Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

INTRODUCTION: We aimed to understand the awareness and attitudes of elderly Southeast Asians towards telehealth services during the coronavirus disease 2019 (COVID-19) pandemic in this study. METHODS: In this qualitative study, 78 individuals from Singapore (51.3% female, mean age 73.0 ± 7.6 years) were interviewed via telephone between 13 May 2020 and 9 June 2020 during Singapore's first COVID-19 'circuit breaker'. Participants were asked to describe their understanding of telehealth, their experience of and willingness to utilise these services, and the barriers and facilitators underlying their decision. Transcripts were analysed using thematic analysis, guided by the United Theory of Acceptance Use of Technology framework. RESULTS: Of the 78 participants, 24 (30.8%) were able to describe the range of telehealth services available and 15 (19.2%) had previously utilised these services. Conversely, 14 (17.9%) participants thought that telehealth comprised solely home medication delivery and 50 (51.3%) participants did not know about telehealth. Despite the advantages offered by telehealth services, participants preferred in-person consultations due to a perceived lack of human interaction and accuracy of diagnoses, poor digital literacy and a lack of access to telehealth-capable devices. CONCLUSION Our results showed poor overall awareness of the range of telehealth services available among elderly Asian individuals, with many harbouring erroneous views regarding their use. These data suggest that public health education campaigns are needed to improve awareness of and correct negative perceptions towards telehealth services in elderly Asians.
Humans ; COVID-19/epidemiology* ; Female ; Telemedicine ; Aged ; Male ; Singapore/epidemiology* ; Qualitative Research ; Health Knowledge, Attitudes, Practice ; SARS-CoV-2 ; Aged, 80 and over ; Middle Aged ; Pandemics ; Awareness ; Asian People ; Southeast Asian People

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events.Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N⁶-methyladenosine (m⁶A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m⁶A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m⁶A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m⁶A methylation and genetic mutations that potentially regulate m⁶A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m⁶A sites derived from 23 human tissues and 499,369 m⁶A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m⁶A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m⁶A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m⁶A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m⁶A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.
Humans ; Methylation ; RNA, Messenger/metabolism* ; Neoplasms/genetics* ; Mutation

Background/Aims: Depression and anxiety are associated with poorer outcomes in patients with hepatocellular carcinoma (HCC). However, the prevalence of depression and anxiety in HCC are unclear. We aimed to establish the prevalence of depression and anxiety in patients with HCC. Methods: MEDLINE and Embase were searched and original articles reporting prevalence of anxiety or depression in patients with HCC were included. A generalized linear mixed model with Clopper-Pearson intervals was used to obtain the pooled prevalence of depression and anxiety in patients with HCC. Risk factors were analyzed via a fractional-logistic regression model. Results: Seventeen articles involving 64,247 patients with HCC were included. The pooled prevalence of depression and anxiety in patients with HCC was 24.04% (95% confidence interval [CI], 13.99–38.11%) and 22.20% (95% CI, 10.07–42.09%) respectively. Subgroup analysis determined that the prevalence of depression was lowest in studies where depression was diagnosed via clinician-administered scales (16.07%;95% CI, 4.42–44.20%) and highest in self-reported scales (30.03%; 95% CI, 17.19–47.01%). Depression in patients with HCC was lowest in the Americas (16.44%; 95% CI, 6.37–36.27%) and highest in South-East Asia (66.67%; 95% CI, 56.68–75.35%). Alcohol consumption, cirrhosis, and college education significantly increased risk of depression in patients with HCC. Conclusions One in four patients with HCC have depression, while one in five have anxiety. Further studies are required to validate these findings, as seen from the wide CIs in certain subgroup analyses. Screening strategies for depression and anxiety should also be developed for patients with HCC.

The personal recovery movement is beginning to gain traction within Singapore's mental healthcare systems. We believe it is timely to give a broad overview of how it developed and provide suggestions on how it can evolve further. From the early custodial care in the 1800s to the community-centric programmes of the 1900s and early 2000s, we now find ourselves at the forefront of yet another paradigm shift towards a more consumer-centric model of care. The following decades will allow personal recovery practitioners and researchers to innovate and identify unique but culturally appropriate care frameworks. We also discuss how the movement can continue to complement existing mental healthcare systems and efforts.
Forecasting ; Humans ; Singapore