bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimates of Single Dose and Full Dose BNT162b2 Vaccine Effectiveness among USAF Academy cadets, 1 Mar - 1 May 2021
Authors: Wickert Douglas P. , Almand Erin A. , Baldovich Kevin J. , Cullenbine Christopher A. , Huckstep Odaro J. , Rohrer Joseph W. , Sitko John C. , Steel J. Jordan , Hasstedt Steven C.M. 
Published: July 2021

AbstractBeginning in early March 2021 and continuing through May 2021, the USAF Academy began vaccinating cadets for protection against the SARS-CoV-2 virus with the BNT162b2 (Pfizer-BioNTech) mRNA vaccine. During this period, vaccination of the almost 4200 cadet population increased from 3% to 85% and prevalence of COVID-19 in the cadet population was constant at approximately 0.4% as indicated by weekly surveillance testing. In this study, vaccine effectiveness at preventing infection is estimated by comparing infection risk as a function of time since vaccination. A statistically significant four-fold reduction in infection risk was observed 14 days after the first vaccine dose and an eleven-fold reduction in infection risk was observed in fully vaccinated cadets. Overall, the Pfizer-BioNTech vaccine was 91% (95% confidence interval = 55-99%) effective at preventing infection in healthy young adults (17-26 years of age) in a university setting and military training environment.
Machine Learning Methods to Predict Survival in Patients Following Traumatic Aortic Injury
Authors: Shiban Nisreen , Gaul Joshua , Zhan Henry , Elhabr Andrew , Kokabi Nima , Johnson Jamlik-Omari , Hanna Tarek , Schrager Justin , Gichoya Judy , Banerjee Imon , Trivedi Hari 
Published: July 2021

AbstractThe National Trauma Data Bank (NTDB) is a resource of diagnostic, treatment, and outcomes information in trauma patients. We leverage the NTDB and machine learning techniques to predict survival following traumatic aortic injury. We create two predictive models using the NTDB – 1) using all data and, 2) using only data available in the first hour after arrival (prospective data). Seven discriminative model types were tested before and after feature engineering to reduce dimensionality. The top performing model was XGBoost, achieving an overall accuracy of 0.893 using all data and 0.855 using prospective data. Feature engineering improved performance of all models. Glasgow Coma Scale score was the most important factor for survival, and thoracic endovascular aortic repair was more common in patients that survived. Smoking, pneumonia, and urinary tract infection predicted poor survival. We also note concerning disparities in outcomes for black and uninsured patients that may reflect differences in care.
Assessing optimal time between doses in two-dose vaccination regimen in an ongoing epidemic of SARS-CoV-2
Authors: Ferreira Leonardo Souto , Canton Otavio , Paixão da Silva Rafael Lopes , Poloni Silas , Sudbrack Vítor , Borges Marcelo Eduardo , Franco Caroline , Darcie Marquitti Flávia Maria , de Moraes José Cássio , de Sousa Mascena Veras Maria Amélia , André Kraenkel Roberto , Coutinho Renato Mendes 
Published: July 2021

AbstractThe SARS-CoV-2 pandemic is a major concern all over the world and, as vaccines became available at the end of 2020, optimal vaccination strategies were subjected to intense investigation. Considering their critical role in reducing disease burden, the increasing demand outpacing production, and that most currently approved vaccines follow a two-dose regimen, the cost-effectiveness of delaying the second dose to increment the coverage of the population receiving the first dose is often debated. Finding the best solution is complex due to the trade-off between vaccinating more people with lower level of protection and guaranteeing higher protection to a fewer number of individuals.Here we present a novel extended age-structured SEIR mathematical model that includes a two-dose vaccination schedule with a between-doses delay modelled through delay differential equations and linear optimization of vaccination rates. Simulations for each time window and for different types of vaccines and production rates were run to find the optimal time window between doses, that is, the one that minimizes the number of deaths.We found that the best strategy depends on an interplay between the vaccine production rate and the relative efficacy of the first dose. In the scenario of low first-dose efficacy, it is always better to apply the second dose as soon as possible, while for high first-dose efficacy, the optimal window depends on the production rate and also on second-dose efficacy provided by each type of vaccine. We also found that the rate of spread of the infection does not affect significantly the thresholds of the optimal window, but is an important factor in the absolute number of total deaths. These conclusions point to the need to carefully take into account both vaccine characteristics and roll-out speed to optimize the outcome of vaccination strategies.
Effects of Various Policy Options on COVID-19 Cases in Nova Scotia including Vaccination Rollout Schedule: A Modelling Study
Authors: Gillis Melissa , Saif Ahmed , Murphy Matthew , Kamal Noreen 
Published: July 2021

