key: cord-0836403-6r57pq8o authors: Heald, Adrian H; Stedman, Michael; Tian, Zixing; Wu, Pensee; Fryer, Anthony A title: Modelling the impact of the mandatory use of face coverings on public transport and in retail outlets in the UK on COVID‐19‐related infections, hospital admissions and mortality date: 2020-10-18 journal: Int J Clin Pract DOI: 10.1111/ijcp.13768 sha: e58e426d6e698d6bbc165b7f9947221416ed0f7a doc_id: 836403 cord_uid: 6r57pq8o INTRODUCTION: The rapid spread of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2/)(COVID‐19) virus resulted in governments around the world instigating a range of measures, including mandating the wearing of face coverings on public transport/in retail outlets. METHODS: We developed a sequential assessment of the risk reduction provided by face coverings using a step‐by‐step approach. The United Kingdom Office of National Statistics (ONS) Population Survey data was utilised to determine the baseline total number of community‐derived infections. These were linked to reported hospital admissions/hospital deaths to create case admission risk ratio and admission‐related fatality rate. We evaluated published evidence to establish an infection risk reduction for face coverings. We calculated an Infection Risk Score (IRS) for a number of common activities and related it to the effectiveness of reducing infection and its consequences, with a face covering, and evaluated their effect when applied to different infection rates over 3 months from 24(th) July 2020, when face coverings were made compulsory in England on public transport/retail outlets. RESULTS: We show that only 7.3% of all community‐based infection risk is associated with public transport/retail outlets. In the week of 24(th) July, The reported weekly community infection rate was 29,400 new cases at the start (24th July). The rate of growth in hospital admissions and deaths for England was around ‐15%/week, suggesting the infection rate, R, in the most vulnerable populations was just above 0.8. In this situation, average infections over the evaluated 13 week follow‐up period, would be 9,517/week with face covering of 40% effectiveness, thus reducing average infections by 844/week, hospital admissions by 8/week and deaths by 0.6/week; a fall of 9% over the period total. If, however, the R‐value rises to 1.0, then average community infections would stay at 29,400/week and mandatory face coverings could reduce average weekly infections by 3,930, hospital admissions by 36 and deaths by 2.9/week; a 13% reduction. If the R‐value rose and stayed at 1.2, then expected average community‐derived hospital admissions would be 975/week and 40% effective face coverings would reduce this by 167/week and reduce possible expected hospital deaths from 80/week to 66/week. These reductions should be seen in the context that there was an average of 102,000/week all‐cause hospital emergency admissions in England in June and 8,900 total reported deaths in the week ending 7th August 2020. CONCLUSION: We have illustrated that the policy on mandatory use of face coverings in retail outlets/on public transport may have been very well followed, but may be of limited value in reducing hospital admissions and deaths, at least at the time that it was introduced, unless infections begin to rise faster than currently seen. The impact appears small compared to all other sources of risk, thereby raising questions regarding the effectiveness of the policy. wearing face masks was associated with a lower risk of infection (relative risk 0·56, 95% CI 0·40 to 117 0·79). The American College of Physicians also raises questions around the evidence to support the 118 effectiveness of face coverings in reducing transmission. 7 The only study we identified that examined the 119 introduction of face coverings on public transport and in retail outlets examined the association between 120 introduction of face coverings in a specific region of Germany relative to when they were introduced in 121 other comparator regions. 8 While this study suggested that 'face masks reduce the daily growth rate of 122 reported infections by around 40%', the design of the study means that it is difficult to assign the 123 observed effect to the introduction of compulsory face coverings in a causal fashion. We developed a sequential assessment of the risk reduction provided by face coverings using a step-by-149 step approach. As a baseline, we utilised the Office of National Statistics (ONS) Population Survey data to 150 determine the baseline total number of community-derived infections. 16 The ONS Population Survey 151 released on the 24 th July 2020 16 provided data that estimated, for the most recent week for which data 152 was available (13 to 19 July 2020). This excluded those in hospitals, care homes or other institutional 153 settings (but not those who work in these settings). This baseline figure of 2,800 cases per day is used in 154 subsequent modelling. Step 1: Source of infection. We considered the impact on the number of infections within the community rather than in hospitals or 157 care homes, as these are where people using retail and public transport will be most reflected. There will 158 be some cross infections but the level of this is beyond the scope of this analysis but is likely to be small. Given that it is unlikely that people displaying more severe symptoms of infection would use public 160 transport or visit retail outlets, we then utilised ONS and wider literature data to estimate the proportion 161 of asymptomatic or pre-symptomatic cases. The ONS data suggests that only around one-third of individuals testing positive for COVID-19 on a swab 163 test reported having symptoms. 