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Background Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators. Results We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report. Conclusion Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: ( https://sites.google.com/view/sr-ln/ssc ).

Current SARS-CoV-2 containment measures rely on controlling viral transmission. Effective prioritization can be determined by understanding SARS-CoV-2 transmission dynamics. We conducted a systematic review and meta-analyses of the secondary attack rate (SAR) in household and healthcare settings. We also examined whether household transmission differed by symptom status of index case, adult and children, and relationship to index case. We searched PubMed, medRxiv, and bioRxiv databases between January 1 and July 25, 2020. High-quality studies presenting original data for calculating point estimates and 95% confidence intervals (CI) were included. Random effects models were constructed to pool SAR in household and healthcare settings. Publication bias was assessed by funnel plots and Egger's meta-regression test. 43 studies met the inclusion criteria for household SAR, 18 for healthcare SAR, and 17 for other settings. The pooled household SAR was 18.1% (95% CI: 15.7%, 20.6%), with significant heterogeneity across studies ranging from 3.9% to 54.9%. SAR of symptomatic index cases was higher than asymptomatic cases (RR: 3.23; 95% CI: 1.46, 7.14). Adults showed higher susceptibility to infection than children (RR: 1.71; 95% CI: 1.35, 2.17). Spouses of index cases were more likely to be infected compared to other household contacts (RR: 2.39; 95% CI: 1.79, 3.19). In healthcare settings, SAR was estimated at 0.7% (95% CI: 0.4%, 1.0%). While aggressive contact tracing strategies may be appropriate early in an outbreak, as it progresses, measures should transition to account for setting-specific transmission risk. Quarantine may need to cover entire communities while tracing shifts to identifying transmission hotspots and vulnerable populations. Where possible, confirmed cases should be isolated away from the household.

Artificial intelligence-based clinical decision support systems (AI-CDSSs) have enhanced personalized medicine and improved the efficiency of health care workers. Despite these opportunities, trust in these tools remains a critical factor for their successful integration into practice. Existing research lacks synthesized insights and actionable recommendations to guide the development of AI-CDSSs that foster trust among health care workers. This systematic review aims to identify and synthesize key factors that influence health care workers' trust in AI-CDSSs and to provide actionable recommendations for enhancing their trust in these systems. We conducted a systematic review of published studies from January 2020 to November 2024, retrieved from PubMed, Scopus, and Google Scholar. Inclusion criteria focused on studies that examined health care workers' perceptions, experiences, and trust in AI-CDSSs. Studies in non-English languages and those unrelated to health care settings were excluded. Two independent reviewers followed the Cochrane Collaboration Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Analysis was conducted using a developed data charter. The Critical Appraisal Skills Programme tool was applied to assess the quality of the included studies and to evaluate the risk of bias, ensuring a rigorous and systematic review process. A total of 27 studies met the inclusion criteria, involving diverse health care workers, predominantly in hospitalized settings. Qualitative methods were the most common (n=16, 59%), with sample sizes ranging from small focus groups to cohorts of over 1000 participants. Eight key themes emerged as pivotal in improving health care workers' trust in AI-CDSSs: (1) System Transparency, emphasizing the need for clear and interpretable AI; (2) Training and Familiarity, highlighting the importance of knowledge sharing and user education; (3) System Usability, focusing on effective integration into clinical workflows; (4) Clinical Reliability, addressing the consistency and accuracy of system performance; (5) Credibility and Validation, referring to how well the system performs across diverse clinical contexts; (6) Ethical Consideration, examining medicolegal liability, fairness, and adherence to ethical standards;(7) Human Centric Design, pioritizing patient centered approaches; (8) Customization and Control, highlighting the need to tailor tools to specific clinical needs while preserving health care providers' decision-making autonomy. Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers' trust in AI-CDSS tools were transparency, usability, and clinical reliability. The findings highlight the need for explainable AI models, comprehensive training, stakeholder involvement, and human-centered design to foster health care workers' trust in AI-CDSSs. Although the heterogeneity of study designs and lack of specific data limit further analysis, this review bridges existing gaps by identifying key themes that support trust in AI-CDSSs. It also recommends that future research include diverse demographics, cross-cultural perspectives, and contextual differences in trust across various health care professions.

