Endometrial cancer combining uterus didelphys is quite rare clinically which partially explains that there is no discussion about young patients’ fertility preservation and follow up of tumor outcome.In this article, we report a case of unilateral endometrial carcinoma found in a young patient with uterus didelphys who was treated with high-efficiency progesterone due to unfinished child-bearing. During the follow-up, the affected uterine endometrium was not reversed by progesterone. So, the patient underwent the abdominal surgery with the left uterus and left fallopian tube resection. We performed three consecutive immunohistochemical studies of the contralateral uterine endometrium to verify the safety of preserving the contralateral uterus and its appendages which preserved her fertility.Endometrial cancer occurring in patients with uterus didelphys is quite rare in child-bearing age. In this case report, we preserved the patient’s contralateral uterus based on patient’s strong needs and negative IHC analysis of the preserved side uterine endometrium. However, the tumor and fertility outcome require more follow-up. Uterus didelphys is a relatively rare type of congenital malformation of female genital tract due to the complete absence of fusion of bilateral paramesonephric tubes during embryonic development , 2. AccoGenitourinary system is originated from mesoderm. The development of mesonephric tube or paramesonephric tube will be impaired with the effects of adverse factors during embryonic development which will lead to abnormalities of urinary system and reproductive system. Therefore, malformations of reproductive system are often accompanied by abnormalities of urinary system .Endometrial carcinoma is one of the most common gynecological malignancies. However, it is extremely rare to have uterus didelphys and endometrial carcinoma in one patient. The standard treatment for endometrial cancer is extra fascial hysterectomy and bilateral adnexectomy, pelvic lymphadenectomy and other biopsy as appropriate . Until nThe female patient was diagnosed as uterus didelphys at 28 years old when she was doing the transvaginal ultra-sound because of long-term abnormal uterine bleeding as 10–15 days/30 days. The gynecological physical and imaging examination confirmed the diagnosis as “double uterus, double cervix, double vagina”. She was operated hysteroscopy with endometrial polyp removal and segmental curettage in October 2018 for the first time. During the operation, it was found that there was a single polyp in the right cavity and multiple polyps in the left cavity. Postoperative paraffin pathology and immunohistochemistry showed that there was endometrial complex atypical hyperplasia and focal carcinoma (moderately differentiated endometrioid carcinoma) in the left cavity. IHC: ER 90% strong positive, PR 90% strong positive, p53+, Pax8+++, PTEN-, p16-, Ki67 10–30%. No lesion was found in the right uterine endometrium. The patient of childbearing age required conservative treatment which was taking oral megestrol acetate after surgery. In March 2019, hysteroscopy and curettage were reexamined. The postoperative pathology reported that there was mucinous metaplasia of endometrial glands in the left uterine cavity which can be explained because of the medication.After the operation, the patient continued to take megestrol acetate. In June 2019, the hysteroscopy and segmental curettage were reexamined. Postoperative pathology showed that the endometrium of left uterus was moderately differentiated endometrioid carcinoma. With strong needs for child-bearing, our patient continuously chose to take megestrol acetate and reviewed hysteroscopy and curettage again in October 2019. Postoperative pathology result of left uterine endometrium was still in accordance with endometrioid carcinoma (moderately differentiated). Doctors recommended surgical treatment due to the unsatisfied outcome of conservative drug treatment. However, the patient refused and continued to take megestrol acetate for outpatient follow-up. Things changed in April 2020 when the pelvic enhanced MRI of our patient showed abnormal enhancement of left endometrium which was more obvious than before. In May 2020, she finally underwent the abdominal surgery with the left uterus and left fallopian tube resection. There were two spindle shaped uterus and accessories in bilateral iliac fossa. During the surgery, we opened the left ureteral tunnel, pushed down the bladder to 3 cm below the external orifice of the cervix and blocked the left uterine artery above it. The left utero-sacral ligament was cut and sewed 2 cm beside the uterus. The vaginal septum was cut open. We removed the left uterus along the space between the two cervixes. The left uterine endometrium was thick and crisp according to the postoperatively dissected specimen. The pathological findings were consistent with moderately differentiated endometrioid carcinoma with changes after treatment in the left uterus. The molecular and gene detection of the specimen showed that it may belong to the molecular type of MMR deficiency type which belongs to sporadic endometrial carcinoma and does not belong to Lynch syndrome.The patient recovered well after operation. Combined with postoperative pathology, the patient was diagnosed as stage IA of moderately differentiated endometrial carcinoma in the left uterus. No abnormalities were found in the postoperative reexamination during which she has been trying to get pregnant in fertility center.Female reproductive tract parenchyma develops from epithelia of the Mullerian Duct and urogenital sinus . The MulWe reviewed published articles about uterus didelphys combining endometrial cancer in PubMed from 1990 to 2020 –22. TherIn this case, an infertile woman with uterus didelphys was diagnosed as endometrial cancer in the left uterine cavity at the age of 28 years. The patient's endometrium did not achieve reversal after the classic high-efficiency progesterone treatment which led to the surgery to remove the left uterus. According to the embryological observations, uterus didelphys is due to the failure of fusion of the Mullerian ducts to form the uterus. Whether to keep the contralateral uterus is worthy of more discussion and attention.By reviewing the previous 15 cases of double uterine malformation with endometrial cancer, we concluded that 7 cases (7/15) occurred in the right uterus with cancer staging from Ia–II and differentiation types from G1–G3. Among the 7 cases, only 1 case combined with atypical hyperplasia in the left endometrium. 4 cases (4/15) occurred in the left uterus with cancer staging from Ia-IIIc and differentiation type from G1 to G3, 1 of 4 cases was associated with atypical hyperplasia in the right endometrium. 