bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mental health consequences of COVID-19: a nationally representative cross-sectional study of pandemic-related stressors and anxiety disorders in the USA
Authors: Abdalla Salma M , Ettman Catherine K , Cohen Gregory H , Galea Sandro 
Published: August 2021

ObjectiveTo document the prevalence of anxiety disorders in the USA during the COVID-19 pandemic.DesignA cross-sectional analysis.SettingA nationally representative sample in the USA between 31 March and 13 April 2020.Participants1450 English-speaking adult participants in the AmeriSpeak Panel. AmeriSpeak is a probability-based panel designed to be representative of households in the USA.Main outcome measuresPrevalence of probable generalised anxiety disorder (GAD) using the GAD-7 and post-traumatic stress symptoms (PTSS) using the four-item PTSD (post-traumatic stress disorder) checklist. Both outcomes were stratified by demographics and COVID-19-related stressors.ResultsThe majority of participants were female (51.8%), non-Hispanic white (62.9%) and reported a household saving of $5000 or more. Those between 18 and 29 years old were the largest age group (38.1%) compared with 40–59 years (32.0%) and 60 years or more (29.9%). The prevalence of probable GAD was 10.9% (95% CI 9.1% to 13.2%) and the prevalence of PTSS was 21.7% (95% CI 19.1% to 24.6%). Among participants reporting five or more COVID-19-related stressors, the prevalence of probable GAD was 20.5% (95% CI 16.1% to 25.8%) and the prevalence of PTSS was 35.7% (95% CI 30.2% to 41.6%). Experiencing five or more COVID-19-related stressors was a predictor of both probable GAD (OR=4.5, 95% CI 2.3 to 8.8) and PTSS (OR=3.3, 95% CI 2.1 to 5.1).ConclusionsThe prevalence of probable anxiety disorders in the USA, as the COVID-19 pandemic and policies implemented to tackle it unfolded, is higher than estimates reported prior to the pandemic and estimates reported following other mass traumatic events. Exposure to COVID-19-related stressors is associated with higher prevalence of both probable GAD and PTSS, highlighting the role these stressors play in increasing the risk of developing anxiety disorders in the USA. Mitigation and recovery policies should take into account the mental health toll the pandemic had on the USA population.
Maternal and fetal characteristics and causes of stillbirth in a tertiary care hospital of Nepal: secondary analysis of registry-based surveillance data
Authors: Sharma Basant , Bhattarai Suraj , Shrestha Sabita , Joshi Rakshya , Tamrakar Renuka , Singh Prekshya , Chaudhary Jully , Pandit Upendra 
Published: August 2021

ObjectivesStillbirth is one of the vital indicators of quality care. This study aimed to determine maternal-fetal characteristics and causes of stillbirth in Nepal.DesignSecondary analysis of single-centred registry-based surveillance data.SettingThe study was conducted at the Department of Obstetrics and Gynecology, Chitwan Medical College Teaching Hospital, a tertiary care hospital located in Bharatpur, Nepal.ParticipantsAll deliveries of intrauterine fetal death, at or beyond 22 weeks’ period of gestation and/or birth weight of 500 g or more, conducted between 16 July 2017 and 15 July 2019 were included in the study.Main outcome measuresThe primary outcome measure of this study was stillbirth, and the secondary outcome measures were maternal and fetal characteristics and cause of stillbirth.ResultsOut of 5282 institutional deliveries conducted over 2 years, 79 (1.5%) were stillbirths, which gives the stillbirth rate of 15 per 1000 births. Of them, the majority (75; 94.9%) were vaginal delivery and only four (5.1%) were caesarean section (p<0.0001). The proportion of the macerated type of stillbirth was more than that of the fresh type (58.2% vs 41.8%; p=0.13). Only half of the mothers who experienced stillbirth had received antenatal care. While the cause of fetal death was unknown in one-third of cases (31.6%; 25/79), among likely causes, the most common was maternal hypertension (29.1%), followed by intrauterine infection (8.9%) and fetal malpresentation (7.6%). Four out of 79 stillbirths (5%) had a birth defect.ConclusionHigh rate of stillbirths in Nepal could be due to the lack of quality antenatal care. The country’s health systems should be strengthened so that pregnancy-related risks such as maternal hypertension and infections are identified early on. Upgrading mothers’ hygiene and health awareness is equally crucial in reducing fetal deaths in low-resource settings.
Factors associated with high compliance behaviour against COVID-19 in the early phase of pandemic: a cross-sectional study in 12 Asian countries
Authors: Chua Chun En , Kew Guan Sen , Demutska Alla , Quek Sabrina , Loo Evelyn Xiu Ling , Gui Hao , Wong Scott , Lau Hui Xing , Low En Xian Sarah , Loh Tze Liang , Ooi Shien Lung , Hung Emily C W , Rahman M Masudur , Ghoshal Uday C , Wong Hei , Cheung Cynthia K Y , Syam Ari F , Tan Niandi , Xiao Yinglian , Liu Jin-Song , Lu Fang , Chen Chien-Lin , Lee Yeong Yeh , Maralit Ruter M , Kim Yong-Sung , Oshima Tadayuki , Miwa Hiroto , Pang Junxiong , Siah Kewin Tien Ho 
Published: August 2021

