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Whilst many with SARS-CoV-2 infection have mild disease, managed in the community, individuals with cardiovascular risk factors experienced often more severe acute disease, requiring hospitalisation. Increasing concern has also developed over long symptom duration in many individuals, including the majority who managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. We examine post-illness metabolomic and gut-microbiome profiles, in community-dwelling participants with SARS-CoV-2, ranging from asymptomatic illness to Post-COVID Syndrome, and participants with prolonged non-COVID-19 illnesses. We also assess a pre-established metabolomic biomarker score for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, and greater biomarker scores, associated with longer illness, both in individuals with and without SARS-CoV-2 infection. We found no association between illness duration and gut microbiome in convalescence. Findings highlight the potential role of cardiometabolic dysfunction to the experience of long illness duration, including after COVID-19.
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AbstractO_ST_ABSBackgroundC_ST_ABSSelf-reported symptom studies rapidly increased our understanding of SARS-CoV-2 during the pandemic and enabled the monitoring of long-term effects of COVID-19 outside the hospital setting. It is now evident that post-COVID syndrome presents with heterogeneous profiles, which need characterisation to enable personalised care among the most affected survivors. This study describes post-COVID profiles, and how they relate to different viral variants and vaccination status. MethodsIn this prospective longitudinal cohort study, we analysed data from 336,652 subjects, with regular health reports through the Covid Symptom Study (CSS) smartphone application. These subjects had reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2. 9,323 individuals subsequently developed Long-COVID, defined as symptoms lasting longer than 28 days. 1,459 had post-COVID syndrome, defined as more than 12 weeks of symptoms. Clustering analysis of the time-series data was performed to identify distinct symptom profiles for post-COVID patients, across variants of SARS-CoV-2 and vaccination status at the time of infection. Clusters were then characterised based on symptom prevalence, duration, demography, and prior conditions (comorbidities). Using an independent testing sample with additional data (n=140), we investigated the impact of post-COVID symptom clusters on the lives of affected individuals. FindingsWe identified distinct profiles of symptoms for post-COVID syndrome within and across variants: four endotypes were identified for infections due to the wild-type variant; seven for the alpha variant; and five for delta. Across all variants, a cardiorespiratory cluster of symptoms was identified. A second cluster related to central neurological, and a third to cases with the most severe and debilitating multi-organ symptoms. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. The three main clusters were confirmed in an independent testing sample, and their functional impact was assessed. InterpretationUnsupervised analysis identified different post-COVID profiles, characterised by differing symptom combinations, durations, and functional outcomes. Phenotypes were at least partially concordant with individuals reported experiences. Our classification may be useful to understand distinct mechanisms of the post-COVID syndrome, as well as subgroups of individuals at risk of prolonged debilitation. FundingUK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimers Society, and ZOE Limited, UK. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a search in the PubMed Central database, with keywords: ("Long-COVID*" OR "post?covid*" OR "post?COVID*" OR postCOVID* OR postCovid*) AND (cluster* OR endotype* OR phenotype* OR sub?type* OR subtype). On 15 June 2022, 161 documents were identified, of which 24 either provided descriptions of sub-types or proposed phenotypes of Long-COVID or post-COVID syndrome(s). These included 16 studies attempting manual sub-grouping of phenotypes, 6 deployments of unsupervised methods for patient clustering and automatic semantic phenotyping (unsupervised k-means=2; random forest classification=1; other=2), and two reports of uncommon presentations of Long-COVID/post-COVID syndrome. Overall, two to eight symptom profiles (clusters) were identified, with three recurring clusters. A cardiopulmonary syndrome was the predominant observation, manifesting with exertional intolerance and dyspnoea (n=10), fatigue (n=8), autonomic dysfunction, tachycardia or palpitations (n=5), lung radiological abnormalities including fibrosis (n=2), and chest pain (n=1). A second common presentation consisted in persistent general autoimmune activation and proinflammatory state (n=2), comprising multi-organ mild sequelae (n=2), gastrointestinal symptoms (n=2), dermatological symptoms (n=2), and/or fever (n=1). A third syndrome was reported, with neurological or neuropsychiatric symptoms: brain fog or dizziness (n=2), poor memory or cognition (n=2), and other mental health issues including mood disorders (n=5), headache (n=2), central sensitization (n=1), paresthesia (n=1), autonomic dysfunction (n=1), fibromyalgia (n=2), and chronic pain or myalgias (n=6). Unsupervised clustering methods identified two to six different post-COVID phenotypes, mapping to the ones described above. 14 further documents focused on possible causes and/or mechanisms of disease underlying one or more manifestations of Long-COVID or post-COVID and identifying immune response dysregulation as a potential common element. All the other documents were beyond the scope of this work. To our knowledge, there are no studies examining the symptom profile of post-COVID syndrome between different variants and vaccination status. Also, no studies reported the modelling of longitudinally collected symptoms, as time-series data, aiming at the characterisation of post-COVID syndrome. Added-value of this studyOur study aimed to identify symptom profiles for post-COVID syndrome across the dominant variants in 2020 and 2021, and across vaccination status at the time of infection, using a large sample with prospectively collected longitudinal self-reports of symptoms. For individuals developing 12 weeks or more of symptoms, we identified three main symptom profiles which were consistent across variants and by vaccination status, differing only in the ratio of individuals affected by each profile and symptom duration overall. Implications of all the available evidenceWe demonstrate the existence of different post-COVID syndromes, which share commonalities across SARS-CoV-2 variant types in both symptoms themselves and how they evolved through the illness. We describe subgroups of patients with specific post-COVID presentations which might reflect different underlying pathophysiological mechanisms. Given the time-series component, our study is relevant for post-COVID prognostication, indicating how long certain symptoms last. These insights could aid in the development of personalised diagnosis and treatment, as well as helping policymakers plan for the delivery of care for people living with post-COVID syndrome.