4 cases (4/15) occurred in bilateral uterus with 1 case in stage IA (differentiation type G1), 1 case in stage II (differentiation type G2), 1 case in stage IIIA (differentiation type G1), and 1 case in stage IV (differentiation type G3).From the limited data point of view, most of the patients (11/15) with uterus didelphys had endometrial cancer on one side of the uterus, two cases had atypical hyperplasia of endometrium on the contralateral uterus. Atypical hyperplasia of endometrium is the precancerous lesion of endometrial adenocarcinoma. 29% of complex atypical hyperplasia without treatment will develop into cancer . AdvanceBack to our case, the patients was diagnosed as stage IA of moderately differentiated endometrial carcinoma in the left uterus. No cancer or precancerous lesions were detected in the contralateral uterus given the previous hysteroscopy results. Only the affected side of the uterus and appendix were removed in the surgery. It is certain that the patient needs reviewing transvaginal ultrasound, MRI, and hysteroscopy of the reserved uterus in the follow-up treatment.In order to ascertain the safety of preserving the fertility of this patient, we would like to search for prognostic biomarkers to predict the progression amongst the normal endometrium, endometrial atypical hyperplasia and endometrial cancer. PTEN is the most commonly investigated biomarker in endometrial cancer. However, PTEN is expressed normally in proliferative endometrial glands and stroma but expressed decreasingly in secretory cycle which may results in variable staining , RecentlPAX2 can be expressed by immunohistochemistry inside normal endometrial glandular cells’ nucleus. In 2018, Emma found a progressive decrease in PAX2 expression from proliferative endometrium to endometrial intraepithelial neoplasia (EIN) to endometrial endometrioid carcinoma (EEC) which is also observed in previous studies. It is acknowledged that it can take 30–40 years from normal endometrium to endometrial cancer. Despite of the small sample size, this study is quite popular since its long follow-up of these patients. In Emma’s study, they presented a method and threshold with PAX2 expression H-score as 75. EIN lesions with a PAX2 expression H-score below or equal to 75 (high risk) had significantly lower progression-free survival than those with an H-score above 75 (low risk). Furthermore, more EIN patients with high-risk PAX2 showed progression  compared to low-risk patients  .In this study, our patient has done hysteroscopic surgeries 4 times in Peking University First Hospital among which the right uterine endometrium was examined 3 times. To better understand the risk of whether the reserved side of uterus will have pathological progress, our pathologist examined PTEN and PAX2 on three right endometrial pathological sections of the patient. The results showed that the right endometrium obtained from three hysteroscopic operations showed strong positive expression of PTEN and PAX2. This may tell clinicians that the possibility of endometrial lesions on the right side is not significant from the onset to the last operation. In the follow-up process, the PTEN and PAX2 immunohistochemical analysis of endometrium can be carried out. If possible, the H score of PAX2 can be used to better complete the follow-up of endometrial lesions, so as to adjust the treatment plan in time.Endometrial cancer occurring in patients with uterus didelphys is quite rare in published articles, especially in young patients with needs for childbearing. In this case report, the patient has the unilateral uterus with endometrial cancer removed due to the unresponsiveness to drugs. According to the IHC analysis of the reserved side uterine endometrium, there is no pathological progress until the last hysteroscopic surgery. However, with less evidence, it requires more follow-up in later period to trace the patient’s tumor and fertility outcome. In order to shorten the time required for fertility, the patient chose assisted reproductive technology in our hospital. During the process of ovarian hyperstimulation and After the achievement of fertility outcome, we will keep monitoring the patient’s transvaginal ultrasound, MRI, and hysteroscopy. The application of H score of PAX2 will be considered to evaluate the risk of reserving the contralateral uterus.


Respiratory protective equipment recommended in the UK for healthcare workers (HCWs) caring for patients with COVID-19 comprises a fluid-resistant surgical mask (FRSM), except in the context of aerosol generating procedures (AGPs). We previously demonstrated frequent pauci- and asymptomatic severe acute respiratory syndrome coronavirus 2 infection HCWs during the first wave of the COVID-19 pandemic in the UK, using a comprehensive PCR-based HCW screening programme .Here, we use observational data and mathematical modelling to analyse infection rates amongst HCWs working on ‘red’  and ‘green’ (non-COVID-19) wards during the second wave of the pandemic, before and after the substitution of filtering face piece 3 (FFP3) respirators for FRSMs.Whilst using FRSMs, HCWs working on red wards faced an approximately 31-fold (and at least fivefold) increased risk of direct, ward-based infection. Conversely, after changing to FFP3 respirators, this risk was significantly reduced (52–100% protection).FFP3 respirators may therefore provide more effective protection than FRSMs for HCWs caring for patients with COVID-19, whether or not AGPs are undertaken.Wellcome Trust, Medical Research Council, Addenbrooke’s Charitable Trust, NIHR Cambridge Biomedical Research Centre, NHS Blood and Transfusion, UKRI. Consistent with World Health Organization (WHO) advice , UK InfeA recent systematic review undertaken for PHE concluded that: ‘patients with SARS-CoV-2 infection who are breathing, talking, or coughing generate both respiratory droplets and aerosols, but FRSM  are considered to provide adequate staff protection’ . NeverthData from the HCW testing programme at Cambridge University Hospitals NHS Foundation Trust (CUHNFT) during the first wave of the UK severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic indicated a higher incidence of infection amongst HCWs caring for patients with COVID-19, compared with those who did not . SubsequCUHNFT is a tertiary hospital in the UK with approximately 1000 beds. During the pandemic, wards were categorised as ‘red’, ‘amber’, or ‘green’. Patients with confirmed COVID-19 were cared for on red wards, and patients who had negative SARS-CoV-2 tests and no clinical features of COVID-19 on green wards. Patients awaiting test results, who had clinical features of COVID-19 but a negative test result, or who may have been exposed to SARS-CoV-2 were cared for on amber wards.The CUHNFT electronic rostering system recorded to which ward(s) individual nurses and healthcare assistants (HCAs) were allocated. Although this does not encompass 100% of ward staff, the data can be used to indicate relative ward size. An average of 42.5 (range 19–72) nurses/HCAs worked on green wards, and 49.6 (range 37–69) worked on red wards. The mean number of beds per green ward was 