Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree-based machine learning models.

Background and Aims: Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students. Methods: A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems. The tree-based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves. Results: Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness-eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia. Conclusion: The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.

Uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the COM-B model.

BACKGROUND: The COVID-19 pandemic has imposed unprecedented suffering on social and individual levels worldwide. Vaccines against COVID-19 have been prioritized as a crucial strategy for ending the pandemic as well as minimizing its consequences. OBJECTIVES: This study aimed to determine the uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the Capability, Opportunity, Motivation, and Behavior (COM-B) model. METHODS: We conducted a web-based cross-sectional study in the Hat Yai district, Songkhla province in Southern Thailand, in September and October 2021. The questionnaire was composed of sections on sociodemographic characteristics, COVID-19 vaccination status, and COM-B constructs. We employed a multivariable logistic regression analysis to determine factors associated with the uptake of the COVID-19 vaccine. We set statistical significance at p < 0.05. RESULTS: In this study, females constituted 54.7% of the total participants (n = 358), and nearly half of the participants (45.8%) were in the younger age group (18-29). Of all the participants, 59.5% (95%CI: 54.2%-64.6%) received at least one dose of the COVID-19 vaccine. Factors associated with the uptake of COVID-19 vaccine and their adjusted OR (95% CI) were being married: 3.59 (2.06-6.24), having a graduate degree: 2.34 (1.38-3.96), gainfully employed: 3.30 (1.91-5.67), having a high level of opportunity: 2.90 (1.48-5.66), and having a high level of motivation: 2.87 (1.17-17.08). CONCLUSION: The uptake of COVID-19 vaccines was moderate in this population. Moreover, the results showed that the COM-B model is useful in predicting COVID-19 vaccine uptake. The findings of this study could be used to aid future public health interventions in any event of outbreaks similar to COVID-19 disease in Thailand and beyond.

A quantile regression approach to identify risk factors for high blood glucose levels among Bangladeshi individuals.

Background and Aims: Diabetes mellitus, characterized by high blood glucose, is an overwhelming public health concern globally, including in Bangladesh. The implication of this trend may pose a significant challenge to the health systems due to the lack of awareness and improper management of this chronic disease. To formulate strategies for public health planning, this study aims to explore the potential risk factors for elevated blood glucose levels among Bangladeshi individuals using advanced statistical methods and a nationally representative data set. Methods: This study utilized data from the 2017-18 Bangladesh Demographic and Health Survey and included 11,863 individuals. A nonparametric Kruskal-Wallis test assessed the significant association between fasting plasma glucose levels and various risk factors. Additionally, a robust quantile regression model was applied to examine the net effects of each risk factor at different quantiles of the distribution. Results: The prevalence of diabetes is 8.1% among individuals in the study population, with variations observed across different administrative divisions in the country. Respondents from the Dhaka division respondents had a higher likelihood (24.1%) of having elevated plasma glucose and the Rangpur division had a lower risk (10.3%) of developing diabetes disease. This study identified several potential risk factors associated with elevated blood glucose levels, including hypertensive disease, overweight and obese body mass index, higher economic status, reduced physical activities, and older age, significantly contributing to develop diabetes mellitus. Conclusion: This study recommends promoting healthy lifestyles, increased physical activity, effective hypertension management, obesity reduction, and nationwide screening programs to control diabetes and noncommunicable diseases in Bangladesh. These preventive measures are crucial for reducing the existing prevalence of diabetes and working toward achieving the Sustainable Development Goals by 2030.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

BACKGROUND: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Assessment of the hypoglycemic and anti-hemostasis effects of Paederia foetida (L.) in controlling diabetes and thrombophilia combining in vivo and computational analysis.

