Development of a prediction model for the acquisition of extended spectrum beta-lactam-resistant organisms in U.S. international travellers.

BACKGROUND: Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. METHODS: We used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. RESULTS: A CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors. CONCLUSIONS: We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

Incidence Rate and Risk Factors Associated with Travelers' Diarrhea in International Travelers Departing from Utah, USA.

Despite knowledge on the causes and prevention strategies for travelers' diarrhea (TD), it continues to be one of the most common illnesses experienced by U.S. international travelers. However, studies of risk factors associated with TD among U.S. travelers are limited. In this study, we aimed to determine the incidence rate of TD, the proportion of travelers who experience TD, and to identify risk factors associated with TD. In this cross-sectional study, we collected and analyzed data from anonymous posttravel questionnaires submitted by international travelers recruited during their pretravel visit at two travel clinics in Salt Lake City, Utah, from October 2016 to March 2020. Of 571 travelers who completed posttravel surveys, 484 (85%) answered the TD question, of which 111 (23%) reported TD, for an incidence rate of 1.1 episodes per 100 travel-days (95% confidence interval [CI]: 0.9-1.4). In a multivariable model, visiting Southeast Asian (odds ratio [OR]: 2.60; 95% CI: 1.45-4.72) and African (OR: 2.06; 95% CI: 1.09-3.93]) WHO regions, having 10 or more individuals in the group (OR: 3.91; 95% CI: 1.50-11.32]), longer trip duration (OR: 1.01; 95% CI: 1.00-1.02), visiting both urban and rural destinations (OR: 1.94; 95% CI: 1.01-3.90), and taking medications/supplements to prevent TD (OR: 2.74; 95% CI: 1.69-4.47) were statistically significantly associated with increased odds of reporting TD. TD continues to be common in international travelers from the United States. Our findings provide insights regarding travelers' behaviors regarding TD in international travelers from high-income countries and shows the need for additional research into prevention strategies for travelers' diarrhea.

Weather variables as important clinical predictors of bacterial diarrhoea among international travellers.

BACKGROUND: Clinicians and travellers often have limited tools to differentiate bacterial from non-bacterial causes of travellers' diarrhoea (TD). Development of a clinical prediction rule assessing the aetiology of TD may help identify episodes of bacterial diarrhoea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhoea among clinical, demographic and weather variables, as well as to develop and cross-validate a parsimonious predictive model. METHODS: We collected de-identified clinical data from 457 international travellers with acute diarrhoea presenting to two healthcare centres in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal aetiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhoea. RESULTS: We identified 195 cases of bacterial aetiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected aetiologies were average location-specific environmental temperature and red blood cell on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an area under the receiver operator curve of 0.73 using 3 variables with calibration intercept -0.01 (standard deviation, SD 0.31) and slope 0.95 (SD 0.36). CONCLUSIONS: We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of aetiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.