Factors associated with SARS-COV-2 positive test in Lifelines
Ontology highlight
ABSTRACT:
SUBMITTER: Sidorenkov G
PROVIDER: S-EPMC10686451 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This study evaluates to what extent symptoms are present before, during, and after a positive SARS-CoV-2 polymerase chain reaction (PCR) test, and to evaluate how the symptom burden and quality of Life (QoL) compares to those with a negative PCR test. Participants from the Dutch Lifelines COVID-19 Cohort Study filled-out as of March 2020 weekly, later bi-weekly and monthly, questions about demographics, COVID-19 diagnosis and severity, QoL, and symptoms. The study population included those with one positive or negative PCR test who filled out two questionnaires before and after the test, resulting in 996 SARS-CoV-2 PCR positive and 3978 negative participants. Nearly all symptoms were more often reported after a positive test versus the period before the test (p < 0.05), except fever. A higher symptom prevalence after versus before a test was also found for nearly all symptoms in negatives (p < 0.05). Before the test, symptoms were already partly present and reporting of nearly all symptoms before did not differ between positives and negatives (p > 0.05). QoL decreased around the test for positives and negatives, with a larger deterioration for positives. Not all symptoms after a positive SARS-CoV-2 PCR test might be attributable to the infection and symptoms were also common in negatives.
| S-EPMC10359407 | biostudies-literature
Project description:We investigate the epistemological consequences of a positive polymerase chain reaction SARS-CoV test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, how much confirmation does a positive test lend to each hypothesis? Second, how much evidence does a positive test provide for each hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that test specificity-and coinfection probabilities when making inferences about C-were key determinants of confirmation and evidence. Tests with <?87% specificity could not provide strong evidence (likelihood ratio?>?8) for V against ¬V regardless of sensitivity. Accordingly, low specificity tests could not provide strong evidence in favor of C in all plausible scenarios modeled. We also show how a positive influenza A test disconfirms C and provides weak evidence against C in dependence on the probability that the patient is influenza A infected given that his/her symptoms are not caused by SARS-CoV-2. Our analysis points out some caveats that should be considered when attributing symptoms or death of a positively tested patient to SARS-CoV-2.
| S-EPMC7473592 | biostudies-literature
Project description:Case reports of patients with coronavirus disease-2019 (COVID-19) who have been discharged and subsequently report positive reverse transcription-polymerase chain reaction again (hereafter referred as "re-positive") do not fully describe the magnitude and significance of this issue. To determine the re-positive rate (proportion) and review probable causes and outcomes, we conduct a retrospective study of all 119 discharged patients in Brunei Darussalam up till April 23. Patients who were discharged are required to self-isolate at home for 14 days and undergo nasopharyngeal specimen collection postdischarge. Discharged patients found to be re-positive were readmitted. We reviewed the clinical and epidemiological records of all discharged patients and apply log-binomial models to obtain risk ratios for re-positive status. One in five recovered patients subsequently test positive again for severe acute respiratory syndrome coronavirus 2-this risk is more than six times higher in persons aged 60 years and above. The average Ct value of re-positive patients was lower predischarge compared with their readmission Ct value. Out of 111 close contacts tested, none were found to be positive as a result of exposure to a re-positive patient. Our findings support prolonged but intermittent viral shedding as the probable cause for this phenomenon. We did not observe infectivity potential in these patients.
| S-EPMC7323238 | biostudies-literature
Project description: Not available
| S-BSST1037 | biostudies-other
Project description:During the COVID-19 pandemic, the reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) assay has been the primary method of diagnosis of SARS-CoV-2 infection. However, RT-qPCR assay interpretation can be ambiguous with no universal absolute cut-off value to determine sample positivity, which particularly complicates the analysis of samples with high Ct values, or weak positives. Therefore, we sought to analyse factors associated with weak positive SARS-CoV-2 diagnosis. We analysed sample data associated with all positive SARS-CoV-2 RT-qPCR diagnostic tests performed by the Victorian Infectious Diseases Reference Laboratory (VIDRL) in Melbourne, Australia, during the Victorian first wave (22 January 2020-30 May 2020). A subset of samples was screened for the presence of host DNA and RNA using qPCR assays for CCR5 and 18S, respectively. Assays targeting the viral RNA-dependent RNA polymerase (RdRp) had higher Ct values than assays targeting the viral N and E genes. Weak positives were not associated with the age or sex of individuals' samples nor with reduced levels of host DNA and RNA. We observed a relationship between Ct value and time post-SARS-CoV-2 diagnosis. High Ct value or weak positive SARS-CoV-2 was not associated with any particular bias including poor biological sampling.
| S-EPMC9239708 | biostudies-literature
Project description: Not available
| S-BSST379 | biostudies-other
Project description:BACKGROUND: Contextual socio-economic factors, health-care access, and general practitioner (GP) involvement may influence colonoscopy uptake and its timing after positive faecal occult blood testing (FOBT). Our objectives were to identify predictors of delayed or no colonoscopy and to assess the role for GPs in colonoscopy uptake. METHODS: We included all residents of a French district with positive FOBTs (n = 2369) during one of the two screening rounds (2007-2010). Multilevel logistic regression analysis was performed to identify individual and area-level predictors of delayed colonoscopy, no colonoscopy, and no information on colonoscopy. RESULTS: A total of 998 (45.2%) individuals underwent early, 989 (44.8%) delayed, and 102 (4.6%) no colonoscopy; no information was available for 119 (5.4%) individuals. Delayed colonoscopy was independently associated with first FOBT (odds ratio, (OR)), 1.61; 95% confidence interval ((95% CI), 1.16-2.25); and no colonoscopy and no information with first FOBT (OR, 2.01; 95% CI, 1.02-3.97), FOBT kit not received from the GP (OR, 2.29; 95% CI, 1.67-3.14), and socio-economically deprived area (OR, 3.17; 95% CI, 1.98-5.08). Colonoscopy uptake varied significantly across GPs (P=0.01). CONCLUSION: Socio-economic factors, GP-related factors, and history of previous FOBT influenced colonoscopy uptake after a positive FOBT. Interventions should target GPs and individuals performing their first screening FOBT and/or living in socio-economically deprived areas.