Paederia foetida is valued for its folk medicinal properties. This research aimed to assess the acute toxicity, hypoglycemic and anti-hemostasis properties of the methanolic extract of P. foetida leaves (PFLE). Acute toxicity of PFLE was performed on a mice model. Hypoglycemic and anti-hemostasis properties of PFLE were investigated on normal and streptozotocin-induced mice models. Deep learning, molecular docking, density functional theory, and molecular simulation techniques were employed to understand the underlying mechanisms through in silico study. Oral administration of PFLE at a dosage of 300 µg/kg body weight (BW) showed no signs of toxicity. Treatment with PFLE (300 µg/kg/BW) for 14 days resulted in a hypoglycemic condition and a 30.47% increase in body weight. Additionally, PFLE mixed with blood exhibited a 44.6% anti-hemostasis effect. Deep learning predicted the inhibitory concentration (pIC50, nM) of Cleomiscosins against SGLT2 and FXa to be 7.478 and 6.017, respectively. Molecular docking analysis revealed strong binding interactions of Cleomiscosins with crucial residues of the target proteins, exhibiting binding energies of -8.2 kcal/mol and -7.1 kcal/mol, respectively. ADME/Tox predictions indicated favorable pharmacokinetic properties of Cleomiscosins, and DFT calculations of frontier molecular orbitals analyzed the stability and reactivity of these compounds. Molecular simulation dynamics, principal component analysis and MM-PBSA calculation demonstrated the stable, compact, and rigid nature of the protein-ligand complexes. The methanolic PFLE exhibited significant hypoglycemic and anti-hemostasis properties. Cleomiscosin may have inhibitory properties for the development of novel drugs to manage diabetes and thrombophilia in the near future.

Risk factors of caesarean deliveries in urban-rural areas of Bangladesh.

Background and aims: The key interest of this research is to identify the causes of the ongoing increasing trends in caesarean section or C-section (CS) deliveries in both urban and rural areas of Bangladesh. Methods: This study analyzed all Bangladesh Demographic and Health Survey (BDHS) datasets through Chi-square and z tests and the multivariable logistic regression model. Results: CS deliveries were found to be more prevalent in urban than in rural areas of Bangladesh. Mothers above 19 years, above 16 years at first birth, overweight mothers, those with higher educational levels, those who received more than one antenatal care (ANC) visit, fathers having secondary/higher education degrees and employed as workers or in business, and mothers living in wealthy households in the cities of Dhaka, Khulna, Mymensingh, Rajshahi, and Rangpur divisions had a significantly higher likelihood of CS deliveries in urban areas. Contrastingly, mothers with ages between 20 and 39 years, above 20 years at first birth, normal weight/overweight mothers, those with primary to higher level of education, those in the business profession, fathers who also received primary to higher education, mothers who received more than one ANC visit, and those living in wealthy households in Dhaka, Khulna, Mymensingh, Rajshahi, and Rangpur divisions were more likely to have CS deliveries in rural areas. The 45-49 age group mothers had a five times higher likelihood of CS deliveries [odds ratio (OR): 5.39] in urban areas than in rural areas. Wealthy mothers were more likely to be CS-delivered in urban (OR: 4.84) than in rural areas (OR: 3.67). Conclusion: The findings reveal a gradual upward alarming trend in CS deliveries with an unequal contribution of significant determinants in urban and rural areas of Bangladesh. Therefore, integrated community-level awareness programs are an urgent need in accordance with the findings on the risks of CS and the benefits of vaginal deliveries in this country.

An online observational study assessing clinical characteristics and impacts of the COVID-19 pandemic on mental health: a perspective study from Bangladesh.

Purpose: The pandemic of coronavirus disease 2019 (COVID-19) has cost numerous lives and induced tremendous mental stress among people. The purpose of this research was to determine anxiety and depression levels, clinical features, and the connections between demographic variables and depression prevalence as well as anxiety prevalence among reported COVID-19 cases in Bangladesh. Methods: For the purpose of data collection, an online cross-sectional survey was carried out from May 26 to June 27, 2020, utilizing a Google adapted preformed questionnaire. The form was shared with a short overview and justification through Facebook, Twitter, Facebook messenger, Viber, and What's App. The Google form contains five parts: a brief introduction, an approval statement, demographics, clinical and radiological data, and mental health assessment by the Generalized Anxiety Disorder 7-item (GAD-7) scale and Patient Health Questionnaire (PHQ-9). Formal ethical clearance was taken from the Institute of Biological Science (IBSc), Bangladesh. Informed consent was ensured before participation. Results: One hundred and fifty-three (153) patients with COVID-19 who had an average age of 39.43 ± 17.59 years with male predominance (72%) were included. A total of 32.7% were doing health-care related jobs, and 17.7% lost their jobs due to COVID-19. Patients had a median income of 30,000 Bangladesh taka (BDT). Of all, 12.4% of the participants showed asymptomatic features, whereas 87.6% of patients were symptomatic and presented with fever (79%), cough (58.8%), myalgia (24.2%), breathlessness (23.5%), sore throat (21.6%), fatigue (19.6%), headache (13.7%), nausea and/or vomiting (11.8%), runny nose (9.8%), chest pain (9.2%), diarrhea (8.5%), stuffy nose (3.2%), ARDS (2.6%), oral ulcer (2.6%), and conjunctivitis (1.9%). Overall, the prevalence of anxiety and depression was 63.5% and 56.6%, respectively. Among the participants, 13.2% had only anxiety, 6.3% had only depression, and 50.3% had both. Conclusion: In most cases, middle age, male, and healthy workers were patients. Fever and cough were the standard presentations. Approximately two-thirds or 66.67% of patients had anxiety and depression, one or both.

