Quality Assurance Sampling Plans in US Stockpiles for Personal Protective Equipment

Abstract

Personal protective equipment (PPE) stockpiles in the United States were established to facilitate rapid deployment of medical assets to sites affected by public health emergencies. Large quantities of PPE were introduced into US stockpiles because of the need to protect healthcare and other professionals during these events. Because most stockpiled PPE was acquired during, or immediately following, large-scale public health events, such as pandemic influenza planning (2005-20080), SARS (2003), H1N1 (2009-10), and Ebola (2014-15), aging PPE poses a significant problem. PPE such as N95 filtering face piece respirators were not designed to be stored for long periods, and much of the currently stored PPE has exceeded its manufacturer-assigned shelf life. Given the significant investment in the procurement and storage of PPE, along with projections of consumption during public health emergencies, discarding large quantities of potentially viable PPE is not an attractive option. Although shelf-life extension programs exist for other stockpiled medical assets, no such option is currently available for stockpiled PPE. This article posits stockpile quality assurance sampling plans as a mechanism through which shelf-life extension programs for stockpiled PPE may be achieved. We discuss some of the nuances that should be considered when developing a plan tailored to stockpiles and provide basic decision tools that may be used in the context of a quality assurance program tailored to stockpiled PPE. We also explore basic information by comparing and contrasting different sample size options.

Figure 1.

PPE sampling depiction for stockpiles based on the lot quality assurance sampling (LQAS) technique

Figure 2.

Probabilities and the cumulative probability of obtaining an exact number of defective units in a sample of 32 from a lot with 15% defective units Note: The solid line in Figure 2 represents the probabilities (on the Y axis) that a sample of 32 will result in a specific number of defective units (on the X axis) when taken from a lot in which 15% of the units are defective. The probability of obtaining 4 and 5 defective units in the sample is greatest—both being approximately 19%. The probability of finding 6 or more defective units in the sample decreases steadily. The dotted line is the sum of the probabilities from right to left in the figure. This line shows that there is nearly a 0% chance of obtaining a sample of 32 with 11 or more defective units if the true number of defects in the lot is 15%. The intersection between the horizontal (α = 0.05) and vertical dotted line (9 defective units in the sample) indicates that obtaining 9 or more defective units in a sample of 32 from a lot with 15% will happen less than 5% of the time.

Figure 3.

Average and standard deviation of sample fail rates across 75 repeated samples