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Multiple studies across global populations have established the primary symptoms characterising COVID-19 (Coronavirus Disease 2019) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID could not be examined. We aimed to characterise patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ( no COVID-19, COVID-19 in last 12 weeks, COVID-19 > 12 weeks ago), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the COVID-19 in last 12 weeks and no COVID-19 groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the COVID-19 > 12 weeks ago and no COVID-19 groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.
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SARS-CoV-2 antibody levels can be used to assess humoral immune responses following SARS-CoV-2 infection or vaccination, and may predict risk of future infection. From cross-sectional antibody testing of 9,361 individuals from TwinsUK and ALSPAC UK population-based longitudinal studies (jointly in April-May 2021, and TwinsUK only in November 2021-January 2022), we tested associations between antibody levels following vaccination and: (1) SARS-CoV-2 infection following vaccination(s); (2) health, socio-demographic, SARS-CoV-2 infection and SARS-CoV-2 vaccination variables. Within TwinsUK, single-vaccinated individuals with the lowest 20% of anti-Spike antibody levels at initial testing had 3-fold greater odds of SARS-CoV-2 infection over the next six to nine months, compared to the top 20%. In TwinsUK and ALSPAC, individuals identified as at increased risk of COVID-19 complication through the UK "Shielded Patient List" had consistently greater odds (2 to 4-fold) of having antibody levels in the lowest 10%. Third vaccination increased absolute antibody levels for almost all individuals, and reduced relative disparities compared with earlier vaccinations. These findings quantify the association between antibody level and risk of subsequent infection, and support a policy of triple vaccination for the generation of protective antibodies. Lay summaryIn this study, we analysed blood samples from 9,361 participants from two studies in the UK: an adult twin registry, TwinsUK (4,739 individuals); and the Avon Longitudinal Study of Parents and Children, ALSPAC (4,622 individuals). We did this work as part of the UK Government National Core Studies initiative researching COVID-19. We measured blood antibodies which are specific to SARS-CoV-2 (which causes COVID-19). Having a third COVID-19 vaccination boosted antibody levels. More than 90% of people from TwinsUK had levels after third vaccination that were greater than the average level after second vaccination. Importantly, this was the case even in individuals on the UK "Shielded Patient List". We found that people with lower antibody levels after first vaccination were more likely to report having COVID-19 later on, compared to people with higher antibody levels. People on the UK "Shielded Patient List", and individuals who reported that they had poorer general health, were more likely to have lower antibody levels after vaccination. In contrast, people who had had a previous COVID-19 infection were more likely to have higher antibody levels following vaccination compared to people without infection. People receiving the Oxford/AstraZeneca rather than the Pfizer BioNTech vaccine had lower antibody levels after one or two vaccinations. However, after a third vaccination, there was no difference in antibody levels between those who had Oxford/AstraZeneca and Pfizer BioNTech vaccines for their first two doses. These findings support having a third COVID-19 vaccination to boost antibodies.
