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0%) were the most frequent clinical manifestation associated with brucellosis. Joint pain was commonly found among children (44.4%). Neurological findings were more frequent among adult patients. The study concluded that brucellosis is endemic in Southern Saudi Arabia and needs local health authority to implement preventive and educational program measures. Infected patients may present with diverse, nonspecific clinical manifestations that require intuition from clinicians to detect the disease.Transgender women are assigned male at birth but identified as women. The incidence of gender dysphoria is estimated to be around 1% of the population. Gender dysphoria may be associated with depression and low quality of life, which in most cases improves during gender-affirming hormonal treatment (GAHT). Feminizing hormonal treatment for transgender women or gender non-binary people typically includes natural estrogen (estradiol). Additional testosterone-blocking treatment is often needed to ensure the suppression of the pituitary-gonadal axis and may include cyproterone acetate, a gonadotropin-releasing hormone agonist (GnRH-a), or spironolactone. The health risks of cyproterone acetate as anti-androgen treatment are debated and randomized protocols with other anti-androgen treatments are requested. Orchiectomy is performed in some transgender women after various duration of GAHT. Currently, natural progesterone is not recommended as part of GAHT due to limited knowledge on the balance between risks and benefits. In the present article, we discuss evidence regarding established and upcoming feminizing treatment for adult transgender women or gender non-binary people seeking feminization. Data on study populations with transgender women are put into a wider context of literature regarding the effects of sex steroid hormones in cisgender study populations. Relevant follow-up and monitoring during feminizing treatment is debated. The review has a special focus on the pharmacotherapy of feminizing hormonal therapy.[This corrects the article DOI 10.2196/21985.].
The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic.
This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge.
Followers of the three main emergency physician professional organizations were identified using Twitter's application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Ipatasertib ic50 Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and top a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states.
COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.
COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.
The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality.
The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression.
The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes deceased, ventilated, or admitted to the intensive care unit (ICU).
The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predictingge of features across different endpoint outcomes and at different hospitalization time points.
Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
The COVID-19 pandemic has resulted in panic among the general public, leading many people to seek out information related to COVID-19 through various sources, including social media and traditional media. Identifying public preferences for obtaining such information may help health authorities to effectively plan successful health preventive and educational intervention strategies.
The aim of this study was to examine the impact of the types of sources used for obtaining COVID-19 information on the attitudes and practices of the general public in Saudi Arabia during the pandemic, and to identify the socioeconomic and demographic factors associated with the use of different sources of information.
This study used data from a cross-sectional online survey conducted on residents of Saudi Arabia from March 20 to 24, 2020. Data were analyzed using descriptive, bivariate, and multivariable logistic regression analyses. Bivariate analysis of categorical variables was performed to determine the associations betobtained their COVID-19 information via the MOH had greater odds of having an optimistic attitude (aOR 1.437, 95% CI 1.234-1.673; P<.001) and adhering to preventive measures (aOR 1.393, 95% CI 1.201-1.615; P<.001) than those who obtained information via other sources.
This study provides evidence that different sources of information influence attitudes and preventive actions differently within a pandemic crisis context. Health authorities in Saudi Arabia should pay attention to the use of appropriate social media channels and sources to allow for more effective dissemination of critical information to the public.
This study provides evidence that different sources of information influence attitudes and preventive actions differently within a pandemic crisis context. Health authorities in Saudi Arabia should pay attention to the use of appropriate social media channels and sources to allow for more effective dissemination of critical information to the public.
Letter to the editor on a JMIR article 'The Present and Future Applications of Technology in Adapting Medical Education Amidst the COVID-19 Pandemic'. We have reflected on the author's main arguments using our experiences as fifth-year medical students at the University of Oxford and Cardiff University. While we support the increased use of technology in medical education, we also invite the author to offer suggestions on how this process can be made more equitable with an aim to widen participation.
Letter to the editor on a JMIR article 'The Present and Future Applications of Technology in Adapting Medical Education Amidst the COVID-19 Pandemic'. We have reflected on the author's main arguments using our experiences as fifth-year medical students at the University of Oxford and Cardiff University. While we support the increased use of technology in medical education, we also invite the author to offer suggestions on how this process can be made more equitable with an aim to widen participation.
Previous studies have shown various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at county or zip code level.
This analysis was carried out at a granular spatial resolution of census tracts, to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of Mexican Americans living in Cameron County, TX on the border of US and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups.
We included all reported COVID-19 positive cases confirmed by reverse transcription-polymerase chain reaction (RT-PCR) testing between March 19th (first case reported) and December 16th, 2020 in Cameron County, TX. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal Negative Binomial model to investigate the COVID-19 ioreover, age-stratified analyses identified different significant contributing factors, and varying magnitude of the "shelter-in-place" effect.
In our study, SDOH including social environment and local emergency measure were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments for COVID-19 control, mitigation and implementation of vaccine strategies.
Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories.
We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide.
Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root me.
Child screen time (ST) has soared during the COVID-19 pandemic as lockdowns and restrictions have forced changes to regular family routines. It is important to investigate how families are navigating ST.
This study aimed to explore families' experiences of ST during the COVID-19 pandemic.
Virtual focus group sessions were conducted between December 2020 and February 2021 in English and Spanish. Transcripts were analyzed using reflexive thematic analysis.
In total, 48 parents (predominantly Hispanic) residing in California participated in 1 of 14 focus group sessions. Children were attending school remotely at the time of the study. A total of 6 themes and 1 subtheme were identified (1) total ST has increased; (2) children are too attached to screens; (3) ST has advantages and disadvantages but parents perceive ST as mostly negative; (4) parents and children have limited options; (5) ST restrictions (subtheme children react negatively when ST is restricted); and (6) parents are concerned that children are not getting enough exercise.
