Efficacy of face masks against respiratory infectious diseases: a systematic review and network analysis of randomized-controlled trials
Bibliographic record
Abstract
Abstract During the ongoing COVID-19 pandemic, face masks are among the most common and practical control measures used globally in reducing the risk of infection and disease transmission. Although several studies have investigated the efficacy of various face masks and respirators in preventing infection, the results have been inconsistent. Therefore, we performed a systematic review and network meta-analysis (NMA) of the randomized-controlled trials (RCTs) to assess the actual efficacy of face masks in preventing respiratory infections. We searched nine electronic databases up to July 2020 to find potential articles. We accepted trials reporting the protective efficacy of face masks against respiratory infections, of which the primary endpoint was the presence of respiratory infections. We used the ROB-2 Cochrane tool to grade the trial quality. We initially registered the protocol for this study in PROSPERO (CRD42020178516). Sixteen RCTs involving 17 048 individuals were included for NMA. Overall, evidence was weak, lacking statistical power due to the small number of participants, and there was substantial inconsistency in our findings. In comparison to those without face masks, participants with fit-tested N95 respirators were likely to have lesser infection risk (RR 0.67, 95% CI 0.38–1.19, P -score 0.80), followed by those with non-fit-tested N95 and non-fit-tested FFP2 respirators that shared the similar risk, (RR 0.73, 95% CI 0.12–4.36, P -score 0.63) and (RR 0.80, 95% CI 0.38–1.71, P -score 0.63), respectively. Next, participants who donned face masks with and without hand hygiene practices showed modest risk improvement alike (RR 0.89, 95% CI 0.67–1.17, P -score 0.55) and (RR 0.92, 95% CI 0.70–1.22, P -score 0.51). Otherwise, participants donning double-layered cloth masks were prone to infection (RR 4.80, 95% CI 1.42–16.27, P -score 0.01). Eleven out of 16 RCTs that underwent a pairwise meta-analysis revealed a substantially lower infection risk in those donning medical face masks (MFMs) than those without face masks (RR 0.83 95% CI 0.71–0.96). Given the body of evidence through a systematic review and meta-analyses, our findings supported the protective benefits of MFMs in reducing respiratory transmissions, and the universal mask-wearing should be applied—especially during the COVID-19 pandemic. More clinical data is required to conclude the efficiency of cloth masks; in the short term, users should not use cloth face masks in the outbreak hot spots and places where social distancing is impossible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.025 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".