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BackgroundWe aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and young people (CYP) in the UK during periods of Delta and Omicron variant predominance. MethodsIn this prospective longitudinal cohort study, we analysed data from 115,775 CYP aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CYP with post-vaccination SARS- CoV-2 infection, compared to unvaccinated CYP, and post-vaccination side-effects. FindingsBetween August 5, 2021 and February 14, 2022, 25,971 UK CYP aged 12-17 years received one dose of BNT162b2 vaccine. Vaccination reduced (proxy-reported) infection risk (-80{middle dot}4% and -53{middle dot}7% at 14-30 days with Delta and Omicron variants respectively, and -61{middle dot}5% and -63{middle dot}7% after 61-90 days). The probability of remaining infection-free diverged soon after vaccination, and was greater in CYP with prior SARS-CoV-2 infection. Vaccinated CYP who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CYP; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CYP. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved quickly. InterpretationOne dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CYP aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CYP also had generally mild disease. Overall, vaccination was well-tolerated. FundingUK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimers Society, and ZOE Limited. Research in context Evidence before this studyWe searched PubMed database for peer-reviewed articles and medRxiv for preprint papers, published between January 1, 2021 and February 15, 2022 using keywords ("SARS-CoV-2" OR "COVID-19") AND (child* OR p?ediatric* OR teenager*) AND ("vaccin*" OR "immunization campaign") AND ("efficacy" OR "effectiveness" OR "symptoms") AND ("delta" or "omicron" OR "B.1.617.2" OR "B.1.1.529"). The PubMed search retrieved 36 studies, of which fewer than 30% specifically investigated individuals <18 years. Eleven studies explored SARS-CoV-2 viral transmission: seroprevalence in children (n=4), including age-dependency of susceptibility to SARS-CoV-2 infection (n=1), SARS-CoV-2 transmission in schools (n=5), and the effect of school closure on viral transmission (n=1). Eighteen documents reported clinical aspects, including manifestation of infection (n=13), symptomatology, disease duration, and severity in children. Other studies estimated emergency department visits, hospitalization, need for intensive care, and/or deaths in children (n=4), and explored prognostic factors (n=1). Thirteen studies explored vaccination-related aspects, including vaccination of children within specific paediatric co-morbidity groups (e.g., children with Down syndrome, inflammatory bowel disease, and cancer survivors, n=4), mRNA vaccine efficacy in children and adolescents from the general population (n=7), and the relation between vaccination and severity of disease and hospitalization cases (n=2). Four clinical trials were conducted using mRNA vaccines in minors, also exploring side effects. Sixty percent of children were found to have side effects after BNT162b2 vaccination, and especially after the second dose; however, most symptoms were mild and transient apart from rare uncomplicated skin ulcers. Two studies focused on severe adverse effects and safety of SARS-CoV-2 vaccines in children, reporting on myocarditis episodes and two cases of Guillain-Barre syndrome. All other studies were beyond the scope of our research. Added value of this studyWe assessed multiple components of the UK vaccination campaign in a cohort of children and young people (CYP) aged 12-17 years drawn from a large UK community-based citizen-science study, who received a first dose of BNT162b2 vaccine. We describe a variant-dependent protective effect of the first dose against both Delta and Omicron, with additional protective effect of pre-vaccination SARS- CoV-2 infection on post-vaccination re-infection. We compare the illness profile in CYP infected post-vaccination with that of unvaccinated CYP, demonstrating overall milder disease with fewer symptoms for vaccinated CYP. We describe local and systemic side-effects during the first week following first-dose vaccination, confirming that local symptoms are common, systemic symptoms uncommon, and both usually transient. Implications of all the available evidenceOur data confirm that first dose BNT162b2 vaccination in CYP reduces risk of infection by SARS-CoV-2 variants, with generally local and brief side-effects. If infected after vaccination, COVID-19 is milder, if manifest at all. The study aims to contribute quantitative evidence to the risk-benefit evaluation of vaccination in CYP to inform discussion regarding rationale for their vaccination and the designing of national immunisation campaigns for this age group; and applies citizen-science approaches in the conduct of epidemiological surveillance and data collection in the UK community. Importantly, this study was conducted during Delta and Omicron predominance in UK; specificity of vaccine efficacy to variants is also illustrated; and results may not be generalizable to future SARS-CoV-2 strains.
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ObjectivesTo assess 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. MethodsParticipants were drawn from the TwinsUK cohort, selected according to 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. Results32 participants were included in final analysis. 14 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 (Chi-squared 28.2, p<0.001). Quantitative T cell responses correlated strongly with fold-change in IgG Spike antibody titre (rho=0.79, p<0.0001) but not to symptom score (rho=0.17, p=0.35). ConclusionsHumoral and cellular immune responses to SARS-CoV-2 are highly correlated, with no evidence that cellular immunity differs from antibody status four months after acute illness.
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BackgroundThe Omicron variant of SARS-CoV-2 infection poses substantial challenges to public health. In England, "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops, but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. MethodsWe developed a rapid online survey of risk mitigation behaviours during the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/Covid Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we describe the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. ResultsOver 95% of survey respondents (NALSPAC=2,686 and NTwins=6,155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 15,000 and 46,000 cumulative deaths, depending on assumptions about vaccine effectiveness. We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. ConclusionsWe conclude that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant, but that voluntary measures alone would be unlikely to completely control transmission.
