Predicting future protection of respirator users: Statistical approaches and practical implications

ABSTRACT

The purpose of this article is to describe a statistical approach for predicting a respirator user's fit factor in the future based upon results from initial tests. A statistical prediction model was developed based upon joint distribution of multiple fit factor measurements over time obtained from linear mixed effect models. The model accounts for within-subject correlation as well as short-term (within one day) and longer-term variability. As an example of applying this approach, model parameters were estimated from a research study in which volunteers were trained by three different modalities to use one of two types of respirators. They underwent two quantitative fit tests at the initial session and two on the same day approximately six months later. The fitted models demonstrated correlation and gave the estimated distribution of future fit test results conditional on past results for an individual worker. This approach can be applied to establishing a criterion value for passing an initial fit test to provide reasonable likelihood that a worker will be adequately protected in the future; and to optimizing the repeat fit factor test intervals individually for each user for cost-effective testing.

KEYWORDS:

• Fit factor
• prediction
• respirator
• respiratory protection
• statistical model

Acknowledgments

The authors gratefully acknowledge the assistance of laboratory staff members including G. Alongi, S. Barret, A. Badilla, R. Boumis, K. Lansey, J. Millet, Amir Rahinian, and C. Xu. The authors particularly appreciate the efforts of the research volunteers in these studies. The authors also thank the editor and a reviewer for constructive suggestions that greatly improved the article.

Funding

The research for this article was supported by grant R01-OH8119 from the National Institute for Occupational Safety and Health of the Centers for Disease Control and Prevention.