Dataset Information

Persistent COVID-19 symptoms in a community study of 606,434 people in England.

ABSTRACT: Long COVID remains a broadly defined syndrome, with estimates of prevalence and duration varying widely. We use data from rounds 3-5 of the REACT-2 study (n = 508,707; September 2020 - February 2021), a representative community survey of adults in England, and replication data from round 6 (n = 97,717; May 2021) to estimate the prevalence and identify predictors of persistent symptoms lasting 12 weeks or more; and unsupervised learning to cluster individuals by reported symptoms. At 12 weeks in rounds 3-5, 37.7% experienced at least one symptom, falling to 21.6% in round 6. Female sex, increasing age, obesity, smoking, vaping, hospitalisation with COVID-19, deprivation, and being a healthcare worker are associated with higher probability of persistent symptoms in rounds 3-5, and Asian ethnicity with lower probability. Clustering analysis identifies a subset of participants with predominantly respiratory symptoms. Managing the long-term sequelae of COVID-19 will remain a major challenge for affected individuals and their families and for health services.

SUBMITTER: Whitaker M

PROVIDER: S-EPMC9005552 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Persistent COVID-19 symptoms in a community study of 606,434 people in England.

Whitaker Matthew M Elliott Joshua J Chadeau-Hyam Marc M Riley Steven S Darzi Ara A Cooke Graham G Ward Helen H Elliott Paul P

Nature communications 20220412 1

Long COVID remains a broadly defined syndrome, with estimates of prevalence and duration varying widely. We use data from rounds 3-5 of the REACT-2 study (n = 508,707; September 2020 - February 2021), a representative community survey of adults in England, and replication data from round 6 (n = 97,717; May 2021) to estimate the prevalence and identify predictors of persistent symptoms lasting 12 weeks or more; and unsupervised learning to cluster individuals by reported symptoms. At 12 weeks in ...[more]

PMID: 35413949

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Project description:BackgroundRapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type.Methods and findingsWe obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England.ConclusionsWhere testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

Project description:BackgroundPeople experiencing homelessness are vulnerable to COVID-19 due to the risk of transmission in shared accommodation and the high prevalence of comorbidities. In England, as in some other countries, preventive policies have been implemented to protect this population. We aimed to estimate the avoided deaths and health-care use among people experiencing homelessness during the so-called first wave of COVID-19 in England-ie, the peak of infections occurring between February and May, 2020-and the potential impact of COVID-19 on this population in the future.MethodsWe used a discrete-time Markov chain model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that included compartments for susceptible, exposed, infectious, and removed individuals, to explore the impact of the pandemic on 46 565 individuals experiencing homelessness: 35 817 living in 1065 hostels for homeless people, 3616 sleeping in 143 night shelters, and 7132 sleeping outside. We ran the model under scenarios varying the incidence of infection in the general population and the availability of prevention measures: specialist hotel accommodation, infection control in homeless settings, and mixing with the general population. We divided our scenarios into first wave scenarios (covering Feb 1-May 31, 2020) and future scenarios (covering June 1, 2020-Jan 31, 2021). For each scenario, we ran the model 200 times and reported the median and 95% prediction interval (2·5% and 97·5% quantiles) of the total number of cases, the number of deaths, the number hospital admissions, and the number of intensive care unit (ICU) admissions.FindingsUp to May 31, 2020, we calibrated the model to 4% of the homeless population acquiring SARS-CoV-2, and estimated that 24 deaths (95% prediction interval 16-34) occurred. In this first wave of SARS-CoV-2 infections in England, we estimated that the preventive measures imposed might have avoided 21 092 infections (19 777-22 147), 266 deaths (226-301), 1164 hospital admissions (1079-1254), and 338 ICU admissions (305-374) among the homeless population. If preventive measures are continued, we projected a small number of additional cases between June 1, 2020, and Jan 31, 2021, with 1754 infections (1543-1960), 31 deaths (21-45), 122 hospital admissions (100-148), and 35 ICU admissions (23-47) with a second wave in the general population. However, if preventive measures are lifted, outbreaks in homeless settings might lead to larger numbers of infections and deaths, even with low incidence in the general population. In a scenario with no second wave and relaxed measures in homeless settings in England, we projected 12 151 infections (10 718-13 349), 184 deaths (151-217), 733 hospital admissions (635-822), and 213 ICU admissions (178-251) between June 1, 2020, and Jan 31, 2021.InterpretationOutbreaks of SARS-CoV-2 in homeless settings can lead to a high attack rate among people experiencing homelessness, even if incidence remains low in the general population. Avoidance of deaths depends on prevention of transmission within settings such as hostels and night shelters.FundingNational Institute for Health Research, Wellcome, and Medical Research Council.

Project description:BackgroundSurveillance remains fundamental to understanding the changes in epidemiological patterns regarding post-COVID conditions and reinfections. Persistent symptoms and reinfection in previously infected individuals are increasing being reported in many countries, but their associations among general populations were seldomly reported. Understanding the association with persistent symptoms of COVID-19 reinfection is essential to develop strategies to mitigate the long-term health and socio-economic impacts of the post-COVID conditions. This study aimed to investigate the incidence of COVID-19 persistent symptoms among previously infected Chinese community residents and explore associations of specific COVID-19 persistent symptoms with reinfection and other factors.MethodsA community-based survey was conducted in a southern city of China with about 20 million residents from August 3 to 24, 2023. Face-to-face questionnaires were distributed to a total of 1,485 residents to collect their information about COVID-19 infection, reinfection, specific ongoing persistent symptoms, and other COVID-19 related information. Multivariable logistic regression analysis was used to examine the association between specific persistent symptoms and reinfection of COVID-19, along with age, gender, and educational level.ResultsOf the 1,485 participants, 1,089 (73.3%) reported they had been infected with COVID-19. Among them, 89.1% reported having ongoing persistent symptoms and 14.2% reported had two or more times of infection. About 20% participants were infected 1 year or more since their initial infection. Fatigue, cough, and headaches were the top 3 symptoms being reported. Participants with reinfection were associated with a higher probability of reporting headaches (OR: 1.54, 95% CI: 1.06-2.25), loss of or change in smell and/or taste (OR: 1.90, 95% CI: 1.27-2.83), impaired sleep (OR: 1.55, 95% CI: 1.02-2.35), and brain fog (OR: 1.76, 95% CI: 1.12-2.76). Participants aged 45 and above and who had a bachelor's or higher degree were more likely to report chest tightness or shortness of breath, impaired sleep, and brain fog.DiscussionDuring the post-emergency period of COVID-19 pandemic, the incidence of ongoing persistent symptoms among Chinese residents remains high. Individuals whose initial infection was longer than 1 year have the highest probability of reporting having multiple symptoms. Reinfection may increase the risk of reporting headaches, loss of or change in smell and/or taste, impaired sleep, and brain fog. It is important to maintain routine syndromic surveillance among previously infected people and provide recommendations for clinical management of individuals with multiple ongoing symptoms.

Dataset Information

Persistent COVID-19 symptoms in a community study of 606,434 people in England.

Publications

Persistent COVID-19 symptoms in a community study of 606,434 people in England.

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