Abstract
Respirators are used to minimise the exposure of healthcare workers (HCWs) to hazardous biological agents (HBAs). A critical element of a comprehensive respiratory protection programme is ensuring that a respirator fits its user. Respirator fit can be assessed either qualitatively or quantitatively. This is necessary to ensure that the wearer is adequately protected from the risk of exposure to airborne hazards. The transmission of airborne infections such as Tuberculosis is a critical public health problem. It is crucial to implement effective infection control interventions especially in healthcare facilities where the HCWs are at high risk. While fit-testing of respirators is widespread, its implementation has been difficult, particularly in resource-constrained settings (Manganyi et al. 2017). The aim of this study was to compare fit-test results from the qualitative fit-test (QLFT) and quantitative fit-test (QNFT) methods with HALYARD Health N95-respirators, models 46827 and 46727, in resource-limited healthcare settings in South Africa. The study also tested whether these N95-Filtering Facepiece Respirator (FFP) models influenced the fit-test outcomes under these settings. Ninety-nine (99) HCWs were recruited from 115 participants approached to participate in the study. As prescribed by the Occupational Safety and Health Administration (OSHA), each participant was assigned a respirator and underwent sequential QLFT and QNFT fit-tests; the test outcomes (either pass or fail) were recorded. The majority of HCWs passed the QLFT compared to the QNFT method (89.2% vs 45.9%), albeit not statistically significant. QNFT was more proficient in detecting failures than the corresponding QLFT for both types of N95-FFRs tested, with HCWs being 80% more likely to pass a QLFT than a corresponding QNFT. The proportion of HCWs obtaining a pass on both tests was roughly 45%, well below the required threshold of 95% as recommended by the American National Standards Institute (ANSI). The degree of uncertainty estimated using the 95% confidence interval showed that a true value of the odds ratio can possibly be found in the range between 0.1 and 6.6. Furthermore, results indicated that the fit-test methods consistently disagreed (K=-0.02). With respect to the effect of the N95-FFR models, an overlap in the confidence intervals indicated that there was no statistically significant difference in fit-test outcomes. Conditions that improve the utilisation and implementation of fit-testing will not only reduce Healthcare Acquired Infections (HAI) such as TB but also 9 reduce the risk of emergence of other epidemics related to respiratory infectious diseases in humans.
M.A. (Environmental Health)