METHODS: This ecological cross-sectional study utilised a geographic information system (GIS) and remote sensing techniques to analyse the spatiotemporal distribution of leptospirosis in Selangor from 2011 to 2019. Laboratory-confirmed leptospirosis cases (n = 1,045) were obtained from the Selangor State Health Department. Using ArcGIS Pro, spatial autocorrelation analysis (Moran's I) and Getis-Ord Gi* (hotspot analysis) was conducted to identify hotspots based on the monthly aggregated cases for each subdistrict. Satellite-derived rainfall and land surface temperature (LST) data were acquired from NASA's Giovanni EarthData website and processed into monthly averages. These data were integrated into ArcGIS Pro as thematic layers. Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. Model performance was then evaluated using cross-validation and metrics such as accuracy, precision, sensitivity, and F1-score.
RESULTS: Moran's I analysis revealed a primarily random distribution of cases across Selangor, with only 20 out of 103 observed having a clustered distribution. Meanwhile, hotspot areas were mainly scattered in subdistricts throughout Selangor with clustering in the central region. Machine learning analysis revealed that the LGBM algorithm had the best performance scores compared to having a cross-validation score of 0.61, a precision score of 0.16, and an F1-score of 0.23. The feature importance score indicated river water level and rainfall contributes most to the model.
CONCLUSIONS: This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. The integration of GIS and machine learning offers a promising framework for disease surveillance, facilitating targeted public health interventions in areas at high risk for leptospirosis.
AIMS AND METHODS: Global Adult Tobacco Survey data from Indonesia (2021), Kazakhstan (2019), and the Philippines (2021) were analyzed. The weighted prevalence rates and 95% CI of EC and HTP awareness, current use, and ever use, and their distribution by cigarette smoking status were calculated. Binary logistic regression analyses assessed socioeconomic, and tobacco control factors associated with EC and HTP use.
RESULTS: The prevalence (%) of EC awareness, ever use and current use were 48.7-69.4, 3.6-8.8, and 1.9-3.0, respectively. The prevalence(%) of HTP awareness, ever use and current use were 2.7-21.7, 0.2-2.1, and 0.1-1.2, respectively. The main reasons for EC and HTP use were attractive flavors(45.8%-73.9%), less harmful than smoking(23.0%-70.1%), and enjoyment(40.8%-76.7%). Avoiding going back to smoking(0.9%-54.4%) and quitting smoking(19.4%-49.8%) were less frequently cited reasons (except in the Philippines). EC/HTP use was associated with younger age, higher education and wealth, current/past smoking, exposure to information about the dangers of tobacco use, and advertisements about tobacco products and smoke-free rules at home.
CONCLUSIONS: The prevalence of EC and HTP use was higher among younger men with higher education and wealth, and current/past smoking. EC and HTP use should be closely monitored. Regulations to restrict the widespread marketing and sales of EC and HTP are needed to prevent the escalation of their use.
IMPLICATIONS: The population-level data provide the benchmark for future monitoring use of e-cigarettes and HTPs and identify population subgroups for future surveillance in low- and middle-income countries. The association of EC/HTP use with tobacco control-related factors provides leads for policies that should be formulated and implemented to regulate the product contents, marketing, and sales of EC and HTP.
METHODS: A cross-sectional study was conducted in 2023 among 285 COMBI CHVs from 5 health operational zones in Kuala Lumpur and Putrajaya. The CHVs were stratified proportionately, with 45.5% of respondents randomly selected from each stratum based on their zones. Self-administered questionnaires assessed their performance level and associated factors, including training, supervision, knowledge, motivation, and job satisfaction. Chi-square and Multiple Logistic Regression tests were used to determine the factors and predictors of high-performance COMBI CHVs.
RESULTS: The prevalence of high-performance COMBI CHVs was 59.3%, significantly associated with training factors (experience, duration, and adequacy of training), supervision factors (being supervised, supervision frequency, and encouragement from supervision), motivation and job satisfaction level. As for significant predictors, having at least one day of training (AOR=14.557), being supervised by medical personnel (AOR=4.997), having a high motivation level (AOR=5.538) and having a high job satisfaction level (AOR=4.562) were more likely to have a high-performance level.
CONCLUSION: Training duration, supervisor background, motivation and job satisfaction can contribute to high-performance levels among COMBI CHVs, which can applied in evidence-based policies and strategies for performance improvement and more effective community healthcare delivery.
METHODS: This cross-sectional study included 500 PUBG players, categorized into Lower, Medium, and High PUBG Users. Data were collected using a self-administered questionnaire, including the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and a gaming disorder screening tool. The Multilayer Perceptron (MLP) methodology was applied to analyze the factors influencing migraine symptoms, sleep quality, and quality of life.
RESULTS: Participants of the study were categorized into lower PUBG users (LPU), medium PUBG users (MPU), and high PUBG users (HPU). Among study participants reporting migraine pain, 259 (51.8%) reported that they were HPU. By examining daytime sleepiness using the ESS, Higher normal Day sleep (DS) was observed in 78 (15.9%) HPU. The cultivation level of PUBG showed a very weak positive correlation with experiencing migraine pain or associated headache symptoms (r = 0.034, p = 0.454). In contrast, the gaming disorder of PUBG showed a weak negative correlation with PSQI (r = -0.092, p = 0.041). The higher levels of gaming disorder are slightly associated with poorer sleep quality. The results of the MLP model suggested that daily PUBG use was the most contributing factor to migraine and related symptoms followed by gaming disorder, gaming addiction, PSQI, and ESS.
CONCLUSION: The study concluded that PUBG playing contributes to migraine and its associated symptoms although is not significant it contributes to less sleep quality and lower quality of life.
AIMS: This study aims to investigate the preferences of older adult individuals in Malaysia regarding digital applications and to propose effective intergenerational instructional strategies that enhance their digital learning experiences.
METHODS: This qualitative study explores older adults' preferences for digital applications and their experiences with intergenerational instructional strategies in Malaysia. A total of 26 older adults and 13 young instructors participated in digital applications workshops at two Pusat Aktiviti Warga Emas (PAWE) centres on August, 2024. Older adults were paired with young instructors in small groups to guide them in using digital applications through hands-on learning. Data collection involved semi-structured interviews before and after the sessions, as well as observations. Thematic analysis is used to analyse interview data, identifying key insights into improving digital learning for older adults in Malaysia.
RESULTS: The study, conducted at two PAWE centres in Perak, Malaysia, explored intergenerational digital learning between 26 older adults (ages 59-82) and 13 young instructors (ages 20-24). Findings highlight the importance of structured instructional strategies, including interactive learning, direct instruction, and clear communication, to enhance digital literacy among older adults. Personalized learning approaches, small group discussions, and adapting content to individual needs improve engagement and comprehension. Smartphone familiarity plays a key role, with participants favouring WhatsApp and Facebook for communication. Older adults face challenges such as fear of complexity, physical limitations, and security concerns, while young instructors benefit from training to improve communication and instructional skills. The program fosters confidence, intergenerational bonding, and digital inclusion, demonstrating the value of tailored learning strategies in bridging the digital divide.
METHODS: The current study used a cross-sectional study design, and data were collected from 384 PMOS patients attending selected healthcare facilities in Lahore. Participants were recruited through a non-probability convenience sampling. Data were collected using a self-structured questionnaire. The Kruskal-Wallis H test was used to assess the differences in dietary patterns, physical activity, and stress levels across PMOS severity groups. Statistical significance was considered at p