In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg⁻¹), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

This study aims to compare the prevalence of hypertension (HTN) and controlled hypertension (CHTN) in US adults and determine the absolute difference in the prevalence of HTN and CHTN between the JNC7 and ACC/AHA 2017 guidelines. Data for this study were derived from the most recent cycle of the National Health and Nutrition Examination Survey (NHANES) 2017–2018. After excluding participants with missing systolic blood pressure (BP) or diastolic BP and aged <18 years, 4730 participants were included in the final analyses. BP was defined as the average of the first three measurements. The prevalence of HTN and CHTN, including absolute differences of these prevalences, were estimated using both JNC7 and ACC/AHA 2017 guidelines. The overall weighted prevalence of HTN was 31.7% (95% CI: 28.7–34.8) based on JNC7, while the corresponding prevalence was 45.6% (95% CI: 43.0–48.3) when new guideline of ACC/AHA was used. Of the people who had HTN according to the JNC7 and ACC/AHA 2017 guidelines, 48.2% (95% CI: 44.4–52.0) and 21.0% (95% CI: 18.1–24.2) had a controlled blood pressure level, respectively. When blood pressure was assessed using both guidelines, the greatest absolute increase in rates of HTN and CHTN was 17.4% and 30.0% in people aged 40–59 years, respectively. Given the high burden of disease due to complications arising from untreated HTN, as well as the higher costs of untreated disease, new guidelines have important public health implications to early detection of patients at risk and prevent complications across different populations.

Background: Although Bangladesh's economy has shown significant improvement over the past two decades, the high population growth rate has hindered development efforts. This study aimed to review the prevalence of different contraceptive methods used among women of reproductive age in Bangladesh, which could help control the population growth rate. Methods: We conducted an extensive literature review and meta-analysis of 82 articles, identifying 20 articles for analysis. The analyses included heterogeneity and publication bias in published papers. Results: The pooled prevalence of various contraceptive methods was as follows: condom use, 7.13%; Female Sterilization, 8.09%; injectables, 12.76%; intrauterine devices (IUDs), 3.76%; male sterilization, 2.34%; periodic abstinence, 6.71%; pills, 33.21%; and withdrawal, 3.27%. Modern contraceptive methods accounted for 62.91% of usage, while traditional methods constituted 8.79%. On average, only 1.95% of women opted for the implant method. The overall prevalence of contraceptive method usage was 59.48%, with 60.59% in urban areas and 54.54% in rural areas. We found statistically significant heterogeneity for all contraceptive methods used by women in Bangladesh. The funnel plot and Egger’s test showed no publication bias for any of the contraceptive methods, except condoms (Z = 2.34, P = 0.0194). The contraceptive methods used by women in rural areas also showed publication bias (Z = −3.04, P = 0.0024). Conclusion: A renewed commitment from government bodies and independent organizations is needed to implement and monitor family planning strategies to ensure adherence to and provision of the most appropriate contraceptive method for couples.

This study aimed to identify the meteorological factors that contribute to dengue epidemics. The monthly incidence of dengue was used as the outcome variable, while maximum temperature, humidity, precipitation, and sunshine hours were used as independent variables. The results showed a consistent increase in monthly dengue cases from 2013 to 2021, with seasonal patterns observed in stationary time-series data. The ARIMA (2, 1, 3) × seasonal (0, 1, 2)₁₂ model was used based on its lowest Akaike Information Criterion (AIC) values. The analysis revealed that a 1-unit increase in rainfall was positively correlated with a small 0.062-unit increase in dengue cases, whereas a 1-unit increase in humidity was negatively associated, leading to a substantial reduction of approximately 16.34 cases. This study highlights the importance of incorporating weather data into national dengue prevention programs to enhance public awareness and to promote recommended safety measures.

This study explores the effect of climate factors on dengue incidence in Bangladesh and Singapore from 2000 to 2020. Various forecasting models, including Seasonal ARIMA, Poisson regression, artificial neural networks (ANN), support vector machines (SVM), and other statistical techniques, were applied to forecast dengue trends and generate future datasets. The results showed that Bangladesh has higher mean temperatures. The Poisson regression indicated that rainfall positively correlated with dengue cases in Bangladesh, while humidity and sunshine showed negative associations. In Singapore, temperature was positively associated with dengue cases, while rainfall and humidity had inverse relationships. ARIMA models predicted Singapore would experience the highest dengue cases by 2023. Based on the Root Mean Square Error (RMSE), the TBATS model was most accurate for predicting dengue cases in Bangladesh, while both ARIMA and TBATS models performed well in Singapore. This study provides valuable insights for policymakers in Singapore and Bangladesh to develop climate-based warning systems.