P2/N95 filtering facepiece respirators: Results of a large-scale quantitative mask fit testing program in Australian health care workers.
P2/N95 filtering facepiece respirators: Results of a large-scale quantitative mask fit testing program in Australian health care workers.
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DOI：
10.1016/j.ajic.2021.12.016
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发表时间：
2022-05
影响因子：
4.9
通讯作者：
Denney-Wilson E
中科院分区：
文献类型：
--
作者：
Milosevic M;Kishore Biswas R;Innes L;Ng M;Mehmet Darendeliler A;Wong A;Denney-Wilson E
为应对COVID-19大流行，6287名澳大利亚医护人员（HCWs）接受了N95过滤式口罩（ffr）的适合测试。本研究确定了HCWs与8个ffr的匹配程度，以及年龄和性别对测试的影响。HCWs按照定量OSHA方案进行了适合性测试。经双变量分析，logistic回归模型评估FFR模型、HCW年龄和性别对拟合检验结果的影响。在接受测试的4,198名女性和2,089名男性医护人员中，93.3%的人成功配戴。55%的人通过了第一次FFR， 21%的人需要2次测试，23%的人需要3个或更多型号的测试。男性通过的可能性比女性低15% （P < 0.001）。与30-59岁的同事相比，18-29岁的人通过考试的可能性要大得多。杯型3M 1860S是最合适的模型（95% CI: 1.94, 2.54），而鸭喙型BSN TN01-11最可能失败（95% CI: 0.11, 0.15）。目前的N95 ffr表现出次优拟合，以至于很大一部分（45%）的HCWs需要在多个模型上进行测试。年龄较大和男性与更高的适合失败率相关。QNFT项目应考虑诸如性别、年龄、种族和面部人体测量等HCW特征，以改善对卫生工作者的保护。
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. 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. 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). 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.
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影响因子：
1.5
作者：
Regli, Adrian;Thalayasingam, Priya;von Ungern-Sternberg, Britta S.
通讯作者：
von Ungern-Sternberg, Britta S.
影响因子：
2.9
作者：
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2.6
作者：
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通讯作者：
Rees, David
DOI：
10.1080/15459621003729909
发表时间：
2010-01-01
影响因子：
2
作者：
Oestenstad, Riedar Kent;Bartolucci, Alfred A.
通讯作者：
Bartolucci, Alfred A.