24.1 (range 5–33) and red 28.1 (range 26–33). The mean number of nurses and HCAs per bed was 0.41 (range 0.24–0.58) on green wards and 0.31 (range 0.24–0.42) on red wards.A change to RPE for staff on red wards from FRSMs to FFP3 respirators was announced on 22/12/20. FFP3 respirators were assigned to staff following fit testing. HCWs on green wards continued to wear FRSMs. HCWs on all wards also wore eye protection. The following types of FFP3 respirator were used during the study period: 3 M 9330+, 3 M 1863, Easimask FSM18, and Mexin MX2016v. HCWs who did not pass fit testing with the masks available used either a JSP half mask respirator or a powered air purifying respirator (Tornado or Easiair).A comprehensive PCR-based HCW screening programme is established at CUHNFT, with symptomatic testing offered as required and asymptomatic testing offered to all HCWs weekly . From 22The start of the study period was taken to be 02/11/20, coinciding with an increase in community incidence of SARS-CoV-2 infection and formal implementation of weekly asymptomatic screening for all staff members. By default new infections on or prior to 27/12/20 were attributed to exposure before the change in RPE. Infections detected later than this date were attributed to exposure after the change in RPE. This timing was chosen to reflect the median incubation period of SARS-CoV-2 (5.1 days), with 27/12/20 falling 5 days (inclusive) after the change in RPE . Since sA programme of SARS-CoV-2 vaccination using the BNT162b2 COVID-19 vaccine commenced at CUHNFT on 08/12/20 . In lineBecause of the rising number of admissions to CUHNFT with COVID-19, the number of red wards was increased from one at the beginning of November 2020 to seven by the week starting 11/01/21. Six wards therefore changed from green to red during the period of data collection. Of 609 positive results over the entire study period, 169 (27.8%) were included in this study. Exclusions encompassed HCWs who were not ward based or worked between different wards with different red/amber/green status , HCW working on amber wards , non-clinical staff , and staff working in critical care areas , where different RPE was used throughout .If a staff member tested positive within 5 days of their ward changing colour, their case was classified according to the red/green status of their ward 5 days before their positive test . The effects of changing the interval from 5 days to between 3 and 7 days explored.The number of ‘HCW days’ for each week of the study was calculated for each category of ward. Rostering information was used to identify the number of nurses and HCAs regularly assigned to each ward on each of the 7 days of the week. Data describing the number of other staff on each ward was not available, but was assumed to be proportional to the number of rostered HCWs, calculations being performed in terms of nurse and HCA numbers.wX,d denotes the number of HCWs on wards of type X on day d, the weekly numbers of ward days for week i, denoted WX,i, were calculated as the sums of these values across that week.Where https://coronavirus.data.gov.uk/details/cases, data downloaded on 12/06/21), and were calculated as the sum of the number of cases reported in each week of the study. Raw data are shown in Details of community incidence were calculated from publicly available data describing the East of England region of the UK  or after that point (R2), giving:Cases on red wards were split according to whether they arose prior to the introduction of FFP3 masks  but not red (R2 = 0.03) wards  wards . Taken tTo further quantify the risk of infection for HCWs working on red and green wards, we generated a simple mathematical model. According to this model, the total risk of infection is divided into a risk from community-based exposure, and a risk from direct, ward-based exposure to patients (ward-specific risk). The risk from direct exposure on red wards was allowed to vary upon the introduction of FFP3 respirators, and was fitted to a maximum likelihood model. Inferred parameters and their confidence intervals are shown in r2/r1).The inferred risk of direct infection from working on a green ward was low throughout the study period, and consistently lower than the risk of community-based exposure, which increased in proportion to rising levels of community incidence . By contr2/r1, 100%), although the lower bound of the effect size varied between 30% and 72% for cutoffs between 3 and 7 days (Where ward designations changed (e.g. from green to red), cases were by default attributed to the type of ward on which each positive-testing HCW worked 5 days prior to reporting symptoms (if symptomatic) or testing positive (if asymptomatic). Altering this cutoff did not alter the maximum likelihood inference for the effect of FFP3 respirators .Whilst using FRSMs, the majority of infections amongst HCWs working on red wards could be attributed to direct exposure to patients with COVID-19. In contrast, as community incidence rose, the majority of infections amongst HCWs working on green wards were attributed by our model to community-based effects. After the change in RPE, cases attributed to ward-based exposure fell significantly, with FFP3 respirators providing an inferred 52–100% (most likely 100%) reduction in the risk of ward-based infection from patients with COVID-19.In keeping with previous observations , our finDuring the study period, the incidence of SARS-CoV-2 in England increased , with spPotential confounders of our observations, should they have differed systematically between HCWs on red and green wards and/or have changed over the course of the study, include:Rates of natural immunity amongst HCWs on red and green wards; however, the frequency of prior SARS-CoV-2 infections was low within CUHNFT. Overall seropositvity revealed by testing in July and August 2020 was 7.2 %  .Rates of vaccination of HCWs on red and green wards; however, the proportion of high-risk HCWs at CUHNFT offered vaccination prior to 08/01/21 was very low, and the study period was ended on 17/01/21 .Frequency of asymptomatic screening of HCWs on red and green wards; however, the proportions of cases ascertained by symptomatic testing versus asymptomatic screening were similar in both settings. In addition, whilst twice-weekly testing was available for red ward staff from week 8 of the study, this would have tended to increase (rather than decrease) the ascertainment of HCW cases on red wards after the change in RPE in week 9.Compliance with infection control measures by HCWs on red and green wards. It is possible that some of the effect of the change in RPE may have been mediated indirectly, by triggering changes in other behaviours; however, this would still be a positive outcome.Exclusion of infections amongst HCWs who worked on wards from multiple categories ; however, this would have tended to minimise any difference in ward-specific risk of infection.Differences in patterns of HCW