ABSTRACT
ObjectiveTo compare the number of accident- and injury-related admissions to pediatric intensive care units (PICU) during the first German COVID-19 lockdown with previous years. To investigate if shifts in types of accidents or injuries occurred, especially regarding non-accidental injuries. DesignRetrospective observational multicenter study. Setting37 German PICUs. Patients1444 children and adolescents < 18 years admitted to German PICUs due to trauma or injuries during the first German lockdown period (16.3.-31.5.2020) and during the same periods of the years 2017-2019. InterventionsNone. Measurements and main resultsStandardized morbidity ratios (SMR) and 95% confidence intervals (CI) were calculated for the severity of disease, admission reasons, types of accidents, injury patterns, surgeries and procedures, and outcomes. Disease severity did not differ from previous years. We found an increase in ingestions (SMR 1.41 (CI 0.88 - 2.16)) and a decrease in aspirations (0.77 (0.41 - 1.32)) and burns (0.82 (0.59 - 1.12)). The total number of admissions for trauma remained constant, but traffic accidents (0.76 (0.56 - 1.01) and school/kindergarten accidents (0.25 (0.05 - 0.74) decreased. Household (1.32 (1.05 - 1.64)) and leisure accidents (1.32 (1.05 - 1.65)) increased. Injured structures did not change, but less neurosurgeries (0.69 (0.42 - 1.07)) and more visceral surgeries (2.00 (1.14 - 3.24)) were performed. Non-accidental non-suicidal injuries declined (0.85 (0.50 - 1.37)). Suicide attempts increased in adolescent boys (1.57 (0.58 - 3.42)), while there was a decrease in adolescent girls (0.86 (0.53 - 1.31)). ConclusionsOur study showed shifts in trauma types and associated surgeries during the lockdown period that are generally in line with current literature. The decreased number of non-accidental non-suicidal injuries we observed does not suggest a fundamental increase in severe child abuse during the lockdown period. The decrease in suicide attempts among adolescent girls confirms previous findings, while the increase among boys has not been described yet and deserves further investigation.
Subject(s)
COVID-19 , Wounds and InjuriesABSTRACT
Objectives: To evaluate the population-based performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection during the pandemic in 2020. Methods: We analysed SARS-CoV-2 RT-PCR results of 162,457 people living in Muenster, Germany screened at nursing homes, testing sites, at schools, regional hospitals, and by general practitioners. All PCRs were done with the same cobas SARS-CoV-2 RT-PCR system (Roche Diagnostics). We stratified positive RT-PCR results by cycle threshold (Ct) values, periods of the national test strategy, age, sex, and symptoms. Results: Among 162,457 individuals, 4164 (2.6%) had a positive RT-PCR test result, defined as Ct<40. Depending on the national test strategy, higher positive rates were associated with testing predominantly symptomatic people. Children (0-9 years) and older adults (70+ years). Only 40.6% of test positives showed low Ct values < 25 (potentially infectious). The percentage of Ct values below 25 was lower among children (0-9), adolescents (10-19), and among the elderly (70+ years). Conclusions: RT-PCR testing as a tool for mass screening should not be used alone as a base for pandemic decision making including measures such as quarantine, isolation, and lockdown.
Subject(s)
COVID-19ABSTRACT
BackgroundSeasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS Corona Virus detection by PCR. MethodsWe performed a retrospective analysis of 12763 respiratory tract sample results (288 positive and 12475 negative) for non-SARS, non-MERS Corona viruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the Corona virus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. ResultsCorona virus infections followed a seasonal pattern peaking from December to March and plunging from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent. Different automatic variable selection processes agreed to select the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased Corona virus detection rates. ConclusionsCorona virus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed. Several meteorological factors were associated with the Corona virus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the Corona virus detection rate.
ABSTRACT
Background It is unknown if the SARS-CoV-2 pandemic will have a second wave. We analysed published data of five influenza pandemics (such as the Spanish Flu and the Swine Flu) and the SARS-CoV-1 pandemic to describe whether there were subsequent waves and how they differed. Methods We reanalysed literature and WHO reports on SARS-CoV-1 and literature on five influenza pandemics. We report frequencies of second and third waves, wave heights, wavelengths and time between subsequent waves. From this, we estimated peak-to-peak ratios to compare the wave heights, and wave-length-to-wave-length ratios to compare the wavelengths differences in days. Furthermore, we analysed the seasonality of the wave peaks and the time between the peak values of two waves. Results Second waves, the Spanish Flu excluded, were usually about the same height and length as first waves and were observed in 93% of the 57 described epidemic events of influenza pandemics and in 42% of the 19 epidemic events of the SARS-CoV-1 pandemic. Third waves occurred in 54% of the 28 influenza and in 11% of the 19 SARS-CoV-1 epidemic events. Third waves, the Spanish Flu excluded, usually peaked higher than second waves with a peak-to-peak ratio of 0.5. Conclusion While influenza epidemics are usually accompanied by 2nd waves, this is only the case in the minority of SARS-Cov1 epidemics.