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Connection regarding Ocular Negative Activities Together with Inactivated COVID-19 Vaccination in Patients within Abu Dhabi.
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This study aimed to compare differences in mortality risk factors between admission and follow-up incorporated models.

A retrospective cohort study of 524 patients with confirmed COVID-19 infection admitted to a tertiary medical center in São Paulo, Brazil from 13 March to 30 April 2020. Data were collected on admission, and the third, eighth and fourteenth days of hospitalization. The hazard ratio (HR) was calculated and 28-day in-hospital mortality risk factors were compared between admission and follow-up models using a time-dependent Cox regression model.

Of 524 patients, 50.4% needed mechanical ventilation. The 28-day mortality rate was 32.8%. Compared with follow-up, admission models under-estimated the mortality HR for peripheral oxygen saturation <92% (1.21 versus 2.09), heart rate >100 bpm (1.19 versus 2.04), respiratory rate >24/min (1.01 versus 1.82) and mechanical ventilation (1.92 versus 12.93). Low oxygen saturation, higher oxygen support and more biomarkers-including lactate dehydrogenase, C-reactive protein, neutrophil-lymphocyte ratio, and urea remained associated with mortality after adjustment for clinical factors at follow-up compared with only urea and oxygen support at admission.

The inclusion of follow-up measurements changed mortality hazards of clinical signs and biomarkers. Low oxygen saturation, higher oxygen support, lactate dehydrogenase, C-reactive protein, neutrophil-lymphocyte ratio, and urea could help with prognosis of patients during follow-up.
The inclusion of follow-up measurements changed mortality hazards of clinical signs and biomarkers. Low oxygen saturation, higher oxygen support, lactate dehydrogenase, C-reactive protein, neutrophil-lymphocyte ratio, and urea could help with prognosis of patients during follow-up.
We aimed to evaluate the clinical and epidemiological behavior of influenza type A versus type B and analyze if there was any correlation or differences between the characteristics of both groups.

An observational, retrospective, descriptive, and population-based study based of children who were hospitalized at the only national pediatric hospital of Costa Rica from January 1, 2010 to December 31, 2018 and had a confirmed influenza virus infection.

336 patients were analyzed. Mean age was 35,6 ± 36,7 months (3,0 ± 3,1 years). The only significant variables at 25% in relation to influenza type A or B virus were sex, month of diagnosis, fever, vomiting, cough, use of antibiotics and admission to the PICU. The hospitalization rate at our hospital increased between the months of October to December, with a higher percentage of cases in November and December, which reveals that the "real peak" in our population begins between 3 to 4 months after the end of the vaccination campaign. Patients with influenza A virus had a 2.5 times greater risk of being admitted to the PICU. Mortality rate was 0.6% and 0% among influenza A and B children, respectively.

Variables in which a causality was found with type A or B virus were admission to the PICU, month of diagnosis, and cough. However, influenza B clinical behavior continues to be unpredictable.
Variables in which a causality was found with type A or B virus were admission to the PICU, month of diagnosis, and cough. However, influenza B clinical behavior continues to be unpredictable.
Mortuary and cemetery workers may be exposed to the bodies of people with SARS-CoV-2 infection; however, prevalence of infection among these groups is unknown.

Nasopharyngeal swabs (NPS) for RT-PCR and serologic testing for SARS-CoV-2 were performed on mortuary and cemetery workers in Qatar. Data on specific job duties, living conditions, contact history, and clinical course were gathered. Environmental sampling was carried out to explore any association with infection. Logistic regression analysis was used to determine the factors associated with infection.

Forty-seven mortuary workers provided an NPS and seven (14.9%) were PCR positive; 32 provided a blood sample and eight (25%) were antibody positive, six (75%) who were seropositive were also PCR positive. Among the 81 cemetery workers, 76 provided an NPS and five (6.6%) were PCR positive; 64 provided a blood sample and 22 (34.4%) were antibody positive, three (13.6%) who were seropositive were also PCR positive. Three (22.2%) and 20 (83.3%) of the infected mortuary and cemetery workers were asymptomatic, respectively. Age <30 years (OR 4.9, 95% CI 1.7-14.6), community exposure with a known case (OR 4.7, 95% CI 1.7-13.3), and presence of symptoms in the preceding 2 weeks (OR 9.0, 95% CI 1.9-42.0) were independently associated with an increased risk of infection (PCR or antibody positive). Of the 46 environmental and surface samples, all were negative or had a Ct value of >35.

A substantial proportion of mortuary and cemetery workers had evidence of SARS-CoV-2 infection, which was incidentally detected upon serologic testing. These data are most consistent with community acquisition rather than occupational acquisition.
A substantial proportion of mortuary and cemetery workers had evidence of SARS-CoV-2 infection, which was incidentally detected upon serologic testing. These data are most consistent with community acquisition rather than occupational acquisition.
The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China, from January to October 2020.

About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases.

The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and prominent clustering regions. Spatio-temporal scanning analysis showed that most COVID-19 high-incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR=491.
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