By: Youlin Long1 Tengyue Hu2 Liqin Liu2 Rui Chen3 Qiong Guo1 Liu Yang1 Yifan Cheng1 Jin Huang4 Liang Du1
1 INTRODUCTION
Severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) have mortality rates about 10% and 37%, respectively.1 Since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facemasks have been considered to be vitally important to reduce the risk of infection because vaccination or specific anti infective treatments are unavailable.2,3 N95 respirators are used to prevent users from inhaling small airborne particles and must fit tightly to the user’s face. Surgical masks are designed to protect wearers from microorganism transmission and fit loosely to the user’s face.5,6 Although surgical masks cannot prevent inhalation of small airborne particles, both of them can protect users from large droplets and sprays.7,8 There are conflicting recommendations for severe acute respiratory syndrome (SARS) and pandemic influenza: the World Health.
Organization (WHO) recommends using masks in low-risk situations and respirators in high-risk situations, but the Centers for Disease Control and Prevention (CDC) recommends using respirators in both low and high-risk situations.9 However, N95 respirators may play a limited role in low-resource settings, where there are a finite number of N95 respirators, or it may be unaffordable.9 Also, previous metaanalyses concluded there was insufficient evidence to determine the effect of N95 respirators due to a small number of studies that is prone to lack of statistical power.10,11 Additionally, these meta-analyses were limited by the small number of included randomized control trials (RCTs). More rigorous RCTs of comparing N95 respirators with surgical masks against influenza published in recent years were not included in previous meta-analyses.12-14
In light of the growing number of RCTs of masks use for protecting against influenza, this systematic review and meta-analysis aimed to assess the effectiveness of N95 respirators versus surgical masks for prevention of` influenza.
2 METHODS
This meta-analysis was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.15
2.1 Inclusion and exclusion criteria
Inclusion criteria were (1) study type: RCT (including clusterrandomized trial) and nonrandomized controlled study; (2) participants: humans with influenza (including pandemic strains, seasonal influenza A or B viruses and zoonotic viruses such as swine or avian influenza), and other respiratory viral infections (as a proxy for influenza); (3) intervention and comparator: N95 respirators versus surgical masks; () primary outcome: laboratory-confirmed influenza; (5) secondary outcomes: laboratory-confirmed respiratory viral infections, laboratory-confirmed bacterial colonization, laboratory-confirmed respiratory infection, and influenzalike illness; and (6) settings: hospital or community. RCTs were selected due to the potential possibility of high evidence level. Exclusion criteria were (1) theoretical models; (2) human ⁄nonhuman experimental laboratory studies; and (3) conference abstract.
2.2 Search strategy
We searched PubMed, EMBASE, and The Cochrane Library databases from inception to January 27, 2020, to identify published systematic reviews on evaluating the use of masks for preventing influenza. Search strategy in PubMed could be found in Table 1, and the strategy was adequately adjusted to use in other databases. Then, primary RCTs included in the systematic reviews were identified. Additionally, we conducted an additional search to identify RCTs published in the past five years from January 27, 2015, to January 27, 2020, using the databases and search strategies described above. We also searched for ClinicalTrials.gov to obtain unpublished data. There were no publication status and language restrictions on selecting the studies.
2.3 Study selection and data extraction
Two reviewers independently screened the articles based on the titles, abstracts and full texts. Then, two reviewers independently exacted the following data from included studies: first author, publication year, country, disease, details of study population and intervention, study design, sample size, settings, and results. All disagreements were resolved by discussion.
2.4 Risk of bias assessment
Two reviewers independently assessed the risk of bias of the selected RCTs using the Cochrane Risk of Bias tool,16 which includes domains on random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessors, incomplete outcome data, and selective reporting. For each RCT, every domain was judged among 3 levels: high risk, unclear risk, and low risk. Disagreements were resolved by discussion.
2.5 Data analysis
All statistical analyses were performed using Review Manager (RevMan) version 5.3. Comparable data from studies with similar interventions and outcomes were pooled using forest plots. Relative risk (RR) with 95% confidence intervals (CIs) for dichotomous data was used as the effect measure. Between-study heterogeneity was assessed using the I 2 for each pooled estimate.17 We adopted a random-effects model for heterogeneity P < .10. We performed a subgroup analysis based on the settings (hospital, community) due to the possibility of clinical heterogeneity. A sensitivity analysis was conducted to evaluate the robustness of the results by excluding individual studies for each forest plot. Funnel plots were planned to assessed publication bias. Because of the small number of studies available for each pooled estimate, we failed to assess publication bias.
3 RESULTS
3.1 Search results and study characteristics
The details on the literature search and screening process can be found in Figure 1. Excluded studies and reasons for exclusion were shown in Table 2. In total, we included six RCTs12,18-22 and found no unpublished data of RCTs from ClinicalTrials.gov. The characteristics of these RCTs were presented in Table 3. The included studies published between 2009 and 2019. A total of 9171 participants in Canada, Australia, China, or America were included, and the number of participants in each RCT ranged from 435 to 5180 patients. The follow-up duration varied from 2 to 15 weeks. Five studies included participants in hospitals,12,18,20-22 and one in households.19 Because of different definitions of outcome in included studies, we redefined the laboratory confirmed respiratory infection as respiratory influenza, other viruses or bacteria infection.