IntroductionRegardless of having effective vaccines against COVID-19, containment measures such as enhanced physical distancing and good practice of personal hygiene remain the mainstay of controlling the COVID-19 pandemic. Countries across Asia have imposed these containment measures to varying extents. However, residents in different countries would have a differing degree of compliance to these containment measures potentially due to differences in the level of awareness and motivation in the early phase of pandemic.ObjectivesIn our study, we aimed to describe and correlate the level of knowledge and attitude with the level of compliance with personal hygiene and physical distancing practices among Asian countries in the early phase of pandemic.MethodsA multinational cross-sectional study was carried out using electronic surveys between May and June 2020 across 14 geographical areas. Subjects aged 21 years and above were invited to participate through social media, word of mouth and electronic mail.ResultsAmong the 2574 responses obtained, 762 (29.6%) participants were from East Asia and 1812 (70.4%) were from Southeast Asia (SEA). A greater proportion of participants from SEA will practise physical distancing as long as it takes (72.8% vs 60.6%). Having safe distancing practices such as standing more than 1 or 2 m apart (AdjOR 5.09 95% CI (1.08 to 24.01)) or more than 3 or 4 m apart (AdjOR 7.05 95% CI (1.32 to 37.67)), wearing a mask when they had influenza-like symptoms before the COVID-19 pandemic, preferring online news channels such as online news websites/applications (AdjOR 1.73 95% CI (1.21 to 2.49)) and social media (AdjOR 1.68 95% CI (1.13 to 2.50) as sources of obtaining information about COVID-19 and high psychological well-being (AdjOR 1.39 95% CI (1.04 to 1.87)) were independent factors associated with high compliance.ConclusionsWe found factors associated with high compliance behaviour against COVID-19 in the early phase of pandemic and it will be useful to consider them in risk assessment, communication and pandemic preparedness.
Behavioural insights (BI) for childhood development and effective public policies in Latin America: a survey and a randomised controlled trial
Authors: Tomio Andrea A , Dottori Martin , Hesse Eugenia , Torrente Fernando , Flichtentrei Daniel , Ibanez Agustin M 
Published: August 2021

ObjectivesWe developed (a) a survey to investigate the knowledge of childhood health experts on public policies and behavioural insights (BI), as well as its use in Latin American and the Caribbean countries (LACs), and (b) an intervention (randomised controlled trial) to test the influence of nudges on the effect of a simulated public health programme communication.Participants and settingsA total of 2003 LACs childhood health professionals participated in the study through a Hispanic online platform.Primary and secondary outcomesWe used regression models analysing expertise-related information, individual differences and location. We extracted several outcome variables related to (a) ‘Public Policy Knowledge Index’ based on the participants’ degree of knowledge on childhood health public policies and (b) BI knowledge, perceived effectiveness and usefulness of a simulated public programme communication. We also analysed a ‘Behavioural Insights Knowledge Index’ (BIKI) based on participants’ performance in BI questions.ResultsIn general, health professionals showed low BI knowledge (knowledge of the term BI: χ2=210.29, df=1 and p<0.001; BIKI: χ2=160.5, df=1 and p<0.001), and results were modulated by different factors (age, academic formation, public policy knowledge and location). The use of BI principles for the communication of the public programme revealed higher impact and clarity ratings from professionals than control messages.ConclusionsOur findings provide relevant knowledge about BI in health professionals to inform governmental and non-governmental organisations’ decision-making processes related with childhood public policies and BI designs.
Social and structural determinants of injecting-related bacterial and fungal infections among people who inject drugs: protocol for a mixed studies systematic review
Authors: Brothers Thomas D , Lewer Dan , Bonn Matthew , Webster Duncan , Harris Magdalena 
Published: August 2021

