COVID Symptom Study App Research Articles

Courage in Decision Making: A Mixed-Methods Study of COVID-19 Vaccine Uptake in Women of Reproductive Age in the U.K.

COVID-19 vaccination rates are lower in women of reproductive age (WRA), including pregnant/postpartum women, despite their poorer COVID-19-related outcomes. We evaluated the vaccination experiences of 3568 U.K. WRA, including 1983 women (55.6%) experiencing a pandemic pregnancy, recruited through the ZOE COVID Symptom Study app. Two staggered online questionnaires (Oct-Dec 2021: 3453 responders; Aug-Sept 2022: 2129 responders) assessed reproductive status, COVID-19 status, vaccination, and attitudes for/against vaccination. Descriptive analyses included vaccination type(s), timing relative to age-based eligibility and reproductive status, vaccination delay (first vaccination >28 days from eligibility), and rationale, with content analysis of free-text comments. Most responders (3392/3453, 98.2%) were vaccinated by Dec 2021, motivated by altruism, vaccination supportiveness in general, low risk, and COVID-19 concerns. Few declined vaccination (by Sept/2022: 20/2129, 1.0%), citing risks (pregnancy-specific and longer-term), pre-existing immunity, and personal/philosophical reasons. Few women delayed vaccination, although pregnant/postpartum women (vs. other WRA) received vaccination later (median 3 vs. 0 days after eligibility, p < 0.0001). Despite high uptake, concerns included adverse effects, misinformation (including from healthcare providers), ever-changing government advice, and complex decision making. In summary, most women in this large WRA cohort were promptly vaccinated, including pregnant/post-partum women. Altruism and community benefit superseded personal benefit as reasons for vaccination. Nevertheless, responders experienced angst and received vaccine-related misinformation and discouragement. These findings should inform vaccination strategies in WRA.

Long COVID: which symptoms can be attributed to SARS-CoV-2 infection?

Long COVID: which symptoms can be attributed to SARS-CoV-2 infection?

Concordance of B- and T-cell responses to SARS-CoV-2 infection, irrespective of symptoms suggestive of COVID-19.

This study assessed T‐cell responses in individuals with and without a positive antibody response to SARS‐CoV‐2, in symptomatic and asymptomatic individuals during the COVID‐19 pandemic. Participants were drawn from the TwinsUK cohort, grouped by (a) presence or absence of COVID‐associated symptoms (S+, S−), logged prospectively through the COVID Symptom Study app, and (b) anti‐IgG Spike and anti‐IgG Nucleocapsid antibodies measured by ELISA (Ab+, Ab−), during the first wave of the UK pandemic. T‐cell helper and regulatory responses after stimulation with SARS‐CoV‐2 peptides were assessed. Thirty‐two participants were included in the final analysis. Fourteen of 15 with IgG Spike antibodies had a T‐cell response to SARS‐CoV‐2‐specific peptides; none of 17 participants without IgG Spike antibodies had a T‐cell response (χ 2: 28.2, p < 0.001). Quantitative T‐cell responses correlated strongly with fold‐change in IgG Spike antibody titer (ρ = 0.79, p < 0.0001) but not to symptom score (ρ = 0.17, p = 0.35). Humoral and cellular immune responses to SARS‐CoV‐2 are highly correlated. We found no evidence of cellular immunity suggestive of SARS‐CoV2 infection in individuals with a COVID‐19‐like illness but negative antibodies.

COVID-19 vaccination for children aged 5–11 years

COVID-19 vaccination for children aged 5–11 years

COVID-19 vaccination antibody responses in patients with aplastic anaemia and paroxysmal nocturnal haemoglobinuria

COVID-19 vaccination antibody responses in patients with aplastic anaemia and paroxysmal nocturnal haemoglobinuria