behaviour on red and green wards, including mixing between HCWs from different areas. For example, staff working on green wards may have been more likely to leave the ward for lunch than staff working on red wards. Whilst such differences could in theory have contributed to the greater risk of HCW infection on red wards, they are unlikely to have changed systematically with the change in RPE. In addition, if mixing between HCWs from different areas led to an increased rate of infection, it would have tended to minimise any difference in ward-specific risk of infection.This observational study includes a small number of cases in a single Trust, and there may be alternative explanations for the different patterns of infection observed before and after the change in RPE. Our maximum likelihood inference that FFP3 masks (in combination with other PPE and infection control measures) provide 100 % protection against ward-based infection should therefore be treated with caution; the large confidence intervals calculated for parameters in our model reflect the limited amount of data available. Nonetheless, our results highlight an urgent need for further studies evaluating the appropriate level of RPE for HCWs caring for patients with COVID-19, as well as other respiratory viruses. In accordance with the precautionary principle, we propose a revision of RPE recommendations until more definitive information is available. Respiratory protective equipment that is recommended in the UK for health-care workers caring for COVID-19 patients comprises a fluid resistant surgical mask (FRSM), and in case of procedures that generate aerosols FFP3 respirators are to be used. In this study, health-care workers using FRSMs, while working on COVID-19 wards faced an approximately 31-fold increased risk of ward-based SARS CoV-2 infection. After changing to FFP3 respirators, this risk was significantly reduced. Thus, FFP3 respirators seem to provide more protection than FRSMs for health-care workers caring for patients with COVID-19. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jos van der Meer as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Sarah Logan (Reviewer #1); Stephanie Evans (Reviewer #2).Thank you for submitting your article "FFP3 respirators protect healthcare workers against infection with SARS-CoV-2" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. The authors need to mention more about the differences between red and green wards: In terms of:Number of patients on the wards (bays and side rooms)Mean number of pts per nurseDemographic of the HCW surveyed on each ward- equal numbers of Nurses/ AHP's/ doctorsVaccine uptake as per below.Staff rest areas.2. The authors also need to describe more about the testing seeking behaviour of the two staff groups; access to testing, more frequently accessed by a group of HCW who were aware of the range of symptoms of covid. This could leave to an ascertainment bias in the red ward prior to the change in mask wearing.3. Vaccine roll out:Most staff working on COVID wards in December 2020 were the first to access the vaccine, and the roll out in most trusts in the UK was not so coordinated. Thus, we imagine this data will be difficult to get, but without it, more needs to be made of that impact.4. Given the sophisticated mathematical modelling and statistical analysis, please address the danger of trying to overanalyse the intervention with relatively small numbers.5. With this in mind it would be nice to see data from critical care ward staff, if they were using FFP3 throughout even though the numbers are small.6. We would recommend to pay some attention in the discussion to the comments from Andrew Goddard from the RCP bulletin.7. Although the manuscript states that a similar number of HCWs worked on each ward, it would be helpful to see the results displayed as a proportion of staff on red/green wards that became infected, or to model as individual exposure hours rather than ward status but I understand this might be difficult with the small sample size.8. It would be helpful for the number of red wards  and C_i+1 to be plotted to give a visual representation of the correlation.9. As ward colour in the model is determined exclusively by the presence of infected patients and there is a possibility that one infected HCW on a green ward could lead to an outbreak within the HCW population. While this would not affect the result that changing the type of RPE used reduced the infections per ward day of HCWs on red wards, ignoring the potential for HCWs to seed outbreaks between ward staff could result in a higher proportion of cases being attributed to the community than actually occurred, and I would consider adding a third component of 'positive result in ward staff' to the model.10. A sensitivity analysis shifting the 5-day cut off for before/after change in RPE and ward classification would also be a nice addition.11. As staff working across wards are excluded, it is not clear if the full numbers of staff present on the red/green wards are included in estimations of both the number of cases and number of infectious sources .Reviewer #1:12. It would be interesting to see the results from the critical care wards that are currently excluded. If these wards used FFP3 respirators throughout,  it would be very helpful to see the data for this period in these wards as a  control group.Understanding how best to protect healthcare workers from infection at work with SARS- CoV2 is absolutely vital. Much of the interventions to date have been done in an era where the evidence was emerging at the same time as interventions instigated. With that in mind the approach by Cambridge University trust to go above PHE guidance and use FFP3 masks on COVID wards and then for the authors to try to evaluate this impact is absolutely the right approach. There is a clear drop off in infections in healthcare workers on COVID wards in December 2020 after this change was made. There may be several reasons for this not just the prevention of inhalation of aerosols.The action of donning and doffing an FFP3 mask produces a change in behaviour in the HCW. The need to change one's mask after leaving a clinical area, the discomfort of an FFP3 necessitating shorter wear times than alternative Fluid resistant surgical masks, the prompt to the HCW when changing the mask of hand hygeine may all have contributed to the drop in rate.The drop-in rate correlates also with vaccine role out. Whilst the authors describe this as not having a major impact in the absence of data on the numbers and date of vaccination of staff on the red and green wards it is impossible to draw this conclusion.Reviewer #2:To be fair, the authors themselves tried not to sensationalise their data but I suspect that many infection control teams will have noted the study and (reasonably) will have pressure put on them to act if they have not done so already. Understanding the risks in double-vaccinated