The experiences of district public health officers during the COVID-19 crisis and its management in the upper southern region of Thailand: A mixed methods approach.

District public health officers (DPHO) are the major health care providers and vital resources for tackling the coronavirus disease 2019 (COVID-19) outbreak in Thailand. No studies have been published on their experiences of combating COVID-19 in Thailand. To guide and improve COVID-19 control efforts, we aimed to describe their experiences and analyze associated factors for tackling the outbreak. This mixed-methods design involved providing structured questionnaires to selected DPHOs across 52 districts of seven provinces in the upper southern region Thailand. We performed data analysis using descriptive and multivariate statistics. The quantitative approach used questionnaires that demonstrated the content validity and reliability. Data collection involved Google forms, analyzed by multivariate statistics. The qualitative approach comprised an online in-depth interview of 11 DPHOs and a thematic analysis. Results found of the 52 DPHOs, 41 were men (78.8%), and the mean age was 50.02 years (SD = 8.52 years). Their proactive experiences were significantly associated with sex (ORadj = 2.38, 95% CI = 1.11-3.30), age (ORadj = 1.73, 95% CI = 1.09-2.76), the length of experience in the current position (ORadj = 2.27, 95% CI = 1.43-3.63), and working time in the current position (ORadj = 2.27, 95% CI = 1.43-3.63). There was no significant association between marital status, knowledge, understanding, opinion, proactive practice, and participation experiences. These results were related to six themes of the qualitative approach as follows: High morbidity and mortality of COVID-19, COVID-19 concomitant with several problems, Reaching out to the community for better COVID-19 solutions, The importance of regular reports and feedback, Solution planning based on the situation, and Providing relief to all stakeholders from COVID-19 issue. Proactive experiences of district public health officers are important for sustainable COVID-19 solutions. Disseminating relevant equipment, guidelines, policy, and government regulations is necessary to promote preparedness and efficacy in the crisis management of COVID-19.

Understanding dengue solution and larval indices surveillance system among village health volunteers in high- and low-risk dengue villages in southern Thailand.

Background: Successful dengue solutions require community collaboration between agencies engaged in human health, vector control and the environment. In Thailand, village health volunteers emphasize the need for a health working group to interact, collaborate, and coordinate actions. The objectives of this study were to acquire an understanding of dengue solutions, as well as the larval indices surveillance system of village health volunteers in high- and low-risk dengue villages. Methods: After 12 months of training in dengue prevention and setting larval indices surveillance systems, an analytical cross-sectional survey was conducted. A total of 117 villages were included in the 18 primary care facilities within one district in southern Thailand, and they were divided into 71 high-risk and 46 low-risk dengue villages. Sample size was determined using the G*power formula. The content validity index and reliability values of Cronbach's alpha coefficient for the questionnaires were 0.91 and 0.83, respectively. A random sampling approach was used to acquire data. The chi-square test, t-test, and odds ratio were used to assess the sample's level of understanding. Results: The study included 1302 village health volunteers, including 895 and 407 from high- and low-risk dengue communities, respectively. In total, 87.9% were female, 51.6% were 20-35 years old, 48.8% had worked as a village health volunteer for 11-20 years, 27.1% had an upper elementary education, and 59.1% had dengue in the previous 12 months. Understanding of the dengue solution and larval indices surveillance system varied across high- and low-risk dengue villages. Village health volunteers with a high level of understanding of the dengue solution and larval indies surveillance system were 1.064 and 1.504 times more likely to stay in high-risk dengue villages, respectively (odds ratio [OR] = 1.064, 95% confidence interval [CI]:0.798-1.419, p = 0.672 and OR = 1.504, 95% CI:1.044-2.167, p = 0.028). Conclusions: Village health volunteers require ongoing training to understand the prevention and control of dengue and larval indices surveillance systems, promote awareness, and monitor dengue in both high- and low-risk dengue villages.

