  The COVID-19 pandemic has heightened the urgency to understand and prevent
pathogen transmission, specifically regarding infectious airborne particles.
Extensive studies validate the understanding of larger (droplets) and smaller
(aerosols) particles in disease transmission. Similarly, N95 respirators, and
other forms of respiratory protection, have proven efficacy in reducing the
risk of infection across various environments. Even though multiple studies
confirm their protective effect when adopted in healthcare and public settings
for infection prevention, studies on their adoption over the last several
decades in both clinical trials and observational studies have not provided as
clear an understanding. Here we show that the standard analytical equations
used in the analysis of these studies do not accurately represent the random
variables impacting study results. By correcting these equations, it is
demonstrated that conclusions drawn from these studies are heavily biased and
uncertain, providing little useful information. Despite these limitations, we
show that when outcome measures are properly analyzed, existing results
consistently point to the benefit of N95 respirators over medical masks, and
masking over its absence. Correcting errors in widely reported meta-analyses
also yields statistically significant estimates. These findings have important
implications for study design and using existing evidence for infection control
policy guidelines.