17 This was based on self-reported symptoms and therefore may be an that between 55 and 75% of infections may be derived from people without symptoms. 18 While posted on 167 the preprint service website, medRxiv, early in the pandemic, these data were reviewed and assessed by 168 the Centre for Evidence-Based Medicine on 23 rd July 2020. 19 According to Yin and Jin, there is no 169 difference in transmissibility between those with and without symptoms. 20 For the modelling, we used a 170 conservative estimate of 80% of infections from pre-or asymptomatic cases. Step 2: Infection risk by activity. We calculated an Infection Risk Score (IRS) for a number of common activities. Firstly, based on location, 174 we categorised daily activities into the following: home, work, public transport, retail outlets, other 175 activities (indoors) and, other activities (outside). We calculated the average length of time spent per day Accepted Article on each of these activities. This was based on the United Kingdom Time Use Survey, 2014-2015, 21 as 177 quoted in a Scottish government report, 22 and a Resolution Foundation report in July 2020. 23 Step 3: Impact of the use of face coverings. The effectiveness of face coverings in reducing infections will be dependent on two broad factors: (i) the range for the UK of 0.7-0.9 and a growth rate was given as -4% to -1% as of 24 th July 2020. 33 Consequently, 263 three R values; namely 0.8 (the accepted level at the time of the introduction of mandatory face 264 coverings), 1.0 (a worsening to equilibrium) and 1.2 (the pandemic restarting) were used in our analysis. For each of these, we calculated the total number of consequent future infections that could be expected 266 to flow from the original infections. Accepted Article Baseline effectiveness of face coverings and the IRS calculated above for retail outlets and public 268 transport was applied to each scenario to calculate the expected infections, hospitalisations and deaths 269 over the next 3 months. The sensitivity of the results to the assumptions on face-covering effectiveness 270 was tested by calculation of the above for no face coverings (0%), 20%, 40%, 60% and 80%. Baseline data & proportion of pre-symptomatic and asymptomatic cases. Based on the ONS survey data, we modelled the impact of face coverings based on 2,800 community 274 cases per day. Of these, 80% are estimated to be due to transmission from pre-symptomatic and 275 asymptomatic cases. These generate a baseline figure for assessment of the impact of face coverings of 276 2,240 community cases. Infection risk by activity. 278 Figure 1a shows graphically the impact of the different assumed R-value (0.8, 1.0, 1.2) on the infection 301 outcomes over the 13 weeks and the potential cumulative numbers for both with or without face 302 coverings for the 3 levels of R then on infections (Figure 1b) , community hospitalisation ( Figure 1c ) and 303 deaths (Figure 1d) We have modelled the potential impact of the use of face coverings worn in retail outlets and on public 336 transport on the number of UK COVID-19 infections and associated hospital admissions and mortality 337 rates. Overall, we demonstrated that only around 7% of all community-based infection risk for those aged 338 more than 16 years of age is associated with public transport and retail outlets. This contrasts with 57% 339 associated with work or study, for those aged 16 years and over. This illustrates the limitations of the 340 impact of any policy to reduce infections in the public transport and retail outlets sectors alone, 341 irrespective of the efficiency of the intervention. It perhaps suggests that measures targeted at the 342 workplace may be more worthwhile. In addition to this, the requirement to wear face coverings may increase anxiety in some people and 344 thereby result in a reluctance to utilise public transport and/or visit retail outlets. This may, therefore, 345 reduce the time spent on these activities. While it is also possible that the use of face coverings may 346 increase the confidence of other people, it is difficult to say whether this will negate the above effect. Certainly, public transport usage and retail footfall does not appear to have returned to pre-pandemic 348 levels, 37,38 and hence the 7.3% may be an overestimate of the contribution of these activities to overall 349 risk. However, in our modelling, given the difficulty in calculating this impact, we assumed this change in 350 behaviour to be neutral. This raises interesting questions around the timing of the implementation of the policies to mandate the 369 use of face coverings in the retail and transport contexts; a time when the R-value was less than one 370 (most UK government reports suggested 0.7-0.9) and the daily infection rate was relatively low in 371 comparison to the peak in April 2020. 33 Use of face coverings in retail outlets and on public transport is of 372 limited value, particularly when the R-value is below 1, in contrast to March/April 2020 when the R-value 373 was much higher. We also used a range of efficiencies of face coverings, reflecting the wide range of types of coverings, 3 375 variability in correct usage (particularly over prolonged periods) and uncertainty around which modes of 376 transmission could be influenced by their use. 27 Realistically, an estimate of around 40% is likely to be a 377 sensible conservative estimate, particularly in the context of the work by van der Sande et al. 29 Advice on the use of masks in the context of COVID-19: interim guidance, 5th Report on face masks for the general public UK Department of Health and Social Care. 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