Depression is the most common mental health problem affecting adolescents globally, wherein its increasing prevalence together with the negative health impacts escalates the need for further research in this area. This work determined the prevalence and factors associated with depressive symptoms among young adolescents in Malaysia. A total of 1350 adolescent aged 13 to 14 years in school across nine secondary schools in Selangor state, Malaysia participated in a cross-sectional study. Independent variables were examined using the using the Global School-Based Student Health Survey included age, gender, ethnicity, alcohol intake, smoking and illicit drug use, loneliness, bullying, parental marital status, income and supervision; and the Health Literacy and Stigma questionnaire examined mental health literacy levels. Depressive symptoms were the dependent variable which was examined using the Center for Epidemiology Study Depression (CESD) instrument. Prevalence of depressive symptoms among all participants was 19 % (95% CI [16.9, 21.2]), with a higher prevalence of depressive symptoms being reported among females 26.3% (95% CI [23.0, 29.8]) compared to males 11.7% (95% CI [9.4, 14.4]). Determinants namely females (AOR = 3.83; 95% CI [2.66, 5.52]), smoking (AOR = 6.16; 95% CI [3.15, 12.05]), been bullied (AOR = 3.70; 95% CI [2.51, 5.47]), felt lonely (AOR = 10.46; 95% CI [7.09, 15.42]) and having no parental supervision (AOR = 1.79; 95% CI [1.26, 2.53]) significantly increased the odds of depressive symptoms among all adolescents in the multivariate model. In addition, feeling lonely, being bullied and smoking were identified as common significant determinants of depressive symptoms across both genders. Feeling lonely (65% to 71%) and being bullied (10% to 19%) were ranked as the most important determinants of depressive symptoms among young adolescents. Tackling these factors would be instrumental in helping decision makers formulate depression prevention strategies and activities for adolescents.

Adherence to the 6-month tuberculosis (TB) treatment regimen is challenging due to its duration and side effects, with various factors influencing patient compliance. A retrospective cross-sectional study was conducted among newly diagnosed pulmonary TB (pTB) patients in Selangor, Malaysia, undergoing treatment in government primary care clinics and hospitals. Patients who were lost to follow-up (LTFU) within the first six months were determined by reviewing patient records and the national TB registry. Logistic regression analysis identified sociodemographic and clinical factors associated with LTFU. Of the 699 pTB patients, 55 (7.9%) were lost to follow-up. Factors significantly associated with LTFU included age (higher in 25–44-year-olds, adjusted odds ratio (aOR): 2.83), unmarried status (aOR: 2.17), lower education level (aOR: 6.13), being a smoker (aOR: 2.65), and unawareness of TB diagnosis (aOR: 38.14). A significant interaction was found between education level and awareness of diagnosis, with unawareness having a stronger association with LTFU among higher-educated patients. Young adults, those with a lower education level, unmarried individuals, smokers, and those unaware of their TB diagnosis are at higher risk of LTFU. These factors can be used for rapid risk assessment. Intensive counselling and health education at treatment initiation, particularly for at-risk patients, are crucial for preventing LTFU.

Endemic channels are widely used in dengue surveillance to detect unusual increases in case counts. However, conventional approaches that rely on historical averages with fixed standard deviation (SD)-based multipliers may produce unstable thresholds and false alerts. This study compared a conventional SD-based endemic channel with a log-scale SD-based endemic channel incorporating an enhanced alert rule to identify the optimal approach for routine dengue surveillance in Malaysia. Weekly national dengue case data from 2014 to 2024 were analyzed. A rolling validation approach was used to evaluate outbreak detection performance from 2017 to 2023 using three-year historical baselines. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and the Youden Index were calculated and pooled across the validation years. The optimal multiplier was selected based on the highest pooled Youden Index using the rolling validation period (2017–2023). The selected approach was then applied independently to the 2024 surveillance data as an operational demonstration of its potential use in routine dengue surveillance. A total of 260 epidemiological weeks were analyzed, of which 42 (16.2%) were classified as outbreak weeks. The log-scale SD-based endemic channel incorporating an enhanced alert rule achieved the highest pooled Youden Index of 0.43 at the optimal multiplier of 0.50. At this multiplier, sensitivity was 0.60, specificity 0.82, positive predictive value 0.35, negative predictive value 0.93, and overall accuracy 0.79. Compared with the conventional SD-based endemic channel at its optimal multiplier (1.00), the proposed approach demonstrated improved sensitivity (0.60 vs. 0.56), specificity (0.82 vs. 0.70), positive predictive value (0.35 vs. 0.23), negative predictive value (0.93 vs. 0.91), accuracy (0.79 vs. 0.68), and Youden Index (0.43 vs. 0.26). The log-scale SD-based endemic channel incorporating an enhanced alert rule demonstrated superior overall outbreak detection performance and was selected as the optimal approach for routine dengue surveillance.

