Differential diagnosis of illness in travelers arriving from sierra Leone, Liberia, or guinea: A cross-sectional study from the Geosentinel surveillance network

Background: The largest-ever outbreak of Ebola virus disease (EVD), ongoing in West Africa since late 2013, has led to export of cases to Europe and North America. Clinicians encountering ill travelers arriving from countries with widespread Ebola virus transmission must be aware of alternate diagnoses associated with fever and other nonspecific symptoms. Objective: To define the spectrum of illness observed in persons returning from areas of West Africa where EVD transmission has been widespread. Design: Descriptive, using GeoSentinel records. Setting: 57 travel or tropical medicine clinics in 25 countries. Patients: 805 ill returned travelers and new mmigrants from Sierra Leone, Liberia, or Guinea seen between September 2009 and August 2014. Measurements: Frequencies of demographic and travelrelated characteristics and illnesses reported. Results: The most common specific diagnosis among 770 nonimmigrant travelers was malaria (n = 310 [40.3%]), with Plasmodium falciparum or severe malaria in 267 (86%) and non–P. falciparum malaria in 43 (14%). Acute diarrhea was the second most common diagnosis among nonimmigrant travelers (n= 95 [12.3%]). Such common diagnoses as upper respiratory tract infection, urinary tract infection, and influenza-like illness occurred in only 26, 9, and 7 returning travelers, respectively. Few instances of typhoid fever (n = 8), acute HIV infection (n = 5), and dengue (n = 2) were encountered. Limitation: Surveillance data collected by specialist clinics may not be representative of all ill returned travelers. Conclusion: Although EVD may currently drive clinical evaluation of ill travelers arriving from Sierra Leone, Liberia, and Guinea, clinicians must be aware of other more common, potentially fatal diseases. Malaria remains a common diagnosis among travelers seen at GeoSentinel sites. Prompt exclusion of malaria and other life-threatening conditions is critical to limiting morbidity and mortality

Comparison of Methods for Clustered Data Analysis in a Non-Ideal Situation: Results from an Evaluation of Predictors of Yellow Fever Vaccine Refusal in the Global TravEpiNet (GTEN) Consortium

Not accounting for clustering in data from multiple centers might yield biased estimates and their standard errors, potentially leading to incorrect inferences. We fit 15 different models with different correlation structures and with/without adjustment for small clusters, including unadjusted logistic regression, Population-averaged models (Generalized Estimating Equations), Cluster-specific models (linear and non-linear with random intercept) and Survey data analysis methods to study the association of variables with the probability of declining yellow fever vaccine among patients seeking pre-travel health consultations at 18 US practices in the Global TravEpiNet Consortium from 1 January, 2009, to 6 June, 2012. Results varied by the method chosen. Generally, when the odds ratio estimates were similar, adjusting for clustering and the small number of clinics increased the standard errors. We chose the random intercept model with the Morel, Bokossa and Neerchal (MBN) adjustment to be the most preferable method for the GTEN dataset since this was one of the more conservative models that accounted for clustering, small sample sizes and also the random effect due to site. Investigators should not ignore clustering and consider the appropriate adjustments necessary for their studies

Illness among US resident student travellers after return to the USA: a GeoSentinel analysis, 2007-17

Background: The number of US students studying abroad more than tripled during the past 20 years. As study abroad programmes' destinations diversify, students increasingly travel to resource-limited countries, placing them at risk for infectious diseases. Data describing infections acquired by US students while travelling internationally are limited. We describe illnesses among students who returned from international travel and suggest how to prevent illness among these travellers. Methods: GeoSentinel is a global surveillance network of travel and tropical medicine providers that monitors travel-related morbidity. This study included the records of US resident student international travellers, 17-24 years old, who returned to the USA, had a confirmed travel-related illness at one of 15 US GeoSentinel sites during 2007-17 and had a documented exposure region. Records were analysed to describe demographic and travel characteristics and diagnoses. Results: The study included 432 students. The median age was 21 years; 69% were female. More than 70% had a pre-travel consultation with a healthcare provider. The most common exposure region was sub-Saharan Africa (112; 26%). Students were most commonly exposed in India (44; 11%), Ecuador (28; 7%), Ghana (25; 6%) and China (24; 6%). The median duration of travel abroad was 40 days (range: 1-469) and presented to a GeoSentinel site a median of 8 days (range: 0-181) after travel; 98% were outpatients. Of 581 confirmed diagnoses, the most common diagnosis category was gastrointestinal (45%). Acute diarrhoea was the most common gastrointestinal diagnosis (113 of 261; 43%). Thirty-one (7%) students had vector-borne diseases [14 (41%) malaria and 11 (32%) dengue]. Three had vaccine-preventable diseases (two typhoid; one hepatitis A); two had acute human immunodeficiency virus infection. Conclusions: Students experienced travel-related infections, despite the majority having a pre-travel consultation. US students should receive pre-travel advice, vaccinations and chemoprophylaxis to prevent gastrointestinal, vector-borne, sexually transmitted and vaccine-preventable infections