We report the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across different settings in Brunei. An initial cluster of SARS-CoV-2 cases arose from 19 persons who had attended the Tablighi Jama'at gathering in Malaysia, resulting in 52 locally transmitted cases. The highest nonprimary attack rates (14.8%) were observed from a subsequent religious gathering in Brunei and in households of attendees (10.6%). Household attack rates from symptomatic case-patients were higher (14.4%) than from asymptomatic (4.4%) or presymptomatic (6.1%) case-patients. Workplace and social settings had attack rates of <1%. Our analyses highlight that transmission of SARS-CoV-2 varies depending on environmental, behavioral, and host factors. We identify red flags for potential superspreading events, specifically densely populated gatherings with prolonged exposure in enclosed settings, persons with recent travel history to areas with active SARS-CoV-2 infections, and group behaviors. We propose differentiated testing strategies to account for differing transmission risk.

Objective Brief-COPE inventory is widely used to assess coping; however, validation evidence is absent and previous results were inconsistent. This study aimed to validate psychometric properties of this inventory to ensure culturally appropriate usage. Methods Preliminary validation study on 423 female nurses from the United Arab Emirates. Confirmatory factor analysis (CFA) with maximum likelihood estimation was computed to test four different previous models. Exploratory factor analysis (EFA) protocol was used to determine underlying domain structure of Brief-COPE in this population. Results The four previous models on CFA had inadequate fit indexes. Two-factor (22-items) second-order model that explained 37.0% of the total variance with Cronbach’s alpha at 0.81 and 0.88, respectively, was suggested. Conclusion This validation of Brief-COPE is timely considering nurses enduring different types of stressors. In addition, cultural diversity needs to be considered in coping research. Re-assessment of this exploratory structure is necessary to ensure accurate measurement of coping strategies.

Background In keeping with nation-wide efforts to contain the spread of COVID-19, Universiti Brunei Darussalam (UBD) transformed fully its pedagogical delivery to online mode, where we investigated teaching and learning experiences, physical and mental health of undergraduate students and lecturers during the COVID-19 pandemic. Methods We conducted a cross-sectional study on undergraduate students and lecturers in a health science faculty using a self-developed pretested questionnaire through anonymous online data collection method. Results Fifty-six lecturers (100% response rate) and 279 students (93.3% response rate) participated. The positive experiences reported by students include becoming independent (72.8%) and adapting to online learning (67.4%), while lecturers learned new teaching techniques (50.0%) and became more innovative (50.0%) by learning new tools (48.2%). However, studying at home caused students to feel more distracted (72.0%) with a feeling of uncertainty towards examinations (66.7%), while lecturers felt that students’ laboratory skills were compromised (44.6%). Even though online delivery of assessments enabled lecturers to explore all options (50.0%), they found it difficult to maintain appropriate questions (41.1%) and fair assessments (37.5%). Majority of students missed eating out (68.8%) and felt a lack of participation in extracurricular activities (64.9%), while lecturers reported more time for exercise (51.8%), despite having more screen time (50.0%) and computer-related physical stress (44.6%). In terms of mental health, increased stress in students was reported (64.9%), though they had more time for self-reflection (54.8%). Although lecturers reported a closer relationship with family (44.6%), they also felt more stressed due to deadlines, unexpected disruptions and higher workloads (44.6%) as well as concerns related to work, family and self (39.3%). Conclusion In this abrupt shift to online teaching, students and lecturers in our study identified both positive and negative experiences including the impact on their physical and mental health. Our findings are important to provide the evidence for online pedagogical benefits and can serve to promote the enhancement and adaptation of digital technology in education. Our findings also aim to promote the importance of addressing physical and mental health issues of the university community’s well-being through provision of emotional and mental health support and appropriate programs.

Background Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored. Purpose This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs. Methods A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024. Results The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency. Conclusion Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Background Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. This review aimed to summarise evidence on the key features, usability and benefits of CVD risk calculators using digital platforms for CVDs prevention and management in populations. Methods We used search engines and thematic analyses to conduct a scoping review. As the reporting guideline for this review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Results A total of 17 studies meeting eligibility criteria were included in the analysis, from which about 70% of the studies have prognostic level I ( n  = 8) and level II ( n  = 4) evidence. The review found that various guidelines are recommending different algorithms for CVD risk prediction. The QRISK® was the most accurate CVD risk calculator for several study populations, whereas World Health Organization/International Society of Hypertension (WHO/ISH) risk scores were the least accurate. The key features of CVD risk calculators are variables, predictive accuracy, discrimination index, applicability, understandability, and cost-effectiveness. Conclusion For the selected risk prediction tool, development and validation research must be done, which considers a mix of stroke-specific risk and CVD risk to establish its usability in the local community and advantages to the particular health-care environment. To get healthcare professionals more involved in preventing and treating CVDs, each healthcare setting should use an online CVD risk assessment tool that is more useful, accurate, and easy to use, based on the population and health system.

