Vaccine effectiveness of primary and booster COVID-19 vaccinations against SARS-CoV-2 infection in the Netherlands from July 12, 2021 to June 6, 2022: A prospective cohort study.

OBJECTIVES: We estimated vaccine effectiveness (VE) of primary and booster vaccinations against SARS-CoV-2 infection overall and in four risk groups defined by age and medical risk condition during the Delta and Omicron BA.1/BA.2 periods. METHODS: VAccine Study COvid-19 is an ongoing prospective cohort study among Dutch adults. The primary end point was a self-reported positive SARS-CoV-2 test from July 12, 2021 to June 06, 2022. The analyses included only participants without a previous SARS-CoV-2 infection based on a positive test or serology. We used Cox proportional hazard models with vaccination status as the time-varying exposure and adjustment for age, sex, educational level, and medical risk condition. RESULTS: A total of 37,170 participants (mean age 57 years) were included. In the Delta period, VE <6 weeks after the primary vaccination was 80% (95% confidence interval 69-87) and decreased to 71% (65-77) after 6 months. VE increased to 96% (86-99) shortly after the first booster vaccination. In the Omicron period, these estimates were 46% (22-63), 25% (8-39), and 57% (52-62), respectively. For the Omicron period, an interaction term between vaccination status and risk group significantly improved the model (P <0.001), with generally lower VEs for those with a medical risk condition. CONCLUSION: Our results show the benefit of booster vaccinations against infection, also in risk groups; although, the additional protection wanes quite rapidly.

Effectiveness of bivalent mRNA booster vaccination against SARS-CoV-2 Omicron infection, the Netherlands, September to December 2022.

We used data of 32,542 prospective cohort study participants who previously received primary and one or two monovalent booster COVID-19 vaccinations. Between 26 September and 19 December 2022, relative effectiveness of bivalent original/Omicron BA.1 vaccination against self-reported Omicron SARS-CoV-2 infection was 31% in 18-59-year-olds and 14% in 60-85-year-olds. Protection of Omicron infection was higher than of bivalent vaccination without prior infection. Although bivalent booster vaccination increases protection against COVID-19 hospitalisations, we found limited added benefit in preventing SARS-CoV-2 infection.

BCG Vaccination of Health Care Workers Does Not Reduce SARS-CoV-2 Infections nor Infection Severity or Duration: a Randomized Placebo-Controlled Trial.

Bacillus Calmette-Guerin (BCG) vaccination has been hypothesized to reduce severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, severity, and/or duration via trained immunity induction. Health care workers (HCWs) in nine Dutch hospitals were randomized to BCG or placebo vaccination (1:1) in March and April 2020 and followed for 1 year. They reported daily symptoms, SARS-CoV-2 test results, and health care-seeking behavior via a smartphone application, and they donated blood for SARS-CoV-2 serology at two time points. A total of 1,511 HCWs were randomized and 1,309 analyzed (665 BCG and 644 placebo). Of the 298 infections detected during the trial, 74 were detected by serology only. The SARS-CoV-2 incidence rates were 0.25 and 0.26 per person-year in the BCG and placebo groups, respectively (incidence rate ratio, 0.95; 95% confidence interval, 0.76 to 1.21; P = 0.732). Only three participants required hospitalization for SARS-CoV-2. The proportions of participants with asymptomatic, mild, or moderate infections and the mean infection durations did not differ between randomization groups. In addition, unadjusted and adjusted logistic regression and Cox proportional hazards models showed no differences between BCG and placebo vaccination for any of these outcomes. The percentage of participants with seroconversion (7.8% versus 2.8%; P = 0.006) and mean SARS-CoV-2 anti-S1 antibody concentration (13.1 versus 4.3 IU/mL; P = 0.023) were higher in the BCG than placebo group at 3 months but not at 6 or 12 months postvaccination. BCG vaccination of HCWs did not reduce SARS-CoV-2 infections nor infection duration or severity (ranging from asymptomatic to moderate). In the first 3 months after vaccination, BCG vaccination may enhance SARS-CoV-2 antibody production during SARS-CoV-2 infection. IMPORTANCE While several BCG trials in adults were conducted during the 2019 coronavirus disease epidemic, our data set is the most comprehensive to date, because we included serologically confirmed infections in addition to self-reported positive SARS-CoV-2 test results. We also collected data on symptoms for every day during the 1-year follow-up period, which enabled us to characterize infections in detail. We found that BCG vaccination did not reduce SARS-CoV-2 infections nor infection duration or severity but may have enhanced SARS-CoV-2 antibody production during SARS-CoV-2 infection in the first 3 months after vaccination. These results are in agreement with other BCG trials that reported negative results (but did not use serological endpoints), except for two trials in Greece and India that reported positive results but had few endpoints and included endpoints that were not laboratory confirmed. The enhanced antibody production is in agreement with prior mechanistic studies but did not translate into protection from SARS-CoV-2 infection.

