bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk
Authors: Polo Gina , Acosta Carlos Mera , Soler-Tovar Diego , Villamil Julián Felipe Porras , Palencia Natalia Polanco , Penagos Marco , Martinez Juan Meza , Bobadilla Juan Nicolás , Martin Laura Victoria , Durán Sandra , Rodriguez Martha , Carvajalino Carlos Meza , Villamil Luis Carlos , Ortiz Efrain Benavides 
Published: August 2020

AbstractThe ongoing outbreak of COVID-19 challenges health systems and epidemiological responses of all countries worldwide. Although mitigation measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we evidenced that factors contributing to poverty are also risk factors for COVID-19 case-fatality, and unexpectedly, their impact on the case-fatality risk is comparable to that produced by health factors. Additionally, we confirm that both case-fatality risk and multidimensional poverty index have a heterogeneous spatial distribution, where the lastest consists of health, educational, dwelling, and employment dimensions. Spatio-temporal analysis reveals that the spatial heterogeneity in case-fatalities is associated with the percentage contribution of the health (RR 1.89 95%CI=1.43-2.48) and dwelling (RR 2.01 95%CI=1.37-2.63) dimensions to the multidimensional poverty, but also with the educational (RR 1.21 95%CI=1.03-1.49), and employment (RR 1.23 95%CI=1.02-1.47) dimensions. This spatial correlation indicates that the case-fatality risk increase by 189% and 201% in regions with a higher contribution of the health dimension (i.e., lack of health insurance and self-reporting) and dwelling dimension (i.e., lack of access to safe water, inadequate disposal of human feces, poor housing construction, and critical overcrowding), respectively. Furthermore, although a temporal decrease is evident, the relative risk of dying by COVID-19 in Colombia is still 200% higher than the established case-fatality risk based on the COVID-19 dynamics in Italy and China. These findings assist policy-makers in the spatial and temporal planning of strategies focused on mitigating the case-fatality risk in most vulnerable communities and preparing for future pandemics by progressively reducing the factors that generate health inequality.
Development of prediction model for trauma assessment using electronic medical records
Authors: Ogura Kentaro , Goto Tadahiro , Shirakawa Toru , Sonoo Tomohiro , Nakano Hidehiko , Nakamura Kensuke 
Published: August 2020

AbstractAbbreviated Injury Score (AIS) and Injury Severity Score (ISS) scores are used to measure the severity of trauma patients in the emergency department, but they have several problems such as a calculation complexity. In this study, we developed a mortality prediction model of trauma patients using the data from electronic medical records and compared it with a model using AIS/ISS scores. This is a prognostic study using the data of patients who were admitted to Hitachi General Hospital Emergency and Critical Care Center from April 2018 to March 2019. The features were age, sex, vital signs, and clinical diagnoses, and the outcome was in-hospital death. Of 337 eligible patients, 11 died during the hospitalization. The predictive performance of our model was comparable to that of the AIS/ISS scores model (AUC 0.912 vs 0.961). Clinical diagnoses were important in predicting the mortality rate. Our study suggests that a trauma severity index calculated by the predicting model using information from electronic medical records might replace AIS/ISS score.
A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States
Authors: Güemes Amparo , Ray Soumyajit , Aboumerhi Khaled , Desjardins Michael R. , Kvit Anton , Corrigan Anne E , Fries Brendan , Shields Timothy , Stevens Robert D , Curriero Frank C , Etienne-Cummings Ralph 
Published: August 2020

AbstractCoronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
Time-use and mental health during the COVID-19 pandemic: a panel analysis of 55,204 adults followed across 11 weeks of lockdown in the UK
Authors: Bu Feifei , Steptoe Andrew , Mak Hei Wan , Fancourt Daisy 
Published: August 2020

