Cargando…

P2/N95 filtering facepiece respirators: Results of a large-scale quantitative mask fit testing program in Australian health care workers

BACKGROUND: In response to the COVID-19 pandemic, 6,287 Australian health care workers (HCWs) were fit tested to N95 filtering facepiece respirators (FFRs). This study determined how readily HCWs were fitted to 8 FFRs and how age and sex influenced testing. METHODS: HCWs were fit tested following th...

Descripción completa

Detalles Bibliográficos
Autores principales: Milosevic, Maxim, Kishore Biswas, Raaj, Innes, Lesley, Ng, Martin, Mehmet Darendeliler, Ali, Wong, Alice, Denney-Wilson, Elizabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767955/
https://www.ncbi.nlm.nih.gov/pubmed/34971710
http://dx.doi.org/10.1016/j.ajic.2021.12.016
Descripción
Sumario:BACKGROUND: In response to the COVID-19 pandemic, 6,287 Australian health care workers (HCWs) were fit tested to N95 filtering facepiece respirators (FFRs). This study determined how readily HCWs were fitted to 8 FFRs and how age and sex influenced testing. METHODS: HCWs were fit tested following the quantitative OSHA protocol. After bivariate analysis, a logistic regression model assessed the effect of FFR model, HCW age and sex on fit test results. RESULTS: Of 4,198 female and 2,089 male HCWs tested, 93.3% were successfully fitted. Fifty-five percent passed the first FFR, 21% required 2 and 23% required testing on 3 or more models. Males were 15% less likely to pass compared to females (P < .001). Individuals aged 18-29 were significantly more likely to pass compared to colleagues aged 30-59. Cup-style 3M 1860S was the most suitable model (95% CI: 1.94, 2.54) while the duckbill BSN TN01-11 was most likely to fail (95% CI: 0.11, 0.15). CONCLUSIONS: Current N95 FFRs exhibit suboptimal fit such that a large proportion (45%) of HCWs require testing on multiple models. Older age and male sex were associated with significantly higher fit failure rates. QNFT programs should consider HCW characteristics like sex, age, racial and facial anthropometric measurements to improve the protection of the health workforce.