staff, ongoing supply chain issues and the fact that at least 20% of FFP3 mask wearers fail a fit test may mean that applying a simple policy to change all to FFP3 will not be without practical challenges.In this paper Ferris et al., attempt to use observational data and mathematical modelling to compare the effect of filtering face piece 3 (FFP3) respirators over fluid resistant surgical masks (FRSM) for reducing infection rates in healthcare workers (HCWs) caring for patients with COVID-19 (working on 'red' wards).The study uses the median incubation period of 5 days to classify new cases as occurring before/after the change in respiratory protective equipment (RPE), and to categorise wards as red and green. This goes some way towards addressing potential confounding that could be introduced by misclassifying either RPE or wards, although this cannot be completely ruled out. Similarly the study is over a sufficient time period that it is reasonable to assume that the effect of vaccines in the HCW population is negligible. Overall the modelling approach, while simple (with only two components of risk: community and ward), is sensible. The Discussion section of this work is fair and balanced, and the concluding statement that this study is not sufficient to provide conclusive evidence but highlights a need for further study into the appropriate RPE for HCWs caring for patients with COVID-19 is faultless.As explained by the authors the main limitations of the study hinge upon the small numbers. While the model itself is broadly sensible, the fitting of this to the very limited data on HCW infection is problematic. Slightly different assumptions (e.g. changing the criteria for classifying wards from the median) may provide an alternative  fit, but give different conclusions.A further limitation of the model used here is the association between Ci-1 and determination of ward 'colour': Ci-1 will determine how many green and red wards there are. Likewise, community prevalence will determine the number of COVID +ve admissions which in turn may impact onward transmission within the hospital, therefore Ci-1 is associated too with ward risk. Essential revisions:1. The authors need to mention more about the differences between red and green wards: In terms of:Many thanks for this point. We have added additional information under the subheadings below:Number of patients on the wards (bays and side rooms)We have added these details to the Methods section, detailing the mean and ranges for numbers of beds on red and green wards:“The mean number of beds per green ward was 24.1 (range 5 to 33) and red 28.1 (range 26 to 33).”Mean number of pts per nurseThis data has been added to the Methods section:“The mean number of nurses and HCAs per bed were 0.41 (range 0.24 to 0.58) on green wards and 0.31 (range 0.24 to 0.42) on red wards.”Demographic of the HCW surveyed on each ward- equal numbers of Nurses/ AHP's/ doctorsThe table details the job roles of the positive-testing HCWs included in the study. It has been included in the paper as Supplementary file 1.This data suggests that a higher proportion of cases (85.2% versus 77.9%) fell amongst nurses and healthcare assistants (HCAs) for red compared to green wards, and that there were no allied health professional (AHP) cases on red wards but 17 (12.1%) cases on green wards. This difference is understandable, because AHPs tend to work on multiple wards, there were many more green than red wards, and HCWs who worked on both red and green wards were excluded from the study.Vaccine uptake as per below.We have clarified in the paper that the HCW vaccination programme did not commence until 08/01/21. We selected 11/01/21 to be the start of the final week of data analysis to mitigate against vaccination confounding results. The proportion of high-risk HCWs at CUHNFT offered vaccination prior to 08/01/21 was very low.Clarification of this is included in two sections of the paper:Discussion“(b) Rates of vaccination of HCWs on red and green wards; however, the proportion of high-risk HCWs at CUHNFT offered vaccination prior to 08/01/21 was very low, and the study period was ended on 17/01/21 .”Methods“A programme of SARS-CoV-2 vaccination using the BNT162b2 COVID-19 vaccine commenced at CUHNFT on 08/12/20 [15]. In line with UK national guidance, the programme initially prioritised local residents over the age of 80. However, some HCWs who had been identified as at high risk from SARS-CoV-2 infection were also vaccinated, and were additionally prevented from working on red wards. From 08/01/21 the programme switched to vaccinating HCWs, with initial priority being given to staff on red wards. To avoid the potential for confounding, the final week of the study period commenced on 11/01/21, since minimal effect is expected in the first seven days after the first dose of vaccine [16].”Staff rest areas.Similar facilities were available on both red and green wards, and there were no changes in rest areas prior to and after the change in PPE. One caveat is that green ward staff were more likely to leave the ward for lunch than red ward staff, however if an increased rate of infections amongst green ward staff resulted, this would tend to underestimate the difference between both ward types. We have added this to the ‘limitations’ section of our discussion as follows:“(f) Differences in patterns of HCW behaviour on red and green wards, including mixing between HCWs from different areas. For example, staff working on green wards may have been more likely to leave the ward for lunch than staff working on red wards. Whilst such differences could in theory have contributed to the greater risk of HCW infection on red wards, they are unlikely to have changed systematically with the change in RPE. In addition, if mixing between HCWs from different areas led to an increased rate of infection, it would have tended to minimise any difference in ward-specific risk of infection.”2. The authors also need to describe more about the testing seeking behaviour of the two staff groups; access to testing, more frequently accessed by a group of HCW who were aware of the range of symptoms of covid. This could leave to an ascertainment bias in the red ward prior to the change in mask wearing.Many thanks for this point. Testing seeking behaviour of the two staff groups was very similar. This was evidenced by the similar proportions of cases ascertained by symptomatic testing and asymptomatic screening on both green and red wards . We have already highlighted this point in the Results section, however have now clarified this point further, modifying the second sentence of the results to read:“Similar proportions of cases were ascertained by symptomatic testing and asymptomatic screening on both green and red wards suggesting similar testing-seeking behaviour between staff groups .”Furthermore, access to testing was identical in the initial part of the study, with symptomatic testing offered as