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BackgroundThe COVID-19 pandemic has led to major economic disruptions. In March 2020, the UK implemented the Coronavirus Job Retention Scheme - known as furlough - to minimize the impact of job losses. We investigate associations between change in employment status and mental and social wellbeing during the early stages of the pandemic. MethodsData were from 25,670 respondents, aged 17 to 66, across nine UK longitudinal studies. Furlough and other employment changes were defined using employment status pre-pandemic and during the first lockdown (April-June 2020). Mental and social wellbeing outcomes included psychological distress, life satisfaction, self-rated health, social contact, and loneliness. Study-specific modified Poisson regression estimates, adjusting for socio-demographic characteristics and pre-pandemic mental and social wellbeing measures, were pooled using meta-analysis. ResultsCompared to those who remained working, furloughed workers were at greater risk of psychological distress (adjusted risk ratio, ARR=1.12; 95% CI: 0.97, 1.29), low life satisfaction (ARR=1.14; 95% CI: 1.07, 1.22), loneliness (ARR=1.12; 95% CI: 1.01, 1.23), and poor self-rated health (ARR=1.26; 95% CI: 1.05, 1.50), but excess risk was less pronounced than that of those no longer employed (e.g., ARR for psychological distress=1.39; 95% CI: 1.21, 1.59) or in stable unemployment (ARR=1.33; 95% CI: 1.09, 1.62). ConclusionsDuring the early stages of the pandemic, those furloughed had increased risk for poor mental and social wellbeing. However, their excess risk was lower in magnitude than that of those who became or remained unemployed, suggesting that furlough may have partly mitigated poorer outcomes.
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BackgroundIdentifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. DesignWe conducted a prospective observational study in UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (other than local symptoms at injection site) and were tested for SARS-CoV-2, aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were also recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models including UK testing criteria. FindingsDifferentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. A majority of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). InterpretationPost-vaccination side-effects per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2, to prevent community spread. FundingZoe Limited, 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, Alzheimers Society, Chronic Disease Research Foundation, Massachusetts Consortium on Pathogen Readiness (MassCPR). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThere are now multiple surveillance platforms internationally interrogating COVID-19 and/or post-vaccination side-effects. We designed a study to examine for differences between vaccination side-effects and early symptoms of COVID-19. We searched PubMed for peer-reviewed articles published between 1 January 2020 and 21 June 2021, using keywords: "COVID-19" AND "Vaccination" AND ("mobile application" OR "web tool" OR "digital survey" OR "early detection" OR "Self-reported symptoms" OR "side-effects"). Of 185 results, 25 studies attempted to differentiate symptoms of COVID-19 vs. post-vaccination side-effects; however, none used artificial intelligence (AI) technologies ("machine learning") coupled with real-time data collection that also included comprehensive and systematic symptom assessment. Additionally, none of these studies attempt to discriminate the early signs of infection from side-effects of vaccination (specifically here: Pfizer-BioNTech mRNA vaccine (BNT162b2) and Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19)). Further, none of these studies sought to provide comparisons with current testing criteria used by healthcare services. Added value of this studyThis study, in a uniquely large community-based cohort, uses prospective data capture in a novel effort to identify individuals with COVID-19 in the immediate post-vaccination period. Our results show that early symptoms of SARS-CoV-2 cannot be differentiated from vaccination side-effects robustly. Thus, post-vaccination systemic symptoms should not be ignored, and testing should be considered to prevent COVID-19 dissemination by vaccinated individuals. Implications of all the available evidenceOur study demonstrates the critical importance of testing symptomatic individuals - even if vaccinated - to ensure early detection of SARS-CoV-2 infection, helping to prevent future pandemic waves in the UK and elsewhere.
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BackgroundMental health issues have been reported after SARS-CoV-2 infection. However, comparison to prevalence in uninfected individuals and contribution from common risk factors (e.g., obesity, comorbidities) have not been examined. We identified how COVID-19 relates to mental health in the large community-based COVID Symptom Study. MethodsWe assessed anxiety and depression symptoms using two validated questionnaires in 413,148 individuals between February and April 2021; 26,998 had tested positive for SARS-CoV-2. We adjusted for physical and mental pre-pandemic comorbidities, BMI, age, and sex. FindingsOverall, 26.4% of participants met screening criteria for general anxiety and depression. Anxiety and depression were slightly more prevalent in previously SARS-CoV-2 positive (30.4%) vs. negative (26.1%) individuals. This association was small compared to the effect of an unhealthy BMI and the presence of other comorbidities, and not evident in younger participants ([≤]40 years). Findings were robust to multiple sensitivity analyses. Association between SARS-CoV-2 infection and anxiety and depression was stronger in individuals with recent (<30 days) vs. more distant (>120 days) infection, suggesting a short-term effect. InterpretationA small association was identified between SARS-CoV-2 infection and anxiety and depression symptoms. The proportion meeting criteria for self-reported anxiety and depression disorders is only slightly higher than pre-pandemic. FundingZoe Limited, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, Medical Research Council UK
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BackgroundThe impact of long COVID is considerable, but risk factors are poorly characterised. We analysed symptom duration and risk factor from 10 longitudinal study (LS) samples and electronic healthcare records (EHR). MethodsSamples: 6907 adults self-reporting COVID-19 infection from 48,901 participants in the UK LS, and 3,327 adults with COVID-19, were assigned a long COVID code from 1,199,812 individuals in primary care EHR. Outcomes for LS included symptom duration lasting 4+ weeks (long COVID) and 12+ weeks. Association with of age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma was assessed. ResultsIn LS, symptoms impacted normal functioning for 12+ weeks in 1.2% (mean age 20 years) to 4.8% (mean age 63 y) of COVID-19 cases. Between 7.8% (mean age 28 y) and 17% (mean age 58 y) reported any symptoms for 12+ weeks, and greater proportions for 4+ weeks. Age was associated with a linear increased risk in long COVID between 20 and 70 years. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), having poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), and overweight or obesity (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) also had higher risk. Non-white ethnic minority groups had lower risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. . Few participants had been hospitalised (0.8-5.2%). ConclusionLong COVID is associated with sociodemographic and pre-existing health factors. Further investigations into causality should inform strategies to address long COVID in the population.