ABSTRACTBackgroundThe COVID-19 pandemic presents a significant challenge to minimize mortality and hospitalizations due to this disease. Vaccinations have begun to roll-out; however, restriction policies required during and after the rollout remain uncertain. A susceptible-exposed-infected-recovered (SEIR) model was developed for Nova Scotia, and it accounted for the province’s policy interventions, demographics, and vaccine rollout schedule.MethodsA modified SEIR model was developed to simulate the spread and outcomes from COVID-19 in Nova Scotia under different policy options. The model incorporated the age distribution and co-morbidity of the province. A system dynamics model was developed in Vensim. Several scenarios were run to determine the effects of various policy options and loosening of restrictions during and after the vaccine roll-out period.ResultsWhen restrictions policy include moderate closure of businesses, restricting travel to Atlantic Canada, and the mandating of masks and physical distancing, the number of cumulative infections after 110 days was less than 120. However, if national travel was opened by July 5 2021 and there were no restrictions by September 2021, the number of active infections will peak at 6,114 by February 16 2022, and there will be a peak of 104 hospitalizations on February 16 2022. Immediate opening of travel and all restrictions on March 15, 2021 will result in 71,731 active infections by June 4 2021.DiscussionModerate restrictions will be required even after the population is fully vaccinated in order to avoid a large number of infections and hospitalizations because herd immunity is not reached due to children under 12 not being vaccinated, the efficacy of the vaccine, and the portion of the population that will choose not to be vaccinated.
Effects of SARS-CoV-2 P.1 introduction and the impact of COVID-19 vaccination on the epidemiological landscape of São José Do Rio Preto, Brazil
Authors: Banho Cecília Artico , Sacchetto Lívia , Campos Guilherme Rodrigues Fernandes , Bittar Cíntia , Possebon Fábio Sossai , Ullmann Leila Sabrina , Marques Beatriz de Carvalho , da Silva Gislaine Ceslestino Dutra , Moraes Marília Mazzi , Parra Maisa Carla Pereira , Negri Andreia Francesli , Boldrin Ana Carolina , Barcelos Michela Dias , dos Santos Thayza M. I. L. , Milhim Bruno H. G. A. , da Rocha Leonardo Cecílio , Dourado Fernanda Simões , dos Santos Andresa Lopes , Ciconi Victoria Bernardi , Patuto Caio , Versiani Alice Freitas , da Silva Rafael Alves , Lobl Edoardo Estevam de Oliveira , Hernandes Victor Miranda , Zini Nathalia , Pacca Carolina Colombelli , Estofolete Cássia Fernanda , Ferreira Helena Lage , Rahal Paula , Araújo João Pessoa , Cohen Jamie A. , Kerr Cliff C. , Althouse Benjamin M. , Vasilakis Nikos , Nogueira Mauricio Lacerda 
Published: July 2021

AbstractThe emergence of the new Brazilian variant of concern, P.1 lineage (Gamma), raised concern about its impact on the epidemiological profile of COVID-19 cases due to its higher transmissibility rate and immune evasion ability. Using 272 whole-genome sequences combined with epidemiological data, we showed that P.1 introduction in São José do Rio Preto, São Paulo, Brazil, was followed by the displacement of eight circulating SARS-CoV-2 variants and a rapid increase in prevalence two months after its first detection. Our findings support that the P.1 variant is associated with an increase in mortality risk and severity of COVID-19 cases in younger aged groups, which corresponds to the unvaccinated population at the time. Moreover, our data highlight the beneficial effects of vaccination indicated by a pronounced reduction of severe cases and deaths in immunized individuals, reinforcing the need for rapid and massive vaccination.
Estimation of the onset ratio and the number of asymptomatic patients of COVID-19 from the proportion of untraceable patients
Authors: Odagaki Takashi 
Published: July 2021

