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Background: In Munich, the first German case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected on 27 January 2020 at the Division of Infectious Diseases and Tropical Medicine of the University Hospital LMU Munich (DIDTM), and consecutively the Covid Testing Unit was established. Germany advocated several public health measures to control the outbreak. This study investigates the effects of measures on health service utilization in the public, which in turn can alter case numbers and test positivity rates., Method: Our retrospective observational study was conducted to determine the effects of public health measures on the utilization of a testing facility and positivity rates from the first operational COVID-19 testing facility in Munich for waves 1 and 2 over a period of 14 months. This was accomplished by comparing trends in client characteristics including age, gender, symptoms, and socio-demographic aspects over time to non-pharmaceutical measures in Germany. To depict trend changes in testing numbers over time, we developed a negative binomial model with multiple breakpoints., Results: In total 9861 tests were conducted on 6989 clients. The clients were mostly young (median age: 34), female (60.58%), and asymptomatic (67.89%). Among those who tested positive for SARS-CoV-2, 67.72% were symptomatic while the percentage was 29.06% among those who tested negative. There are other risk factors, but a SARS-CoV-2-positive colleague at work is the most prominent factor. Trend changes in the clients' testing numbers could be attributed to the implementation of various public health measures, testing strategies, and attitudes of individuals toward the pandemic. However, test positivity rates did not change substantially during the second wave of the pandemic., Conclusion: We could show that implementation or changes in public health measures have a strong effect on the utilization of testing facilities by the general public, which independently of the true epidemiological background situation can result in changing test numbers., (© 2023. The Author(s).)

Background: Numerous scoring tools have been developed for assessing the probability of SARS-COV-2 test positivity, though few being suitable or adapted for outpatient triage of health care workers., Methods: We retrospectively analysed 3069 patient records of health care workers admitted to the COVID-19 Testing Unit of the Ludwig-Maximilians-Universität of Munich between January 27 and September 30, 2020, for real-time polymerase chain reaction analysis of naso- or oropharyngeal swabs. Variables for a multivariable logistic regression model were collected from self-completed case report forms and selected through stepwise backward selection. Internal validation was conducted by bootstrapping. We then created a weighted point-scoring system from logistic regression coefficients., Results: 4076 (97.12%) negative and 121 (2.88%) positive test results were analysed. The majority were young (mean age: 38.0), female (69.8%) and asymptomatic (67.8%). Characteristics that correlated with PCR-positivity included close-contact professions (physicians, nurses, physiotherapists), flu-like symptoms (e.g., fever, rhinorrhoea, headache), abdominal symptoms (nausea/emesis, abdominal pain, diarrhoea), less days since symptom onset, and contact to a SARS-COV-2 positive index-case. Variables selected for the final model included symptoms (fever, cough, abdominal pain, anosmia/ageusia) and exposures (to SARS-COV-positive individuals and, specifically, to positive patients). Internal validation by bootstrapping yielded a corrected Area Under the Receiver Operating Characteristics Curve of 76.43%. We present sensitivity and specificity at different prediction cut-off points. In a subgroup with further workup, asthma seems to have a protective effect with regard to testing result positivity and measured temperature was found to be less predictive than anamnestic fever., Conclusions: We consider low threshold testing for health care workers a valuable strategy for infection control and are able to provide an easily applicable triage score for the assessment of the probability of infection in health care workers in case of resource scarcity., (© 2022. The Author(s).)

Background: The 2013-2016 West Africa Ebola Virus Disease (EVD) outbreak recorded the highest incidence and mortality since the discovery of the virus in Zaire in 1976; with more than 28,000 probable and confirmed EVD cases and 11,000 deaths. Studies relating to previous outbreaks usually involved small sample sizes. In this study we are set to identify those sociodemographic and clinical features that predict in-facility mortality among EVD patients using a large sample size., Methods: We analysed the anonymized medical records of 938 laboratory-confirmed EVD patients 15 years old and above who received treatment at The 34 Military Hospital and The Police Training School EVD Treatment Centers in Sierra Leone in the period June 2014 to April 2015. We used both univariable and multivariable logistic regression to determine the predictors for in-facility mortality of these patients based on their sociodemographic and clinical characteristics., Results: The median age of the EVD cases was 33 years (interquartile range = 25 to 40 years). The majority of the EVD cases were male (59.0%) and had secondary level education (79.3%). We reported a low overall in-facility case fatality rate of 26.4%. The associations between case fatality rates and EVD patients who reported fever, abdominal pain, cough, diarrhoea, vomiting, fatigue, haemorrhage, dysphagia, conjunctival injection, dyspnea, and skin rash at the time of admission were all statistically significant (p <  0.05). Our preferred model with the age group 65 years and above alongside the following clinical symptoms; diarrhoea, vomiting, fatigue, dysphagia, conjunctival injection, dyspnea and cough produced a receiver operating characteristic (ROC) curve with an AUC (area under the curve) value of 0.93., Conclusions: We constructed a simple model that can be optimally used alongside other rapid EVD diagnostic tools to identify EVD in-facility treatment mortality predictors based on the sociodemographic characteristics and clinical symptoms of adult EVD patients. We also reported low EVD cases among patients with secondary and tertiary education. These subpopulations of our patients who are generally informed about the signs and symptoms of EVD, alongside our treatment regimen may have been responsible for our comparatively lower case fatality rate.

Background: The West Africa Ebola Virus Disease (EVD) outbreak in 2014-2016 was declared by the World Health Organization (WHO) a public health emergency of international concern. Most of the previous studies done in Sierra Leone relating to the clinical and epidemiological features of EVD during the 2014-2016 West African outbreak focused on adult EVD patients. There have been conflicting reports about the effects of EVD on children during previous outbreaks., Methods: This is an observational retrospective analysis of medical data of all laboratory confirmed paediatric EVD patients below 15 years of age who were admitted at the 34 Military Hospital Ebola Treatment Center (ETC) in Wilberforce, Sierra Leone between June 2014 to April 2015. We analyzed the sociodemographic and clinical characteristics of paediatric EVD cases contained in case report forms that were collected by Ebola surveillance officers and clinicians at the 34 Military Hospital ETC. Both univariate and multivariate logistic regression models were used to determine the sociodemographic and clinical characteristics of paediatric EVD patients that were associated with EVD facility-based mortality., Results: The majority of the paediatric EVD cases in this study were female (56.1%), pupils (51.1%), and 43.2% belonged to the age group between 10 years and below 15 years. The median age of the paediatric EVD cases was 9 years (interquartile range = 4 to 11 years). Adjusting for other covariates in the model, male paediatric EVD patient (AOR = 13.4, 95% CI = [2.07-156-18], p <  0.05), EVD patient with abdominal pain (AOR = 11.0, 95% CI = [1.30-161.81], p <  0.05), vomiting (AOR = 35.7, 95% CI = [3.43-833.73], p <  0.05), signs of conjunctivitis (AOR = 17.4, 95% CI = [1.53-342.21], p <  0.05) and difficulty in breathing (AOR = 23.3, 95% CI = [1.92-713.01], p <  0.05) at the time of admission had increased odds of dying during EVD treatment., Conclusions: We recommend the adoption of case definitions currently in vigour to cater for specific characteristics of paediatric patients. Subgroups that can be identified by applying the model developed in this study may require special attention and intensified care.