AbstractThere is currently major concern about the impact of the global COVID-19 outbreak on mental health. But it remains unclear how individual behaviors could exacerbate or protect against adverse changes in mental health. This study aimed to examine the associations between specific activities (or time-use) and mental health and wellbeing amongst people during the Covid-19 pandemic. Data were from the UCL COVID-19 Social Study; a panel study collecting data weekly during the COVID-19 pandemic. The analytical sample consisted of 55,204 adults living in the UK who were followed up for the strict 11-week lockdown period from 21st March to 31st May 2020. Data were analyzed using fixed-effects and Arellano–Bond models. We found that changes in time spent on a range of activities were associated with changes in mental health and wellbeing. After controlling for bidirectionality, behaviors involving outdoor activities including gardening and exercising predicted subsequent improvements in mental health and wellbeing, while increased time spent on following news about COVID-19 predicted declines in mental health and wellbeing. These results are relevant to the formulation of guidance for people obliged to spend extended periods in isolation during health emergencies, and may help the public to maintain wellbeing during future pandemics.
Comprehensive Surveillance of SARS-CoV-2 Spread Using Wastewater-based Epidemiology Studies
Authors: Hemalatha Manupati , Kiran Uday , Kuncha Santosh Kumar , Kopperi Harishankar , Gokulan C. G. , Mohan S Venkata , Mishra Rakesh K 
Published: August 2020

AbstractSARS-CoV-2 pandemic is having a devastating effect on human lives. Individuals who are symptomatic/asymptomatic or have recovered are reported to have/will have serious health complications in the future, which is going to be huge economic burden globally. Given the wide-spread transmission of SARS-CoV-2 it is almost impossible to test each and every individual for the same and isolate them. Recent reports have shown that sewage can be used as a holistic approach to estimate the epidemiology of the virus. Here we have estimated the spread of SARS-CoV-2 in the city of Hyderabad, India which is populated with nearly 10 million people. The sewage samples were collected from all the major sewage treatment plants (STPs) and were processed for detecting the viral genome using the standard RT-PCR method. Based on the average viral particle shedding per individual, the total number of individuals exposed to SARS-CoV-2 (in a window of 35 days) is about 6.6% of the population, which clearly indicates the rate of community transmission and asymptomatic carriers is higher than the number of reported cases. It is important to note here that the samples collected from the inlet of STPs were positive for SARS-CoV-2, while the outlets were negative indicating the efficient treatment of sewage at STPs. These studies are going to be essential to manage the pandemic better and also to assess the effectiveness of control measure.
Absence of relevant QT interval prolongation in not critically ill COVID-19 patients
Authors: Jiménez-Jáimez Juan , Macías-Ruiz Rosa , Bermúdez-Jiménez Francisco , Rubini-Costa Ricardo , Taboada Jessica Ramírez , Flores Paula Isabel García , Padilla Laura Gallo , García Juan Diego Mediavilla , García Concepción Morales , Suárez Sara Moreno , Molina Celia Fignani , Álvarez López Miguel , Tercedor Luis 
Published: August 2020

ABSTRACTObjectivesSARS-CoV-2 is a rapidly evolving pandemic causing great morbimortality. Medical therapy with hydroxicloroquine, azitromycin and protease inhibitors is being empirically used, with reported data of QTc interval prolongation. Our aim is to assess QT interval behaviour in a not critically ill and not monitored cohort of patients.DesignWe evaluated admitted and ambulatory patients with COVID-19 patients with 12 lead electrocardiogram at 48 hours after treatment initiation. Other clinical and analytical variables were collected. Statistical analysis was performed to assess the magnitude of the QT interval prolongation under treatment and to identify clinical, analytical and electrocardiographic risk markers of QT prolongation independent predictors.ResultsWe included 219 patients (mean age of 63.6 ± 17.4 years, 48.9% were women and 16.4% were outpatients. The median baseline QTc was 416 ms (IQR 404–433), and after treatment QTc was prolonged to 423 ms (405–438) (P < 0.001), with an average increase of 1.8%. Most of the patients presented a normal QTc under treatment, with only 31 cases (14,1 %) showing a QTc interval > 460 ms, and just one case with QTc > 500 ms. Advanced age, longer QTc basal at the basal ECG and lower potassium levels were independent predictors of QTc interval prolongation.ConclusionsAmbulatory and not critically ill patients with COVID-19 treated with hydroxychloroquine, azithromycin and/or antiretrovirals develop a significant, but not relevant, QT interval prolongation.What is already known about this subject?Treatment for COVID 19 with empirical drugs hydroxicloroquine, azitromycin and protease inhibitors affects ventricular repolarization by prolonging the QT interval in a variable degree basing upon heterogeneous studiesWhat does this study add?Not critically ill patients with COVID 19 develop a non relevant QT interval prolongation, leading to a modest arrhythmic risk increase, overall in patients with normal basal ECGHow might this impact on clinical practice?Our findings are significant as ECG monitoring of COVID 19 patients is costly and difficult. Ambulatory and stable patients with normal basal ECG might nor benefit from QT assessment afeter treatment initiation
Comparison Between Influenza and COVID-19 at a Tertiary Care Center
Authors: Donnino Michael W. , Moskowitz Ari , Thompson Garrett S. , Heydrick Stanley J. , Pawar Rahul D , Berg Katherine M. , Mehta Shivani , Patel Parth V. , Grossestreuer Anne V. 
Published: August 2020