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The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants ([≥]18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19,161 self-reported PCR tests were used to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities were used to estimate daily regional COVID-19 prevalence, which were in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We found that this hospital prediction model demonstrated a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates were similar. When applying the same model to an English dataset, not including local COVID-19 test data, we observed MdAPEs of 22.3% and 19.0%, respectively, highlighting the transferability of the prediction model.
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BackgroundHealth systems worldwide have faced major disruptions due to COVID-19 which could exacerbate health inequalities. The UK National Health Service (NHS) provides free healthcare and prioritises equity of delivery, but the pandemic may be hindering the achievement of these goals. We investigated associations between multiple social characteristics (sex, age, occupational social class, education and ethnicity) and self-reported healthcare disruptions in over 65,000 participants across twelve UK longitudinal studies. MethodsParticipants reported disruptions from March 2020 up to late January 2021. Associations between social characteristics and three types of self-reported healthcare disruption (medication access, procedures, appointments) and a composite of any of these were assessed in logistic regression models, adjusting for age, sex and ethnicity where relevant. Random-effects meta-analysis was conducted to obtain pooled estimates. ResultsPrevalence of disruption varied across studies; between 6.4% (TwinsUK) and 31.8 % (Understanding Society) of study participants reported any disruption. Females (Odd Ratio (OR): 1.27 [95%CI: 1.15,1.40]; I2=53%), older persons (e.g. OR: 1.39 [1.13,1.72]; I2=77% for 65-75y vs 45-54y), and Ethnic minorities (excluding White minorities) (OR: 1.19 [1.05,1.35]; I2=0% vs White) were more likely to report healthcare disruptions. Those in a more disadvantaged social class (e.g. OR: 1.17 [1.08, 1.27]; I2=0% for manual/routine vs managerial/professional) were also more likely to report healthcare disruptions, but no clear differences were observed by education levels. ConclusionThe COVID-19 pandemic has led to unequal healthcare disruptions, which, if unaddressed, could contribute to the maintenance or widening of existing health inequalities.
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BackgroundIn children, SARS-CoV-2 is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, the largest UK citizen participatory epidemiological study to date. MethodsData from 258,790 children aged 5-17 years were reported by an adult proxy between 24 March 2020 and 22 February 2021. Illness duration and symptom profiles were analysed for all children testing positive for SARS-CoV-2 for whom illness duration could be determined, considered overall and within younger (5-11 years) and older (12-17 years) groups. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. Findings1,734 children (588 younger, 1,146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study time frame. The commonest symptoms were headache (62.2%) and fatigue (55.0%). Median illness duration was six days (vs. three days in children testing negative), and was positively associated with age (rs 0.19, p<1.e-4) with median duration of seven days in older vs. five days in younger children. Seventy-seven (4.4%) children had illness duration [≥]28 days (LC28), more commonly experienced by older vs. younger children (59 (5.1%) vs. 18 (3.1%), p=0.046). The commonest symptoms experienced by these children were fatigue (84%), headache (80%) and anosmia (80%); however, by day 28 the symptom burden was low (median, two). Only 25 (1.8%) of 1,379 children experienced symptoms for [≥]56 days. Few children (15 children, 0.9%) in the negatively-tested cohort experienced prolonged symptom duration; however, these children experienced greater symptom burden (both throughout their illness and at day 28) than children positive for SARS-CoV-2. InterpretationSome children with COVID-19 experience prolonged illness duration. Reassuringly, symptom burden in these children did not increase with time, and most recovered by day 56. Some children who tested negative for SARS-CoV-2 also had persistent and burdensome illness. A holistic approach for all children with persistent illness during the pandemic is appropriate. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSARS-CoV-2 in children is usually asymptomatic or manifests as a mild illness of short duration. Concerns have been raised regarding prolonged illness in children, with no clear resolution of symptoms several weeks after onset, as is observed in some adults. How common this might be in children, the clinical features of such prolonged illness in children, and how it might compare with illnesses from other respiratory viruses (and with general population prevalence of these symptoms) is unclear. Added value of this studyWe provide systematic description of COVID-19 in UK school-aged children. Our data, collected in a digital surveillance platform through one of the largest UK citizen science initiatives, show that long illness duration after SARS-CoV-2 infection in school-aged children does occur, but is uncommon, with only a small proportion of children experiencing illness duration beyond four weeks; and the symptom burden in these children usually decreases over time. Almost all children have symptom resolution by eight weeks, providing reassurance about long-term outcomes. Additionally, symptom burden in children with long COVID was not greater than symptom burden in children with long illnesses due to causes other than SARS-CoV-2 infection. Implications of all the available evidenceOur data confirm that COVID-19 in UK school-aged children is usually of short duration and of low symptom burden. Some children do experience longer illness duration, validating their experience; however, most of these children usually recover with time. Our findings highlight that appropriate resources will be necessary for any child with prolonged illness, whether due to COVID-19 or other illness. Our study provides timely and critical data to inform discussions around the impact and implications of the pandemic on paediatric healthcare resource allocation.
