Non-device related right-sided infective endocarditis (ND-RSIE) is not well characterized. We aimed to characterize patients with infective endocarditis (IE) with emphasis on the epidemiology, clinical characteristics and complications of ND-RSIE. Methods: In this population-based cohort study, we identified patients with IE using ICD-10 codes from the Danish National Hospital Register in the Region of Southern Denmark between January 2007 and May 2017. Hospital records were reviewed, and characteristics and outcomes recorded. Results: We included 1243 confirmed IE episodes of which 82% were left-sided IE, 11% were cardiac device right sided infective endocarditis (RSIE) and 7% were ND-RSIE. Patients with ND-RSIE were considerably younger, had less comorbidity and had a lower 30-day mortality (6%) compared with patients with device RSIE (24%) and left-sided IE (26%) (p < 0.01). ND-RSIE was associated with underlying heart disease, involvement of the tricuspid valve (57%), Staphylococcus species (53%) and complications (61%). Forty percent of ND-RSIE occurred among people who inject drugs (PWID) for whom significant differences were observed compared with non-PWID with regards to tricuspid valve involvement (96% vs. 32%), causative microorganisms (Staphylococcus aureus 79% vs. 27%), complications (86% vs. 45%), recurrence (29% vs. 11%) and 30-day mortality (0% vs. 7%). Conclusion: ND-RSIE is relatively rare and differs with regards to epidemiology, clinical characteristics and complications compared with left-sided IE and cardiac device RSIE, but has a favourable prognosis. Forty percent of ND-RSIE occurs among PWID, which is associated with frequent complications but a very low mortality.

The development of MM4SIGHT, a machine learning system that enables annual forecasts of mixed-migration flows, is presented. Mixed migration refers to cross-border movements of people that are motivated by a multiplicity of factors to move including refugees fleeing persecution and conflict, victims of trafficking, and people seeking better lives and opportunity. Such populations have a range of legal status, some of which are not reflected in official government statistics. The system combines institutional estimates of migration along with in-person monitoring surveys to establish a migration volume baseline. The surveys reveal clusters of migratory drivers of populations on the move. Given macrolevel indicators that reflect migratory drivers found in the surveys, we develop an ensemble model to determine the volume of migration between source and host country along with uncertainty bounds. Using more than 80 macroindicators, we present results from a case study of migratory flows from Ethiopia to six countries. Our evaluations show error rates for annual forecasts to be within a few thousand persons per year for most destinations.

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