The UK Leicester COVID-19 'exceedance' May-July 2020: An analysis of hospitalised cases.
Ontology highlight
ABSTRACT:
SUBMITTER: Tang JW
PROVIDER: S-EPMC8164510 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC8393009 | biostudies-literature
Project description:BackgroundRisk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses.MethodsWe investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10-14 years before the COVID-19 time periods.ResultsHere we show, for example, "participant having son and/or daughter in household" was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively).ConclusionsOur data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.
| S-EPMC10062272 | biostudies-literature
Project description:BackgroundIn March 2020, the COVID-19 outbreak was declared a pandemic by the World Health Organization.AimOur objective was to identify risk factors predictive of severe disease and death in France.MethodsIn this prospective cohort study, we included patients ≥ 18 years old with confirmed COVID-19, hospitalised in Strasbourg and Mulhouse hospitals (France), in March 2020. We respectively compared patients who developed severe disease (admission to an intensive care unit (ICU) or death) and patients who died, to those who did not, by day 7 after hospitalisation.ResultsAmong 1,045 patients, 424 (41%) had severe disease, including 335 (32%) who were admitted to ICU, and 115 (11%) who died. Mean age was 66 years (range: 20-100), and 612 (59%) were men. Almost 75% of patients with body mass index (BMI) data (n = 897) had a BMI ≥ 25 kg/m2 (n = 661). Independent risk factors associated with severe disease were advanced age (odds ratio (OR): 1.1 per 10-year increase; 95% CrI (credible interval): 1.0-1.2), male sex (OR: 2.1; 95% CrI: 1.5-2.8), BMI of 25-29.9 kg/m2 (OR: 1.8; 95% CrI: 1.2-2.7) or ≥ 30 (OR: 2.2; 95% CrI: 1.5-3.3), dyspnoea (OR: 2.5; 95% CrI: 1.8-3.4) and inflammatory parameters (elevated C-reactive protein and neutrophil count, low lymphocyte count). Risk factors associated with death were advanced age (OR: 2.7 per 10-year increase; 95% CrI: 2.1-3.4), male sex (OR: 1.7; 95% CrI: 1.1-2.7), immunosuppression (OR: 3.8; 95% CrI: 1.6-7.7), diabetes (OR: 1.7; 95% CrI: 1.0-2.7), chronic kidney disease (OR: 2.3; 95% CrI: 1.3-3.9), dyspnoea (OR: 2.1; 95% CrI: 1.2-3.4) and inflammatory parameters.ConclusionsOverweightedness, obesity, advanced age, male sex, comorbidities, dyspnoea and inflammation are risk factors for severe COVID-19 or death in hospitalised patients. Identifying these features among patients in routine clinical practice might improve COVID-19 management.
| S-EPMC7716399 | biostudies-literature
Project description:Circulating microRNAs (miRNAs) have been shown to be excellent disease diagnostic or prognostic biomarkers in a wide range of chronic and acute inflammatory and infectious diseases including viral respiratory infection. Crucially, circulating miRNA levels are thought to reflect the state of the diseased tissue. Despite their proven value as mechanism-based clinical stratification indicators, miRNAs have only started being explored in the context of COVID-19. here, we aimed to explore whether integrating miRNA with other clinical and biological measurements would reveal more accurate correlates of COVID-19 severity and outcome, and to identify severity-specific correlations of miRNAs with COVID-19-associated inflammatory mediators, clinical parameters, and otucome.
2021-12-09 | GSE189610 | GEO
Project description:BackgroundExposure to COVID-19 is more likely among certain occupations compared with others. This descriptive study seeks to explore occupational differences in mortality due to COVID-19 among workers in Massachusetts.MethodsDeath certificates of those who died from COVID-19 in Massachusetts between March 1 and July 31, 2020 were collected. Occupational information was coded and age-adjusted mortality rates were calculated according to occupation.ResultsThere were 555 deaths among MA residents of age 16-64, with usable occupation information, resulting in an age-adjusted mortality rate of 16.4 per 100,000 workers. Workers in 11 occupational groups including healthcare support and transportation and material moving had mortality rates higher than that for workers overall. Hispanic and Black workers had age-adjusted mortality rates more than four times higher than that for White workers overall and also had higher rates than Whites within high-risk occupation groups.ConclusionEfforts should be made to protect workers in high-risk occupations identified in this report from COVID-19 exposure.