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers.

COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (ß = 11.91, 95% CI: 4.77, 19.05) and India (ß = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (ß = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (ß = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.

Digital technologies are the need of today to predict, prevent and control emerging infectious diseases. Bangladesh is one of the world's poorest and most densely populated countries and faces a double burden of two deadly diseases, COVID-19 and dengue. In response to both these diseases, the absence of a digital healthcare system and insufficient preparedness, lack of public awareness pose unique challenges and a large threat to the population, resulting in epidemics of escalating severity. This paper suggests a digital health care and surveillance system based on the internet of things (IoT) and artificial intelligence (AI) for timely identification of COVID-19 and dengue cases and improving the prevention and control strategies in the country.

Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.

Accurate predictive time series modelling is important in public health planning and response during the emergence of a novel pandemic. Therefore, the aims of the study are three-fold: (a) to model the overall trend of COVID-19 confirmed cases and deaths in Bangladesh; (b) to generate a short-term forecast of 8 weeks of COVID-19 cases and deaths; (c) to compare the predictive accuracy of the Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost) for precise modelling of non-linear features and seasonal trends of the time series. The data were collected from the onset of the epidemic in Bangladesh from the Directorate General of Health Service (DGHS) and Institute of Epidemiology, Disease Control and Research (IEDCR). The daily confirmed cases and deaths of COVID-19 of 633 days in Bangladesh were divided into several training and test sets. The ARIMA and XGBoost models were established using those training data, and the test sets were used to evaluate each model's ability to forecast and finally averaged all the predictive performances to choose the best model. The predictive accuracy of the models was assessed using the mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The findings reveal the existence of a nonlinear trend and weekly seasonality in the dataset. The average error measures of the ARIMA model for both COVID-19 confirmed cases and deaths were lower than XGBoost model. Hence, in our study, the ARIMA model performed better than the XGBoost model in predicting COVID-19 confirmed cases and deaths in Bangladesh. The suggested prediction model might play a critical role in estimating the spread of a novel pandemic in Bangladesh and similar countries.

Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand.

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4-40%) of districts in Laos (mean r = 0.5) and 27% (15-53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12-40%) of districts in Laos and in 13% (3-38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0-28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.

Analyzing Predictors of Control Measures and Psychosocial Problems Associated with COVID-19 Pandemic: Evidence from Eight Countries.

COVID-19 has harshly impacted communities globally. This study provides relevant information for creating equitable policy interventions to combat the spread of COVID-19. This study aims to predict the knowledge, attitude, and practice (KAP) of the COVID-19 pandemic at a global level to determine control measures and psychosocial problems. A cross-sectional survey was conducted from July to October 2020 using an online questionnaire. Questionnaires were initially distributed to academicians worldwide. These participants distributed the survey among their social, professional, and personal groups. Responses were collected and analyzed from 67 countries, with a sample size of 3031. Finally, based on the number of respondents, eight countries, including Bangladesh, China, Japan, Malaysia, Mexico, Pakistan, the United States, and Zambia were rigorously analyzed. Specifically, questionnaire responses related to COVID-19 accessibility, behavior, knowledge, opinion, psychological health, and susceptibility were collected and analyzed. As per our analysis, age groups were found to be a primary determinant of behavior, knowledge, opinion, psychological health, and susceptibility scores. Gender was the second most influential determinant for all metrics except information about COVID-19 accessibility, for which education was the second most important determinant. Respondent profession was the third most important metric for all scores. Our findings suggest that health authorities must promote health educations, implement related policies to disseminate COVID-19-awareness that can prevent and control the spread of COVID-19 infection.

Ecological, Social, and Other Environmental Determinants of Dengue Vector Abundance in Urban and Rural Areas of Northeastern Thailand.

Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants' KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.

Clinical manifestations and socio-demographic status of COVID-19 patients during the second-wave of pandemic: A Bangladeshi experience.

