key: cord-0869911-fj6l76hq
authors: Kim, Min Seo; Seong, Dawon; Li, Han; Chung, Seo Kyoung; Park, Youngjoo; Lee, Minho; Lee, Seung Won; Yon, Dong Keon; Kim, Jae Han; Lee, Keum Hwa; Solmi, Marco; Dragioti, Elena; Koyanagi, Ai; Jacob, Louis; Kronbichler, Andreas; Tizaoui, Kalthoum; Cargnin, Sarah; Terrazzino, Salvatore; Hong, Sung Hwi; Abou Ghayda, Ramy; Radua, Joaquim; Oh, Hans; Kostev, Karel; Ogino, Shuji; Lee, I‐Min; Giovannucci, Edward; Barnett, Yvonne; Butler, Laurie; McDermott, Daragh; Ilie, Petre‐Cristian; Shin, Jae Il; Smith, Lee
title: Comparative effectiveness of N95, surgical or medical, and non‐medical facemasks in protection against respiratory virus infection: A systematic review and network meta‐analysis
date: 2022-02-26
journal: Rev Med Virol
DOI: 10.1002/rmv.2336
sha: 59a218b5850a3f2d88c0f90ae3bdc0357a2f5715
doc_id: 869911
cord_uid: fj6l76hq

The aim of this systematic review and network meta‐analysis is to evaluate the comparative effectiveness of N95, surgical/medical and non‐medical facemasks as personal protective equipment against respiratory virus infection. The study incorporated 35 published and unpublished randomized controlled trials and observational studies investigating specific mask effectiveness against influenza virus, SARS‐CoV, MERS‐CoV and SARS‐CoV‐2. We searched PubMed, Google Scholar and medRxiv databases for studies published up to 5 February 2021 (PROSPERO registration: CRD42020214729). The primary outcome of interest was the rate of respiratory viral infection. The quality of evidence was estimated using the GRADE approach. High compliance to mask‐wearing conferred a significantly better protection (odds ratio [OR], 0.43; 95% confidence interval [CI], 0.23–0.82) than low compliance. N95 or equivalent masks were the most effective in providing protection against coronavirus infections (OR, 0.30; CI, 0.20–0.44) consistently across subgroup analyses of causative viruses and clinical settings. Evidence supporting the use of medical or surgical masks against influenza or coronavirus infections (SARS, MERS and COVID‐19) was weak. Our study confirmed that the use of facemasks provides protection against respiratory viral infections in general; however, the effectiveness may vary according to the type of facemask used. Our findings encourage the use of N95 respirators or their equivalents (e.g., P2) for best personal protection in healthcare settings until more evidence on surgical and medical masks is accrued. This study highlights a substantial lack of evidence on the comparative effectiveness of mask types in community settings.

The coronavirus disease pandemic has led to an unprecedented increase in the demand for facemasks globally. The types of facemasks currently in use include N95 respirators, surgical masks, medical masks and non-medical masks (e.g., cloth or cotton masks). [1] [2] [3] [4] However, there is no established evidence or consensus on which type of facemask is superior in preventing respiratory viral infection either by the wearer or those they encounter. Different facemask guidelines recommend the use of different facemasks against COVID-19, [1] [2] [3] [4] and this is an area of concern as certain mask types may not be as capable as others in preventing respiratory viral infections. Previous systematic reviews exclusively performed pairwise comparisons of mask types, [5] [6] [7] and did not evaluate the capacities of all existing mask types simultaneously, leading to the unconsolidated information on the comparative effectiveness of different facemask types. Therefore, we conducted the first network meta-analysis (NMA) to evaluate the comparative prevention effectiveness of the most common types of facemasks (N95 respirators, surgical or medical masks, and non-medical masks) that have been used as personal protective equipment (PPE). NMA is an analytical tool that enables a single coherent ranking of multiple interventions; thus, it can provide information that helps policy-makers and healthcare workers choose appropriate equipment from an array of protective equipment. 8, 9 To inform optimized protective strategies for different causative viruses and clinical settings, we separately analysed comparative mask effects in various respiratory viral infections, including influenza, Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS) and COVID-19, in both community and healthcare settings.

We conducted a meta-analysis following a pre-registered protocol in PROSPERO (CRD42020214729). Two researchers (Min Seo Kim and Dawon Seong) independently searched the PubMed, Google Scholar and medRxiv databases from inception to 5 February 2021 using the search strategy detailed in the Supplementary Appendix (p. 2). The manual research and screening of reference lists of review articles were also conducted to include additional relevant studies that have not been retrieved through the primary search. Any conflicts were resolved by consensus, with the mediation of a third independent investigator (Jae Il Shin).

Our research question could be summarized in PICOS (Population, Intervention, Comparator, Outcomes and Setting) as follows:

people at risk of respiratory virus infection (P), adhered to facemask wearing (I), compared with either no mask-wearing or little maskwearing (C), reduction in the risk of laboratory-confirmed viral infection (O), in health care or community settings (S). Eligible studies met the following criteria: (1) randomized controlled trials (RCTs), cluster RCTs, prospective cohort studies, retrospective cohort studies, case-control studies and cross-sectional studies; (2) studies comparing the effectiveness of N95 respirators or their equivalent (e.g., P2), surgical masks, medical masks or non-medical (e.g., cloth or cotton) masks with each other or with not wearing masks/very low compliance to wearing masks. Studies were excluded if they did not specify the types of mask used, and did not present isolated outcomes for individual mask types. There was no limitation regarding the type of mask, compliance to wearing masks, and the fitting of the mask; however, we preferentially used results from high compliance and better mask fitting when stratified results were presented within a study. Pre-prints have been used relatively frequently in metaanalyses for the urgent topic of COVID-19 10-14 as a large amount of relevant data is still unpublished. We included pre-prints to reduce the risk of selection and publication bias and increase network density, as done elsewhere. 15 We included both RCTs and observational studies in our NMA; inclusion of real-world data from nonrandomized studies has the potential to improve precision of findings from RCTs if appropriately integrated 16, 17 and many previous NMAs have increased the density of network and enhanced the statistical power of findings using the approach. 18-21