Diagnostic accuracy of SARS-CoV-2 rapid antigen self-tests in asymptomatic individuals in the omicron period: a cross-sectional study.

OBJECTIVES: To assess the performances of three commonly used antigen rapid diagnostic tests used as self-tests in asymptomatic individuals in the Omicron period. METHODS: We performed a cross-sectional diagnostic test accuracy study in the Omicron period in three public health service COVID-19 test sites in the Netherlands, including 3600 asymptomatic individuals aged ≥ 16 years presenting for SARS-CoV-2 testing for any reason except confirmatory testing after a positive self-test. Participants were sampled for RT-PCR (reference test) and received one self-test (either Acon Flowflex [Flowflex], MP Biomedicals (MPBio), or Siemens-Healthineers CLINITEST [CLINITEST]) to perform unsupervised at home. Diagnostic accuracies of each self-test were calculated. RESULTS: Overall sensitivities were 27.5% (95% CI, 21.3-34.3%) for Flowflex, 20.9% (13.9-29.4%) for MPBio, and 25.6% (19.1-33.1%) for CLINITEST. After applying a viral load cut-off (≥5.2 log10 SARS-CoV-2 E-gene copies/mL), sensitivities increased to 48.3% (37.6-59.2%), 37.8% (22.5-55.2%), and 40.0% (29.5-51.2%), respectively. Specificities were >99% for all tests in most analyses. DISCUSSION: The sensitivities of three commonly used SARS-CoV-2 antigen rapid diagnostic tests when used as self-tests in asymptomatic individuals in the Omicron period were very low. Antigen rapid diagnostic test self-testing in asymptomatic individuals may only detect a minority of infections at that point in time. Repeated self-testing in case of a negative self-test is advocated to improve the diagnostic yield, and individuals should be advised to re-test when symptoms develop.

Bacillus Calmette-Guérin vaccine for prevention of COVID-19 and other respiratory tract infections in older adults with comorbidities: a randomized controlled trial

Objectives To test whether BCG vaccination would reduce the incidence of COVID-19 and other respiratory tract infections in older adults with one or more comorbidities. Methods Community-dwelling adults over 60 years old with one or more underlying comorbidities and no contra-indications for BCG vaccination were randomized 1:1 to BCG or placebo vaccination and followed for six months. The primary endpoint was self-reported test-confirmed COVID-19 incidence. Secondary endpoints included COVID-19 hospital admissions and clinically relevant RTI (i.e. RTI including but not limited to COVID-19 requiring medical intervention). COVID-19 and clinically relevant RTI episodes were adjudicated. Incidences were compared using Fine and Gray regression, accounting for competing events. Results A total of 6,112 participants with a median age of 69 years (inter-quartile range 65-74) and median of 2 (inter-quartile range 1-3) comorbidities were randomized to BCG (n=3,058) or placebo (n=3,054) vaccination. COVID-19 infections were reported by 129 BCG recipients compared to 115 placebo recipients (hazard ratio (HR) 1.12;95% confidence interval (CI) 0.87-1.44). COVID-19-related hospitalization occurred in 18 BCG and 21 placebo recipients (HR 0.86;95% CI 0.46-1.61). During the study period 13 BCG recipients compared to 18 placebo recipients died (HR 0.71;95% CI 0.35 - 1.43) of which 11 deaths (35%) were COVID-19 related six in the placebo group and five in the BCG group. Clinically relevant RTI was reported by 66 BCG and 72 placebo recipients (HR 0.92;95% CI 0.66-1.28). Conclusion BCG vaccination does not protect older adults with comorbidities against COVID-19, COVID-19 hospitalization or clinically relevant RTI.

