PMID- 31433277
OWN - NLM
STAT- MEDLINE
DCOM- 20200518
LR  - 20200701
IS  - 2326-5108 (Electronic)
IS  - 2326-5094 (Print)
IS  - 2326-5094 (Linking)
VI  - 17
IP  - 4
DP  - 2019 Jul/Aug
TI  - Quality Assurance Sampling Plans in US Stockpiles for Personal Protective 
      Equipment: A Computer Simulation to Examine Degradation Rates.
PG  - 324-333
LID - 10.1089/hs.2019.0042 [doi]
AB  - Medical countermeasure stockpiles in the United States are designed to support 
      healthcare workers and the public during public health emergencies; they include 
      supplies of personal protective equipment (PPE). As part of typical PPE 
      manufacturing processes, appropriate test methods are used to ensure that the 
      devices provide adequate protective performance. At the time of manufacture, 
      performance is often measured and weighed against an objective standard of 
      quality, resulting in a pass or fail attribute being assigned to individual PPE 
      items and thence to production lots. Incorporating periodic performance testing 
      for stockpiled PPE can ensure that they maintain their protective qualities and 
      integrity over time while in storage. There is an absence of guidance regarding 
      how to design quality assurance programs for stockpiled PPE. The applicability of 
      the Lot Quality Assurance Sampling (LQAS) approach to stockpiled PPE was examined 
      in a previous study that compared and contrasted different sample sizes in 
      recovering the true percentage of defective units in large lots in the LQAS 
      framework. The current study carries this line of inquiry forward by integrating 
      PPE degradation over time and comparing different sampling time intervals in 
      recovering the true underlying degradation rate. The results suggest that product 
      degradation is more easily detected when tested at shorter time intervals and for 
      higher degradation rates. They further suggest that sampling interval groupings 
      can be made based on the proficiency with which they recover the true underlying 
      degradation rate.
FAU - Dubaniewicz, Mitchell T
AU  - Dubaniewicz MT
AD  - Mitchell T. Dubaniewicz is a student researcher, Department of Bioengineering, 
      Swanson School of Engineering, University of Pittsburgh, and was on assignment 
      with CDC/NIOSH/NPPTL.
FAU - Rottach, Dana R
AU  - Rottach DR
AD  - Dana R. Rottach, PhD, is a Physical Scientist, and Patrick L. Yorio, PhD, is a 
      Health Statistician; both at the National Personal Protective Technology 
      Laboratory, National Institute for Occupational Safety and Health, Centers for 
      Disease Control and Prevention, Pittsburgh, PA.
FAU - Yorio, Patrick L
AU  - Yorio PL
AD  - Dana R. Rottach, PhD, is a Physical Scientist, and Patrick L. Yorio, PhD, is a 
      Health Statistician; both at the National Personal Protective Technology 
      Laboratory, National Institute for Occupational Safety and Health, Centers for 
      Disease Control and Prevention, Pittsburgh, PA.
LA  - eng
GR  - CC999999/Intramural CDC HHS/United States
PT  - Journal Article
PL  - United States
TA  - Health Secur
JT  - Health security
JID - 101654694
SB  - IM
MH  - *Computer Simulation
MH  - Humans
MH  - Lot Quality Assurance Sampling/*statistics & numerical data
MH  - Personal Protective Equipment/*standards
MH  - Public Health
MH  - United States
PMC - PMC6823634
MID - NIHMS1054665
OTO - NOTNLM
OT  - Countermeasures
OT  - Personal protective equipment
OT  - Stockpile
EDAT- 2019/08/23 06:00
MHDA- 2020/05/19 06:00
PMCR- 2020/07/01
CRDT- 2019/08/22 06:00
PHST- 2019/08/22 06:00 [entrez]
PHST- 2019/08/23 06:00 [pubmed]
PHST- 2020/05/19 06:00 [medline]
PHST- 2020/07/01 00:00 [pmc-release]
AID - 10.1089/hs.2019.0042 [doi]
PST - ppublish
SO  - Health Secur. 2019 Jul/Aug;17(4):324-333. doi: 10.1089/hs.2019.0042.