Bangladesh remains one of the most vulnerable countries in the world to the effects of climate change. Given the reliance of a large segment of the population on the agricultural sector for both their livelihoods as well as national food security, climate change adaptation in the agricultural sector is crucial for continued national food security and economic growth. Using household data from lowland rice farmers of selected haor areas in Sylhet, the current work presents an analysis of the determinants behind the implementation of different climate change adaptation strategies by lowland rice farmers. The first objective of this study was to explore the extent of awareness of climate change within this population as well as the type of opinions held by lowland rice farmers with respect to climate change. To serve this purpose, a severity index (SI) was developed and subsequently employed to evaluate the perceptions and attitudes of 378 farmers with respect to climate change vulnerability. Respondents were interviewed with respect to climate change related circumstances they faced in their daily lives. Attained SI index values ranged from 69.18% to 93.52%. The SI for the perception “Climate change affects rice production” was measured as 93.52%. Using data collected from the same 378 farmers, a logistic regression was carried out to investigate the impact of socio-economic and institutional factors on adaptation. The results show that credit from non-government organizations is highly statistically significant for adaptation, and that rural market structure also has a positive effect on adaptation. Among the studied factors, credit from non-governmental organizations (NGOs) was found to be the most important factor for adaptation. The results of this work further indicate that marginal farmers would benefit from government (GoB) funded seasonal training activities that cover pertinent information regarding adaptation after flash floods. Additionally, the authors of this piece recommend timely issuance of government-assisted credit during early flash floods to afflicted farmers, as such an initiative can aid farmers in adapting different strategies to mitigate losses and enhance their productivity as well as livelihood.

Background and Aims: Depression is a major public health issue, particularly among elderly populations, and is influenced by sociodemographic, health, and lifestyle factors. Ethnic variations in depression remain underexplored in Bangladesh. This study aimed to determine the prevalence of depression among elderly individuals from different ethnic groups and to identify associated risk factors. Methods: This cross‐sectional study was conducted among 632 respondents aged 55 years and above in Rangamati, Sylhet, and Mymensingh, Bangladesh. Depression symptoms were assessed using the validated Patient Health Questionnaire (PHQ‐9). Respondents were categorized as “not depressed” (score 0–9) and “depressed” (score ≥ 10). Data were collected using a structured questionnaire on sociodemographic, economic, and lifestyle factors. Descriptive statistics and logistic regression were used to estimate odds ratios (OR) with 95% confidence intervals (CI) at a significance level of p < 0.05. Analyses were performed in Stata 14.2. Results: The prevalence of depression was 26.6% (168/632). The mean age of participants was 59.73 years (SD = 8.08). Older participants had higher odds of depression: aged 60–70 years (OR = 1.25, 95% CI: 1.01–1.56, p < 0.05) and aged ≥ 71 years (OR = 2.85, 95% CI: 2.15–3.79, p < 0.001) compared to those aged < 60 years. Ethnic differences observable Chakma (43.45%, 73/632), Manipuri (41.67%, 70/632), and Garo (14.88%, 25/632) showed significant variation ( p < 0.001). Longstanding health problems increased depression risk (OR = 6.47, 95% CI: 4.12–9.82, p < 0.001). Hypertension, frequent doctor visits, and poor wealth were also associated with depression. Dietary factors, such as sugar, alcohol, and red meat, showed exploratory associations. Conclusions: Depression is prevalent among elderly populations in Bangladesh, with significant variation across ethnic groups. Age, chronic illness, and socioeconomic disadvantage were key predictors. Findings highlight the need for culturally sensitive mental health interventions and targeted support for elderly minorities and those with chronic conditions.

Background: The prevalence of dengue in Bangladesh has markedly risen, resulting in the most lethal epidemic in 2023 and persistently elevated levels in 2024. Regardless of stringent control measures, dengue persists with comorbidities in Dhaka City. This research explored the correlation between laboratory findings and clinical parameters with severe dengue infection. Methods: A case–control study was performed on 435 dengue patients (145 cases with severe dengue and 290 controls with non-severe dengue) hospitalized in medical college hospitals in Dhaka City from July to December 2024. Participants were matched by age (±5 years), sex, and admission date (±2 weeks). Univariate and multivariable logistic regression analyses were conducted to find out factors associated with dengue severity. Results: Comorbidities, such as chronic pulmonary disease (AOR: 2.14; 95% confidence interval (CI): 0.90–5.10), were associated with an increased odds of severe dengue. Symptoms such as abdominal pain (AOR: 2.30; 95% CI: 1.45–3.68), lethargy (AOR: 2.70; 95% CI: 1.35–5.47), and nausea (AOR: 2.21; 95% CI: 1.15–4.22) were associated with increased dengue infection severity. Severe dengue cases exhibited an elevation in serum creatinine, ALT, AST, and pulse rate, but total bilirubin, albumin, and globulin levels were associated with a reduced odds of severity. Conclusion: This study delineates characteristics associated with severe dengue infection, including comorbidities, such as chronic pulmonary disease, and signs and symptoms, such as abdominal pain, lethargy, and nausea. Laboratory results indicating higher serum creatinine, liver enzymes, and pulse rate, coupled with decreased total bilirubin, albumin, and globulin, were significantly associated with severity. These findings emphasize the significance of early surveillance, especially in patients with comorbidities, and offer critical insights for enhancing the management of severe dengue. Future studies should investigate the transition from non-severe to severe dengue.

Background: In Bangladesh, diabetes mellitus has become a substantial public health burden, imposing a strain on the population both economically and clinically. Robust methods for identifying high‐risk populations using national data are urgently needed due to the increasing prevalence of metabolic noncommunicable illnesses throughout South Asia. Using data from the most recent wave of a reliable national survey, this study was aimed at creating and testing a diabetes prediction model for adult female Bangladeshis. Methods: The 2022 Bangladesh Demographic and Health Survey (BDHS) biomarker data were examined, where key factors of undiagnosed diabetes were identified using stacked ensemble machine learning (ML) algorithms together with explainable AI (XAI) approaches after adjusting for selection bias using a propensity score model. A nomogram, a user‐friendly clinical tool for risk assessment, was created. The area under the receiver operating characteristic (AUROC) curve was used to assess the model′s performance. Results: Of the 18,547 weighted female participants (unweighted n = 7833), the prevalence of undiagnosed diabetes was 6.26% (95% CI: 5.52%–7.00%). Significant predictors identified included age (mean 42 ± 16 years vs. 38 ± 16 years; p < 0.001), BMI (mean 24.7 ± 4.6 kg/m ² ; p < 0.001), and hypertension (mean 120/78 mmHg; p = 0.001). ML models demonstrated high predictive accuracy (AUROC > 0.80), and a simplified clinical nomogram was developed to provide personalized risk scores. XAI (SHAP) analysis emphasized nonlinear influences, particularly in urban residents, who accounted for 40% of undiagnosed cases, and the richest quintile, which bore a disproportionate burden of 33% ( p < 0.001) compared to just 13% in the poorest quintile. Conclusion: For early diabetes screening in Bangladesh, the established predictive model and nomogram provide an evidence‐based method. These technologies can help healthcare providers and policymakers tailor interventions toward high‐risk groups by using nationally representative data. This could potentially lessen the sustained health and financial effects of the diabetes epidemic.