required and asymptomatic testing offered to all HCWs weekly. From 22/12/20, twice-weekly swabbing was offered on red wards and on wards where the most vulnerable patients were cared for. We have already stated these points in the Methods section of the paper, however have now added as a limitation:“Potential confounders of our observations, should they have differed systematically between HCWs on red and green wards and/or have changed over the course of the study, include: …… (c) Frequency of asymptomatic screening of HCWs on red and green wards; however, the proportions of cases ascertained by symptomatic testing versus asymptomatic screening were similar in both settings. In addition, whilst twice-weekly testing was available for red ward staff from week 8 of the study, this would have tended to increase (rather than decrease) the ascertainment of HCW cases on red wards after the change in RPE in week 9.”3. Vaccine roll out:Most staff working on COVID wards in December 2020 were the first to access the vaccine, and the roll out in most trusts in the UK was not so coordinated. Thus, we imagine this data will be difficult to get, but without it, more needs to be made of that impact.Many thanks for requesting this clarification. We have already detailed how vaccination was prioritised in the Methods section:“A programme of SARS-CoV-2 vaccination using the BNT162b2 COVID-19 vaccine commenced at CUHNFT on 08/12/20 [15]. In line with UK national guidance, the programme initially prioritised local residents over the age of 80. However, some HCWs who had been identified as at high risk from SARS-CoV-2 infection were also vaccinated, and were additionally prevented from working on red wards. From 08/01/21 the programme switched to vaccinating HCWs, with initial priority being given to staff on red wards. To avoid the potential for confounding, the end of the study period was therefore taken to be 17/01/21, since minimal effect is expected in the first seven days after the first dose of vaccine [16].”To clarify this point, we have added details in the discussion about potential confounders of our observations:“Potential confounders of our observations, should they have differed systematically between HCWs on red and green wards and/or have changed over the course of the study, include: …… (b) Rates of vaccination of HCWs on red and green wards; however, the proportion of high-risk HCWs at CUHNFT offered vaccination prior to 08/01/21 was very low, and the study period was ended on 17/01/21 .”4. Given the sophisticated mathematical modelling and statistical analysis, please address the danger of trying to overanalyse the intervention with relatively small numbers.We do not believe that our mathematical model is particularly complex. In our model HCWs can be infected either via exposure to the virus while they are on a ward (which we describe in terms of a ward-specific infection risk), or they can be infected via exposure to the virus while they are not on a ward (which we describe as a non-ward-specific infection risk). We assume that ward-specific risks are constant within each type of ward, with the exception of a potential change following the change in PPE in red wards, and that non-ward-specific risks are constant across all types of ward. We are not certain that the questions addressed by our study could be evaluated with a less complicated model.A very valuable point as suggested by the reviewer is that relatively small numbers of cases were available for our study, which additionally considered data from a single hospital and during a specific period of the SARS-CoV-2 pandemic. The size of these confidence intervals for all of the estimated parameters from our study directly reflects the amount of data we were able to collect. Given more data, we could be more precise in our estimates. We have added a sentence to our Discussion clarifying the extent to which sample size constrains the precision of our estimates.“Our maximum likelihood inference that FFP3 masks (in combination with other PPE and infection control measures) provide 100% protection against ward-based infection should therefore be treated with caution; the large confidence intervals calculated for parameters in our model reflect the limited amount of data available.”5. With this in mind it would be nice to see data from critical care ward staff, if they were using FFP3 throughout even though the numbers are small.We have added data from critical care ward staff to Supplementary File 1. As anticipated by the reviewer, numbers were small. For completeness, we produced an extended version of our model in which critical care wards were included as a separate category to red and green wards . This did not substantially change the outcome with respect to the effect of introducing FFP3 respirators, which is the central focus of our model.6. We would recommend to pay some attention in the discussion to the comments from Andrew Goddard from the RCP bulletin.Very many thanks for this suggestion. We have already communicated with Andrew Goddard about his comments in the RCP bulletin. Of note, Dr. Goddard accepted all of the points we made, stating “I accept all your points”. As the reviewer suggests, some useful additions to the paper can be gleaned from our communications with Dr Goddard, and we note changes to the manuscript that have been introduced below:– “More regular routine testing of ‘green’ areas appears to have started when the change was introduced.” Staff on green wards were asked to test weekly over the 11 weeks of the study. From 22/12/20 we asked staff on red wards and those green wards with more vulnerable patients  to test twice weekly.” This is included in the Methods section: “From 22/12/20, twice-weekly swabbing was offered on red wards and on wards where the most vulnerable patients were cared for .”– “The authors state that vaccination didn’t start in the relevant healthcare workers until after the intervention.” We set up our vaccination clinic to start vaccinating staff in early December but were required to vaccinate the over 80s first and so did not start the staff vaccination programme until 08/01/21. We gave spare vaccines to staff during December and early January from a list of those identified to be vulnerable (who were restricted from working on red wards) and the ED (whose staff were not included in the study). This has been addressed in the Methods section in a paragraph about vaccination, starting “A programme of SARS-CoV-2 vaccination using the BNT162b2 COVID-19 vaccine…”– “Infection rates in healthcare workers in the second wave were 25% of those seen in the first wave due to previous infection, general improvement in infection control and vaccination.” Our first wave infection rates were possibly lower than some, with positive serology July/August 2020 being 7.2% . Whilst this may have been a confounding factor I think it would have a similar effect on red and green ward staff and so not impact substantially on the findings. However, it is another issue for us to address. We have addressed this point by adding the following to the discussion “Overall seropositvity revealed by testing in July and August 2020 was 7.2% ”We have added a further note to our discussion (below), highlighting that the inference of 100% protection from FFP3 respirators should be treated with caution in light of the small numbers in our study. While our study suggests an advantage being gained from FFP3 masks we are keen that they should not be regarded as ‘bullet-proof vests’.