Bacillus Calmette-Guérin vaccine for prevention of COVID-19 and other respiratory tract infections in older adults with comorbidities: a randomized controlled trial.

OBJECTIVES: To test whether Bacillus Calmette-Guérin (BCG) vaccination would reduce the incidence of COVID-19 and other respiratory tract infections (RTIs) in older adults with one or more comorbidities. METHODS: Community-dwelling adults aged 60 years or older with one or more underlying comorbidities and no contraindications to BCG vaccination were randomized 1:1 to BCG or placebo vaccination and followed for 6 months. The primary endpoint was a self-reported, test-confirmed COVID-19 incidence. Secondary endpoints included COVID-19 hospital admissions and clinically relevant RTIs (i.e. RTIs including but not limited to COVID-19 requiring medical intervention). COVID-19 and clinically relevant RTI episodes were adjudicated. Incidences were compared using Fine-Gray regression, accounting for competing events. RESULTS: A total of 6112 participants with a median age of 69 years (interquartile range, 65-74) and median of 2 (interquartile range, 1-3) comorbidities were randomized to BCG (n = 3058) or placebo (n = 3054) vaccination. COVID-19 infections were reported by 129 BCG recipients compared to 115 placebo recipients [hazard ratio (HR), 1.12; 95% CI, 0.87-1.44]. COVID-19-related hospitalization occurred in 18 BCG and 21 placebo recipients (HR, 0.86; 95% CI, 0.46-1.61). During the study period, 13 BCG recipients died compared with 18 placebo recipients (HR, 0.71; 95% CI, 0.35-1.43), of which 11 deaths (35%) were COVID-19-related: six in the placebo group and five in the BCG group. Clinically relevant RTI was reported by 66 BCG and 72 placebo recipients (HR, 0.92; 95% CI, 0.66-1.28). DISCUSSION: BCG vaccination does not protect older adults with comorbidities against COVID-19, COVID-19 hospitalization, or clinically relevant RTIs.

Head-to-head comparison of the accuracy of saliva and nasal rapid antigen SARS-CoV-2 self-testing: cross-sectional study.

BACKGROUND: The diagnostic accuracy of unsupervised self-testing with rapid antigen diagnostic tests (Ag-RDTs) is mostly unknown. We studied the diagnostic accuracy of a self-performed SARS-CoV-2 saliva and nasal Ag-RDT in the general population. METHODS: This large cross-sectional study consecutively included unselected individuals aged ≥ 16 years presenting for SARS-CoV-2 testing at three public health service test sites. Participants underwent molecular test sampling and received two self-tests (the Hangzhou AllTest Biotech saliva self-test and the SD Biosensor nasal self-test by Roche Diagnostics) to perform themselves at home. Diagnostic accuracy of both self-tests was assessed with molecular testing as reference. RESULTS: Out of 2819 participants, 6.5% had a positive molecular test. Overall sensitivities were 46.7% (39.3-54.2%) for the saliva Ag-RDT and 68.9% (61.6-75.6%) for the nasal Ag-RDT. With a viral load cut-off (≥ 5.2 log10 SARS-CoV-2 E-gene copies/mL) as a proxy of infectiousness, these sensitivities increased to 54.9% (46.4-63.3%) and 83.9% (76.9-89.5%), respectively. For the nasal Ag-RDT, sensitivities were 78.5% (71.1-84.8%) and 22.6% (9.6-41.1%) in those symptomatic and asymptomatic at the time of sampling, which increased to 90.4% (83.8-94.9%) and 38.9% (17.3-64.3%) after applying the viral load cut-off. In those with and without prior SARS-CoV-2 infection, sensitivities were 36.8% (16.3-61.6%) and 72.7% (65.1-79.4%). Specificities were > 99% and > 99%, positive predictive values > 70% and > 90%, and negative predictive values > 95% and > 95%, for the saliva and nasal Ag-RDT, respectively, in most analyses. Most participants considered the self-performing and result interpretation (very) easy for both self-tests. CONCLUSIONS: The Hangzhou AllTest Biotech saliva self Ag-RDT is not reliable for SARS-CoV-2 detection, overall, and in all studied subgroups. The SD Biosensor nasal self Ag-RDT had high sensitivity in individuals with symptoms and in those without prior SARS-CoV-2 infection but low sensitivity in asymptomatic individuals and those with a prior SARS-CoV-2 infection which warrants further investigation.