AbstractA simple method is devised to estimate the onset ratio of COVID-19 patients from the proportion of untraceable patients tested positive, which allows us to obtain the number of asymptomatic patients, the number of infectious patients and the effective reproduction number. The recent data in Tokyo indicate that there are about six to ten times as many infectious patients in the city as the daily confirmed new cases. It is shown that, besides social distancing and use of effective masks, a quarantine measure on non-symptomatic patients is critically important in controlling the pandemic.
Robust image-based risk predictions from the deep learning of lung tumors in motion
Authors: Bajaj Amishi , Teo P. Troy , Randall James , Lou Bin , Shah Jainil , Gopalakrishnan Mahesh , Kamen Ali , Abazeed Mohamed E. 
Published: July 2021

ABSTRACTIntroductionDeep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e. degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT).MethodsAn IRB-approved study (NU00212113) was used to identify patients treated with lung SBRT. Pre-therapy free breathing (FB) CT images from 849 patients were input into a multi-task deep neural network to generate an image fingerprint (or DL score) that predicts time-to-event local failure outcomes. The impact of tumor motion was examined on the DL scores for a hold-out set comprising 228 image series from 19 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images.FindingsVariance in image-derived DL scores were associated with tumor motion (R2 = 0.45). Specifically, DL score variance was deterministic with undulations in phase with the respiratory cycle (R2 = 0.77-0.99). DL scores, but not tumor volumes, peaked near end-exhalation (∼50% phase). The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r = 0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p < 0.0001).InterpretationWe show that an image-based DL risk score derived from a series of four-dimensional CT images varies in a deterministic, sinusoidal trajectory in phase with the respiratory cycle. These results indicate that deep learning models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.
Predictors of COVID-19 vaccination uptake and reasons for decline of vaccination: a systematic review
Authors: Galanis Petros , Vraka Irene , Siskou Olga , Konstantakopoulou Olympia , Katsiroumpa Aglaia , Kaitelidou Daphne 
Published: July 2021

AbstractBackgroundVarious COVID-19 vaccines with proven safety and effectiveness are available now but vaccine hesitancy remains a public threat. COVID-19 vaccines uptake appears to have an essential role in the successful control of the COVID-19 pandemic.ObjectiveTo examine predictors of COVID-19 vaccination uptake and reasons for decline of vaccination.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines for this systematic review. We searched Medline, PubMed, Web of Science, Scopus, ProQuest, CINAHL, and a pre-print service (medRxiv) from inception to July 12, 2021. We used the following key-words: vaccin*, COVID-19, and uptake. We included all types of studies (quantitative, qualitative, and mixed methods) reporting COVID-19 vaccination uptake. The review protocol was registered with PROSPERO (CRD42021267460).ResultsTwelve studies met the inclusion and exclusion criteria. COVID-19 vaccination uptake ranged from 28.6% to 98% in the general population, while among healthcare workers ranged from 33.3% to 94.5%, and among patients ranged from 36% to 80%. The main predictors of COVID-19 vaccination uptake were male gender, white race, older age, higher socioeconomic status, higher self-perceived COVID-19 vulnerability, increased information about COVID-19 vaccines, and chronic illness. The most important reasons for decline of vaccination were concerns about the safety and effectiveness of vaccines, illness, medication, pregnancy, fertility, breastfeeding, religious reasons, ethical reasons, previous COVID-19 diagnosis, self-estimation that COVID-19 is not a severe disease, and limited knowledge about the vaccines.ConclusionsSeveral factors affect COVID-19 vaccination uptake, while various reasons affect people’s decision to refuse to take a COVID-19 vaccine. These findings are essential to further enhance our understanding of COVID-19 vaccination uptake and design specific interventions. Given the high prevalence of COVID-19 vaccine hesitancy, our findings have major implications for the delivery of COVID-19 vaccination programmes in the public with special attention to people who are undecided or unlikely to take a COVID-19 vaccine.
Motivators and barriers to research participation for individuals with cerebral palsy and their families
Authors: Zvolanek Kristina M. , Goyal Vatsala , Hruby Alexandra , Ingo Carson , Sukal-Moulton Theresa 
Published: July 2021