“Our maximum likelihood inference that FFP3 masks (in combination with other PPE and infection control measures) provide 100% protection against ward-based infection should therefore be treated with caution; the large confidence intervals calculated for parameters in our model reflect the limited amount of data available.”7. Although the manuscript states that a similar number of HCWs worked on each ward, it would be helpful to see the results displayed as a proportion of staff on red/green wards that became infected, or to model as individual exposure hours rather than ward status but I understand this might be difficult with the small sample size.We have converted the denominator of our statistics to consider representative numbers of HCW days, rather than ward days, based upon rostering information for each of the wards in our study. We do not have readily available information about how many individual HCWs worked on each ward so as to calculate the proportion of staff who became infected. However, the change we have made leads to an estimated risk of infection per HCW day, rather than simply per ward day. Some of the derived numerical values have changed slightly as a consequence of this adjustment, for example the ratio of red/green risk.8. It would be helpful for the number of red wards  and C_i+1 to be plotted to give a visual representation of the correlation.We thank the reviewer for this suggestion. We found a positive and significant correlation between the number of hours worked on red wards and community incidence, and have shown this in a new Figure 1S2.9. As ward colour in the model is determined exclusively by the presence of infected patients and there is a possibility that one infected HCW on a green ward could lead to an outbreak within the HCW population. While this would not affect the result that changing the type of RPE used reduced the infections per ward day of HCWs on red wards, ignoring the potential for HCWs to seed outbreaks between ward staff could result in a higher proportion of cases being attributed to the community than actually occurred, and I would consider adding a third component of 'positive result in ward staff' to the model.This is an important point. The ward colour in the model is determined by the theoretical presence of infected patients, since infected patients can still be present on green wards if undetected as such . Furthermore, the models include the possibility of HCW-to-HCW infection, as they do not distinguish the origin of infections (patients vs HCWs). To address this point, we have added further clarity to the meaning of ‘ward-specific risk’ and ‘non-ward-specific’, or ‘community risk’. HCW-to-HCW infection can occur in two settings: (a) whilst working on the wards. This type of infection would be part of ‘ward-based risk’, and would be susceptible to modification by a change in RPE. This is because throughout the study, FFP3 masks were worn on a sessional basis, being changed at lunchtime and at the end of the day. (b) outside wards. Here, HCWs from both ward settings would experience the same risk of infecting each other, and this would be considered to be part of a ‘community risk’. Of note, one caveat is that HCWs on red wards more frequently ate lunch within the facilities available on the wards, to avoid the need to completely change out of the surgical scrubs worn as part of their PPE. If HCW-to-HCW infections acquired during these lunch breaks accounted for a significant part of the red ‘ward-based risk’, this would tend to act to reduce the effect of the change in RPE. A more nuanced understanding of ‘community’ risks could be gained with more information about patterns of mixing between HCWs during the period of the study, but this information was not available to us.We have added the following to address these points:Results:(ward-specific risk). The risk from direct exposure on red wards was allowed to vary upon the introduction of FFP3 respirators, and was fitted to a maximum likelihood model.”“To further quantify the risk of infection for HCWs working on red and green wards, we generated a simple mathematical model. According to this model, the total risk of infection is divided into a risk from community-based exposure, and a risk from direct, ward-based exposure to patients Discussion:“Whilst using FRSMs, the majority of infections among HCWs working on red wards could be attributed to direct exposure to patients with COVID-19. In contrast, as community incidence rose, the majority of infections among HCWs working on green wards were attributed by our model to community-based effects. After the change in RPE, cases attributed to ward-based exposure fell significantly, with FFP3 respirators providing an inferred 52-100% (most likely 100%) reduction in the risk of ward-based infection from patients with COVID-19.”“Potential confounders of our observations, should they have differed systematically between HCWs on red and green wards and/or have changed over the course of the study, include:(f) Differences in patterns of HCW behaviour on red and green wards, including mixing between HCWs from different areas. For example, staff working on green wards may have been more likely to leave the ward for lunch than staff working on red wards. Whilst such differences could in theory have contributed to the greater risk of HCW infection on red wards, they are unlikely to have changed systematically with the change in RPE. In addition, if mixing between HCWs from different areas led to an increased rate of infection, it would have tended to minimise any difference in ward-specific risk of infection.”10. A sensitivity analysis shifting the 5-day cut off for before/after change in RPE and ward classification would also be a nice addition.We have carried out a sensitivity analysis, shifting the cutoff to values between three and seven days . Altering the cutoff did not alter the maximum likelihood inference (100%) for the effect of FFP3 respirators, although the lower bound of the effect changed to between 30% and 72% with cutoffs between 3 and 7 days.11. As staff working across wards are excluded, it is not clear if the full numbers of staff present on the red/green wards are included in estimations of both the number of cases and number of infectious sources .Many thanks for this point. Calculations of staff numbers on red and green wards were performed in terms of the numbers of nurses and health care assistants rostered to each ward on each day, based upon weekday and weekend patterns of working on each ward in the study. This provides a proxy for the total number of HCWs on any given ward. Differences in the interactions between HCWs and patients on red and green wards will contribute to differences in the ward-specific risk. Differences in the interactions between HCWs on red and green wards outside of the ward could not be measured.We have added the following to address these points:Methods“The CUHNFT electronic rostering system recorded to which ward(s) individual nurses and healthcare assistants (HCAs) were allocated. Although this does not encompass 100% of ward staff, the data can be used to indicate relative ward size.”