Diagnostic accuracy of covid-19 rapid antigen tests with unsupervised self-sampling in people with symptoms in the omicron period: cross sectional study.

OBJECTIVE: To assess the performance of rapid antigen tests with unsupervised nasal and combined oropharyngeal and nasal self-sampling during the omicron period. DESIGN: Prospective cross sectional diagnostic test accuracy study. SETTING: Three public health service covid-19 test sites in the Netherlands, 21 December 2021 to 10 February 2022. PARTICIPANTS: 6497 people with covid-19 symptoms aged ≥16 years presenting for testing. INTERVENTIONS: Participants had a swab sample taken for reverse transcription polymerase chain reaction (RT-PCR, reference test) and received one rapid antigen test to perform unsupervised using either nasal self-sampling (during the emergence of omicron, and when omicron accounted for >90% of infections, phase 1) or with combined oropharyngeal and nasal self-sampling in a subsequent (phase 2; when omicron accounted for >99% of infections). The evaluated tests were Flowflex (Acon Laboratories; phase 1 only), MPBio (MP Biomedicals), and Clinitest (Siemens-Healthineers). MAIN OUTCOME MEASURES: The main outcomes were sensitivity, specificity, and positive and negative predictive values of each self-test, with RT-PCR testing as the reference standard. RESULTS: During phase 1, 45.0% (n=279) of participants in the Flowflex group, 29.1% (n=239) in the MPBio group, and 35.4% ((n=257) in the Clinitest group were confirmatory testers (previously tested positive by a self-test at own initiative). Overall sensitivities with nasal self-sampling were 79.0% (95% confidence interval 74.7% to 82.8%) for Flowflex, 69.9% (65.1% to 74.4%) for MPBio, and 70.2% (65.6% to 74.5%) for Clinitest. Sensitivities were substantially higher in confirmatory testers (93.6%, 83.6%, and 85.7%, respectively) than in those who tested for other reasons (52.4%, 51.5%, and 49.5%, respectively). Sensitivities decreased from 87.0% to 80.9% (P=0.16 by χ2 test), 80.0% to 73.0% (P=0.60), and 83.1% to 70.3% (P=0.03), respectively, when transitioning from omicron accounting for 29% of infections to >95% of infections. During phase 2, 53.0% (n=288) of participants in the MPBio group and 44.4% (n=290) in the Clinitest group were confirmatory testers. Overall sensitivities with combined oropharyngeal and nasal self-sampling were 83.0% (78.8% to 86.7%) for MPBio and 77.3% (72.9% to 81.2%) for Clinitest. When combined oropharyngeal and nasal self-sampling was compared with nasal self-sampling, sensitivities were found to be slightly higher in confirmatory testers (87.4% and 86.1%, respectively) and substantially higher in those testing for other reasons (69.3% and 59.9%, respectively). CONCLUSIONS: Sensitivities of three rapid antigen tests with nasal self-sampling decreased during the emergence of omicron but was only statistically significant for Clinitest. Sensitivities appeared to be substantially influenced by the proportion of confirmatory testers. Sensitivities of MPBio and Clinitest improved after the addition of oropharyngeal to nasal self-sampling. A positive self-test result justifies prompt self-isolation without the need for confirmatory testing. Individuals with a negative self-test result should adhere to general preventive measures because a false negative result cannot be ruled out. Manufacturers of MPBio and Clinitest may consider extending their instructions for use to include combined oropharyngeal and nasal self-sampling, and other manufacturers of rapid antigen tests should consider evaluating this as well.

