Ten months of temporal variation in the clinical journey of hospitalised patients with COVID-19: an observational cohort

Background: There is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high density unit (HDU). On the other hand, limited resources at times of high demand may lead to rationing. Nevertheless, these variables may be used as static proxies for disease severity, as outcome measures for trials, and to inform planning and logistics. Methods: We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay. Results: Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors. Conclusions: Our results establish that variables of these kinds have limitations when used as outcome measures in a rapidly-evolving situation. Funding: This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z] and the Bill and Melinda Gates Foundation [OPP1209135]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

StopCOVID cohort : An observational study of 3,480 patients admitted to the Sechenov University hospital network in Moscow city for suspected COVID-19 infection

© 2020 Oxford University Press. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Clinical Infectious Diseases following peer review. The version of record is available online at: https://doi.org/10.1093/cid/ciaa1535.BACKGROUND: The epidemiology, clinical course, and outcomes of COVID-19 patients in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically-diagnosed COVID-19 in real-life settings is lacking. METHODS: We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow, between April 8 and May 28, 2020. RESULTS: Of the 4261 patients hospitalised for suspected COVID-19, outcomes were available for 3480 patients (median age 56 years (interquartile range 45-66). The commonest comorbidities were hypertension, obesity, chronic cardiac disease and diabetes. Half of the patients (n=1728) had a positive RT-PCR while 1748 were negative on RT-PCR but had clinical symptoms and characteristic CT signs suggestive of COVID-19 infection.No significant differences in frequency of symptoms, laboratory test results and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive SARS-CoV-2 RT-PCR.In a multivariable logistic regression model the following were associated with in-hospital mortality; older age (per 1 year increase) odds ratio [OR] 1.05 (95% confidence interval (CI) 1.03 - 1.06); male sex (OR 1.71, 1.24 - 2.37); chronic kidney disease (OR 2.99, 1.89 - 4.64); diabetes (OR 2.1, 1.46 - 2.99); chronic cardiac disease (OR 1.78, 1.24 - 2.57) and dementia (OR 2.73, 1.34 - 5.47). CONCLUSIONS: Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features were sufficient to diagnoseCOVID-19 infection indicating that laboratory testing is not critical in real-life clinical practice.Peer reviewe

Structural identifiability of compartmental models for infectious disease transmission is influenced by data type

If model identifiability is not confirmed, inferences from infectious disease transmission models may not be reliable, so they might lead to misleading recommendations. Structural identifiability analysis characterizes whether it is possible to obtain unique solutions for all unknown model parameters, given the model structure. In this work, we studied the structural identifiability of some typical deterministic compartmental models for infectious disease transmission, focusing on the influence of the data type considered as model output on the identifiability of unknown model parameters, including initial conditions. We defined 26 model versions, each having a unique combination of underlying compartmental structure and data type(s) considered as model output(s). Four compartmental model structures and three common data types in disease surveillance (incidence, prevalence and detected vector counts) were studied. The structural identifiability of some parameters varied depending on the type of model output. In general, models with multiple data types as outputs had more structurally identifiable parameters, than did models with a single data type as output. This study highlights the importance of a careful consideration of data types as an integral part of the inference process with compartmental infectious disease transmission models

Estimating vaccination threshold and impact in the 2017-2019 hepatitis A virus outbreak among persons experiencing homelessness or who use drugs in Louisville, Kentucky, United States

Background Between September 2017 and June 2019, an outbreak of hepatitis A virus (HAV) occurred in Louisville, Kentucky, resulting in 501 cases and 6 deaths, predominantly among persons who experience homelessness or who use drugs (PEH/PWUD). The critical vaccination threshold () required to achieve herd immunity in this population is unknown. We investigated and vaccination impact using epidemic modeling. Methods To determine which population subgroups had high infection risks, we employed a technique based on comparing the proportion of cases arising before and after the epidemic peak, across subgroups. We also developed a dynamic deterministic model of HAV transmission among PEH/PWUD to estimate the basic reproduction number (), herd immunity threshold, and the effect of timing of the vaccination intervention on epidemic and economic outcomes. Results Of the 501 confirmed or probable cases, 385 (76.8%) were among PEH/PWUD. Among PEH/PWUD and within the general population, homelessness was a significant risk factor for infection in the initial stages of the outbreak (odds ratios for homeless versus not homeless: 2.62; 95% confidence interval (CI): 1.62–4.25 for PEH/PWUD and 2.39; 95% CI: 1.51–3.78 for all detected cases). Our estimate for R0 ranges between 2.85 and 3.54, corresponding to an estimate of 69% (95% CI: 65–72) for herd immunity threshold and 76% (95% CI: 72%-80%) for , assuming a vaccine with 90% efficacy. The observed vaccination program was estimated to have averted 30 hospitalizations (95% CI: 19–43), associated with over US$490 000 (95% CI: $310 000–700 000) in hospitalization cost. Greater impact was observed with earlier and faster vaccination implementation. Conclusions Vaccination coverage of at least 77% is likely required to prevent outbreaks of HAV among PEH/PWUD in Louisville, assuming a 90% vaccine efficacy. Proactive hepatitis A vaccination programs among PEH/PWUD will maximize health and economic benefits of these programs and reduce the likelihood of another outbreak

StopCOVID cohort: an observational study of 3,480 patients admitted to the Sechenov University hospital network in Moscow city for suspected COVID-19 infection

Background The epidemiology, clinical course, and outcomes of patients with coronavirus disease 2019 (COVID-19) in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically diagnosed COVID-19 in real-life settings is lacking. Methods We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow between 8 April and 28 May 2020. Results Of the 4261 patients hospitalized for suspected COVID-19, outcomes were available for 3480 patients (median age, 56 years; interquartile range, 45–66). The most common comorbidities were hypertension, obesity, chronic cardiovascular disease, and diabetes. Half of the patients (n = 1728) had a positive reverse transcriptase–polymerase chain reaction (RT-PCR), while 1748 had a negative RT-PCR but had clinical symptoms and characteristic computed tomography signs suggestive of COVID-19. No significant differences in frequency of symptoms, laboratory test results, and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR. In a multivariable logistic regression model the following were associated with in-hospital mortality: older age (per 1-year increase; odds ratio, 1.05; 95% confidence interval, 1.03–1.06), male sex (1.71; 1.24–2.37), chronic kidney disease (2.99; 1.89–4.64), diabetes (2.1; 1.46–2.99), chronic cardiovascular disease (1.78; 1.24–2.57), and dementia (2.73; 1.34–5.47). Conclusions Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features was sufficient to diagnose COVID-19 infection, indicating that laboratory testing is not critical in real-life clinical practice

Connecting the Human Variome Project to nutrigenomics

Nutrigenomics is the science of analyzing and understanding gene–nutrient interactions, which because of the genetic heterogeneity, varying degrees of interaction among gene products, and the environmental diversity is a complex science. Although much knowledge of human diversity has been accumulated, estimates suggest that ~90% of genetic variation has not yet been characterized. Identification of the DNA sequence variants that contribute to nutrition-related disease risk is essential for developing a better understanding of the complex causes of disease in humans, including nutrition-related disease. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) is an international effort to systematically identify genes, their mutations, and their variants associated with phenotypic variability and indications of human disease or phenotype. Since nutrigenomic research uses genetic information in the design and analysis of experiments, the HVP is an essential collaborator for ongoing studies of gene–nutrient interactions. With the advent of next generation sequencing methodologies and the understanding of the undiscovered variation in human genomes, the nutrigenomic community will be generating novel sequence data and results. The guidelines and practices of the HVP can guide and harmonize these efforts