COVID-19 mRNA vaccine safety during the first 6 months of roll-out in the USA

COVID-19 mRNA vaccine safety during the first 6 months of roll-out in the USA

Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study

SummaryBackgroundCOVID-19 vaccines show excellent efficacy in clinical trials and effectiveness in real-world data, but some people still become infected with SARS-CoV-2 after vaccination. This study aimed to identify risk factors for post-vaccination SARS-CoV-2 infection and describe the characteristics of post-vaccination illness.MethodsThis prospective, community-based, nested, case-control study used self-reported data (eg, on demographics, geographical location, health risk factors, and COVID-19 test results, symptoms, and vaccinations) from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile phone app. For the risk factor analysis, cases had received a first or second dose of a COVID-19 vaccine between Dec 8, 2020, and July 4, 2021; had either a positive COVID-19 test at least 14 days after their first vaccination (but before their second; cases 1) or a positive test at least 7 days after their second vaccination (cases 2); and had no positive test before vaccination. Two control groups were selected (who also had not tested positive for SARS-CoV-2 before vaccination): users reporting a negative test at least 14 days after their first vaccination but before their second (controls 1) and users reporting a negative test at least 7 days after their second vaccination (controls 2). Controls 1 and controls 2 were matched (1:1) with cases 1 and cases 2, respectively, by the date of the post-vaccination test, health-care worker status, and sex. In the disease profile analysis, we sub-selected participants from cases 1 and cases 2 who had used the app for at least 14 consecutive days after testing positive for SARS-CoV-2 (cases 3 and cases 4, respectively). Controls 3 and controls 4 were unvaccinated participants reporting a positive SARS-CoV-2 test who had used the app for at least 14 consecutive days after the test, and were matched (1:1) with cases 3 and 4, respectively, by the date of the positive test, health-care worker status, sex, body-mass index (BMI), and age. We used univariate logistic regression models (adjusted for age, BMI, and sex) to analyse the associations between risk factors and post-vaccination infection, and the associations of individual symptoms, overall disease duration, and disease severity with vaccination status.FindingsBetween Dec 8, 2020, and July 4, 2021, 1 240 009 COVID Symptom Study app users reported a first vaccine dose, of whom 6030 (0·5%) subsequently tested positive for SARS-CoV-2 (cases 1), and 971 504 reported a second dose, of whom 2370 (0·2%) subsequently tested positive for SARS-CoV-2 (cases 2). In the risk factor analysis, frailty was associated with post-vaccination infection in older adults (≥60 years) after their first vaccine dose (odds ratio [OR] 1·93, 95% CI 1·50–2·48; p<0·0001), and individuals living in highly deprived areas had increased odds of post-vaccination infection following their first vaccine dose (OR 1·11, 95% CI 1·01–1·23; p=0·039). Individuals without obesity (BMI <30 kg/m2) had lower odds of infection following their first vaccine dose (OR 0·84, 95% CI 0·75–0·94; p=0·0030). For the disease profile analysis, 3825 users from cases 1 were included in cases 3 and 906 users from cases 2 were included in cases 4. Vaccination (compared with no vaccination) was associated with reduced odds of hospitalisation or having more than five symptoms in the first week of illness following the first or second dose, and long-duration (≥28 days) symptoms following the second dose. Almost all symptoms were reported less frequently in infected vaccinated individuals than in infected unvaccinated individuals, and vaccinated participants were more likely to be completely asymptomatic, especially if they were 60 years or older.InterpretationTo minimise SARS-CoV-2 infection, at-risk populations must be targeted in efforts to boost vaccine effectiveness and infection control measures. Our findings might support caution around relaxing physical distancing and other personal protective measures in the post-vaccination era, particularly around frail older adults and individuals living in more deprived areas, even if these individuals are vaccinated, and might have implications for strategies such as booster vaccinations.FundingZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, and the Alzheimer's Society.

Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study

SummaryBackgroundMultiple voluntary surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of population-based COVID-19 epidemiology. During this time, testing criteria broadened and health-care policies matured. We aimed to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three surveillance platforms in three countries (two platforms per country), during periods of testing and policy changes.MethodsFor this observational study, we used data of observations from three volunteer COVID-19 digital surveillance platforms (Carnegie Mellon University and University of Maryland Facebook COVID-19 Symptom Survey, ZOE COVID Symptom Study app, and the Corona Israel study) targeting communities in three countries (Israel, the UK, and the USA; two platforms per country). The study population included adult respondents (age 18–100 years at baseline) who were not health-care workers. We did logistic regression of self-reported symptoms on self-reported SARS-CoV-2 test status (positive or negative), adjusted for age and sex, in each of the study cohorts. We compared odds ratios (ORs) across platforms and countries, and we did meta-analyses assuming a random effects model. We also evaluated testing policy changes, COVID-19 incidence, and time scales of duration of symptoms and symptom-to-test time.FindingsBetween April 1 and July 31, 2020, 514 459 tests from over 10 million respondents were recorded in the six surveillance platform datasets. Anosmia–ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test (robust aggregated rank one, meta-analysed random effects OR 16·96, 95% CI 13·13–21·92). Fever (rank two, 6·45, 4·25–9·81), shortness of breath (rank three, 4·69, 3·14–7·01), and cough (rank four, 4·29, 3·13–5·88) were also highly associated with test positivity. The association of symptoms with test status varied by duration of illness, timing of the test, and broader test criteria, as well as over time, by country, and by platform.InterpretationThe strong association of anosmia–ageusia with self-reported positive SARS-CoV-2 test was consistently observed, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform, country, phase of illness, or testing policy. These findings show that associations between COVID-19 symptoms and test positivity ranked similarly in a wide range of scenarios. Anosmia, fever, and respiratory symptoms consistently had the strongest effect estimates and were the most appropriate empirical signals for symptom-based public health surveillance in areas with insufficient testing or benchmarking capacity. Collaborative syndromic surveillance could enhance real-time epidemiological investigations and public health utility globally.FundingNational Institutes of Health, National Institute for Health Research, Alzheimer's Society, Wellcome Trust, and Massachusetts Consortium on Pathogen Readiness.