“The number of ‘HCW days’ for each week of the study was calculated for each category of ward. Rostering information was used to identify the number of nurses and HCAs regularly assigned to each ward on each of the seven days of the week. Data describing the number of other staff on each ward was not available, but was assumed to be proportional to the number of rostered health care workers, calculations being performed in terms of nurse and health care assistant numbers.”“In order to quantify the effect of the change in RPE upon cases in red wards, a mathematical model was developed, considering the numbers of cases observed among HCWs as arising from a combination of ward-specific infection risks, which relate directly to working on a red or green ward, and non-ward-specific risks, which include infections arising from the community.”Discussion, ‘limitations’ section:“Potential confounders of our observations, should they have differed systematically between HCWs on red and green wards and/or have changed over the course of the study, include:(f) Differences in patterns of HCW behaviour on red and green wards, including mixing between HCWs from different areas. For example, staff working on green wards may have been more likely to leave the ward for lunch than staff working on red wards. Whilst such differences could in theory have contributed to the greater risk of HCW infection on red wards, they are unlikely to have changed systematically with the change in RPE. In addition, if mixing between HCWs from different areas led to an increased rate of infection, it would have tended to minimise any difference in ward-specific risk of infection.”12. It would be interesting to see the results from the critical care wards that are currently excluded. If these wards used FFP3 respirators throughout,  it would be very helpful to see the data for this period in these wards as a  control group.Many thanks for this suggestion; we have now addressed this in point 5 above.


This cross-sectional study examines the association of full approval of the BNT162b2 mRNA vaccine by the US Food and Drug Administration (FDA) with subsequent US uptake of COVID-19 vaccines of any kind. It is unclear whether FDA approval allayed this hesitancy. To examine this notion, we conducted a cross-sectional analysis of the association between the FDA full approval of BNT162b2 mRNA vaccine with subsequent US COVID-19 vaccinations of any kind.On August 23, 2021, the US Food and Drug Administration (FDA) gave its first full approval of a COVID-19 vaccine, the BNT162b2 mRNA vaccine (Pfizer-BioNTech).3 Daily UK vaccinations came from the Coronavirus (COVID-19) in the UK website.4 The vaccination trend in the United Kingdom (UK) was used as a covarying series, which was combined with information about the preapproval trend in the US to create a synthetic control group against which observed US vaccination counts were compared. The period for analysis began 30 days before full FDA approval  and ended the day before the Biden administration’s vaccine mandate announcement on September 9, 2021. Because we used publicly available, deidentified data, this study is not viewed as human participants research by the Department of Health and Human Services; thus, no institutional review board approval or informed consent was sought, in accordance with 45 CFR §46. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.Daily US vaccination data were obtained from the Centers for Disease Control and Prevention’s COVID Data Tracker.5 We separately analyzed daily numbers of dose-agnostic vaccinations, first doses, and series-completing doses using R Studio statistical software version 2020 (RStudio Team). We generated 95% credible intervals (CrIs) using Markov Chain Monte Carlo methods. P < .05 in 2-sided hypothesis tests was considered significant.In this analysis, which used bayesian structural time-series modeling, a counterfactual time trend that would have been observed without the impact of an intervention was constructed using a synthetic control. More information on the analytical approach are available in the eAppendix in the In this analysis of daily dose-agnostic vaccinations, FDA approval was associated with an increase of 3.51 million cumulative vaccinations  over the study period, a 36% relative increase . HoweverTo our knowledge, this cross-sectional study is the first to examine the association of the FDA’s full approval of the BNT162b2 mRNA COVID-19 vaccine with subsequent US COVID-19 vaccinations. We estimated that approval was significantly associated with an increase in the overall number of vaccines administered; however, this appears to be largely related to an increase in series completions after approval. Our analysis suggests that approval may have been associated with a decrease in the number of first doses administered compared with what would have been expected without approval. Potential limitations of this study include the possibility of spillover effects and the relative shortness of the postintervention period considered.Additional research is needed to understand the underlying reasons for this differential response to FDA approval. Individuals who have not received any dose of a COVID-19 vaccine may be behaviorally distinct from those who have chosen to receive at least 1 dose. The results in the unvaccinated population may be related to negative perceptions of government sources of vaccine information. Alternatively, some may have decided to postpone taking vaccines from other manufacturers to reevaluate their vaccine choices considering approval of the Pfizer-BioNTech vaccine. The contrasting relative increase of series completions may reflect that approval was associated with the decisions of individuals who forgot or had barriers to scheduling a second appointment or previously had misgivings after taking the first dose.Our results call for more research into the association of FDA approval with US vaccine uptake. They also call for more work into the distinctions between unvaccinated individuals and those who have received 1 dose to improve our understanding of how interventions could motivate the 2 groups toward full vaccination.