METHODS: A cross-sectional study was conducted in a locality within Selangor, sampling a total of 1449 young adults. The Cyberbullying and Online Aggression Survey was used to measure cyberbullying victimisation. The Family APGAR scale, General Health Questionnaire, Pittsburgh Sleep Quality Index and single-item measures were used to assess family dysfunction, psychological distress and health behaviour, respectively.
RESULTS: The 1-month prevalence of cyberbullying victimisation among young adults was 2.4%. The most common cyberbullying act experienced was mean or hurtful comments about participants online (51.7%), whereas the most common online environment for cyberbullying to occur was social media (45.8%). Male participants (adjusted OR (AOR)=3.60, 95% CI=1.58 to 8.23) had at least three times the odds of being cyberbullied compared with female participants. Meanwhile, participants with higher levels of psychological distress had increased probability of being cyberbullied compared with their peers (AOR=1.13, 95% CI=1.05 to 1.21).
CONCLUSIONS: As evident from this study, cyberbullying victimisation prevails among young adults and is significantly related to gender and psychological distress. Given its devastating effects on targeted victims, a multipronged and collaborative approach is warranted to reduce incidences of cyberbullying and safeguard the health and well-being of young adults.
MATERIALS AND METHODS: This cross- sectional study was conducted among 286 non-smokers from two healthcare training centres and two nearby colleges in Malaysia from January 2015 to April 2015. A standardized questionnaire was administered via staff and student emails. The questionnaire collected information on sociodemographic characteristics, support for a tobacco-free policy and perceived respiratory and sensory symptoms due to tobacco exposure. Bivariate and multivariate logistic regression analyses were performed to estimate the independent effects of supporting a tobacco-free campus.
RESULTS: The percentage of individuals supporting completely tobacco-free facilities was 83.2% (N=238), as opposed to 16.7% (N=48) in support of partially tobacco-free facilities. Compared to the supporters of partially tobacco-free facilities, non-smokers who supported completely tobacco-free health facilities were more likely to be female, have higher education levels, to be very concerned about the effects of other people smoking on their health and to perceive a tobacco-free policy as very important. In addition, they perceived that tobacco smoke bothered them at work by causing headaches and coughs and, in the past 4 weeks, had experienced difficulty breathing. In the multivariate model, after adjusting for sociodemographic characteristics and other factors, only experiencing coughs and headaches increased the odds of supporting a completely tobacco-free campus, up to 2.5- and 1.9-fold, respectively.
CONCLUSIONS: Coughs and headaches due to other people smoking at work enhances support for a completely tobacco-free campus among non-smokers.
OBJECTIVE: This study aimed to document the process of designing, developing, and validating the gamification, motivation, and real-time elements in the Gamified Real-time Video Observed Therapies (GRVOTS) mobile app.
METHODS: The modified nominal group technique via a panel of 11 experts was used to validate the presence of the gamification and motivation elements inside the app, which were assessed based on the percentage of agreement among the experts.
RESULTS: The GRVOTS mobile app, which can be used by patients, supervisors, and administrators, was successfully developed. For validation purposes, the gamification and motivation features of the app were validated as they achieved a total mean percentage of agreement of 97.95% (SD 2.51%), which was significantly higher than the minimum agreement score of 70% (P
METHODS: Secondary data from MyTB version 2.1, a national database, were analysed using R version 3.6.1. Descriptive analysis and multivariable logistic regression were conducted to identify treatment success and its determinants.
RESULTS: In total, 3630 cases of TB cases were registered among children in Malaysia between 2013 and 2017. The overall treatment success rate was 87.1% in 2013 and plateaued between 90.1 and 91.4% from 2014 to 2017. TB treatment success was positively associated with being a Malaysian citizen (aOR = 3.43; 95% CI = 2.47, 4.75), being a child with BCG scars (aOR = 1.93; 95% CI = 1.39, 2.68), and being in the older age group (aOR = 1.06; 95% CI = 1.03, 1.09). Having HIV co-infection (aOR = 0.31; 95% CI = 0.16, 0.63), undergoing treatment in public hospitals (aOR = 0.38; 95% CI =0.25, 0.58), having chest X-ray findings of advanced lesion (aOR = 0.48; 95% CI = 0.33, 0.69), having EPTB (aOR = 0.58; 95% CI = 0.41, 0.82) and having sputum-positive PTB (aOR = 0.58; 95% CI = 0.43, 0.79) were negatively associated with TB treatment success among children.
CONCLUSIONS: The overall success rate of treatment among children with TB in Malaysia has achieved the target of 90% since 2014 and remained plateaued until 2017. The socio-demographic characteristics of children, place of treatment, and TB disease profile were associated with the likelihood of TB treatment success among children. The treatment success rate can be increased by strengthening contact tracing activities and promoting early identification targeting the youngest children and non-Malaysian children.