Citizen scientists - the power of people data to manage public health [research - citizen data

This article discusses various current medical citizen science projects that span across a range of medical applications to help aid gathering and processing of information. These include a mobile Covid symptom study app that is used to monitor and detect Covid19 symptoms, with machine learning and artificial intelligence enabled symptom/test result clustering and Covid infection classification prediction. Other projects discussed include a self-blinding LSD psychedelic micro-dosing trial that uses QR codes, a Dreamlab app that allows users to share the computing power of their phone, and `Cochrane crowd&apos; that asks users to characterise data and research which is then fed into a classification algorithm. These projects reduce the time frame and cost of medical and public health research by providing proactive information at minimal cost. The limitations and challenges of these projects are also discussed, including the need for data cleaning, along with their applicability to the Governments future public health care strategy.

Diagnosis of COVID‐19 using skin rashes

Several studies have observed that patients hospitalized with COVID‐19 experienced unusual skin rashes, such as urticaria (‘nettle rash’), chickenpox‐type rash, and reddish and purplish bumps on the fingers or toes.Using data from about 336,000 UK users of the COVID Symptom Study app, we observed that 8.8% of people reporting a positive SARS‐CoV‐2 swab test had experienced a skin rash, compared with 5.4% of those with a negative test result. Next, we analysed data collected using an independent online survey in nearly 12,000 people with skin rashes and a suspected or confirmed SARS‐CoV‐2 infection. We observed that 17% of respondents testing positive reported a rash as the first symptom of the disease, while for 21% of them this was their only symptom.Taking these observations together, we advise that skin rashes should be considered when diagnosing COVID‐19 infection. To increase awareness regarding these symptoms we have created, in collaboration with the British Association of Dermatologists, an online catalogue of images of the most common skin manifestations of COVID‐19, available at https://covidskinsigns.com. Linked Article: Visconti et al. Br J Dermatol 2021; 184:880–887.

Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study

SummaryBackgroundThe Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and effectiveness of these vaccines in a UK community setting.MethodsIn this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI <30 kg/m2vs ≥30 kg/m2), and comorbidities (binary variable, with or without comorbidities).FindingsBetween Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49–68) for ChAdOx1 nCoV-19 and 69% (66–72) for BNT162b2 at 21–44 days and 72% (63–79) for BNT162b2 after 45–59 days.InterpretationSystemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days.FundingZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association.

Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

SummaryBackgroundThe SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility.MethodsWe did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates.FindingsFrom Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant.InterpretationThe lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant.FundingZoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.

Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Vaccine after Effects and Post-Vaccine Infection in a Real World Setting: Results from the COVID Symptom Study App

