Predicting Health Care Workers' Tolerance of Personal Protective Equipment: An Observational Simulation Study

Self-contamination following removal of two personal protective equipment suits: a randomized, controlled, crossover simulation trial

BACKGROUND

Healthcare personnel are often at high risk of contamination when participating in airway management and other aerosol-generating procedures.

AIM

To explore the differences in self-contamination after removal of gown and coverall personal protective equipment (PPE) using an ultraviolet-fluorescent solution.

METHODS

This prospective, randomized, controlled crossover trial was set in a third-level university health centre in Buenos Aires, Argentina between August and October 2020. The study included 60 anaesthesia personnel volunteers, and no participants were excluded from the study. A two-period/two-intervention design was chosen; each intervention comprised audio-guided placement of PPE, full-body spraying of fluorescent solution, audio-guided removal of PPE, and self-contamination assessment through ultraviolet light scanning. The primary outcome was the mean within-participant difference (any traces) between PPE suits. Statistical significance was tested using t-tests for paired data. The allocation ratio was 25/35 (gown followed by coverall/coverall followed by gown).

FINDINGS

Self-contamination after removal of coveralls was greater than that after removal of gowns, with a mean within-participant difference of 11.45 traces (95% confidence interval 8.26-14.635; P<0.001). Significant differences were found for the number of self-contaminated body zones, small fluorescent traces and large fluorescent traces. Removal of a gown was associated with a markedly lower risk of self-contamination.

CONCLUSIONS

Quick one-step removal of a gown and gloves may reduce self-contamination in the arm/hand area. Fluorescent solutions can help to identify self-contamination and compare outcomes between available PPE suits. Repeated training sessions and enhanced knowledge on self-contamination following removal of PPE are paramount.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04763304 (on ClinicalTrials.gov).

Using data mining techniques to fight and control epidemics: A scoping review

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.