Singapore tuberculosis (TB) clinical management guidelines 2024: A modified Delphi adaptation of international guidelines for drug-susceptible TB infection and pulmonary disease.

Introduction: Tuberculosis (TB) remains endemic in Singapore. Singapore's clinical practice guidelines for the management of tuberculosis were first published in 2016. Since then, there have been major new advances in the clinical management of TB, ranging from diagnostics to new drugs and treatment regimens. The National TB Programme convened a multidisciplinary panel to update guidelines for the clinical management of drug-susceptible TB infection and disease in Singapore, contextualising current evidence for local practice. Method: Following the ADAPTE framework, the panel systematically reviewed, scored and synthesised English-language national and international TB clinical guidelines published from 2016, adapting recommendations for a prioritised list of clinical decisions. For questions related to more recent advances, an additional primary literature review was conducted via a targeted search approach. A 2-round modified Delphi process was implemented to achieve consensus for each recommendation, with a final round of edits after consultation with external stakeholders. Results: Recommendations for 25 clinical questions spanning screening, diagnosis, selection of drug regimen, monitoring and follow-up of TB infection and disease were formulated. The availability of results from recent clinical trials led to the inclusion of shorter treatment regimens for TB infection and disease, as well as consensus positions on the role of newer technologies, such as computer-aided detection-artificial intelligence products for radiological screening of TB disease, next-generation sequencing for drug-susceptibility testing, and video observation of treatment. Conclusion: The panel updated recommendations on the management of drug-susceptible TB infection and disease in Singapore.

On Orthorexia Nervosa: A Systematic Review of Reviews.

INTRODUCTION: Orthorexia nervosa (ON), characterized by a pathological preoccupation with "extreme dietary purity," is increasingly observed as a mental health condition among young adults and the general population. However, its diagnosis is not formally recognized and has remained contentious. OBJECTIVE: In this systematic review, we attempt to overview previous reviews on ON, focusing on the methodological and conceptual issues with ON. This would serve both as a summary and a way to highlight gaps in earlier research. METHODS: This systematic review took reference from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines, and using combinations of the search terms ("orthorexia" OR "orthorexia nervosa" OR "ON") AND ("review" OR "systematic review" OR "meta-analysis"), a literature search was performed on EMBASE, Medline and PsycINFO databases from inception up to October 31, 2023. Articles were included if (1) they were written or translated into English and (2) contained information pertaining to the diagnostic stability or validity of ON, or instruments used to measure ON symptoms and behaviors. Only review articles with a systematic literature search approach were included. RESULTS: A total of 22 reviews were qualitatively reviewed. Several studies have reported variable prevalence of ON and highlighted the lack of thoroughly evaluated measures of ON with clear psychometric properties, with no reliable estimates. ORTO-15 and its variations such as ORTO-11, ORTO-12 are popularly used, although their use is discouraged. Existing instruments lack specificity for pathology and several disagreements on the conceptualization and hence diagnostic criteria of ON exist. DISCUSSION: Previous reviews have consistently highlighted the highly variable (and contradictory) prevalence rates with different instruments to measure ON, lack of stable factor structure and psychometrics across ON measures, paucity of data on ON in clinical samples, and a need for a modern re-conceptualization of ON. The diagnosis of ON is challenging as it likely spans a spectrum from "normal" to "abnormal," and "functional" to "dysfunctional." "Non-pathological" orthorexia is not related to psychopathological constructs in the same way that ON is.

Socioeconomic disadvantage and long-term survival duration in out-of-hospital cardiac arrest patients: A population-based cohort study.

Background: Socioeconomic status (SES) is a well-established determinant of cardiovascular health. However, the relationship between SES and clinical outcomes in long-term out-of-hospital cardiac arrest (OHCA) is less well-understood. The Singapore Housing Index (SHI) is a validated building-level SES indicator. We investigated whether SES as measured by SHI is associated with long-term OHCA survival in Singapore. Methods: We conducted an open cohort study with linked data from the Singapore Pan-Asian Resuscitation Outcomes Study (PAROS), and the Singapore Registry of Births and Deaths (SRBD) from 2010 to 2020. We fitted generalized structural equation models, calculating hazard ratios (HRs) using a Weibull model. We constructed Kaplan-Meier survival curves and calculated the predicted marginal probability for each SHI category. Results: We included 659 cases. In both univariable and multivariable analyses, SHI did not have a significant association with survival. Indirect pathways of SHI mediated through covariates such as Emergency Medical Services (EMS) response time (HR of low-medium, high-medium and high SHI when compared to low SHI: 0.98 (0.88-1.10), 1.01 (0.93-1.11), 1.02 (0.93-1.12) respectively), and age of arrest (HR of low-medium, high-medium and high SHI when compared to low SHI: 1.02 (0.75-1.38), 1.08 (0.84-1.38), 1.18 (0.91-1.54) respectively) had no significant association with OHCA survival. There was no clear trend in the predicted marginal probability of survival among the different SHI categories. Conclusions: We did not find a significant association between SES and OHCA survival outcomes in residential areas in Singapore. Among other reasons, this could be due to affordable healthcare across different socioeconomic classes.

Examining Public Messaging on Influenza Vaccine over Social Media: Unsupervised Deep Learning of 235,261 Twitter Posts from 2017 to 2023.