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BackgroundCOVID-19 vaccines show excellent efficacy in clinical trials and real-world data, but some people still contract SARS-CoV-2 despite vaccination. This study sought to identify risk factors associated with SARS-CoV-2 infection post-vaccination and describe characteristics of post-vaccination illness. MethodsAmongst 1,102,192 vaccinated UK adults from the COVID Symptom Study, 2394 (0.2%) cases of post-vaccination SARS-CoV-2 infection were identified between 8th December 2020 and 1st May 2021. Using a control group of vaccinated individuals testing negative, we assessed the associations of age, frailty, comorbidity, area-level deprivation and lifestyle factors with infection. Illness profile post-vaccination was assessed using a second control group of unvaccinated cases. FindingsOlder adults with frailty (OR=2.78, 95% CI=[1.98-3.89], p-value<0.0001) and individuals living in most deprived areas (OR=1.22 vs. intermediate group, CI[1.04-1.43], p-value=0.01) had increased odds of post-vaccination infection. Risk was lower in individuals without obesity (OR=0.6, CI[0.44-0.82], p-value=0.001) and those reporting healthier diet (OR=0.73, CI[0.62-0.86], p-value<0.0001). Vaccination was associated with reduced odds of hospitalisation (OR=0.36, CI[0.28-0.46], p-value<0.0001), and high acute-symptom burden (OR=0.51, CI[0.42-0.61], p-value<0.0001). In older adults, risk of [≥]28 days illness was lower following vaccination (OR=0.72, CI[0.51-1.00], p-value=0.05). Symptoms were reported less in positive-vaccinated vs. positive-unvaccinated individuals, except sneezing, which was more common post-vaccination (OR=1.24, CI[1.05-1.46], p-value=0.01). InterpretationOur findings suggest that older individuals with frailty and those living in most deprived areas are at increased risk of infection post-vaccination. We also showed reduced symptom burden and duration in those infected post-vaccination. Efforts to boost vaccine effectiveness in at-risk populations, and to targeted infection control measures, may still be appropriate to minimise SARS-CoV-2 infection. FundingThis work is supported by UK Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre (BRC) award to Guys & St Thomas NHS Foundation Trust in partnership with Kings College London and Kings College Hospital NHS Foundation Trust and via a grant to ZOE Global; the Wellcome Engineering and Physical Sciences Research Council (EPSRC) Centre for Medical Engineering at Kings College London (WT 203148/Z/16/Z). Investigators also received support from the Chronic Disease Research Foundation, the Medical Research Council (MRC), British Heart Foundation, the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, the Wellcome Flagship Programme (WT213038/Z/18/Z and Alzheimers Society (AS-JF-17-011), and the Massachusetts Consortium on Pathogen Readiness (MassCPR). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSTo identify existing evidence for risk factors and characteristics of SARS-CoV-2 infection post-vaccination, we searched PubMed for peer-reviewed articles published between December 1, 2020 and May 18, 2021 using keywords ("COVID-19" OR "SARS-CoV-2") AND ("Vaccine" OR "vaccination") AND ("infection") AND ("risk factor*" OR "characteristic*"). We did not restrict our search by language or type of publication. Of 202 articles identified, we found no original studies on individual risk and protective factors for COVID-19 infection following vaccination nor on nature and duration of symptoms in vaccinated, community-based individuals. Previous studies in unvaccinated populations have shown that social and occupational factors influence risk of SARS-CoV-2infection, and that personal factors (age, male sex, multiple morbidities and frailty) increased risk for adverse outcomes in COVID-19. Phase III clinical trials have demonstrated good efficacy of BNT162b2 and ChAdOx1 vaccines against SARS-CoV-2 infection, confirmed in published real-world data, which additionally showed reduced risk of adverse outcomes including hospitalisation and death. Added value of this studyThis is the first observational study investigating characteristics of and factors associated with SARS-CoV-2 infection after COVID-19 vaccination. We found that vaccinated individuals with frailty had higher rates of infection after vaccination than those without. Adverse determinants of health such as increased social deprivation, obesity, or a less healthy diet were associated with higher likelihood of infection after vaccination. In comparison with unvaccinated individuals, those with post-vaccination infection had fewer symptoms of COVID-19, and more were entirely asymptomatic. Fewer vaccinated individuals experienced five or more symptoms, required hospitalisation, and, in the older adult group, fewer had prolonged illness duration (symptoms lasting