Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions.

Introduction: Type 2 diabetes mellitus (T2DM) and depression are closely linked. People with T2DM are at increased risk of developing depression and vice versa. T2DM and depression comorbid conditions adversely affect Health-Related Quality of Life (HRQOL) and management of T2DM. In this study, we assessed depression and HRQOL among patients with T2DM in Dhaka, Bangladesh. Methods: A cross-sectional study was conducted in two tertiary-level hospitals in Dhaka, Bangladesh. Data were collected from 318 patients with T2DM. A set of standard tools, PHQ-9 (for assessing depression) and EuroQol-5D-5L (for assessing the HRQOL), were used. Statistical analyses, including Chi-square and Fisher's exact tests, Wilcoxon (Mann–Whitney), and Spearman's correlation coefficient tests, were performed using SPSS (v.20). Results: The majority of the patients (58%) were females, with a mean age (standard deviation) of 52 ± 10 years, and 74% of patients lived in urban areas. The prevalence of depression was 62% (PHQ-9 score ≥ 5). Over three-quarters (76%) reported problems in the anxiety/ depression dimension of EQ-5D, followed by pain/discomfort (74%), mobility (40%), self-care (36%), and usual activities (33%). The depression and T2DM comorbid condition were associated with all the five dimensions of EQ-5D (χ2 statistics with df = 1 was 52.33, 51.13, 52.67, 21.61, 7.92 for mobility, self-care, usual activities, pain/discomfort, and anxiety/ depression dimensions respectively, p - < 0.01). The mean EQ-5D index (0.53 vs. 0.75) and the mean EQ-5D VAS (65 vs. 76) both showed lower values in T2DM patients with depression compared to T2DM patients without depression (Wilcoxon test, p - < 0.001). Conclusions: We conclude that the majority of the patients with T2DM had comorbid conditions, and the HRQOL was negatively affected by comorbid depression in T2DM patients. This suggests the importance of timely screening, diagnosis, treatment, and follow-up of comorbid depression in T2DM patients to improve overall health and QOL.

Prolonged exposure to high ambient temperatures, heat stress, and inadequate mitigation measures increases the health and safety risks of construction workers. Following the PRISMA guidelines, our goal was to synthesize recent evidence on the impacts of ambient heat stress on construction workers. A literature review of articles published between 2019 and 2024 was conducted, selecting 42 out of 883 articles that focused on occupational heat stress, extreme ambient temperatures, and their effects on the health, safety, and injury risks of construction workers. The findings highlighted a relationship between occupational heat exposure, increased injury, illness, and mortality among construction workers. Elevated ambient temperatures, during summer and peak work hours, significantly increased the risk of falls, cardiovascular events, and thermal discomfort. Younger, unacclimatized workers in regions with extreme ambient heat and a lack of regulation, observation, and regulation enforcement were at risk. Evidence revealed gaps in worker training, compliance, enforcement, and the integration of individualized monitoring. This review highlights the increasing risks associated with occupational heat exposure in construction workers, driven by rising ambient temperatures. It emphasizes the need for integrated strategies combining personalized wearable technologies, inclusive training, and regulatory reform to improve worker safety and productivity and promote policy development.

Objective This study aims to examine the relation between daily maximum exposure to Particulate Matter (PM_2.5), high wind speed, and minimum visibility, and COVID-19 cases in El Paso County, Texas, a dust-prone region. A time-series analysis using a generalized linear model with a Poisson model was employed to analyze relative risks of COVID-19 cases in El Paso (March 2020 to November 2021). A total of 156,299 cases were diagnosed during the study period. A 10 μg/m³ increase in PM_2.5 levels was linked with higher risk ratio (RR) of COVID-19 (Lag Days 1 to 3) [lag1: RR = 1.004; 95% CI: (1.003–1.005), lag2: RR = 1.006; 95% CI: (1.005–1.007), & lag 3: RR = 1.004; 95% CI: (1.003–1.005)], followed by a decrease in cases. Similarly, a 4.47 m/s rise in maximum wind speed was associated with an elevated RR of cases on Lag Day 4 [lag4: RR = 1.009; 95% CI: (1.003–1.014)], after which numbers begin to drop. Finally, a 4.83 km decrease in minimum daily visibility was correlated with an increased RR of cases on Lag Days 1 and 2 [lag1: RR = 1.031; 95% CI: (1.023–1.039), & lag2: RR = 1.018; 95% CI: (1.011–1.024)], with a decrease on Lag days 3, 4 and 5 and resurgence on Lag days 6 and 7 [lag6: RR = 1.028; 95% CI: (1.021–1.036), & lag7: RR = 1.046; 95% CI: (1.038–1.055)]. PM_2.5, wind speed, and visibility influencing COVID-19 cases in El Paso highlight the need for evidence-based interventions, including information, education, and communication programs, early-warning systems, cross-border air-quality management, real-time monitoring, and stricter emission controls.

