Predictors of professional burnout and fulfilment in a longitudinal analysis on nurses and healthcare workers in the COVID-19 pandemic.

AIMS AND OBJECTIVES: (1) To investigate the vulnerability of nurses to experiencing professional burnout and low fulfilment across 5 months of the COVID-19 pandemic. (2) To identify modifiable variables in hospital leadership and individual vulnerabilities that may mitigate these effects. BACKGROUND: Nurses were at increased risk for burnout and low fulfilment prior to the COVID-19 pandemic. Hospital leadership factors such as organisational structure and open communication and consideration of employee opinions are known to have positive impacts on work attitudes. Personal risk factors for burnout include symptoms of depression and anxiety. METHODS: Healthcare workers (n = 406 at baseline, n = 234 longitudinal), including doctors (n = 102), nurses (n = 94), technicians (n = 90) and non-clinical administrative staff (n = 120), completed 5 online questionnaires, once per month, for 5 months. Participants completed self-report questionnaires on professional fulfilment and burnout, perceptions of healthcare leadership, and symptoms of anxiety and depression. Participants were recruited from various healthcare settings in the southeastern United States. The STROBE checklist was used to report the present study. RESULTS: Both at baseline and across the 5 months, nurses working during the COVID-19 pandemic reported increased burnout and decreased fulfilment relative to doctors. For all participants, burnout remained largely steady and fulfilment decreased slightly. The strongest predictors of both burnout and fulfilment were organisational structure and depressive symptoms. Leadership consideration and anxiety symptoms had smaller, yet significant, relationships to burnout and fulfilment in longitudinal analyses. CONCLUSIONS: Burnout and reduced fulfilment remain a problem for healthcare workers, especially nurses. Leadership styles and employee symptoms of depression and anxiety are appropriate targets for intervention. RELEVANCE TO CLINICAL PRACTICE: Leadership wishing to reduce burnout and increase fulfilment among employees should increase levels of organisational support and consideration and expand supports to employees seeking treatment for depression and anxiety.

Analysis of the three dimensional structure of the kidney glomerulus capillary network.

The capillary network of the kidney glomerulus filters small molecules from the blood. The glomerular 3D structure should help to understand its function, but it is poorly characterized. We therefore devised a new approach in which an automated tape collecting microtome (ATUM) was used to collect 0.5 µm thick serial sections from fixed mouse kidneys. The sections were imaged by scanning electron microscopy at ~ 50 nm/pixel resolution. With this approach, 12 glomeruli were reconstructed at an x-y-z resolution ~ 10 × higher than that of paraffin sections. We found a previously undescribed no-cross zone between afferent and efferent branches on the vascular pole side; connections here would allow blood to exit without being adequately filtered. The capillary diameters throughout the glomerulus appeared to correspond with the amount of blood flow within them. The shortest path (minimum number of branches to travel from afferent to efferent arterioles) is relatively independent of glomerular size and is present primarily on the vascular pole size. This suggests that new branches and longer paths form on the urinary pole side. Network analysis indicates that the glomerular network does not form by repetitive longitudinal splitting of capillaries. Thus the 3D structure of the glomerular capillary network provides useful information with which to understand glomerular function. Other tissue structures in the body may benefit from this new three dimensional approach.

Sensitivity of comorbidity network analysis.

OBJECTIVES: Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques. MATERIALS AND METHODS: We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters. RESULTS: Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative. DISCUSSION: Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach. CONCLUSION: We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.

ggalluvial: Layered Grammar for Alluvial Plots.

Alluvial diagrams use stacked bar plots and variable-width ribbons to represent multi-dimensional or repeated-measures data comprising categorical or ordinal variables (Bojanowski & Edwards, 2016; Rosvall & Bergstrom, 2010). The ggalluvial package extends the layered grammar of graphics of ggplot2 (Wickham, 2016) to generate alluvial diagrams from tidy data (Wickham, 2014). The package makes two key contributions to the R ecosystem. First, ggalluvial anchors the imprecise notion of an alluvial diagram to the rigid grammar of graphics (Wilkinson, 2006), which lends the plots more precise meaning and opens up many combinatorial possibilities. Second, ggalluvial adopts a distinctive geological nomenclature to distinguish "alluvial plots" and their graphical elements from Sankey diagrams and parallel sets plots, which I hope prove useful as these visualization tools converge toward common standards.

Applications of network analysis to routinely collected health care data: a systematic review.

Objective: To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research. Materials and Methods: A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis. Results: The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities. Discussion: We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine.

BACKGROUND: Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes. OBJECTIVES: We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines. METHODS: Article-level bibliographic data from PubMed were combined with publicly available bibliometric data from Web of Science and SCImago over the years 1999-2007. We selected four predictors of coauthorship were selected, two (study type, topical scope of the study) associated with complexity and two (financial support for the project, popularity of the publishing journal) associated with competition. A negative binomial regression model was used to estimate the effects of each predictor on coauthorship incidence and growth. A second, mixed-effect model included the journal as a random effect. RESULTS: Coauthorship increased at about one author per article per decade. Clinical trials, supported research, and research of broader scope produced articles with more authors, while review articles credited fewer; and more popular journals published higher-authorship articles. Incidence and growth rates varied widely across journals and were themselves uncorrelated. Most effects remained statistically discernible after controlling for the publishing journal. The effects of complexity-associated factors held constant or diminished over time, while competition-related effects strengthened. These trends were similar in size but not discernible from subject-specific subdata. CONCLUSIONS: Coauthorship incidence rates are multifactorial and vary with factors associated with both complexity and competition. Coauthorship growth is likewise multifactorial and increasingly associated with research competition.