ABSTRACTBackgroundWidespread reports suggest the characteristics and disease course of coronavirus disease 2019 (COVID-19) and influenza differ, yet detailed comparisons of their clinical manifestations are lacking.ObjectiveComparison of the epidemiology and clinical characteristics of COVID-19 patients with those of influenza patients in previous seasons at the same hospitalDesignAdmission rates, clinical measurements, and clinical outcomes from confirmed COVID-19 cases between March 1 and April 30, 2020 were compared with those from confirmed influenza cases in the previous five influenza seasons (8 months each) beginning September 1, 2014.SettingLarge tertiary care teaching hospital in Boston, MassachusettsParticipantsLaboratory-confirmed COVID-19 and influenza inpatientsMeasurementsPatient demographics and medical history, mortality, incidence and duration of mechanical ventilation, incidences of vasopressor support and renal replacement therapy, hospital and intensive care admissions.ResultsData was abstracted from medical records of 1052 influenza patients and 583 COVID-19 patients. An average of 210 hospital admissions for influenza occurred per 8-month season compared to 583 COVID-19 admissions over two months. The median weekly number of COVID-19 patients requiring mechanical ventilation was 17 (IQR: 4, 34) compared to a weekly median of 1 (IQR: 0, 2) influenza patient (p=0.001). COVID-19 patients were significantly more likely to require mechanical ventilation (31% vs 8%), and had significantly higher mortality (20% vs. 3%; p<0.001 for all). Relatively more COVID-19 patients on mechanical ventilation lacked pre-existing conditions compared with mechanically ventilated influenza patients (25% vs 4%, p<0.001).LimitationThis is a single-center study which could limit generalization.ConclusionCOVID-19 resulted in more hospitalizations, higher morbidity, and higher mortality than influenza at the same hospital.
International Randomised Controlled Trial evaluating metabolic syndrome in type 2 Diabetic Cigarette Smokers following switching to Combustion-Free Nicotine Delivery Systems: the DIASMOKE protocol
Authors: Krysiński Arkadiusz , Russo Cristina , John Sarah , Belsey Jonathan , Campagna Davide , Caponnetto Pasquale , Vudu Lorina , Wei Lim Chong , Purrello Francesco , Di Mauro Maurizio , Iqbal Farrukh , Fluck David , Franek Edward , Polosa Riccardo , Sharma Pankaj 
Published: August 2020

ABSTRACTReducing exposure to cigarette smoke is an imperative for public health, and even more so for diabetic patients. Increasingly, combustion-free technologies for nicotine delivery such as e-cigarettes and heated tobacco products are substituting conventional cigarettes and accelerating the current downward trends in smoking prevalence. However, there is limited information about the long-term health impact in diabetics who use these technologies.This international randomised controlled trial of type 2 diabetic cigarette smokers will test the hypothesis that following a switch from conventional cigarettes to Combustion-Free Nicotine Delivery Systems (C-F NDS), a measurable improvement in metabolic syndrome (MetS) risk factors and functional parameters will be shown over the course of 2 years.A total of 576 patients will be randomized (1:2 ratio) to either a control arm (Study Arm A), in which they will be offered referral to smoking cessation programs or to an intervention arm (Study Arm B), in which they will be assigned to C-F NDS use. The primary outcome will be the prevalence of the a difference in MetS score between baseline and 2 years follow-up, with comparison being made between diabetic patients randomized to each arm of the study. Patient recruitment will start in October 2020 and enrolment is expected to be completed by August 2021.This will be the first study determining the overall health impact of using such technologies in diabetic patients. Data from this study will provide valuable insights into the overall potential of C-F NDS to reduce the risk of cardiovascular disease in individuals, particularly diabetic patients.Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04231838
This time is different: model-based evaluation of the implications of SARS-CoV-2 infection kinetics for disease control
Authors: Johnson Kaitlyn E. , Stoddard Madison , Nolan Ryan P. , White Douglas E. , Hochberg Natasha , Chakravarty Arijit 
Published: August 2020