IntroductionInjecting-related bacterial and fungal infections are a common complication among people who inject drugs (PWID), associated with significant morbidity and mortality. Invasive infections, including infective endocarditis, appear to be increasing in incidence. To date, preventive efforts have focused on modifying individual-level risk behaviours (eg, hand-washing and skin-cleaning) without much success in reducing the population-level impact of these infections. Learning from successes in HIV prevention, there may be great value in looking beyond individual-level risk behaviours to the social determinants of health. Specifically, the risk environment conceptual framework identifies how social, physical, economic and political environmental factors facilitate and constrain individual behaviour, and therefore influence health outcomes. Understanding the social and structural determinants of injecting-related bacterial and fungal infections could help to identify new targets for prevention efforts in the face of increasing incidence of severe disease.Methods and analysisThis is a protocol for a systematic review. We will review studies of PWID and investigate associations between risk factors (both individual-level and social/structural-level) and the incidence of hospitalisation or death due to injecting-related bacterial infections (skin and soft-tissue infections, bacteraemia, infective endocarditis, osteomyelitis, septic arthritis, epidural abscess and others). We will include quantitative, qualitative and mixed methods studies. Using directed content analysis, we will code risk factors for these infection-related outcomes according to their contributions to the risk environment in type (social, physical, economic or political) and level (microenvironmental or macroenvironmental). We will also code and present risk factors at each stage in the process of drug acquisition, preparation, injection, superficial infection care, severe infection care or hospitalisation, and outcomes after infection or hospital discharge.Ethics and disseminationAs an analysis of the published literature, no ethics approval is required. The findings will inform a research agenda to develop and implement social/structural interventions aimed at reducing the burden of disease.PROSPERO registration numberCRD42021231411.
‘…the way it was staffed during COVID is the way it should be staffed in real life…’: a qualitative study of the impact of COVID-19 on the working conditions of junior hospital doctors
Authors: Byrne John-Paul , Creese Jennifer , Matthews Anne , McDermott Aoife M , Costello Richard W , Humphries Niamh 
Published: August 2021

ObjectivesCOVID-19 has prompted the reconfiguration of hospital services and medical workforces in countries across the world, bringing significant transformations to the work environments of hospital doctors. Before the pandemic, the working conditions of hospital doctors in Ireland were characterised by understaffing, overload, long hours and work–life conflict. As working conditions can affect staff well-being, workforce retention and patient outcomes, the objective of this study was to analyse how the pandemic and health system response impacted junior hospital doctors’ working conditions during the first wave of COVID-19 in Ireland.Methods and analysisUsing a qualitative study design, the article draws on semi-structured interviews with 30 junior hospital doctors. Informed by an abductive approach that draws iteratively on existing literature and empirical data to explain unexpected observations, data were analysed using inductive and deductive coding techniques to identify the key themes reflecting the experiences of working in Irish hospitals during the first wave of COVID-19. We use the Consolidated Criteria for Reporting Qualitative Research to present this research.ResultsOur analysis generated three themes which demonstrate how COVID-19 prompted changes in medical staffing which in turn enhanced interviewees’ work environments. First, interviewees felt there were more doctors staffing the hospital wards during the first wave of the pandemic. Second, this had positive implications for a range of factors important to their experience of work, including the ability to take sick leave, workplace relationships, collective workplace morale, access to senior clinical support and the speed of clinical decision-making. Third, interviewees noted how it took a pandemic for these improvements to occur and cautioned against a return to pre-pandemic medical staffing levels, which had negatively impacted their working conditions and well-being.ConclusionsInterviewees’ experience of the first wave of COVID-19 illustrates how enhanced levels of medical staffing can improve junior hospital doctors’ working conditions. Given the pervasive impact of staffing on the quality of interviewees’ work experience, perhaps it is time to consider medical staffing standards as a vital job resource for hospital doctors and a key policy lever to enhance medical workforce retention. In a global context of sustained COVID-19 demands, pressures from delayed care and international health worker shortages, understanding frontline experiences and identifying strategies to improve them are vital to the development of more sustainable work practices and to improve doctor retention.
Operation “Denver”
Authors: Selvage Douglas 
Published: August 2021