The global surge in Autism Spectrum Disorder (ASD) cases, coupled with evidence linking prenatal Particulate Matter (PM) exposure to developmental disruption, demands a comprehensive review to design targeted health interventions. This systematic review and meta-analysis aim to evaluate the strength and consistency of evidence linking prenatal PM exposure to ASD across studies, quantifying this relation to identify actionable environmental risk thresholds. This study employed PRISMA protocols to systematically extract and evaluate evidence from PubMed, Web of Science, Scopus, and ScienceDirect (2010–2024), and screened 4,013 articles to identify qualified case-control and cohort studies (n=29). Data synthesis employed random-effects modeling, accompanied by comprehensive assessment through I ² statistics, Q-tests, funnel plots, Duval and Tweedie’s trim-and-fill analysis, and Egger’s regression, to ensure validity. A meta-analysis of 16 case-control studies revealed a 34 % increased risk of ASD associated with prenatal PM exposure (pooled OR=1.34; 95 % CI: 1.13–1.54), despite substantial between-study heterogeneity (I ² =94.02 %, p<0.001). Publication bias was not significant (Egger’s test p value=0.114). Critical trimester-specific analysis uncovered that third-trimester exposure significantly increased ASD risk (OR=1.17; 95 % CI: 1.01–1.34), while first-trimester (OR=1.02; 95 % CI: 0.92–1.11; I ² =49.18 %, p<0.10) and second-trimester exposures (OR=1.13; 95 % CI: 0.88–1.38; I ² =92.59 %, p<0.001) showed non-significant associations. This review identified prenatal and early life exposure to PM as a risk factor for ASD, indicating a trimester-specific vulnerability. It highlighted the necessity of focused air quality interventions and targeted guidance to reduce prenatal PM exposure to alleviate ASD risk during the critical-window.

Background: Child mental health is a critical public health concern in the United States. This study examines the multifaceted determinants of child mental health, including demographic, socioeconomic, family, healthcare access, and chronic health condition information. Methods: This study utilized data from the National Survey of Children’s Health (NSCH). Univariate, bivariate, and multivariable logistic regression models were fitted to identify the determinants of each outcome variable. Results: We pooled data from two years (2020 & 2021) of NSCH and included a nationally representative sample of 6- to 17-year-old U.S. children (N = 60,809; male, 51.1%; female, 48.9%; 12-17-year-old, 50.9%; 6-11-year-old, 49.1%). There was an increase in the prevalence of depression, anxiety, and ADHD/ADD among 6-17-year-old U.S. children from 2020 to 2021, while the prevalence of behavioral problems decreased during the same period (the prevalences were 4.8%, 10.7%, 8.8%, and 11% respectively for the year of 2020 and 5.3%, 11%, 7.9% and 11.6% in 2021). Our findings reveal significant gender disparities, with girls experiencing higher rates of depression (AOR: 1.64; 95% CI: 1.34-2.01, P <0.001) and anxiety (AOR: 1.62; 95% CI: 1.43-1.84, P <0.001). Teens (12-17 years) were more susceptible to depression and anxiety than younger children (6-11 years). Ethnic and racial disparities were observed, with lower rates of these conditions among non-Hispanic Asian and Hispanic/Latino children. Neighborhood support was a protective factor against anxiety and behavioral problems. Physical activity for at least 60 minutes every day was significantly associated with lower odds of depression (AOR: 0.51; 95% CI: 0.37-0.71; P <0.001) and anxiety (AOR: 0.67; 95% CI: 0.54 - 0.83; P <0.001) as opposed to no physical activities. Adverse childhood experiences (ACEs) were consistently linked to a higher likelihood of mental health conditions. Conclusion: This study underscores the complex interplay of factors influencing child mental health and offers actionable insights for policy and practice. Future research should explore causal relationships and longitudinal trajectories to guide effective interventions and policies.