AbstractAs the ongoing COVID-19 pandemic passes from an acute to a chronic situation, countries and territories are grappling with the issue of how to reopen safely. The unique kinetics of infectivity of SARS-CoV-2, with its significant presymptomatic transmission, presents an unprecedented challenge to our intuitions. In this context, a generalizable quantitative understanding of the impact of SARS-CoV-2 infectivity on disease control strategies is vital. We used a previously published time-dependent model of SARS-CoV-2 infectivity (Heet al., 2020) to parameterize an epidemiological model of transmission, which was then used to explore the effect of various disease control measures. Our analysis suggests that using symptom-based isolation alone as a control strategy is ineffective in limiting the spread of COVID-19, in contrast to its effectiveness in other diseases, such as SARS and influenza. Additionally, timeliness of testing and tracing strategies to reduce time to isolation, along with widespread adoption of measures to limit transmission are critical for any containment strategy. Our findings suggest that for symptom-based isolation and testing strategies to be effective, reduced transmission is required, reinforcing the importance of measures to limit transmission. From a public health strategy perspective, our findings lend support to the idea that symptomatic isolation should not form the primary basis for COVID-19 disease control.
Impact of Bilateral Transmuscular Quadratus Lumborum Block Using Different Doses of Dexmedetomidine as an Adjuvant Combined with Ropivacaine for Postoperative Analgesia in Laparoscopic Myomectomy
Authors: Li Yue , Zhang Le , Jiao Jing , Yu Xinhua , Huang Shaoqiang 
Published: August 2020

AbstractIntroductionThe optimal dosage of dexmedetomidine (DEX) as an adjuvant combined with ropivacaine for postoperative analgesia in laparoscopic myomectomy is still controversial. The main aim of this prospective clinical trial was to evaluate the analgesic effect and relevant adverse effects of different doses of dexmedetomidine applied locally for ropivacaine-induced bilateral transmuscular quadratus lumborum block (TQLB).MethodsTQLB was conducted using different doses of dexmedetomidine per side (R group: control group; DEX1 group: 0.1μg/kg; DEX2 group: 0.3μg/kg; DEX3 group: 0.5μg/kg). Numeric rating scales (NRS) of pain score, heart rate (HR) and blood pressure (BP) were assessed at different time points after performing TQLB. Dosage of additional analgesics via patient-controlled analgesia pump, recovery time for anal exsufflation, hospital stay and clinical events such as nausea, vomiting and pruritus were also compared among groups.ResultsA total of 200 patients underwent laparoscopic myomectomy were enrolled in this study and divided into 4 groups (R, DEX1, DEX2 and DEX3 group) randomly, 50 for each group. Compared with R group, NRS pain score was significantly lower in DEX2 group and DEX3group (p<0.001) 10 minutes after TQLB. Twenty minutes after TQLB, statistical significance of NRS pain score appeared between DEX1 group and R group (p<0.05) and between DEX2, 3 group and DEX1 group (p<0.001). In comparison to R group and DEX1 group, NRS pain score in DEX2 and DEX3 group were lower within 24 hours postoperatively (p<0.05). HR in DEX2 group and DEX3 group was significantly lower than DEX1 group and R group (p<0.05). 2 patients in DEX2 and DEX3 group were developed severe low HR of which HR was below 50bpm. Even 6 hours after surgery, patients in DEX3 group still remained severe low HR. Patients in DEX2 group and DEX3 group needed fewer PCA bolus deliveries than patients in R group and DEX1 group (p<0.001). Satisfaction score of postoperative analgesia was higher in DEX2 group and DEX3 group in comparison to DEX1 group and R group (p<0.001).group and DEX3 group in comparison to DEX1 group and R group (p<0.001).Conclusion0.3μg/kg per side of dexmedetomidine as an adjuvant for TQLB was recommended to effectively relieve postoperative pain after laparoscopic myomectomy.Trial registrationChinese Clinical Trial Registry with registration number ChiCTR1900028509. Date of registration: 24 November 2019. http://www.chictr.org.cn/edit.aspx?pid=42635=htm=4
Testing for coronavirus (SARS-CoV-2) infection in populations with low infection prevalence: the largely ignored problem of false positives and the value of repeat testing
Authors: Sudlow Cathie , Diggle Peter , Warlow Oliver , Seymour David , Gordon Ben , Walker Rhos , Warlow Charles 
Published: August 2020