Abstract
This second part of a two-part article moves ahead in showing how the East German Ministry for State Security (Stasi) came to play a key role in the disinformation campaign launched by the Soviet State Security Committee (KGB) in 1983 regarding the origins of the Human Immunodeficiency Virus (HIV) and the Acquired Immune Deficiency Syndrome (AIDS). The KGB launched the campaign itself, but in the mid-1980s it sought to widen the effort by enlisting the cooperation of intelligence services in other Warsaw Pact countries, especially the Stasi. From the autumn of 1986 until November 1989, the Stasi played a central role in the disinformation campaign. Despite pressure from the U.S. government and a general inclination of Soviet leader Mikhail Gorbachev to curtail the campaign by the end of 1987, both the KGB and the USSR's official Novosti press agency continued until 1989 to spread false allegations that HIV was a U.S. biological weapon. Even after the KGB curtailed its disinformation in 1989, the Stasi continued to disseminate falsehoods, not least because it had successfully maintained plausible deniability regarding its role in the campaign. The Stasi worked behind the scenes to support the work of Soviet–East German scientists Jakob Segal and Lilli Segal and to facilitate dissemination of the Segals’ views in West Germany and Great Britain, especially through the leftwing media, and to purvey broader disinformation about HIV/AIDS by attacking U.S. biological and chemical weapons in general.
Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOSX
Authors: Zhang Zhi , Wang Dagang , Qiu Jianxiu , Zhu Jinxin , Wang Tingli 
Published: August 2021

AbstractThe Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatio-temporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) by using four machine learning approaches, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN), and Extreme Gradient Boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All the four approaches show similar improvement, with 53%-60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, i.e., the Dongjiang River Basin (DJR) in southeastern China. The improvements are even greater in a semi-arid area, i.e., the Fenhe River Basin (FHR) in central China, the RMSE reduction ranges from 63%-66%. The products generated by the machine learning methods performs similarly to or even outperform than the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that requires NRT precipitation information including flood monitoring.
YouTube as a platform for knowledge and awareness of peritoneal dialysis: A content analysis
Authors: Yeter Hasan Haci , Akcay Omer Faruk 
Published: August 2021

Background: One of the main barriers to choosing peritoneal dialysis (PD) is the lack of awareness and PD knowledge. There is an increasing trend in the use of the internet as a search tool for health-related information. This study aims to determine how useful YouTube videos are to get information about PD. Methods: YouTube videos were evaluated independently by two nephrologists. The videos’ quality was assessed with DISCERN scoring system, global quality score (GQS) and the Journal of the American Medical Association (JAMA) scoring system. We determined the quartile (Q) of the videos as follows: most reliable top 25% videos Q1 and others Q2–4. Results: A total of 295 videos were evaluated. University or society-sourced videos made up 15% ( n = 43) of all videos, and healthcare providers were the primary target audience compared to patients ( p < 0.001). JAMA, GQS and DISCERN scores were significantly higher for the videos that were targeted healthcare providers compared to the patients ( p < 0.001, for all). A total of 34% of the videos in Q1 were obtained from the university or society. Nevertheless, only 17% of the videos prepared for the patients were among the Q1. A small number of videos mentioned that PD maintains the residual kidney function (RKF) longer compared to haemodialysis. Conclusions: Universities and societies should upload videos to provide easy-to-understand information on PD. Also, the important benefits of PD, like the preservation of RKF, should be further highlighted in these videos. It may increase the PD penetrance by increasing patients’ awareness.
Identifying potential association on gene-disease network via dual hypergraph regularized least squares
Authors: Yang Hongpeng , Ding Yijie , Tang Jijun , Guo Fei 
Published: August 2021

Abstract
Background
Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases.

Results
In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations.

Conclusion
Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.
Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension
Authors: Zheng Xifeng , Fang Fang , Nong Weidong , Feng Dehui , Yang Yu 
Published: August 2021

AbstractObjectivesThis study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension.MethodsThis retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke.ResultsThe prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710.ConclusionThe prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.