The 2023 Dengue Outbreak in Bangladesh: Exploring the Epidemiology in Hospitalized Patients.

For the past two decades, Bangladesh has faced recurrent dengue outbreaks, with the most recent occurring in 2023. We investigated the socioeconomic, clinical, and laboratory aspects of patients diagnosed with dengue during this outbreak. This observational study was conducted from July to September 2023 at Dhaka Medical College Hospital and Chittagong Medical College Hospital, and included 450 confirmed cases of dengue. Sociodemographic information was collected via face-to-face interviews, clinical examinations, and laboratory testing, which was done within 24 hours of admission. Dengue severity was classified according to the 2009 WHO dengue guidelines. Notably, 17% of patients experienced severe dengue, and 89% of those with nonsevere cases exhibited at least one warning sign. Most patients were young adults (mean age, 33 years), with a nearly equal male-to-female ratio. Common clinical presentations included fever (95%), myalgia (62%), and headache (58%), whereas warning signs such as vomiting (54%) and abdominal pain (39%) were prevalent. Plasma leakage indicators, including ascites, pleural effusion, and edema, were found predominantly in severe cases. Laboratory findings revealed leukopenia, thrombocytopenia, and elevated hepatic enzymes (alanine aminotransferase and aspartate aminotransferase) in nearly half the patients. An elevated hematocrit level was associated with severe dengue. We report that a substantial number of patients developed severe dengue during the epidemic in 2023, and provide detailed clinical-epidemiological profiles of the patients, offering valuable insight into management of dengue cases.

Leveraging machine learning to evaluate factors influencing vitamin D insufficiency in SLE patients: A case study from southern Bangladesh.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student's t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.