AbstractObjective(s)Our objective was to investigate the motivators and barriers associated with the individual or family decision to participate in cerebral palsy research. Based on this information, we offer suggestions to increase the likelihood of participation in future CP studies.MethodsA digital survey was administered to stakeholders affected by cerebral palsy across the US. Our analysis focused on variables related to personal interests, travel, and study-specific elements. Statistical tests investigated the effects of responder type, cerebral palsy type, and Gross Motor Function Classification System level on travel and study-specific element variables. Recommendations were informed by responses reflecting the majority of respondents.ResultsBased on 233 responses, we found that respondents highly valued research participation (on average 88.2/100) and compensation (on average 62.3/100). Motivators included the potential for direct benefit (62.2%) and helping others (53.4%). The primary barriers to participation were schedule limitations (48.9%) and travel logistics (32.6%). Schedule limitations were especially pertinent to caregivers, while individuals with more severe cerebral palsy diagnoses reported the necessity of additional items to comfortably travel.ConclusionsOverall, we encourage the involvement of stakeholders affected by cerebral palsy in the research process. Researchers should consider offering flexible study times, accommodating locations, and compensation for time and travel expenses. We recommend a minimum compensation of $15/hour and a maximum time commitment of 4 hours/day to respect participants’ time and increase likelihood of research participation. Future studies should track how attitudes toward research change with time and experience.
Emerging and Continuing Trends in Opioid Overdose Decedent Characteristics during COVID-19
Authors: Garcia Gian-Gabriel P. , Stringfellow Erin J. , DiGennaro Catherine , Poellinger Nicole , Wood Jaden , Wakeman Sarah , Jalali Mohammad S. 
Published: July 2021

AbstractBackgroundSince COVID-19 erupted in the United States, little is known about how state-level opioid overdose trends and decedent characteristics have varied throughout the country.ObjectiveInvestigate changes in annual overdose death rates, substances involved, and decedent demographics in opioid overdose deaths across nine states; assess whether 2019-2020 trends were emerging (i.e., change from 2019-2020 was non-existent from 2018-2019) or continuing (i.e., change from 2019-2020 existed from 2018-2019).DesignCross-sectional study using vital statistics data to conduct a retrospective analysis comparing 2020 to 2019 and 2019 to 2018 across nine states.SettingAlaska, Colorado, Connecticut, Indiana, Massachusetts, North Carolina, Rhode Island, Utah, and Wyoming.ParticipantsOpioid-related overdose deaths in 2018, 2019, and 2020.MeasurementsAnnual overdose death rate, proportion of overdose deaths involving specific substances, and decedent demographics (age, sex, race, and ethnicity).ResultsWe find emerging increases in annual opioid-related overdose death rates in Alaska (55.3% [P=0.020]), Colorado (80.2% [P<0.001]), Indiana (40.1% [P=0.038]), North Carolina (30.5% [P<0.001]), and Rhode Island (29.6% [P=0.011]). Decreased heroin-involved overdose deaths were emerging in Alaska (−49.5% [P=0.001]) and Indiana (−58.8% [P<0.001]), and continuing in Colorado (−33.3% [P<0.001]), Connecticut (−48.2% [P<0.001]), Massachusetts (39.9% [P<0.001]), and North Carolina (−34.8% [P<0.001]). Increases in synthetic opioid presence were emerging in Alaska (136.5% [P=0.019]) and Indiana (27.6% [P<0.001]), and continuing in Colorado (44.4% [P<0.001]), Connecticut (3.6% [P<0.05]), and North Carolina (14.6% [P<0.001]). We find emerging increases in the proportion of male decedents in Colorado (15.2% [P=0.008]) and Indiana (12.0% [P=0.013]).LimitationsDelays from state-specific death certification processes resulted in varying analysis periods across states.ConclusionThese findings highlight emerging changes in opioid overdose dynamics across different states, which can inform state-specific public health interventions.
Clinical Determinants and Predictors for Prognosis of SARS-CoV-2 Infected Pediatric Patients in Saudi Arabia
Authors: Adam Khalid Mohamed , Elamin Bahaeldin K. , Alfaifi Jaber A. , Abbas Mohammed 
Published: July 2021