Although influenza vaccines are safe and efficacious, vaccination rates have remained low globally. Today, with the advent of new media, many individuals turn to social media for personal health questions and information. However, misinformation may be rife, and health communications may be suboptimal. This study, therefore, aimed to investigate the public messaging related to influenza vaccines by organizations over Twitter, which may have a far-reaching influence. The theoretical framework of the COM-B (capacity, opportunity, and motivation component of behavior) model was used to interpret the findings to aid the design of messaging strategies. Employing search terms such as "flu jab", "flu vaccine", "influenza vaccine", and '" influenza jab", tweets posted in English and by organizations from 1 January 2017 to 1 March 2023 were extracted and analyzed. Using topic modeling, a total of 235,261 tweets by organizations over Twitter were grouped into four main topics: publicizing campaigns to encourage influenza vaccination, public education on the safety of influenza vaccine during pregnancy, public education on the appropriate age to receive influenza vaccine, and public education on the importance of influenza vaccine during pregnancy. Although there were no glaring pieces of misinformation or misconceptions, the current public messaging covered a rather limited scope. Further information could be provided about influenza and the benefits of vaccination (capability), promoting community, pharmacist-led influenza vaccination, and other avenues (opportunity), and providing greater incentivization and support for vaccination (motivation).

Performance of the ROX index in predicting high flow nasal cannula failure in COVID-19 patients: a systematic review and meta-analysis.

COVID-19 patients with acute hypoxemic respiratory failure (AHRF) benefit from high flow nasal cannula (HFNC) oxygen therapy. However, delays in initiating invasive ventilation after HFNC failure are associated with poorer outcomes. The respiratory oxygenation (ROX) index, combining SpO2/FiO2 and respiratory rate, can predict HFNC failure. This meta-analysis evaluated the optimal ROX index cut-offs in predicting HFNC failure among COVID-19 patients at different measurement timings and clinical settings. Three databases were searched for eligible papers. From each study, we reconstructed the confusion matrices at different cut-offs, fitted linear mixed models to estimate the ROX index distribution function, and derived the area under the summary receiver operator characteristic curve (sAUC) and optimal cut-offs to predict HFNC failure. 24 studies containing 4790 patients were included. Overall sAUC was 0.771 (95% CI: 0.666-0.847) (optimal cut-off: 5.23, sensitivity: 0.732, specificity: 0.690). The cut-off values to achieve 80%, 90% sensitivity, 80%, 90% specificity were 5.70, 6.69, 4.45, 3.37, respectively. We stratified the analysis by ROX measurement time and estimated optimal cut-offs and cut-offs to achieve 80% sensitivity and specificity. For 2-6 h and 6-12 h post-HFNC initiation, we propose the use of 80% specific cut-offs to rule in HFNC failure of < 5.33 and < 3.69, respectively. For 12-24 h post-HFNC initiation, we propose the use of the 80% sensitive cut-off of > 6.07 to rule out HFNC failure. Our analysis confirms the overall utility of the ROX index in risk stratification of COVID-19 patients with AHRF receiving HFNC and provides potentially useful cut-offs for different times from HFNC initiation.

Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts.

Several countries are witnessing significant increases in influenza cases and severity. Despite the availability, effectiveness and safety of influenza vaccination, vaccination coverage remains suboptimal globally. In this study, we examined the prevailing negative sentiments related to influenza vaccination via a deep learning analysis of public Twitter posts over the past five years. We extracted original tweets containing the terms 'flu jab', '#flujab', 'flu vaccine', '#fluvaccine', 'influenza vaccine', '#influenzavaccine', 'influenza jab', or '#influenzajab', and posted in English from 1 January 2017 to 1 November 2022. We then identified tweets with negative sentiment from individuals, and this was followed by topic modelling using machine learning models and qualitative thematic analysis performed independently by the study investigators. A total of 261,613 tweets were analyzed. Topic modelling and thematic analysis produced five topics grouped under two major themes: (1) criticisms of governmental policies related to influenza vaccination and (2) misinformation related to influenza vaccination. A significant majority of the tweets were centered around perceived influenza vaccine mandates or coercion to vaccinate. Our analysis of temporal trends also showed an increase in the prevalence of negative sentiments related to influenza vaccination from the year 2020 onwards, which possibly coincides with misinformation related to COVID-19 policies and vaccination. There was a typology of misperceptions and misinformation underlying the negative sentiments related to influenza vaccination. Public health communications should be mindful of these findings.

Examining the Public Messaging on 'Loneliness' over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade.

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.

Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts.

The World Health Organization (WHO) started the initiative on healthy ageing from 2016 to 2020, which has now continued into the United Nations (UN) Decade of Healthy Ageing 2021-2030. Research into healthy ageing and healthy ageing communities have emphasized that the concept of healthy ageing encompasses a plurality of views and has multiple dimensions. Anchored in a transdisciplinary approach, the present report thus aimed to investigate public perceptions of healthy ageing via a deep analysis of social media posts on Twitter. Original tweets, containing the terms "Healthy Ageing" OR "healthy aging" OR "healthyageing" OR "healthyaging", and posted in English between 1 January 2012 and 30 June 2022 were extracted. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition was applied to select for individual users. Topic modelling, specifically BERTopic was used to generate interpretable topics and descriptions pertaining to the concept of healthy ageing. Subsequently, manual thematic analysis was performed by the study investigators, with independent reviews of the topic labels and themes. A total of 63,809 unique tweets were analyzed and clustered semantically into 16 topics. The public perception of healthy ageing could be broadly grouped into three themes: (1) healthy diet and lifestyle, (2) maintaining normal bodily functions and (3) preventive care. While most perceptions dovetail WHO's definition, there are some points regarding skin appearances, beauty and aging that should be closely considered in the design of initiatives in the UN Decade of Healthy Ageing and beyond.

Trends in Pseudomonas aeruginosa (P. aeruginosa) Bacteremia during the COVID-19 Pandemic: A Systematic Review.

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.