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BMC Med Res Methodol. 2019 May 31;19(1):110. doi: 10.1186/s12874-019-0745-5.

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.

Author information

1
Sorbonne Université, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F-75012, Paris, France. ana-maria.vilcu@upmc.fr.
2
Sorbonne Université, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F-75012, Paris, France.
3
Real World Insight, IQVIA, F-92099, La Défense Cedex, France.
4
Assistance Publique - Hôpitaux de Paris (APHP), hôpital Lariboisière, Service de Rhumatologie, F-75010, Paris, France.
5
Université de Versailles Saint-Quentin-en-Yvelines, UVSQ, UFR de Médecine, F-78000, Versailles, France.
6
Assistance Publique - Hôpitaux de Paris (APHP), hôpital Ambroise Paré, Service de Médecine Interne, F-92100, Boulogne Billancourt, France.
7
Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France.
8
Assistance Publique - Hôpitaux de Paris (APHP), hôpital Tenon, Service de Médecine Interne, F-75020, Paris, France.

Abstract

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.

KEYWORDS:

Epidemiology; Gastroenteritis; Pharmacy; Primary care physicians; Public health surveillance; Sentinel surveillance

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