Two investigators (Dawon Seong and Min Seo Kim) extracted data on the PICOS for each study. Moreover, information on the following was collected: first author, publication year, study design, estimated effect sizes or number of events, population information, type of respiratory virus, details of interventions and comparisons (mask type and compliance, if applicable), and outcome of interest. The intervention group included participants wearing a specific type of mask for protection, and the control group consisted of participants not wearing a mask or those who had a very low compliance to wearing a mask. For studies involving facemask and other non-pharmaceutical interventions (i.e., hand hygiene), we extracted data from selective groups to make the facemask the only difference. The primary outcome of the current NMA was laboratory-confirmed infection of various respiratory viruses-influenza virus, SARS-CoV, MERS-CoV and SARS-CoV-2. Disagreements were resolved by consensus, with any persistent conflict resolved by a third independent investigator (Jae Il Shin).

Two investigators (Dawon Seong and Min Seo Kim) evaluated the risk of bias for all included studies according to meta-analysis guidelines.

The risk of bias of RCTs was assessed using the ROB2 tool. 22 The risk of bias of observational studies was assessed using the ROBINS-I tool. 23 The certainty of evidence for primary outcomes was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach specifically designed for NMA. [24] [25] [26] [27] Using the GRADE approach, outcomes were classified as high, moderate, low or very low certainty of evidence. 

This NMA assessed the effectiveness of facemasks in preventing respiratory viral infection by presenting binary outcomes as odds ratio (OR) with 95% confidence interval (CI). The frequentist framework was used to perform the NMA using STATA (Stata Corp, version 15.0) and R software (version 3.6.0) 28 ; self-programmed routines of STATA 29, 30 and the 'netmeta' package in R 31 were used as described in the previous studies. 15, 32 The 'netmeta' package utilises graph theoretical approach, which constructs the Moore-Penrose pseudoinverse matrix and calculates the fitted values of the network model using a weighted least squares approach. 33 Review Manager (REVMAN version 5.3; Nordic Cochrane Centre) was used for pairwise meta-analysis using inverse variance randomeffects model. We applied random-effects model as we deemed that the expected heterogeneity between studies is likely to be due to real differences between studies rather than by chance.

In this NMA, the rank hierarchy for each mask type was investigated using the surface under the cumulative rank curve (SUCRA) of the P rank score of R. 34 We assessed the consistency of evidence between direct and indirect comparisons where p < 0.05 under the design-by-treatment interaction random-effects model or inconsistency factors with 95% credible intervals containing 0 was deemed a lack of consistency. 35 As consistency could be considered as statistical measure of transitivity, 36 transitivity assumption was estimated along with consistency test. The net heat plot was constructed to visualise the inconsistency matrix. 35 Heterogeneity was measured using the I 2 value, with I 2 > 50% indicating moderate-to-high heterogeneity. Publication bias was assessed using comparison-adjusted funnel plots and Egger's test. 29 A two-sided p-value of <0.05 was considered statistically significant.

Subgroup analyses were performed for virus types (influenza virus, SARS-CoV, MERS-CoV and SARS-CoV-2), clinical settings (healthcare setting and community setting), and study design (RCT and observational study) as planned in priori. Post-hoc subgroup analysis for usual healthcare setting (patient contact) versus aerosol-generating procedure (AGP) was further conducted given that increasing evidence has supported the difference in the risk of infection in those settings. 37,38 3 | RESULTS for the primary outcomes is depicted in Table 1 .

Wearing masks, regardless of the type, was associated with a 

Only wearing N95 or equivalent masks (OR, 0. (Figures 3 and 4) ; given the imprecision of the effect estimates for wearing masks against influenza according to GRADE (Table 1) , we cannot yet discount facemasks' effectiveness in prevention of influenza infection. Third, the poor effectiveness of masks against influenza may be attributable to the higher aerosol transmission potency of influenza virus compared to that of coronaviruses. 44, 45 The higher aerosol potency of influenza virus may allow more particles to be penetrated through unfitted masks. Lastly, the High quality: We are very confident that the true effect lies close to that of the estimate of the effect.

Moderate quality: We are moderately confident in the effect estimate that is the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.

Low quality: Our confidence in the effect estimate is limited that is the true effect may be substantially different from the estimate of the effect. Very low quality: We have very little confidence in the effect estimate that is the true effect is likely to be substantially different from the estimate of effect.

difference in the findings can be possibly explained by a higher adherence to wearing masks in pandemic settings than during the seasonal spread of influenza. 6 The global effect of SARS, MERS and COVID-19 led to unprecedentedly high standards, regulations and education regarding facemask usage, and this may have contributed to a significant reduction in the numbers of coronavirus infections. This is also supported by our result that higher compliance to masks significantly reduced respiratory viral infection (Figure 3 ). ranging from 48% to 87%). 5 Lastly, it is observed in the GRADE framework that certainty of evidence for medical or surgical masks are generally lower than that for N95 or equivalent (Table 1) . This may support the necessity for reappraisal of surgical/medical masks after more studies are published. Although the certainty of evidence is yet suboptimal, this study presents the highest level of evidence to date.

Coronaviruses are a serious public health threat, as demon- 

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