Background: The Pfizer-BioNTech (BNT162b2) and the Oxford/AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in Phase III trials. Here we report results from a real world setting on the two most administered vaccines in the UK.Methods: We investigated self-reported systemic and local effects within eight days of vaccination in 387,471 individuals from the COVID Symptom Study app who received one (n=209,251) or two (n=13,478) doses of the BNT162b2 vaccine, or one dose of ChAdOx1 nCoV-19 vaccine (n=178,220) between December 8 and February 15 2021. A subset of individuals subsequently tested for SARS-CoV-2 were studied for infection rates from PCR or lateral flow test results post-vaccination (59,639 vaccinated vs 277,599 controls).Findings: Systemic side effects were reported in 11.8% of participants after the first BNT162b2 dose, 20.3% after the second BNT162b2 dose, and 29.4% after the first ChAdOx1 nCoV-19 dose. Systemic effects were more prevalent among individuals with pre-existing COVID-19 infection (BNT162b2:34.1%; ChAdOx1 nCoV-19:51.6%) than among individuals without known prior infection (BNT162b2:10.6%; ChAdOx1 nCoV-19:28.6%) and among those aged 55 years (BNT162b2:9.2%, ChAdOx1 nCoV-19: 26.9%). We observed significant reduction in infection risk 12-21 days after the first dose (BNT162b2:-57% [-71%, -38%], ChAdOx1 nCoV-19:-42% [-71%, -17%]). Interpretation: This phase IV-type study assessing both BNT162b2 and ChAdOx1 nCoV-19 vaccines identifies mild systemic side effects affecting 11-30% of individuals post-vaccination, lower than in published Phase III trials. Our data on infection post-vaccine were also reassuring.Funding: Zoe, NIHR, CDRF, NIH, MRCDeclaration of Interests: TDS and AMV are consultants to Zoe Global Ltd (“Zoe”). JW, AM, LP and JC are employees of Zoe Global Limited. ALG is a regional PI on the COV002 trial and the Novavax COVID-19 vaccine trial and as such her organisation has received grants from Novavax. Other authors have no conflict of interest to declare.Ethics Approval Statement: Ethical approval for use of the app for research purposes in the UK was obtained from King’s College London Ethics Committee (review reference LRS-19/20-18210), and all users provided consent for non-commercial use.

Diagnostic value of cutaneous manifestation of SARS-CoV-2 infection.

SummaryBackgroundOne of the challenging aspects of SARS‐CoV‐2 infection is its diverse multisystemic disease presentation.ObjectivesTo evaluate the diagnostic value of cutaneous manifestations of SARS‐CoV‐2 infection and investigate their duration and timing in relation to other COVID‐19 symptoms.MethodsWe used data from 336 847 UK users of the COVID Symptom Study app to assess the diagnostic value of body rash or an acral rash in SARS‐CoV‐2 infection, and data from an independent online survey of 11 544 respondents to investigate skin‐specific symptoms and collect their photographs.ResultsUsing data from the app, we show significant association between skin rashes and a positive swab test result (odds ratio 1·67, 95% confidence interval 1·42–1·97). Strikingly, among the respondents of the independent online survey, we found that 17% of SARS‐CoV‐2‐positive cases reported skin rashes as the first presentation, and 21% as the only clinical sign of COVID‐19. Together with the British Association of Dermatologists, we have compiled a catalogue of images of the most common skin manifestations of COVID‐19 from 400 individuals (https://covidskinsigns.com), which we have made publicly available to assist clinicians in recognition of this early clinical feature of COVID‐19.ConclusionsSkin rashes cluster with other COVID‐19 symptoms, are predictive of a positive swab test, and occur in a significant number of cases, either alone or before other classical symptoms. Recognizing rashes is important in identifying new and earlier cases of COVID‐19.

Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

BackgroundAs many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention.MethodsIn this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots.FindingsFrom March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data.InterpretationOur method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance.FundingZoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation.

Abstract S09-01: Cancer and race: Two important risk factors for COVID-19 incidence as captured by the COVID Symptom Study real-time epidemiology tool