ABSTRACTBackgroundCalls are increasing for widespread SARS-CoV-2 infection testing of people from populations with a very low prevalence of infection. We quantified the impact of less than perfect diagnostic test accuracy on populations, and on individuals, in low prevalence settings, focusing on false positives and the role of confirmatory testing.MethodsWe developed a simple, interactive tool to assess the impact of different combinations of test sensitivity, specificity and infection prevalence in a notional population of 100,000. We derived numbers of true positives, true negatives, false positives and false negatives, positive predictive value (PPV – the percentage of test positives that are true positives) and overall test accuracy for three testing strategies: (1) single test for all; (2) add repeat testing in test positives; (3) add further repeat testing in those with discrepant results. We also assessed the impact on test results for individuals having one, two or three tests under these three strategies.ResultsWith sensitivity of 80%, infection prevalence of 1 in 2,000, and specificity 99.9% on all tests, PPV in the tested population of 100,000 will be only 29% with one test, increasing to > 99.5% (100% when rounded to the nearest %) with repeat testing in strategies 2 or 3. More realistically, if specificity is 95% for the first and 99.9% for subsequent tests, single test PPV will be only 1%, increasing to 86% with repeat testing in strategy 2, or 79% with strategy 3 (albeit with 6 fewer false negatives than strategy 2). In the whole population, or in particular individuals, PPV increases as infection becomes more common in the population but falls to unacceptably low levels with lower test specificity.ConclusionTo avoid multiple unnecessary restrictions on whole populations, and in particular individuals, from widespread population testing for SARS-CoV-2, the crucial roles of extremely high test specificity and of confirmatory testing must be fully appreciated and incorporated into policy decisions.
A Case Study of Transfer of Lesion-Knowledge
Authors: Krishnan Soundarya , Khincha Rishab , Vig Lovekesh , Dash Tirtharaj , Srinivasan Ashwin 
Published: August 2020

AbstractAll organs in the human body are susceptible to cancer, and we now have a growing store of images of lesions in different parts of the body. This, along with the acknowledged ability of neural-network methods to analyse image data, would suggest that accurate models for lesions can now be constructed by a deep neural network. However an important difficulty arises from the lack of annotated images from various parts of the body. Our proposed approach to address the issue of scarce training data for a target organ is to apply a form of transfer learning: that is, to adapt a model constructed for one organ to another for which there are minimal or no annotations. After consultation with medical specialists, we note that there are several discriminating visual features between malignant and benign lesions that occur consistently across organs. Therefore, in principle, these features boost the case for transfer learning on lesion images across organs. However, this has never been previously investigated. In this paper, we investigate whether lesion knowledge can be transferred across organs. Specifically, as a case study, we examine the transfer of a lesion model from the brain to lungs and lungs to the brain. We evaluate the efficacy of transfer of a brain-lesion model to the lung, and the transfer of a lung-lesion model to the brain by comparing against a model constructed: (a) without model-transfer (i.e.random weights); and (b) using model-transfer from a lesion-agnostic dataset (ImageNet). In all cases, our lesion models perform substantially better. These results point to the potential utility of transferring lesionknowledge across organs other than those considered here.