A new respirator fit test panel based on principal component analysis.
Authors
Zhuang Z; Shaffer R; Bradtmiller B
Source
American Industrial Hygiene Conference and Exposition, May 13-16, 2006, Chicago, Illinois. Fairfax, VA: American Industrial Hygiene Association, 2006 May; :9
The respirator fit test panels currently used are 25-subject panels, developed by Los Alamos National Laboratory (LANL). The LANL panels are based on data from the 1967 and 1968 anthropometric surveys of U.S. Air Force men and women. Previous work has shown that military data do not represent the great diversity in face size and shape seen in today's civilian populations. This paper presents the development of a new respirator fit test panel representative of the current U.S. civilian workers using principal component analysis (PCA). A database containing 19 neck, head, and face measurements for 3,997 respirator users was created in 2003 from a nationwide anthropometric survey. Correlation analyses were conducted to identify dimensions that can be predicted well by other dimensions. Dimensions that have been shown to be associated with respirator fit were also identified. A set of 10 face dimensions was then selected for PCA. A respirator fit test panel was developed using the scores from the first two principal components obtained from the 10 face dimensions (age- and race-adjusted). The 10 face dimensions on which the panel is based have good correlations with, and can predict, the 9 dimensions that were excluded in the model. The new PCA panel is expected to accommodate more than 95% of the current U.S. civilian work force and may be appropriate for testing half-mask and full-facepiece respirators. Interpretation of the PCA loadings and scores provided insight into the relationships between key facial dimensions. These data suggest that new sizing systems that incorporate both the size and shape of the face may be appropriate for use with the new PCA panel. A software program was also developed to assist in the measurement of the 10 dimensions and to calculate principal component scores.
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