This study presents the trends of age-standardised incidence and mortality rates of common cancers in Brunei Darussalam from 2011 to 2020. All cancer cases diagnosed among Brunei Darussalam citizens and permanent residents in the period 2011 to 2020 were included in the study. De-identified data were provided by the CanReg5 based BDCR, Ministry of Health Brunei Darussalam. The annual age-standardised incidence and mortality rates per 100,000 persons were standardised by the direct method using the World Health Organization (WHO) world standard population distribution. Joinpoint regression analyses were used to study the incidence and mortality trends of cancer in Brunei Darussalam over the 2011–2020 period. Trends were expressed as average annual percent change (AAPC) over 2011 to 2020, or annual percent change (APC) for a given time period. There were a total of 6,495 new cancer cases diagnosed and 3,359 death cases recorded from 2011 to 2020, in Brunei Darussalam. The five common cancers for males were colorectal, lung and bronchus, prostate, liver, and non-Hodgkin lymphoma. Among females, the five most common cancers were breast, colorectal, lung and bronchus, corpus uteri and cervix uteri. The five leading cancer deaths for males were lung and bronchus, colorectal, liver, prostate, and stomach, while for females, the five leading cancer deaths were breast, lung and bronchus, colorectal, ovary, and cervix uteri. There was a significant increase in the incidence trend of corpus uteri (AAPC : 13.3 ) and a significant decline in the incidence trend for cervical cancer (AAPC : - 4.5 ) from 2011 to 2020. There was a significant increase in the mortality trend of female breast cancer from 2011 to 2015 (APC : 16.3 ), but the trend significantly declined from 2015 to 2020 (APC : - 12.5 ). We also found a significant decrease in mortality trends for stomach cancer (AAPC : - 4.7 ) from 2011 to 2020 for both genders combined. The burden of common cancers is expected to continue to grow with ageing population, effective public health interventions targeting high burden cancers and high-risk groups, and control of modifiable risk factors will continue to be the essential approaches in reducing cancer burden.

Background The widespread use of digital devices among adolescents has raised concerns about the potential impact of screen time on sleep quality. This study aimed to investigate the relationship between screen time and self-reported sleep quality in adolescents. Methods A cross-sectional study was conducted on adolescents (13- to 19-year-olds) using multi-stage cluster sampling with probability proportional to the size of public schools. Data were collected in November 2022 through self-administered questionnaires. The questionnaire collected sociodemographic characteristics, sleep quality using the Pittsburgh Sleep Quality Index (PSQI), and screen time on weekdays and weekends using the Screen Time Questionnaire (STQ). A scoring system was used in the PSQI and a global score of more than 5 indicates poor sleep quality. The relationship between screen time and sleep quality was analysed using simple and multiple linear regression. Results A total of 547 adolescents participated in the study, with a mean (SD) age of 16.66 (1.54) years. The mean (SD) PSQI score was 5.98 (2.70), and 52% of participants had poor sleep quality. The sleep disturbance component had the highest mean (SD) score at 1.35 (0.5) out of a total score of 3.0. The mean (SD) screen time for a weekday was 537.6 (301.5) minutes, and a weekend day was 725.5 (339.2) minutes. The highest median screen time was spent on smartphones during the whole week. A significant linear relationship was observed between age and PSQI global score ( p  = 0.008), with a 0.2 increase in PSQI global score for each year increase in age (95% CI: 0.05, 0.35). Being female was also significantly associated with a high PSQI global score ( p  < 0.001). Additionally, a significant linear relationship was observed between screen time on a weekend and PSQI score ( p  = 0.032). Conclusions The study found that half of the adolescents had poor sleep quality, which was associated with being female, increased screen time on weekends, and older age. Future research endeavours should focus on conducting longitudinal studies to assess the temporal relationship between screen time and sleep quality.