Factors Associated With Using the COVID-19 Mobile Contact-Tracing App Among Individuals Diagnosed With SARS-CoV-2 in Amsterdam, the Netherlands: Observational Study.

BACKGROUND: Worldwide, efforts are being made to stop the COVID-19 pandemic caused by SARS-CoV-2. Contact tracing and quarantining are key in limiting SARS-CoV-2 transmission. Mathematical models have shown that the time between infection, isolation of cases, and quarantining of contacts are the most important components that determine whether the pandemic can be controlled. Mobile contact-tracing apps could accelerate the tracing and quarantining of contacts, including anonymous contacts. However, real-world observational data on the uptake and determinants of contact-tracing apps are limited. OBJECTIVE: The aim of this paper is to assess the use of a national Dutch contact-tracing app among notified cases diagnosed with SARS-CoV-2 infection and investigate which characteristics are associated with the use of the app. METHODS: Due to privacy regulations, data from the app could not be used. Instead, we used anonymized SARS-CoV-2 routine contact-tracing data collected between October 28, 2020, and February 26, 2021, in the region of Amsterdam, the Netherlands. Complete case logistic regression analysis was performed to identify which factors (age, gender, country of birth, municipality, number of close contacts, and employment in either health care or education) were associated with using the app. Age and number of close contacts were modelled as B-splines due to their nonlinear relationship. RESULTS: Of 29,766 SARS-CoV-2 positive cases, 4824 (16.2%) reported app use. Median age of cases was 41 (IQR 29-55) years, and 46.7% (n=13,898) were male. In multivariable analysis, males (adjusted odds ratio [AOR] 1.11, 95% CI 1.04-1.18) and residents of municipalities surrounding Amsterdam were more likely to use the app (Aalsmeer AOR 1.34, 95% CI 1.13-1.58; Ouder-Amstel AOR 1.96, 95% CI 1.54-2.50), while people born outside the Netherlands, particularly those born in non-Western countries (AOR 0.33, 95% CI 0.30-0.36), were less likely to use the app. Odds of app use increased with age until the age of 58 years and decreased sharply thereafter (P<.001). Odds of app use increased with number of contacts, peaked at 8 contacts, and then decreased (P<.001). Individuals working in day care, home care, and elderly nursing homes were less likely to use the app. CONCLUSIONS: Contact-tracing app use among people with confirmed SARS-CoV-2 infection was low in the region of Amsterdam. This diminishes the potential impact of the app by hampering the ability to warn contacts. Use was particularly low among older people, people born outside the Netherlands, and people with many contacts. Use of the app was also relatively low compared to those from some other European countries, some of which had additional features beyond contact tracing, making them potentially more appealing. For the Dutch contact-tracing app to have an impact, uptake needs to be higher; therefore, investing more into promotional efforts and additional features could be considered.

Bacillus Calmette-Guérin vaccine to reduce healthcare worker absenteeism in COVID-19 pandemic, a randomized controlled trial.

