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Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.
To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.
Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.
Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.
Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.
Variance in how citizens interact with the political world constitutes one of many classes of individual difference. Understanding the antecedents of this variance is the central objective for students of political behaviour, and researchers draw on numerous factors in addressing this task. Unfortunately, one potentially vital factor, personality, has received only sporadic attention in recent decades. Neglect of personality was understandable for many years, as psychological research on personality failed to produce concise taxonomies applicable to the study of politics. As the present analysis demonstrates, however, this situation has changed. Research on personality has gained new footing with the emergence of a series of five-factor models, and these frameworks hold great potential for the study of political behaviour. This thesis is advanced in a two-part analysis. First, we outline how and why our understanding of citizen politics may be improved through application of five-factor models of personality. In doing so, we focus on the components of one specific taxonomy, the Big Five lexical model. Secondly, using three datasets, we explore the link between the Big Five personality factors and a wide array of political attitudes and behaviours. Results reveal that all facets of personality captured by the Big Five framework matter for citizen politics, and that personality effects operate on virtually all aspects of political behaviour. These findings demonstrate the insight that can emerge with further application of broad-scale models of personality.
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