Abstract Background: The COVID-19 pandemic and response underscore the urgent need for real-time population-level data, especially for vulnerable populations (e.g., cancer patients, racial and ethnic minorities). Smartphone applications (“apps”) facilitate the collection of self-reported data at scale, the results of which can then be rapidly redeployed to inform the public health response. The COVID Symptom Study is an app that was launched March 24, 2020, and is now used by nearly 4 million people in the U.S., U.K., and Sweden. Methods: COVID Symptom Study app users self-report health status (e.g., symptoms, COVID-19 testing, health care utilization), comorbidities, demographics, and key risk factors for infection on a daily basis. Multivariable adjusted logistic regression models were used to determine the association of cancer and race with COVID-19 prevalence, adjusting for age, sex, comorbidities, and risk factors for infection, from app launch through May 25, 2020. Results: Among 23,266 individuals with cancer and 1,784,293 without cancer, we documented 155 and 10,249 self-reports of COVID-19, respectively. Compared to individuals without cancer, those with cancer had an increased risk of COVID-19 (adjusted odds ratio (aOR): 1.60; 95% confidence interval (CI): 1.36-1.88). The association was stronger among older participants &amp;gt;65 compared to younger participants (Pinteraction&amp;lt;0.001) and among males (aOR: 1.71; 95%CI: 1.36-2.15) compared to females (aOR: 1.43; 95%CI: 1.14-1.79; Pinteraction=0.02). Chemotherapy/immunotherapy was associated with a 2-fold increased risk of COVID-19 (aOR: 2.22; 95% CI: 1.68-2.94) and risk of COVID-related hospitalization (aOR:2.47; 95% CI: 2.22-2.76). In a separate analysis, we documented 8,990 self-reported cases of positive COVID-19 testing among 2,304,472 non-Hispanic white participants (93.6% of cohort); 93 among 19,498 Hispanic participants; 204 among 19,498 Black participants; 608 among 64,429 Asian participants; and 352 among 65,046 mixed race/other racial minorities. Compared with non-Hispanic white participants, the ORs for reporting a positive COVID-19 test for racial minorities ranged from 1.44 (mixed race/other races) to 2.59 (Black). After accounting for risk factors for infection, comorbidities, and sociodemographic characteristics, the aORs were 1.37 (95% CI 1.09-1.72) for Hispanic participants, 1.42 (95% CI 1.23-1.64) for Black participants, 1.44 (95% CI 1.33-1.57) for Asian participants, and 1.18 (95% CI 1.06-1.32) for mixed race/other minorities. Conclusion: Our results demonstrate an increase in COVID-19 risk among ethnic minorities and individuals with cancer, particularly those on treatment with chemotherapy/immunotherapy. The association with minorities was not completely explained by other known risk factors for COVID-19 or sociodemographic characteristics. These findings highlight the utility of app-based syndromic surveillance for quantifying the impact of the COVID-19 pandemic on at-risk populations. Citation Format: David A. Drew, Long H. Nguyen, Wenjie Ma, Chun-Han Lo, Amit D. Joshi, Daniel Sikavi, Christina M. Astley, Karla Lee, Mary Ni Lochlainn, Maria Gomez, Sebastien Ourselin, Andrew T. Chan. Cancer and race: Two important risk factors for COVID-19 incidence as captured by the COVID Symptom Study real-time epidemiology tool [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S09-01.

Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study

BackgroundData for front-line health-care workers and risk of COVID-19 are limited. We sought to assess risk of COVID-19 among front-line health-care workers compared with the general community and the effect of personal protective equipment (PPE) on risk.MethodsWe did a prospective, observational cohort study in the UK and the USA of the general community, including front-line health-care workers, using self-reported data from the COVID Symptom Study smartphone application (app) from March 24 (UK) and March 29 (USA) to April 23, 2020. Participants were voluntary users of the app and at first use provided information on demographic factors (including age, sex, race or ethnic background, height and weight, and occupation) and medical history, and subsequently reported any COVID-19 symptoms. We used Cox proportional hazards modelling to estimate multivariate-adjusted hazard ratios (HRs) of our primary outcome, which was a positive COVID-19 test. The COVID Symptom Study app is registered with ClinicalTrials.gov, NCT04331509.FindingsAmong 2 035 395 community individuals and 99 795 front-line health-care workers, we recorded 5545 incident reports of a positive COVID-19 test over 34 435 272 person-days. Compared with the general community, front-line health-care workers were at increased risk for reporting a positive COVID-19 test (adjusted HR 11·61, 95% CI 10·93–12·33). To account for differences in testing frequency between front-line health-care workers and the general community and possible selection bias, an inverse probability-weighted model was used to adjust for the likelihood of receiving a COVID-19 test (adjusted HR 3·40, 95% CI 3·37–3·43). Secondary and post-hoc analyses suggested adequacy of PPE, clinical setting, and ethnic background were also important factors.InterpretationIn the UK and the USA, risk of reporting a positive test for COVID-19 was increased among front-line health-care workers. Health-care systems should ensure adequate availability of PPE and develop additional strategies to protect health-care workers from COVID-19, particularly those from Black, Asian, and minority ethnic backgrounds. Additional follow-up of these observational findings is needed.FundingZoe Global, Wellcome Trust, Engineering and Physical Sciences Research Council, National Institutes of Health Research, UK Research and Innovation, Alzheimer's Society, National Institutes of Health, National Institute for Occupational Safety and Health, and Massachusetts Consortium on Pathogen Readiness.

Une nouvelle complication de l'implant contraceptif : l'atteinte du nerf ulnaire au bras. À propos de deux cas

The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and effectiveness of these vaccines in a UK community setting.In this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI <30 kg/m2 vs ≥30 kg/m2), and comorbidities (binary variable, with or without comorbidities).Between Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49–68) for ChAdOx1 nCoV-19 and 69% (66–72) for BNT162b2 at 21–44 days and 72% (63–79) for BNT162b2 after 45–59 days.Systemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days.ZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association.