longer than 28 days). Implications of all the available evidenceSome individuals still contract COVID-19 after vaccination and our data suggest that frail older adults and those living in more deprived areas are at higher risk. However, in most individuals illness appears less severe, with reduced need for hospitalisation and lower risk of prolonged illness duration. Our results are relevant for health policy post-vaccination and highlight the need to prioritise those most at risk, whilst also emphasising the balance between the importance of personal protective measures versus adverse effects from ongoing social restrictions. Strategies such as timely prioritisation of booster vaccination and optimised infection control could be considered for at-risk groups. Research is also needed on how to enhance the immune response to vaccination in those at higher risk.
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BackgroundThe COVID-19 pandemic and associated virus suppression measures have disrupted lives and livelihoods and people already experiencing mental ill-health may have been especially vulnerable. AimTo quantify mental health inequalities in disruptions to healthcare, economic activity and housing. Method59,482 participants in 12 UK longitudinal adult population studies with data collected prior to and during the COVID-19 pandemic. Within each study we estimated the association between psychological distress assessed pre-pandemic and disruptions since the start of the pandemic to three domains: healthcare (medication access, procedures, or appointments); economic activity (employment, income, or working hours); and housing (change of address or household composition). Meta-analyses were used to pool estimates across studies. ResultsAcross the analysed datasets, one to two-thirds of participants experienced at least one disruption, with 2.3-33.2% experiencing disruptions in two or more domains. One standard deviation higher pre-pandemic psychological distress was associated with: (i) increased odds of any healthcare disruptions (OR=1.30; [95% CI:1.20-1.40]) with fully adjusted ORs ranging from 1.24 [1.09-1.41] for disruption to procedures and 1.33 [1.20- 1.49] for disruptions to prescriptions or medication access; (ii) loss of employment (OR=1.13 [1.06-1.21]) and income (OR=1.12 [1.06 -1.19]) and reductions in working hours/furlough (OR=1.05 [1.00-1.09]); (iii) no associations with housing disruptions (OR=1.00 [0.97-1.03]); and (iv) increased likelihood of experiencing a disruption in at least two domains (OR=1.25 [1.18-1.32]) or in one domain (OR=1.11 [1.07-1.16]) relative to no disruption. ConclusionPeople experiencing psychological distress pre-pandemic have been more likely to experience healthcare and economic disruptions, and clusters of disruptions across multiple domains during the pandemic. Failing to address these disruptions risks further widening the existing inequalities in mental health.
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BackgroundRacial and ethnic minorities have been disproportionately impacted by COVID-19. In the initial phase of population-based vaccination in the United States (U.S.) and United Kingdom (U.K.), vaccine hesitancy and limited access may result in disparities in uptake. MethodsWe performed a cohort study among U.S. and U.K. participants in the smartphone-based COVID Symptom Study (March 24, 2020-February 16, 2021). We used logistic regression to estimate odds ratios (ORs) of COVID-19 vaccine hesitancy (unsure/not willing) and receipt. ResultsIn the U.S. (n=87,388), compared to White non-Hispanic participants, the multivariable ORs of vaccine hesitancy were 3.15 (95% CI: 2.86 to 3.47) for Black participants, 1.42 (1.28 to 1.58) for Hispanic participants, 1.34 (1.18 to 1.52) for Asian participants, and 2.02 (1.70 to 2.39) for participants reporting more than one race/other. In the U.K. (n=1,254,294), racial and ethnic minorities had similarly elevated hesitancy: compared to White participants, their corresponding ORs were 2.84 (95% CI: 2.69 to 2.99) for Black participants, 1.66 (1.57 to 1.76) for South Asian participants, 1.84 (1.70 to 1.98) for Middle East/East Asian participants, and 1.48 (1.39 to 1.57) for participants reporting more than one race/other. Among U.S. participants, the OR of vaccine receipt was 0.71 (0.64 to 0.79) for Black participants, a disparity that persisted among individuals who specifically endorsed a willingness to obtain a vaccine. In contrast, disparities in uptake were not observed in the U.K. ConclusionsCOVID-19 vaccine hesitancy was greater among racial and ethnic minorities, and Black participants living in the U.S. were less likely to receive a vaccine than White participants. Lower uptake among Black participants in the U.S. during the initial vaccine rollout is attributable to both hesitancy and disparities in access.