BACKGROUND: Bangladesh is a densely populated country with a substandard healthcare system and a mediocre economic framework. Due to the enormous number of people who have been unaware until now, the development of COVID-19's second-wave infection has become a severe threat. The present investigation aimed to characterize the clinical and socio-demographic characteristics of COVID-19 in Bangladesh. METHODS: A cross-sectional analysis was carried out from all the other COVID-19 patients and confirmed by RT-PCR undergoing a specialized COVID-19 hospital. From March 1 to April 15, 2021, a total of 1326 samples were collected. Samples were only obtained from non-critical COVID-19 patients as critically ill patients required emergency intensive care medications. Then, from April 17 to May 03, 2021, SARS-CoV-2 infection and clinical assessment was performed based on interim guidelines from the WHO. The diagnosis was conducted through RT-PCR. Later, identifying the symptomatic and asymptomatic patient based on checking the Clinical Observation Form (COF). The patients filled the COF form. Finally, statistical analyses were done using the SPSS 20 statistical program. RESULTS: In this investigation, a total of 326 patients were diagnosed as COVID-19 positive. Among them, approximately 19.02% (n = 62) were asymptomatic, and 80.98% (n = 264) were symptomatic. Here, the finding shows that the occurrence of this infection was varied depending on age, sex, residence, occupation, smoking habit, comorbidities, etc. However, Males (60.12%) were more affected than females (39.88%), and, surprisingly, this pandemic infected both urban and rural residents almost equally (urban = 50.92%; rural = 49.08%). Approximately 19% of the asymptomatic and 62% of symptomatic cases had at least one comorbid disorder. Interestingly, an unexpected result was exhibited in the case of smokers, where non-smokers were more affected than smokers. The study indicates community transmission of COVID 19, where people were highly infected at their occupations (35.58%), at houses (23.93%) and by traveling (12.88%). Noteworthy, according to this report, a large number (19.33%) of individuals did not know exactly how they were contaminated with SARS-CoV-2. Patients were most commonly treated by an antibiotic 95.09%, followed in second by corticosteroid 46.01%. Anti-viral drugs, remdesivir, and oxygenation are also needed for other patients. Among those, who were being treated, approximately 69.33% were isolated at home, 27.91% were being treated at dedicated COVID-19 hospitals. Finally, 96.63% were discharged without complications, and 0.03% has died. CONCLUSION: This investigation concludes that males became more infected than females. Interestingly, both urban and rural people became nearly equally infected. It noticed community transmission of SARS-CoV-2, where people were highly infected at their workplaces. A higher rate of silent transmission indicates that more caution is needed to identify asymptomatic patients. Most of the infected people were isolated at home whereas nearly one-fourth were treated at hospitals. Clinically, antibiotics were the most widely used treatment. However, the majority of the patients were discharged without complications. The current investigation would be helpful to understand the clinical manifestations and socio-demographic situations during the second wave of the COVID-19 pandemic in Bangladesh.

Correction: Doum, D., et al. Dengue Seroprevalence and Seroconversion in Urban and Rural Populations in Northeastern Thailand and Southern Laos. Int. J. Environ. Res. Public Health 2020, 17, 9134.

There was an error in the original article [...].

Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media-based cross-sectional survey.

BACKGROUND: Bangladesh experienced its worst dengue fever (DF) outbreak in 2019. This study investigated the knowledge, attitudes and practices (KAP) among university students in Bangladesh and significant factors associated with their prevention practices related to climate change and DF. METHODS: A social media-based (Facebook) cross-sectional KAP survey was conducted and secondary data of reported DF cases in 2019 extracted. Logistic regression and spatial analysis were run to examine the data. RESULTS: Of 1500 respondents, 76% believed that climate change can affect DF transmission. However, participants reported good climate change knowledge (76.7%), attitudes (87.9%) and practices (39.1%). The corresponding figures for DF were knowledge (47.9%), attitudes (80.3%) and practices (25.9%). Good knowledge and attitudes were significantly associated with good climate change adaptation or mitigation practices (p<0.05). Good knowledge, attitudes and previous DF experiences were also found to be significantly associated with good DF prevention practices (p<0.001). There was no significant positive correlation between climate change and DF KAP scores and the number of DF cases. CONCLUSIONS: Findings from this study provide baseline data that can be used to promote educational campaigns and intervention programs focusing on climate change adaptation and mitigation and effective DF prevention strategies among various communities in Bangladesh and similar dengue-endemic countries.