OBJECTIVES: The COVID-19 pandemic increases healthcare worker (HCW) absenteeism. The bacillus Calmette-Guérin (BCG) vaccine may provide non-specific protection against respiratory infections through enhancement of trained immunity. We investigated the impact of BCG vaccination on HCW absenteeism during the COVID-19 pandemic. METHODS: HCWs exposed to COVID-19 patients in nine Dutch hospitals were randomized to BCG vaccine or placebo in a 1:1 ratio, and followed for one year using a mobile phone application. The primary endpoint was the self-reported number of days of unplanned absenteeism for any reason. Secondary endpoints included documented COVID-19, acute respiratory symptoms or fever. This was an investigator-funded study, registered at ClinicalTrials.gov (NCT03987919). RESULTS: In March/April 2020, 1511 HCWs were enrolled. The median duration of follow-up was 357 person-days (interquartile range [IQR], 351 to 361). Unplanned absenteeism for any reason was observed in 2.8% of planned working days in the BCG group and 2.7% in the placebo group (adjusted relative risk 0.94; 95% credible interval, 0.78-1.15). Cumulative incidences of documented COVID-19 were 14.2% in the BCG and 15.2% in the placebo group (adjusted hazard ratio (aHR) 0.94; 95% confidence interval (CI), 0.72-1.24). First episodes of self-reported acute respiratory symptoms or fever occurred in 490 (66.2%) and 443 (60.2%) participants, respectively (aHR: 1.13; 95% CI, 0.99-1.28). Thirty-one serious adverse events were reported (13 after BCG, 18 after placebo), none considered related to study medication. CONCLUSIONS: During the COVID-19 pandemic, BCG-vaccination of HCW exposed to COVID-19 patients did not reduce unplanned absenteeism nor documented COVID-19.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review. OBJECTIVES: Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions. SELECTION CRITERIA: We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. MAIN RESULTS: We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7). AUTHORS' CONCLUSIONS: Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

Interventions to control nosocomial transmission of SARS-CoV-2: a modelling study.

BACKGROUND: Emergence of more transmissible SARS-CoV-2 variants requires more efficient control measures to limit nosocomial transmission and maintain healthcare capacities during pandemic waves. Yet the relative importance of different strategies is unknown. METHODS: We developed an agent-based model and compared the impact of personal protective equipment (PPE), screening of healthcare workers (HCWs), contact tracing of symptomatic HCWs and restricting HCWs from working in multiple units (HCW cohorting) on nosocomial SARS-CoV-2 transmission. The model was fit on hospital data from the first wave in the Netherlands (February until August 2020) and assumed that HCWs used 90% effective PPE in COVID-19 wards and self-isolated at home for 7 days immediately upon symptom onset. Intervention effects on the effective reproduction number (RE), HCW absenteeism and the proportion of infected individuals among tested individuals (positivity rate) were estimated for a more transmissible variant. RESULTS: Introduction of a variant with 56% higher transmissibility increased - all other variables kept constant - RE from 0.4 to 0.65 (+ 63%) and nosocomial transmissions by 303%, mainly because of more transmissions caused by pre-symptomatic patients and HCWs. Compared to baseline, PPE use in all hospital wards (assuming 90% effectiveness) reduced RE by 85% and absenteeism by 57%. Screening HCWs every 3 days with perfect test sensitivity reduced RE by 67%, yielding a maximum test positivity rate of 5%. Screening HCWs every 3 or 7 days assuming time-varying test sensitivities reduced RE by 9% and 3%, respectively. Contact tracing reduced RE by at least 32% and achieved higher test positivity rates than screening interventions. HCW cohorting reduced RE by 5%. Sensitivity analyses show that our findings do not change significantly for 70% PPE effectiveness. For low PPE effectiveness of 50%, PPE use in all wards is less effective than screening every 3 days with perfect sensitivity but still more effective than all other interventions. CONCLUSIONS: In response to the emergence of more transmissible SARS-CoV-2 variants, PPE use in all hospital wards might still be most effective in preventing nosocomial transmission. Regular screening and contact tracing of HCWs are also effective interventions but critically depend on the sensitivity of the diagnostic test used.