METHODS: 71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.
RESULTS: This study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p
MATERIALS AND METHODS: A multicentre prospective cohort study was conducted among employees from 2 different public universities in Malaysia. Interventions include at least 2 sessions of behavioural therapy combined with free nicotine replacement therapy (NRT) for 8 weeks. Participants were followed up for 6 months. Independent variables assessed were on sociodemographic and environmental tobacco smoke. Their quit status were determined at 1 week, 3 months and 6 months.
RESULTS: One hundred and eighty- five smokers volunteered to participate. Among the participants, 15% and 13% sustained quit at 3 months and 6 months respectively. Multivariate analysis revealed that at 6 months, attending all 3 behavioural sessions predicted success. None of the environmental tobacco exposure variables were predictive of sustained cessation.
CONCLUSION: Individual predictors of success in intra-workplace smoking cessation programmes do not differ from the conventional clinic-based smoking cessation. Furthermore, environmental tobacco exposure in low intensity smoke-free workplaces has limited influence on smokers who succeeded in maintaining 6 months quitting.
MATERIALS AND METHODS: The development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. Estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c-statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration plot.
RESULTS: The model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The prognostic scores were categorized into three groups that predict the risk for LTFU: low-risk (<15 points), medium-risk (15 to 25 points) and high-risk (> 25 points). The model exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2 = 4.893 and accompanying p value of 0.769.
CONCLUSION: Predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use.
METHODS: The WoCKSS was developed with 20 and 31 items for knowledge and stigma domains, respectively, based on an extensive review of COVID-19 literature. Content validation was conducted by four experts using a content validation form to assess the relevancy of each item to the intended construct. Content Validity Index (CVI) was calculated to measure the agreement between the experts on the relevance of each item to the intended construct. Face validation was then conducted by randomly selecting 10 respondents from the manufacturing industry, who rated the clarity and comprehension of each item using a face validation form. The Item Face Validity Index (I-FVI) was calculated to determine the clarity and comprehension of each question, and only items with an I-FVI ≥ 0.83 were retained.
RESULTS: The WoCKSS achieved excellent content validity in both knowledge and stigma domains. Only 19 items from the knowledge domain and 24 items from the stigma domain were retained after CVI analysis. All retained items received a CVI score of 1.00, indicating perfect agreement among the experts. FVI analysis resulted in 17 items for the knowledge domain and 22 items for the stigma domain. The knowledge domain achieved a high level of agreement among respondents, with a mean I-FVI of 0.91 and a S-FVI/UA of 0.89. The stigma domain also showed high agreement, with a mean I-FVI of 0.99 and a S-FVI/UA of 0.86.
CONCLUSION: In conclusion, the WoCKSS demonstrated high content and face validity. However, further testing on a larger sample size is required to establish its construct validity and reliability.
METHODS: A structured, systematic review was performed according to the PRISMA guidelines. Databases of PubMed, Scopus, Web of Science, IDEAS/REPEC, OSHLINE, HSELINE, and NIOSHTIC-2 were reviewed. Study quality appraisal was performed using the Table of Evidence Levels from Cincinnati Children's Hospital Medical Center, Joanna Briggs Institute tools, Mixed Methods Appraisal Tool, and Center of Evidence Based Management case study critical appraisal checklist. Quantitative analysis was not attempted due to the heterogeneity of included studies. A qualitative synthesis of primary studies examining socioeconomic impact of airborne and droplet-borne infectious diseases outbreaks in any industry was performed and a framework based on empirical findings was conceptualized.
RESULTS: A total of 55 studies conducted from 1984 to 2021 were included, reporting on 46,813,038 participants working in multiple industries across the globe. The quality of articles were good. On the whole, direct socioeconomic impacts of Coronavirus Disease 2019, influenza, influenza A (H1N1), Severe Acute Respiratory Syndrome, tuberculosis and norovirus outbreaks include increased morbidity, mortality, and health costs. This had then led to indirect impacts including social impacts such as employment crises and reduced workforce size as well as economic impacts such as demand shock, supply chain disruptions, increased supply and production cost, service and business disruptions, and financial and Gross Domestic Product loss, attributable to productivity losses from illnesses as well as national policy responses to contain the diseases.
CONCLUSIONS: Evidence suggests that airborne and droplet-borne infectious diseases have inflicted severe socioeconomic costs on regional and global industries. Further research is needed to better understand their long-term socioeconomic impacts to support improved industry preparedness and response capacity for outbreaks. Public and private stakeholders at local, national, and international levels must join forces to ensure informed systems and sector-specific cost-sharing strategies for optimal global health and economic security.