Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.
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ABSTRACT:
SUBMITTER: Sudre CH
PROVIDER: S-EPMC7978420 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC8282115 | biostudies-literature
Project description:Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.
| S-EPMC8262614 | biostudies-literature
Project description:BackgroundApproximately 80% of people with COVID-19 do not require hospitalization. Studies examining the outpatient experience have not tracked symptoms to resolution leading to unknown expected symptom duration. Our objectives were to (1) determine symptom duration among patients with COVID-19 who do not require hospitalization and (2) identify potential risk factors associated with prolonged symptom duration.DesignThis is a retrospective cohort study conducted across an academic healthcare system including adult patients with laboratory-confirmed SARS-CoV-2 infection between March 18th and April 28th, 2020 who were not hospitalized. Symptom duration encompassed time from patient-reported symptom onset as documented in the chart until documented symptom resolution. We calculated the median symptom duration and tested if demographics, comorbidities, or reported symptoms were associated with symptom duration.Key resultsOf 294 patients meeting inclusion criteria, 178 (60.5%) had documented symptom resolution. The median [interquartile range (IQR)] symptom duration for included patients was 15 (8-24) days. No associations were found between comorbidities and symptom duration. Factors associated with prolonged symptom duration were presence vs lack of lower respiratory symptoms [median (IQR) 16.5 (10.75-33.5) vs 14.5 (7-21.75) days respectively, P < .001] and neurologic symptoms [median (IQR) 17 (9-28) vs 9.5 (4-17) days, P < .001] at disease onset.ConclusionsThe median symptom duration in outpatients is 15 days and over 25% of patients have symptoms longer than 21 days.
| S-EPMC8590478 | biostudies-literature
Project description:We estimated the generation interval distribution for coronavirus disease on the basis of serial intervals of observed infector-infectee pairs from established clusters in Singapore. The short mean generation interval and consequent high prevalence of presymptomatic transmission requires public health control measures to be responsive to these characteristics of the epidemic.
| S-EPMC7853533 | biostudies-literature
Project description:BackgroundThe early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic.ObjectiveThe aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants.MethodsThis report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.ResultsThe study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022.ConclusionsThe @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.International registered report identifier (irrid)DERR1-10.2196/30444.
| S-EPMC8496683 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the world and reports of children during early epidemic period showed features of family clusters. The aim of this study is to assess clinical profiles of COVID-19 in family clusters with children.MethodsWe performed a systematic literature review of English database (PubMed, Web of Science) and Chinese database (" www.cnki.net ", " www.cqvip.com " and " www.Wanfangdata.com.cn ") to identify papers on family clusters of COVID-19 with children and their family members.ResultsEighteen studies involving 34 children and 98 adults from 28 families were included. Fever, cough and ground-grass opacity change of chest computed tomography (CT) were the dominant features, whereas proportion of asymptomatic infections for children was higher than adults with statistical significance (32.4% and 13.3%, respectively, P < 0.05). Median time of longer incubation period (10 days) and shorter duration of pharyngeal swab nucleic acid test positive period (11 days) were seen in children than adults (7 and 17 days, respectively) with statistical significance (P < 0.05). There were statistically significant differences in lymphopenia, increased C-reactive protein and abnormal chest CT between children and adult patients (P < 0.05). Twenty-seven families reported adults as first case of COVID-19 in family clusters.ConclusionsThe same virus strain can cause milder disease in children compared with their caregivers. Children of COVID-19 were infected by adults in family during the early epidemic period. Asymptomatic patients can transmit the virus.
| S-EPMC8229253 | biostudies-literature
Project description:Blood collected from adults pre vaccination and post vaccination to study the immune effects of COVID-19 vaccination and how they relate to antibody and T-cell responses.
2023-01-12 | GSE199750 | GEO
Project description:It has been widely observed that adult men of all ages are at higher risk of developing serious complications from COVID-19 when compared with women. This study aimed to investigate the association of COVID-19 positivity and severity with estrogen exposure in women, in a population based matched cohort study of female users of the COVID Symptom Study application in the UK. Analyses included 152,637 women for menopausal status, 295,689 women for exogenous estrogen intake in the form of the combined oral contraceptive pill (COCP), and 151,193 menopausal women for hormone replacement therapy (HRT). Data were collected using the COVID Symptom Study in May-June 2020. Analyses investigated associations between predicted or tested COVID-19 status and menopausal status, COCP use, and HRT use, adjusting for age, smoking and BMI, with follow-up age sensitivity analysis, and validation in a subset of participants from the TwinsUK cohort. Menopausal women had higher rates of predicted COVID-19 (P = 0.003). COCP-users had lower rates of predicted COVID-19 (P = 8.03E-05), with reduction in hospital attendance (P = 0.023). Menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P = 2.22E-05) for HRT users alone. The findings support a protective effect of estrogen exposure on COVID-19, based on positive association between predicted COVID-19 with menopausal status, and negative association with COCP use. HRT use was positively associated with COVID-19, but the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential unaccounted for confounders and comorbidities.
| S-EPMC8432854 | biostudies-literature
Project description:The COVID-19 pandemic has been spreading worldwide with more than 246 million confirmed cases and 5 million deaths across more than 200 countries as of October 2021. There have been multiple disease clusters, and transmission in South Korea continues. We aim to analyze COVID-19 clusters in Seoul from 4 March to 4 December 2020. A branching process model is employed to investigate the strength and heterogeneity of cluster-induced transmissions. We estimate the cluster-specific effective reproduction number Reff and the dispersion parameter κ using a maximum likelihood method. We also compute Rm as the mean secondary daily cases during the infection period with a cluster size m. As a result, a total of 61 clusters with 3088 cases are elucidated. The clusters are categorized into six groups, including religious groups, convalescent homes, and hospitals. The values of Reff and κ of all clusters are estimated to be 2.26 (95% CI: 2.02-2.53) and 0.20 (95% CI: 0.14-0.28), respectively. This indicates strong evidence for the occurrence of superspreading events in Seoul. The religious groups cluster has the largest value of Reff among all clusters, followed by workplaces, schools, and convalescent home clusters. Our results allow us to infer the presence or absence of superspreading events and to understand the cluster-specific characteristics of COVID-19 outbreaks. Therefore, more effective suppression strategies can be implemented to halt the ongoing or future cluster transmissions caused by small and sporadic clusters as well as large superspreading events.
| S-EPMC8701974 | biostudies-literature