A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the Remote Early Detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but the infectious period starts on average 2 days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: • The algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) • The algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. TRIAL DESIGN: The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial learning phase (maximum of 3 months), followed by period 1 (3 months) and period 2 (3 months). Subjects entering the study at the end of the recruitment period may directly start with period 1 and will not be part of the learning phase. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in either period 1 or period 2 and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either sequence 1 (experimental condition first) or sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. PARTICIPANTS: The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6500 normal-risk individuals and 3500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal and self-sampling serology and PCR kits. More information on the study can be found in www.covid-red.eu . During recruitment, subjects will be invited to visit the COVID-RED web portal. After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria: Inclusion criteria: • Resident of the Netherlands • At least 18 years old • Informed consent provided (electronic) • Willing to adhere to the study procedures described in the protocol • Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, the study team should be notified) • Be able to read, understand and write Dutch Exclusion criteria: • Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) • Current suspected (e.g. waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) • Participating in any other COVID-19 clinical drug, vaccine or medical device trial (self-reported) • Electronic implanted device (such as a pacemaker; self-reported) • Pregnant at the time of informed consent (self-reported) • Suffering from cholinergic urticaria (per the Ava bracelet's user manual; self-reported) • Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronize it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 h, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess the intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). Note that both algorithms will also instruct to seek testing when any SARS-CoV-2 symptoms are reported in line with those defined by the Dutch national institute for public health and the environment 'Rijksinstituut voor Volksgezondheid en Milieu' (RIVM) guidelines. MAIN OUTCOMES: The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with the self-reported Daily Symptom Diary data and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional twenty secondary and exploratory objectives which address, among others, infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2-infected participants and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (between month 0 and 3.5 months after the start of subject recruitment), at the end of the learning phase (month 3; note that this sampling moment is skipped if a subject entered the study at the end of the recruitment period), period 1 (month 6) and period 2 (month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the learning phase is positive, or if the subject entered the study at the end of the recruitment period, and samples collected at the end of period 1 will only be analysed if the sample collected at the end of period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called COVID-positive mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using the data collected in period 2 (months 6 through 9). Within this period, serology tests (before and after period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. RANDOMIZATION: All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimentalcondition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in approximately equal numbers of high-risk and normal-risk individuals between the sequences. BLINDING (MASKING): In this study, subjects will be blinded to the study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on the data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. NUMBERS TO BE RANDOMIZED (SAMPLE SIZE): A total of 20,000 subjects will be recruited and randomized 1:1 to either sequence 1 (experimental condition followed by control condition) or sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6500 normal-risk and 3500 high-risk individuals per sequence. TRIAL STATUS: Protocol version: 3.0, dated May 3, 2021. Start of recruitment: February 19, 2021. End of recruitment: June 3, 2021. End of follow-up (estimated): November 2021 TRIAL REGISTRATION: The Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.

Diagnostic accuracy of rapid antigen tests in asymptomatic and presymptomatic close contacts of individuals with confirmed SARS-CoV-2 infection: cross sectional study.

OBJECTIVE: To assess the diagnostic test accuracy of two rapid antigen tests in asymptomatic and presymptomatic close contacts of people with SARS-CoV-2 infection on day 5 after exposure. DESIGN: Prospective cross sectional study. SETTING: Four public health service covid-19 test sites in the Netherlands. PARTICIPANTS: 4274 consecutively included close contacts (identified through test-and-trace programme or contact tracing app) aged 16 years or older and asymptomatic for covid-19 when requesting a test. MAIN OUTCOME MEASURES: Sensitivity, specificity, and positive and negative predictive values of Veritor System (Beckton Dickinson) and Biosensor (Roche Diagnostics) rapid antigen tests, with reverse-transcriptase polymerase chain reaction (RT-PCR) testing as reference standard. The viral load cut-off above which 95% of people with a positive RT-PCR test result were virus culture positive was used as a proxy of infectiousness. RESULTS: Of 2678 participants tested with Veritor, 233 (8.7%) had a RT-PCR confirmed SARS-CoV-2 infection of whom 149 were also detected by the rapid antigen test (sensitivity 63.9%, 95% confidence interval 57.4% to 70.1%). Of 1596 participants tested with Biosensor, 132 (8.3%) had a RT-PCR confirmed SARS-CoV-2 infection of whom 83 were detected by the rapid antigen test (sensitivity 62.9%, 54.0% to 71.1%). In those who were still asymptomatic at the time of sampling, sensitivity was 58.7% (51.1% to 66.0%) for Veritor (n=2317) and 59.4% (49.2% to 69.1%) for Biosensor (n=1414), and in those who developed symptoms were 84.2% (68.7% to 94.0%; n=219) for Veritor and 73.3% (54.1% to 87.7%; n=158) for Biosensor. When a viral load cut-off was applied for infectiouness (≥5.2 log10 SARS-CoV-2 E gene copies/mL), the overall sensitivity was 90.1% (84.2% to 94.4%) for Veritor and 86.8% (78.1% to 93.0%) for Biosensor, and 88.1% (80.5% to 93.5%) for Veritor and 85.1% (74.3% to 92.6%) for Biosensor, among those who remained asymptomatic throughout. Specificities were >99%, and positive and negative predictive values were >90% and >95%, for both rapid antigen tests in all analyses. CONCLUSIONS: The sensitivities of both rapid antigen tests in asymptomatic and presymptomatic close contacts tested on day 5 onwards after close contact with an index case were more than 60%, increasing to more than 85% after a viral load cut-off was applied as a proxy for infectiousness.

