Objective
With the epidemic of coronavirus disease 2019 (COVID-19), the healthcare workers (HCWs) require proper respiratory personal protective equipment (rPPE) against viral respiratory infectious diseases (VRIDs). It is necessary to evaluate which type of mask and manner of wearing is the best suitable rPPE for preventing the VRID.
Study design
A Bayesian network meta-analysis was performed to comprehensively analyze the protective efficacy of various rPPE.
Methods
This network meta-analysis protocol was registered in an international prospective register of systematic reviews (CRD42020179489). Electronic databases were searched for cluster randomized control trials (RCTs) of comparing the effectiveness of rPPE and wearing manner in preventing HCWs from VRID. The primary outcome was the incidence of laboratory-confirmed viral respiratory infection reported as an odds ratio (OR) with the associated 95% credibility interval (CrI). The secondary outcome was the incidence of clinical respiratory illness (CRI) reported as an OR with the associated 95% CrI. Surface under the cumulative ranking curve analysis (SUCRA) provided a ranking of each rPPE according to the primary outcome and the secondary outcome as data supplement.
Results
Six studies encompassing 12,265 HCWs were included. In terms of the incidence of laboratory-confirmed viral respiratory infection, the continuous wearing of N95 respirators (network OR, 0.48; 95% CrI: 0.27 to 0.86; SUCRA score, 85.4) showed more effective than the control group. However, in terms of reducing the incidence of CRI, there was no rPPE showing superior protective effectiveness.
Conclusions
There are significant differences in preventive efficacy among current rPPE. Our result suggests that continuous wearing of N95 respirators on the whole shift can serve as the best preventive rPPE for HCWs from the VRID.
In April 2012, highly pathogenic avian influenza virus of the H5N1 subtype (HPAIV H5N1) emerged in poultry layers in Ningxia. A retrospective case-control study was conducted to identify possible risk factors associated with the emergence of H5N1 infection and describe and quantify the spatial variation in H5N1 infection. A multivariable logistic regression model was used to identify risk factors significantly associated with the presence of infection; residual spatial variation in H5N1 risk unaccounted by the factors included in the multivariable model was investigated using a semivariogram. Our results indicate that HPAIV H5N1-infected farms were three times more likely to improperly dispose farm waste [adjusted OR = 0.37; 95% CI: 0.12-0.82] and five times more likely to have had visitors in their farm within the past month [adjusted OR = 5.47; 95% CI: 1.97-15.64] compared to H5N1-non-infected farms. The variables included in the final multivariable model accounted only 20% for the spatial clustering of H5N1 infection. The average size of a H5N1 cluster was 660 m. Bio-exclusion practices should be strengthened on poultry farms to prevent further emergence of H5N1 infection. For future poultry depopulation, operations should consider H5N1 disease clusters to be as large as 700 m.
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