Objectives: The hospital standardised mortality ratio (HSMR) is the ratio of the observed number of hospital deaths to the expected number of deaths, with the latter estimated using statistical models that adjust for available case-mix factors. This study aimed to develop and validate in-hospital or 30-day post-discharge mortality prediction models for 40 diagnosis groups within the HSMR framework, using penalized logistic regression (pLR) and random forest (RF), and to compare the performance of these two approaches.Methods: We analysed 1,144,890 hospital admissions from 14 Malaysian state hospitals between 2012 and 2016. Separate models were developed for each diagnosis group using nine administrative features, including age, comorbidities, and admission category. Model performance was evaluated using mean Brier scores and Cstatistics across multiple bootstrapped datasets to obtain less biased performance estimates. Aggregate expected mortality counts were also compared with observed counts.Results: The overall observed mortality rate was 10.2%. The pLR models consistently showed better discrimination and calibration than the RF models, with lower Brier scores and higher C-statistics across the 40 diagnosis groups. On average, the C-statistic for pLR exceeded that for RF by 0.062. Although the RF model produced aggregate mortality predictions that were numerically closer to the observed counts, it showed high variance and poorer probabilistic calibration than pLR.Conclusions: The pLR model tended to underestimate mortality more than RF but still demonstrated better calibration and discrimination, making it the preferable model for HSMR analysis in this dataset.

OBJECTIVES: This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak.METHODS: SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison.RESULTS: In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario).CONCLUSIONS: Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.

Introduction: Tuberculosis (TB) remains a major global public health challenge requiring urgent interventions to achieve the World Health Organization (WHO) End TB Strategy targets. This study aimed to develop and validate a compartmental model to estimate the TB reproduction number (R ₀ ) and TB incidence in Malaysia, identify parameters influencing R ₀ and TB incidence, and simulate TB incidence using baseline and intervention scenarios to 2035 to assess compatibility with achieving the WHO End TB Strategy target. Methods: A Susceptible-Latent Fast-Latent Slow-Infectious-Recovered (SL _F L _S IR) compartmental model was developed and calibrated using national TB data from 2013 to 2023. Model calibration was performed using initial Monte Carlo exploration followed by Particle Swarm Optimisation to identify final calibrated parameters. R ₀ was estimated using the next-generation matrix approach. Uncertainty was quantified using parametric bootstrapping with negative binomial observation model. Model validation used observed TB cases in 2024 and 2025 and was assessed using performance metrics. Global sensitivity analysis using Latin Hypercube Sampling and Partial Rank Correlation Coefficients identified parameters influencing R ₀ and simulated TB incidence in 2035. TB incidence scenarios were simulated to 2035 by varying the transmission rate ( β ), rate of progression from active TB to recovery ( γ ), and rate of progression from latent fast to active TB ( ε ). Results: The calibrated model-estimated TB cases and incidence showed good fit to observed TB cases and incidence during calibration and short-term validation periods, with satisfactory performance metrics. The estimated R ₀ was 1.09 (95% CI: 1.01–1.27). Parameters influencing R ₀ were β and γ , while parameters influencing simulated TB incidence in 2035 were β , γ , and rate of progression from latent slow to active TB ( κ ). TB incidence showed minimal reduction by 2035 in the baseline scenario. The most intensive intervention scenario reduced TB incidence by 66.21% by 2035, relative to 2015. Conclusion: The SL _F L _S IR model showed that TB transmission in Malaysia remains self-sustaining, with estimated R ₀ slightly above 1. Although combined TB-specific interventions could reduce simulated TB incidence, achieving the WHO End TB Strategy targets by 2035 will likely require integrated TB control with broader improvements in population health and social determinants of TB risk.