Measures used to assess the burden of ESBL-producing Escherichia coli infections in humans: a scoping review.

BACKGROUND: ESBL-producing bacteria pose a serious challenge to both clinical care and public health. There is no standard measure of the burden of illness (BOI) of ESBL-producing Escherichia coli (ESBL-EC) in the published literature, indicating a need to synthesize available BOI data to provide an overall understanding of the impact of ESBL-EC infections on human health. OBJECTIVES: To summarize the characteristics of BOI reporting in the ESBL-EC literature to (i) describe how BOI associated with antimicrobial resistance (AMR) is measured and reported; (ii) summarize differences in other aspects of reporting between studies; and (iii) highlight the common themes in research objectives and their relation to ESBL-EC BOI. METHODS AND RESULTS: Two literature searches, run in 2013 and 2018, were conducted to capture published studies evaluating the BOI associated with ESBL-EC infections in humans. These searches identified 1723 potentially relevant titles and abstracts. After relevance screening of titles and abstracts and review of full texts, 27 studies were included for qualitative data synthesis. This review identified variability in the reporting and use of BOI measures, study characteristics, definitions and laboratory methods for identifying ESBL-EC infections. CONCLUSIONS: Decision makers often require BOI data to make science-based decisions for the implementation of surveillance activities or risk reduction policies. Similarly, AMR BOI measures are important components of risk analyses and economic evaluations of AMR. This review highlights many limitations to current ESBL-EC BOI reporting, which, if improved upon, will ensure data accessibility and usefulness for ESBL-EC BOI researchers, decision makers and clinicians.

The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis.

BACKGROUND: All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. METHODS: Using self-reported food frequency and physical activity data from Alberta's Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). RESULTS: The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie's adjusted Rand Index, Kappa and Cramer's V statistics < 0.8). CONCLUSIONS: Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.