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IntroductionAgeing affects immune function resulting in aberrant fever response to infection. We assess the effects of biological variables on basal temperature and temperature in COVID-19 infection, proposing an updated temperature threshold for older adults. MethodsParticipants: O_LIUnaffected twin volunteers: 1089 adult TwinsUK participants. C_LIO_LILondon hospitalised COVID-19+: 520 adults with emergency admission. C_LIO_LIBirmingham hospitalised COVID-19+: 757 adults with emergency admission. C_LIO_LICommunity-based COVID-19+: 3972 adults self-reporting a positive test using the COVID Symptom Study mobile application. C_LI AnalysisHeritability assessed using saturated and univariate ACE models; Linear mixed-effect and multivariable linear regression analysing associations between temperature, age, sex and BMI; multivariable logistic regression analysing associations between fever ([≥]37.8{degrees}C) and age; receiver operating characteristic (ROC) analysis to identify temperature threshold for adults [≥] 65 years. ResultsAmong unaffected volunteers, lower BMI (p=0.001), and older age (p<0.001) associated with lower basal temperature. Basal temperature showed a heritability of 47% (95% Confidence Interval 18-57%). In COVID-19+ participants, increasing age associated with lower temperatures in cohorts (c) and (d) (p<0.001). For each additional year of age, participants were 1% less likely to demonstrate a fever (OR 0.99; p<0.001). Combining healthy and COVID-19+ participants, a temperature of 37.4{degrees}C in adults [≥]65 years had similar sensitivity and specificity to 37.8{degrees}C in adults <65 years for discriminating fever in COVID-19. ConclusionsAgeing affects temperature in health and acute infection. Significant heritability indicates biological factors contribute to temperature regulation. Our observations indicate a lower threshold (37.4{degrees}C) should be considered for assessing fever in older adults. Key PointsO_LIOlder adults, particularly those with lower BMI, have a lower basal temperature and a lower temperature in response to infection C_LIO_LIBasal temperature is heritable, suggesting biological factors underlying temperature regulation C_LIO_LIOur findings support a lower temperature threshold of 37.4{degrees}C for identifying possible COVID-19 infection in older adults C_LIO_LIThis has implications for case detection, surveillance and isolation and could be incorporated into observation assessment C_LI
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ObjectivesDiagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. MethodsUK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FindingsUK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. InterpretationWe confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings. HighlightsO_LIWidely recommended symptoms identified only [~]70% COVID-19 cases C_LIO_LIAdditional symptoms increased case finding to > 90% but tests needed doubled C_LIO_LIOptimal symptom combinations maximise case capture considering available resources C_LIO_LIImplications for COVID-19 vaccine efficacy trials and wider public health C_LI
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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. MethodsWe performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots. FindingsMore than 2.8 million app users in England provided 120 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys: the ONS and REACT-1 studies. On 28 September 2020 we estimated 15,841 (95% CI 14,023-17,885) daily cases, a prevalence of 0.53% (95% CI 0.45-0.60), and R(t) of 1.17 (95% credible interval 1.15-1.19) in England. On a geographically granular level, on 28 September 2020 we detected 15 of the 20 regions with highest incidence according to Government test data, with indications that our method may be able to detect rapid case increases in regions where Government testing provision is more limited. InterpretationSelf-reported data from mobile applications can provide an agile resource to inform policymakers during a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance. FundingZoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimers Society. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSTo identify instances of the use of digital tools to perform COVID-19 surveillance, we searched PubMed for peer-reviewed articles between 1 January and 14 October 2020, using the keywords COVID-19 AND ((mobile application) OR (web tool) OR (digital survey)). Of the 382 results, we found eight that utilised user-reported data to ascertain a users COVID-19 status. Of these, none sought to provide disease surveillance on a national level, or to compare these predictions to other tools to ascertain their accuracy. Furthermore, none of these papers sought to use their data to highlight geographical areas of concern. Added value of this studyTo our knowledge, we provide the first demonstration of mobile technology to provide national-level disease surveillance. Using over 120 million reports from more than 2.8 million users across England, we estimate incidence, prevalence, and the effective reproduction number. We compare these estimates to those from national community surveys to understand the effectiveness of these digital tools. Furthermore, we demonstrate the large number of users can be used to provide disease surveillance with high geographical granularity, potentially providing a valuable source of information for policymakers seeking to understand the spread of the disease. Implications of all the available evidenceOur findings suggest that mobile technology can be used to provide real-time data on the national and local state of the pandemic, enabling policymakers to make informed decisions in a fast-moving pandemic.