Sentinel Surveillance in Travel Medicine: 20 Years of GeoSentinel Publications (1999–2018)
Notice bibliographique
Résumé
A recent comprehensive literature review highlighted that between 6% and 87% of travellers become ill during or as a result of their travel.1 In this issue of the Journal of Travel Medicine, the value of sentinel surveillance in international travellers to identify and describe rare medical problems such as mycoses was highlighted.2 In the time period from 1997 through 2017, 61 cases of mycoses were identified out of more than 60 000 included case records reported to GeoSentinel. GeoSentinel is a global surveillance network now consisting of 70 travel and tropical medicine centres situated in 31 countries across 6 continents. Although sentinel surveillance in individual travel medicine clinics is helpful,3 rare diseases such as mycoses, or emerging, and other novel epidemiological features of infectious diseases in travellers can only be studied through a global surveillance system of returning travellers such as GeoSentinel. GeoSentinel was founded in 1995 by the International Society of Travel Medicine (ISTM) and is supported by ISTM the US Centers for Disease Control and Prevention and the Public Health Agency of Canada. GeoSentinel is based on the concept that travel medicine providers who encounter returning travellers are ideally situated to detect geographic and temporal trends in morbidity among travellers, and that by reporting such data centrally, such trends will be detected with greater frequency and expedience. Much of our knowledge on health problems and infections encountered by international travellers has evolved as a result of such sentinel surveillance.4 As travellers serve as a vehicle for the spread of diseases,5 the initial intent of GeoSentinel as a provider-based sentinel network was to track emerging infections at their point of entry, for example, influenza,6 or to identify outbreaks that may otherwise have gone unnoticed such as the outbreak of leptospirosis in a sporting event that involved many international visitors.7 The scope has broadened over time to monitor global trends in disease occurrence among travellers;8 to determine travel destinations with the highest risk exposure;9 to ascertain risk factors and morbidity in groups of travellers categorised by travel purpose and type of traveller;10 and to describe specific diseases,11 including those that are of extreme public health importance.12,13 GeoSentinel is now a worldwide communication and data collection network for the surveillance of travel-related morbidity, the largest of its kind. Limitations of the network include the absence of denominator data precluding the estimation of relative risk of specific travel-acquired illnesses; regional variation in the availability and application of microbiological diagnostics and therapeutics; lack of full clinical linkage of records, which limits the scope of data collected to basic demographic and travel data; localisation of sites to predominantly ambulatory referral-based centres staffed by specialists in travel and tropical medicine, which translates to underrepresentation in the network of paediatric and hospitalised cases, as well as mild self-limited illnesses or those with either very short (e.g. influenza) or very long (e.g. hepatitis B) incubation periods. CanTravNet is also an initiative of the International Society of Travel Medicine (ISTM), in collaboration with the Public Health Agency of Canada (PHAC), founded in 2012. All core sites are members of the GeoSentinel Global Surveillance Network. EuroTravNet was first funded by the European Centre for Disease Prevention and Control (ECDC). It was funded by ECDC from 2008 to 2012 and the ISTM. EuroTravNet is now funded by the ISTM and the Institutes Hospitalo-Universitaire (IHU) Méditerranée Infection Foundation in Marseille. The EuroTravNet founding core sites and members all belong to the GeoSentinel Global Surveillance Network. Given the substantial efforts towards tracking, defining and analysing infectious diseases trends amongst travellers and migrants over the past two decades, it is timely to highlight the publications stemming from GeoSentinel sentinel surveillance. Table 1 summarises the 99 peer-reviewed publications arising from network-wide (global) analyses of GeoSentinel data as well as publications arising from the two regional networks of GeoSentinel, EuroTravNet and CanTravNet, from 1999 to 2018. Such knowledge products are a testament to the wide scope of GeoSentinel activities, the global collaborative nature of its leaders, the breadth of the research ranging from high-level epidemiology of illness in travellers to specific travel-acquired diseases such as dengue and malaria, and its impact. Publications arising from analysis of GeoSentinel Surveillance Network Data Publications arising from analysis of GeoSentinel Surveillance Network Data Given the recognised globalisation of infectious diseases and the high degree of mobility of populations, vigilance to combat threats to local, national and global public health is imperative. To this end, continued sentinel surveillance for new and emerging infectious diseases, as well as detection of existing pathogens in novel epidemiologic niches, is imperative. Conflict of interest: None declared.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».