to identify AG episodes from the drugstore database. We used Pearson’s correlation coefficient to evaluate the agreement between weekly AG signals obtained from the two data sources during winter months, in the overall population, by specific age-groups and by regions. Results Correlations between AG signals for all ages were 0.84 [95%CI 0.69; 0.92] for season 2014/15, 0.87 [95%CI 0.75; 0.93] for season 2015/16 and 0.94 [95%CI 0.88; 0.97] for season 2016/17. The association between AG signals estimated from two data sources varied significantly across age groups in season 2016/17 ( p -value < 0.01), and across regions in all three seasons studied ( p -value < 0.01). Conclusions There is a strong agreement between the dynamic of AG activity estimated from drug dispensing data and from validated primary care surveillance data collected during winter months in the overall population but the agreement is poorer in several age groups and in several regions. Once automated, the reuse of drug dispensing data, already collected for reimbursement purposes, could be a cost-efficient method to monitor AG activity at the national level. Electronic supplementary material The online version of this article (10.1186/s12874-019-0745-5) contains supplementary material, which is available to authorized users.

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