| S-EPMC3776987 | biostudies-other
Project description:ObjectiveTo identify risk factors for severe disease in children hospitalised for SARS-CoV-2 infection.DesignMulticentre retrospective cohort study.Setting18 hospitals in Canada, Iran and Costa Rica from 1 February 2020 to 31 May 2021.PatientsChildren<18 years of age hospitalised for symptomatic PCR-positive SARS-CoV-2 infection, including PCR-positive multisystem inflammatory syndrome in children (MIS-C).Main outcome measureSeverity on the WHO COVID-19 Clinical Progression Scale was used for ordinal logistic regression analyses.ResultsWe identified 403 hospitalisations. Median age was 3.78 years (IQR 0.53-10.77). At least one comorbidity was present in 46.4% (187/403) and multiple comorbidities in 18.6% (75/403). Eighty-one children (20.1%) met WHO criteria for PCR-positive MIS-C. Progression to WHO clinical scale score ≥6 occurred in 25.3% (102/403). In multivariable ordinal logistic regression analyses adjusted for age, chest imaging findings, laboratory-confirmed bacterial and/or viral coinfection, and MIS-C diagnosis, presence of a single (adjusted OR (aOR) 1.90, 95% CI 1.13 to 3.20) or multiple chronic comorbidities (aOR 2.12, 95% CI 1.19 to 3.79), obesity (aOR 3.42, 95% CI 1.76 to 6.66) and chromosomal disorders (aOR 4.47, 95% CI 1.25 to 16.01) were independent risk factors for severity. Age was not an independent risk factor, but different age-specific comorbidities were associated with more severe disease in age-stratified adjusted analyses: cardiac (aOR 2.90, 95% CI 1.11 to 7.56) and non-asthma pulmonary disorders (aOR 3.07, 95% CI 1.26 to 7.49) in children<12 years old and obesity (aOR 3.69, 1.45-9.40) in adolescents≥12 years old. Among infants<1 year old, neurological (aOR 10.72, 95% CI 1.01 to 113.35) and cardiac disorders (aOR 10.13, 95% CI 1.69 to 60.54) were independent predictors of severe disease.ConclusionWe identified risk factors for disease severity among children hospitalised for PCR-positive SARS-CoV-2 infection. Comorbidities predisposing children to more severe disease may vary by age. These findings can potentially guide vaccination programmes and treatment approaches in children.
| S-EPMC9358955 | biostudies-literature
Project description:The ongoing COVID-19 pandemic caused by SARS-CoV-2 has affected millions of people worldwide and has significant implications for public health. Host transcriptomics profiling provides comprehensive understanding of how the virus interacts with host cells and how the host responds to the virus. COVID-19 disease alters the host transcriptome, affecting cellular pathways and key molecular functions. To contribute to the global effort to understand the virus’s effect on host cell transcriptome, we have generated a dataset from nasopharyngeal swabs of 35 individuals infected with SARS-CoV-2 from the Campania region in Italy during the three outbreaks, with different clinical conditions. This dataset will help to elucidate the complex interactions among genes and can be useful in the development of effective therapeutic pathways
2023-05-25 | E-MTAB-13028 | biostudies-arrayexpress
Project description:BackgroundSARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection.MethodsWe linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1).FindingsAmong 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78-91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 tested positive in early and late wave 1, respectively. In the late phase (when restriction of visitors, closure of communal spaces, and universal masking in LTC were routine), regional-level characteristics comprised 33 of the top 50 factors associated with testing positive, while laboratory values and comorbidities were also predictive. The c-index of the final model was 0.934, and sensitivity was 0.887. In the highest versus lowest risk quartiles, the odds ratio for infection was 114.3 (95% CI 38.6-557.3). LTC-related geographic variations existed in the distribution of observed infection rates and the proportion of residents at highest risk.InterpretationMachine learning informed evaluation of predicted and observed risks of SARS-CoV-2 infection at the resident and LTC levels, and may inform initiatives to improve care quality in this setting.FundingFunded by a Canadian Institutes of Health Research, COVID-19 Rapid Research Funding Opportunity grant (# VR4 172736) and a Peter Munk Cardiac Centre Innovation Grant. Dr. D. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Austin is supported by a Mid-Career investigator award from the Heart and Stroke Foundation. Dr. McAlister is supported by an Alberta Health Services Chair in Cardiovascular Outcomes Research. Dr. Kaul is the CIHR Sex and Gender Science Chair and the Heart & Stroke Chair in Cardiovascular Research. Dr. Rochon holds the RTO/ERO Chair in Geriatric Medicine from the University of Toronto. Dr. B. Wang holds a CIFAR AI chair at the Vector Institute.
| S-EPMC8763396 | biostudies-literature