| S-EPMC8013546 | biostudies-literature
Project description:BackgroundCoronavirus disease of 2019 (COVID-19) is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2 Virus). It was reported for the first time in Wuhan city, Hubei province of China. The first cases of COVID-19 in Burundi were identified on 31st March 2020. Several signs and symptoms, including mainly; fever, dry cough, fatigue, myalgia, and dyspnea are the most prominent characteristics of the disease. The aim of this study was to provide description of the clinical and epidemiological characteristics of COVID-19 cases identified during the mass screening campaign conducted between July and October, 2020 in Burundi.MethodsWe conducted a retrospective secondary analysis of data of clients to the mass screening campaign in Bujumbura city that was run between July and October 2020. Clients with complete data and tested for COVID-19 with Reverse Transcription Polymerase Chain Reaction (RT-PCR) were included in the study. Epi-Info 7.2.2.6 was used to perform descriptive and analytical statistics and Quantum Geographic Information System (QGIS) was used for cases mapping. Association between positive cases and independent variables such as sex, history of contact with confirmed COVID-19 case was measured using chi-square statistical test at a p-value of .05.ResultsThe study included 20,114 participants. 243 (1.2%) were tested positive for COVID-19. The mean age for confirmed cases was 33 (±15) years. The majority of cases (72.8%) were between 20 and 59 years of age and they were predominantly males (67.9%). 164 (67.5%) were symptomatic and cough was the most frequent symptom observed 109 (66.5%), followed by rhinorrhea 69 (42.1%). Fever was present in only 18 (11.0%) of symptomatic patients. Participants with a history of contact with a COVID-19 confirmed case (aOR=2.2; 95%CI [1.6-3.0]; p-value <.001), were more likely to be positive for COVID-19. Also, those who were coughing (aOR=1.47; 95%CI [1.06-2.05]; p-value=.023) and having sore throat (aOR=2.4; 95%CI [1.1-4.9]; p-value=.02) were more likely to test positive for COVID-19.ConclusionThis study revealed that a significant proportion (32.5%) of COVID-19 patients were silent carriers of the virus. Data highlighted that high proportion of cases were among the active age group and contacts with confirmed cases, and noted high proportion of asymptomatic cases at diagnosis. Measures including routine testing of asymptomatic contacts could contribute to tackling corona virus in Burundi.
| S-EPMC9887485 | biostudies-literature
Project description:ObjectivesTo quantify and characterise non-household contact and to identify the effect of shielding and isolating on contact patterns.DesignCross-sectional study.Setting and participantsAnyone living in the UK was eligible to take part in the study. We recorded 5143 responses to the online questionnaire between 28 July 2020 and 14 August 2020.Outcome measuresOur primary outcome was the daily non-household contact rate of participants. Secondary outcomes were propensity to leave home over a 7 day period, whether contacts had occurred indoors or outdoors locations visited, the furthest distance travelled from home, ability to socially distance and membership of support bubble.ResultsThe mean rate of non-household contacts per person was 2.9 d-1. Participants attending a workplace (adjusted incidence rate ratio (aIRR) 3.33, 95% CI 3.02 to 3.66), self-employed (aIRR 1.63, 95% CI 1.43 to 1.87) or working in healthcare (aIRR 5.10, 95% CI 4.29 to 6.10) reported significantly higher non-household contact rates than those working from home. Participants self-isolating as a precaution or following Test and Trace instructions had a lower non-household contact rate than those not self-isolating (aIRR 0.58, 95% CI 0.43 to 0.79). We found limited evidence that those shielding had reduced non-household contacts compared with non-shielders.ConclusionThe daily rate of non-household interactions remained lower than prepandemic levels measured by other studies, suggesting continued adherence to social distancing guidelines. Individuals attending a workplace in-person or employed as healthcare professionals were less likely to maintain social distance and had a higher non-household contact rate, possibly increasing their infection risk. Shielding and self-isolating individuals required greater support to enable them to follow the government guidelines and reduce non-household contact and therefore their risk of infection.
| S-EPMC9748508 | biostudies-literature
Project description:In April 2020, during the peak of the coronavirus disease 2019 (COVID-19) pandemic in Europe, a cluster of children with hyperinflammatory shock with features similar to Kawasaki disease and toxic shock syndrome was reported in England* (1). The patients' signs and symptoms were temporally associated with COVID-19 but presumed to have developed 2-4 weeks after acute COVID-19; all children had serologic evidence of infection with SARS-CoV-2, the virus that causes COVID-19 (1). The clinical signs and symptoms present in this first cluster included fever, rash, conjunctivitis, peripheral edema, gastrointestinal symptoms, shock, and elevated markers of inflammation and cardiac damage (1). On May 14, 2020, CDC published an online Health Advisory that summarized the manifestations of reported multisystem inflammatory syndrome in children (MIS-C), outlined a case definition,† and asked clinicians to report suspected U.S. cases to local and state health departments. As of July 29, a total of 570 U.S. MIS-C patients who met the case definition had been reported to CDC. A total of 203 (35.6%) of the patients had a clinical course consistent with previously published MIS-C reports, characterized predominantly by shock, cardiac dysfunction, abdominal pain, and markedly elevated inflammatory markers, and almost all had positive SARS-CoV-2 test results. The remaining 367 (64.4%) of MIS-C patients had manifestations that appeared to overlap with acute COVID-19 (2-4), had a less severe clinical course, or had features of Kawasaki disease.§ Median duration of hospitalization was 6 days; 364 patients (63.9%) required care in an intensive care unit (ICU), and 10 patients (1.8%) died. As the COVID-19 pandemic continues to expand in many jurisdictions, clinicians should be aware of the signs and symptoms of MIS-C and report suspected cases to their state or local health departments; analysis of reported cases can enhance understanding of MIS-C and improve characterization of the illness for early detection and treatment.
| S-EPMC7440126 | biostudies-literature
Project description:As part of a multiyear project that monitored illness-related school closures, we conducted systematic daily online searches during July 27, 2020-June 30, 2022, to identify public announcements of COVID-19-related school closures (COVID-SCs) in the United States lasting >1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient [r] = 0.73, 95% CI 0.56-0.84) and new cases (r = 0.72, 95% CI 0.54-0.83) during 2020-21 and with hospitalization rates among all ages (r = 0.81, 95% CI 0.67-0.89) during 2021-22. The numbers of reactive COVID-SCs during 2020-21 and 2021-22 greatly exceeded previously observed numbers of illness-related reactive school closures in the United States, notably being nearly 5-fold greater than reactive closures observed during the 2009 influenza (H1N1) pandemic.
| S-EPMC10756369 | biostudies-literature
Project description:BackgroundHospitalized coronavirus disease 2019 (COVID-19) patients have a high morbidity and mortality and are often dependent on intensive care, especially mechanical ventilation. Little is as yet known about COVID-19 patient allocation.ObjectivesAnalysis of the structures of German hospital care for COVID-19 patients up to July 2020 in terms of number of beds and previous ventilation experience.Data and methodsFor the analysis of the care structures, only completed COVID-19 cases in which the virus was detected by a PCR test were evaluated. Claims data from the German Local Health Care Funds (Allgemeine Ortskrankenkassen, AOK) were analysed. The sample includes 17,094 COVID-19 cases that were treated in 1082 hospitals.ResultsA total of 77% of all hospitals participated in the treatment COVID-19 patients and 48% of all hospitals provided intensive care for these patients. One half of the hospitals that treated COVID-19 cases cared for 88% of all cases. Although this suggests a centralization effect of COVID-19 cases in specific hospitals, the remaining 12% of the cases were distributed among many hospitals with often very small numbers of cases. Furthermore, 23% of the ventilated COVID-19 cases were treated in hospitals with below-average ventilation experience.ConclusionsIn the context of increasing numbers of infections, it is both necessary to improve the allocation of hospitalized, and therefore potentially ventilated, COVID-19 cases by means of clearly defined and centrally controlled pyramid-type concepts and to continue to care for patients without COVID-19. For Germany, a comprehensive pyramid-type concept with a greater concentration in the best-qualified hospitals seems reasonable for the care of these patients with complex diseases.
| S-EPMC7837335 | biostudies-literature