3.2 Risk of bias
The results of the risk of bias assessment can be found in Figure 2. Five studies reported the computer-generated random sequences, while only one mentioned randomization. All studies did not mention allocation concealment. Participants and trial staff were not blinded in two studies, and the other two studies failed to mention the blinding of participants and personnel. Four studies did not report whether the outcome assessors were blinded. All studies had complete outcome data or described comparable numbers and reasons for withdrawal across groups and prespecified outcomes.
3.3 Effectiveness
Five RCTs involving 8444 participants reported laboratory-confirmed influenza.12,18-21 Meta-analysis with fixed-effects model revealed that there was no statistically significant differences in preventing influenza using N95 respirators and surgical masks (RR = 1.09, 95% CI 0.92-1.28, P > .05) (Figure 3). The results of subgroup analyses were consistent with this regardless of the hospital or the community. The results of the sensitivity analysis were not altered after excluding each trial. Four RCTs18-21 involving 3264 participants reported laboratoryconfirmed respiratory viral infections. Meta-analysis with fixed-effects model revealed that there were no statistically significant differences in preventing respiratory viral infections using N95 respirators and surgical masks (RR = 0.89, 95% CI 0.70-1.11, P > .05) (Figure 4). The results of subgroup analyses were consistent regardless of the hospital or the community. However, the sensitivity analysis after excluding the trial by Loeb et al18 showed a significant effect of N95 respirators on preventing respiratory viral infections (RR=0.61, 95% CI 0.39-0.98, P < .05). Two RCTs21,22 involving 2538 participants reported laboratoryconfirmed bacterial colonization. Meta-analysis with fixed-effects model revealed that compared with surgical masks, N95 respirators significantly reduced bacterial colonization in hospitals (RR = 0.58, 95% CI 0.43-0.78, P < .05) (Figure 5). The sensitivity analysis showed that the results did not change after excluding each trial. Two RCTs12,22 involving 6621 participants reported laboratoryconfirmed respiratory infection. Meta-analysis with random-effects model revealed that there were no statistically significant differences in preventing respiratory infection using N95 respirators and surgical masks in hospitals (RR = 0.74, 95% CI 0.42-1.29, P > .05) (Figure 6). However, the sensitivity analysis after excluding the trial by Radonovich et al12 showed a significant effect of N95 respirators on preventing respiratory infection (RR = 0.53, 95% CI 0.35-0.82, P < .05). Five RCTs involving 8444 participants reported influenza like illness.12,18-21 Meta-analysis with random-effects model revealed that there were no statistically significant differences in preventing influenza like illness using N95 respirators and surgical masks (RR = 0.61, 95% CI 0.33-1.14, P > .05) (Figure 7). The results of subgroup analyses indicated that statistically significant superiority of N95 respirators over surgical masks against influenza like illness (RR = 0.37, 95% CI 0.20-0.71, P < .05) in the community (only one RCT). The sensitivity analysis showed results remained unchanged after excluding each trial.
4 DISCUSSION
This meta-analysis showed that there were no statistically significant differences in preventing laboratory-confirmed influenza, laboratoryconfirmed respiratory viral infections, laboratory-confirmed respiratory infection and influenza-like illness using N95 respirators and surgical masks. N95 respirators provided a protective effect against laboratory-confirmed bacterial colonization. In subgroup analysis, similar results could be found in the hospital and community for laboratory-confirmed influenza and laboratory-confirmed respiratory viral infections. However, sensitivity analysis showed unstable results for the prevention of laboratory-confirmed respiratory viral infections and laboratory-confirmed respiratory infection. Through the course of influenza pandemics, large numbers of facemasks may be required to use in long periods to protect people from infections.23 Using N95 respirators is likely to result in discomfort, for example, headaches.23 A previous study3 reported that there was an inverse relationship between the level of compliance with wearing an N95 respirator and the risk of clinical respiratory illness. It is difficult to ensure high compliance due to this discomfort of N95 respirators in all studies. The reason for the similar effects on preventing influenza for the use of N95 respirators versus surgical masks may be related to low compliance to N95 respirators wear,23 which may lead to more frequent doffing compared with surgical masks.13 Although N95 respirators may confer superior protection in laboratory studies designing to achieve 100% intervention adherence,24 the routine use of N95 respirators seems to be less acceptable due to more significant discomfort in real-world practice.11 Therefore, the benefit of N95 respirators of fitting tightly to faces is offset or subjugated.13 However, it should be noted that the surgical masks are primarily designed to protect the environment from the wearer, whereas the respirators are supposed to protect the wearer from the environment.25 There are several limitations to this study. First, some RCTs had a high risk of bias due to lack of allocation concealment and blinding; although it is impractical to blind participants who would know the type of masks they are wearing. Second, the number of included studies focusing on the community was small. Consequently, the results of the subgroup analysis might be unreliable. Third, we identified RCTs from published systematic reviews, which may result in the omission of relative RCTs. Finally, there might be publication bias, and we cannot assess it due to an insufficient number of included RCTs. In conclusion, the current meta-analysis shows the use of N95 respirators compared with surgical masks is not associated with a lower risk of laboratory-confirmed influenza. It suggests that N95 respirators should not be recommended for the general public and nonhigh risk medical staffs those are not in close contact with influenza patients or suspected patients.
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