A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but that the infectious period starts on average two days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) the algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. TRIAL DESIGN: The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. All subjects will participate in an initial Learning Phase (varying from 2 weeks to 3 months depending on enrolment date), followed by two contiguous 3-month test phases, Period 1 and Period 2. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in one of these periods and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either Sequence 1 (experimental condition first) or Sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. PARTICIPANTS: The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6,500 normal-risk individuals and 3,500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal, and self-sampling serology and PCR kits. During recruitment, subjects will be invited to visit the COVID-RED web portal ( www.covid-red.eu ). After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria. INCLUSION CRITERIA: Resident of the Netherlands At least 18 years old Informed consent provided (electronic) Willing to adhere to the study procedures described in the protocol Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, study team should be notified) Be able to read, understand and write Dutch Exclusion criteria: Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) Previously received a vaccine developed specifically for COVID-19 or in possession of an appointment for vaccination in the near future (self-reported) Current suspected (e.g., waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) Participating in any other COVID-19 clinical drug, vaccine, or medical device trial (self-reported) Electronic implanted device (such as a pacemaker; self-reported) Pregnant at time of informed consent (self-reported) Suffering from cholinergic urticaria (per the Ava bracelet's User Manual; self-reported) Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronise it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 hours, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that: no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). MAIN OUTCOMES: The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature, and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava Bracelet data when coupled with the self-reported Daily Symptom Diary data, and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional seventeen secondary outcomes which address infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2 infected participants, and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme, and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (Month 0), and at the end of the Learning Phase (Month 3), Period 1 (Month 6) and Period 2 (Month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the Learning Phase is positive, and samples collected at the end of Period 1 will only be analysed if the sample collected at the end of Period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called "COVID-positive" mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using data collected in Period 2 (Month 6 through 9). Within this period, serology tests (before and after Period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. RANDOMISATION: All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in equal numbers of high-risk and normal-risk individuals between the sequences. BLINDING (MASKING): In this study, subjects will be blinded as to study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED appfor the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 20,000 subjects will be recruited and randomized 1:1 to either Sequence 1 (experimental condition followed by control condition) or Sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6,500 normal-risk and 3,500 high-risk individuals per sequence. TRIAL STATUS: Protocol version: 1.2, dated January 22nd, 2021 Start of recruitment: February 22nd, 2021 End of recruitment (estimated): April 2021 End of follow-up (estimated): December 2021 TRIAL REGISTRATION: The trial has been registered at the Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic.

The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (Re) with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced Re below 1, with unchanged non-school-based contacts.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 22 June 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study. DATA COLLECTION AND ANALYSIS: We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS-2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases). Twenty-six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow. For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1). Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity. For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may have limited capability in differentiating SARS-CoV-2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of suspected COVID-19 cases should be carefully interpreted. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. 'threshold' effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable. OBJECTIVES: To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020. SELECTION CRITERIA: We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them. DATA COLLECTION AND ANALYSIS: We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without. AUTHORS' CONCLUSIONS: The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways.