How to Answer Introduction Hospital Acquired Questions (Complete Guide)
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Original Question
Introduction: Hospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral infections, and subsequently higher rates of morbidity and mortality. Materials and methods: We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets, including individual-level factors, engineering control factors, and administrative control factors. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies. Results: Regarding the individual-level risk model, the variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California’s higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level. Conclusion: The accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies. [ABSTRACT FROM AUTHOR]Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)Author Affiliations:1Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, United States of America 2Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, North Dakota, United States of America 3University of North Dakota, Fargo, North Dakota, United States of America 4Sanford Hospital, Fargo, North Dakota, United States of America 5Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America 6Department of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, North Carolina, United States of AmericaFull Text Word Count:9713ISSN:1932-6203DOI:10.1371/journal.pone.0272919Accession Number:160329955Database:Academic Search Complete Please use the article above to answer the question show below in the chart, Thanks Reviewer name(s) Article number Author, date, and title Type of evidence Population, size, and setting Intervention Findings that help answer the EBP question Measures used Limitations Evidence level and quality
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