ABSTRACT
Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as Twitter and Reddit as well as online forums, e.g., loneliness forums, to seek advice and solace regarding their health and well-being. The present study therefore aimed to investigate the public messaging on loneliness via an unsupervised machine learning analysis of posts made by organisations on Twitter. We specifically examined tweets put out by organisations (companies, agencies or common interest groups) as the public may view them as more credible information as opposed to individual opinions. A total of 68,345 unique tweets in English were posted by organisations on Twitter from 1 January 2012 to 1 September 2022. These tweets were extracted and analysed using unsupervised machine learning approaches. BERTopic, a topic modelling technique that leverages state-of-the-art natural language processing, was applied to generate interpretable topics around the public messaging of loneliness and highlight the key words in the topic descriptions. The topics and topic labels were then reviewed independently by all study investigators for thematic analysis. Four key themes were uncovered, namely, the experience of loneliness, people who experience loneliness, what exacerbates loneliness and what could alleviate loneliness. Notably, a significant proportion of the tweets centred on the impact of the COVID-19 pandemic on loneliness. While current online interactions are largely descriptive of the complex and multifaceted problem of loneliness, more targeted prosocial messaging appears to be lacking to combat the causes of loneliness brought up in public messaging.
ABSTRACT
Pseudomonas aeruginosa (P. aeruginosa) is among the most common pathogens associated with healthcare-acquired infections, and is often antibiotic resistant, causing significant morbidity and mortality in cases of P. aeruginosa bacteremia. It remains unclear how the incidence of P. aeruginosa bacteremia changed during the Coronavirus Disease 2019 (COVID-19) pandemic, with studies showing almost contradictory conclusions despite enhanced infection control practices during the pandemic. This systematic review sought to examine published reports with incidence rates for P. aeruginosa bacteremia during (defined as from March 2020 onwards) and prior to the COVID-19 pandemic. A systematic literature search was conducted in accordance with PRISMA guidelines and performed in Cochrane, Embase, and Medline with combinations of the key words (pseudomonas aeruginosa OR PAE) AND (incidence OR surveillance), from database inception until 1 December 2022. Based on the pre-defined inclusion criteria, a total of eight studies were eligible for review. Prior to the pandemic, the prevalence of P. aeruginosa was on an uptrend. Several international reports found a slight increase in the incidence of P. aeruginosa bacteremia during the COVID-19 pandemic. These findings collectively highlight the continued importance of good infection prevention and control and antimicrobial stewardship during both pandemic and non-pandemic periods. It is important to implement effective infection prevention and control measures, including ensuring hand hygiene, stepping up environmental cleaning and disinfection efforts, and developing timely guidelines on the appropriate prescription of antibiotics.
ABSTRACT
Prompt detection and isolation of COVID-19 cases is vital for preventing further viral transmission, and lateral flow or rapid antigen tests have been an important diagnostic tool in this pandemic. However, concerns have emerged regarding the sensitivity of these devices for the new BA.1, BA.2, and BA.4/5 omicron variants, which have greater transmissibility and extensive mutations in its spike (S) and nucleocapsid (N) proteins. N protein is an important target protein for existing lateral flow devices. This paper therefore aimed to provide a rapid review of available literature on the performance of the lateral flow tests for detecting the omicron coronavirus variant. A systematic literature search of PubMed, EMBASE, OVID Medline, and Google Scholar found six published studies and four preprints investigating the performance of existing lateral flow devices for the omicron variant, as compared to the B.1.617.2 (Delta) variant. Overall, it appears that the devices have poorer performance for the omicron variant and when testing samples with cycle threshold (Ct) values greater than 25 and in asymptomatic individuals. As most available data were preliminary and had small sample sizes, it is recommended that these data be further studied to better inform and adapt our public health responses.
ABSTRACT
Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to "COVID-19", "suicide", or "self-harm". An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments' responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.
Subject(s)
COVID-19 , Social Media , Humans , COVID-19/epidemiology , Pandemics , Unsupervised Machine LearningABSTRACT
Despite numerous technological and medical advances, out-of-hospital cardiac arrests (OHCAs) still suffer from suboptimal survival rates and poor subsequent neurological and functional outcomes amongst survivors. Multiple studies have investigated the implementation of high-quality prehospital resuscitative efforts, and across these studies, different terms describing high-quality resuscitative efforts have been used, such as high-performance CPR (HP CPR), multi-tiered response (MTR) and minimally interrupted cardiac resuscitation (MICR). There is no universal definition for HP CPR, and dissimilar designs have been employed. This systematic review thus aimed to review current evidence on HP CPR implementation and examine the factors that may influence OHCA outcomes. Eight studies were systematically reviewed, and seven were included in the final meta-analysis. Random-effects meta-analysis found a significantly improved likelihood of prehospital return of spontaneous circulation (pooled odds ratio (OR) = 1.46, 95% CI: 1.16 to 1.82, p < 0.001), survival-to-discharge (pooled OR = 1.32, 95% CI: 1.16 to 1.50, p < 0.001) and favourable neurological outcomes (pooled OR = 1.24, 95% CI: 1.11 to 1.39, p < 0.001) with HP CPR or similar interventions. However, the studies had generally high heterogeneity (I2 greater than 50%) and overall moderate-to-severe risk for bias. Moving forward, a randomised, controlled trial is necessary to shed light on the subject.