AbstractBackgroundThe significant variations in clinical characteristics and outcomes of COVID-19 that range from asymptomatic to severe fatal illness entail searching for potential prognostic determinants to help predict the disease course and early detection of patients at risk of developing life-threatening complications. Although children are less commonly infected by SARS-CoV-2 than their adult counterparts, and their symptoms are generally milder, a severe type of COVID-19 cannot be precluded.MethodsAt first, demographic, clinical, laboratory measurement data, and outcomes for 26 COVID-19 infected children of less than 12 years of age, admitted to King Abdallah Hospital, Bisha, Saudi Arabia, were retrieved from the electronic medical records for the observational retrospective study.Later, electronic and manual database searches were carried out for pediatric severe COVID-19-related articles. The relevant data from 20 eligible studies and the present retrospective study were analyzed to assess the association of demographic characteristics and comorbidities with COVID-19 severity.ResultsIn the retrospective study, 5 (19%) of the children presented with severe symptoms admitted to PICU, 18 (69%) presented with cough, 5 (19%) with diarrhea, 7 (27%) with underlying comorbidities, 4 (15%) with respiratory illnesses, 3 (12%) with cardiovascular diseases and 2 (8%) were obese. None of the patient characteristics showed any significant association with COVID-19 severity.Of the 21 studies selected for meta-analyses, 14 studies were included in the analysis of the association between any comorbidity and disease severity, resulting in OR: 2.69, 95%CI: 1.38 – 5.26, P < 0.05, for analysis of the association between cardiovascular comorbidities and disease severity 14 studies were included giving OR: 4.06, 95%CI: 1.86 – 8.87, P < 0.05, for analysis of the association between respiratory comorbidity and disease severity 15 studies were included giving OR: 2.05, 95%CI: 1.54 – 2.74, P < 0.05, for analysis of the association between obesity and disease severity 10 studies were included, giving OR: 2.48, 95%CI: 1.16 – 5.32, P < 0.05, for analysis of the association between age <10 years old and diseases severity, 16 studies were included, giving OR: 0.80, 95%CI: 0.65 – 0.97, P < 0.05, and for analysis of the association between female gender and disease severity, 19 studies were included, giving OR: 0.83, 95%CI: 0.59 – 1.18, P > 0.05.ConclusionIt can be concluded that COVID-19 pediatric patients with underlying comorbidities, being cardiovascular, respiratory, or obesity, are at high risk of developing severe illness, and young age has a protective role against the disease severity.
Variations in Non-Pharmaceutical Interventions by State Correlate with COVID-19 Disease Outcomes
Authors: Avery Annika J. , Wang Jiayi , Ma Xinyu , Pan Qingkai , McGrady Elizabeth E. , Yuan Zongyuan , Liang Yuqing , Nugent Rebecca , Lakdawala Seema S. 
Published: July 2021

AbstractThe COVID-19 pandemic highlighted the lack of understanding around effective public health interventions to curtail the spread of an emerging respiratory virus. Here, we examined the public health approaches implemented by each state to limit the spread and burden of COVID-19. Our analysis revealed that stronger statewide interventions positively correlated with fewer COVID-19 deaths, but some neighboring states with distinct intervention strategies had similar SARS-CoV-2 case trajectories. Additionally, more than two weeks is needed to observe an impact on SARS-CoV-2 cases after an intervention is implemented. These data provide a critical framework to inform future interventions during emerging pandemics.