Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh.

OBJECTIVE: To estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location. METHOD: We conducted this study using data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured. We utilized chi-square tests and ordinal logistic regression to analyze the association between various selected variables in both urban and rural settings and their relationship with diabetes and prediabetes. RESULTS: The prevalence of T2D was 10.8% in urban areas and 7.4% in rural areas, while pre-diabetes affected 31.4% and 27% of the populations in these respective settings. The study found significant factors influencing diabetes in both urban and rural regions, particularly in the 55-64 age group (Urban: AOR = 1.88, 95% CI [1.46, 2.42]; Rural: AOR = 1.87, 95% CI [1.54, 2.27]). Highly educated individuals had lower odds of T2D, while wealthier and overweight participants had higher odds in both areas. In rural regions, T2D risk was higher among caffeinated drink consumers and those not engaged in occupation-related physical activity, while these factors did not show significant influence in urban areas. Furthermore, urban participants displayed a significant association between T2D and hypertension. CONCLUSION: Our study outlines a comprehensive strategy to combat the increasing prevalence of T2D in both urban and rural areas. It includes promoting healthier diets to control BMI level, encouraging regular physical activity, early detection through health check-ups, tailored awareness campaigns, improving healthcare access in rural regions, stress management in urban areas, community involvement, healthcare professional training, policy advocacy like sugary drink taxation, research, and monitoring interventions. These measures collectively address the T2D challenge while accommodating the distinct features of urban and rural settings.

Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach.

BACKGROUND: The prevalence of suicidal ideation has become an urgent issue, particularly among adolescents. The primary objective of this research is to determine the prevalence of suicidal ideation among students in the southern region of Bangladesh and to predict this phenomenon using machine learning (ML) models. METHODS: The data collection process involved using a simple random sampling technique to gather information from university students located in the southern region of Bangladesh during the period spreading from April 2022 to June 2022. Upon accounting for missing values and non-response rates, the ultimate sample size was determined to be 584, with 51.5 % of participants identifying as male and 48.5 % female. RESULTS: A significant proportion of students, precisely 19.9 %, reported experiencing suicidal ideation. Most participants were female (77 %) and unmarried (78 %). Within the machine learning (ML) framework, KNN exhibited the highest accuracy score of 91.45 %. In addition, the Random Forest (RF), and Categorical Boosting (CatBoost) algorithms exhibited comparable levels of accuracy, achieving scores of 90.60 and 90.59 respectively. LIMITATIONS: Using a cross-sectional design in research limits the ability to establish causal relationships. CONCLUSION: Mental health practitioners can employ the KNN model alongside patients' medical histories to detect those who may be at a higher risk of attempting suicide. This approach enables healthcare professionals to take appropriate measures, such as counselling, encouraging regular sleep patterns, and addressing depression and anxiety, to prevent suicide attempts.

A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.

An unprecedented case report of primary cardiac lymphoma exclusive to left ventricle: a diagnostic and therapeutic challenge.

INTRODUCTION: Primary cardiac lymphoma accounts for <2% of all primary cardiac tumours. It is uncommon in immunocompetent patients, often fatal and diagnosed at autopsy. Tumour usually involves the right heart chambers and pericardium. With advances in imaging, early diagnosis is possible and treatment including chemotherapy and surgery affords good prognosis. CASE PRESENTATION: We present a 50-year-old woman with abdominal pain and fevers for 5 days. Computed tomography of the abdomen showed splenic and renal infarcts but no mass or vegetation was noted on echocardiography. Thoracic computed tomography divulged a large left ventricular filling defect. Cardiac magnetic resonance imaging delineated a 3.5 × 4.5 cm anterobasal mass with frond-like projections and endocardial invasion without extracardiac involvement suggestive of a low-vascularity tumour. Echo-guided endomyocardial biopsy and minithoracotomy with needle biopsy were inconclusive. A sarcoid-protocol cardiac positron emission tomography-fluorodeoxyglucose scan showed focally elevated uptake in the basal anteroseptum without extracardiac uptake, supporting a malignant entity. This prompted open heart mass resection. Pathology revealed diffuse large B-cell lymphoma. DISCUSSION: Our case is a unique report of cardiac lymphoma isolated to the left ventricle. Location of the tumour and lack of specific imaging characteristics made it a diagnostic challenge. It underscores the importance of including lymphoma in the differential for intracardiac masses as it is responsive to chemotherapy. Additionally, it emphasizes the complementary role of imaging modalities and multidisciplinary team approach in diagnosis. Early diagnosis and therapy is the key to establishing successful outcomes.

Infection and readmission rate of cardiac implantable electronic device insertions: An observational single center study.

BACKGROUND: Infection is one of the most serious complications following surgical placement of cardiac implantable electronic devices (CIEDs). Infection prevention efforts are necessary in reducing CIED infectious outcomes. These devices, however, are commonly inserted in higher risk patients, which may explain the ongoing risk of surgical site infection (SSI) in this population. The rates of CIED infection and utilization vary widely in the literature. The definitions of infection may also vary between clinical definitions and the National Healthcare Safety Network (NHSN) criteria. METHODS: The primary objective of this study was to review patient data to identify risk factors for infection and readmission after CIED placement at an academic medical center. The secondary objectives were to compare the rates of SSI identified by NHSN criteria compared to that obtained by applying clinical infection definitions. RESULTS: The overall rate of infection (SSI) was 1.9%, which was identical in both the clinical definition and NHSN reported data. The 30 day readmission rate and the 90 day readmission rate were 12.7% and 25.6% respectively with the most readmissions related to the patients' underlying medical conditions. A lower ejection fraction (EF) was identified as an independent risk factor for readmission, inpatient procedures, smoking and device infection were also significantly associated with readmission after CIED insertion.

Cardiac Implantable Electronic Device Infection: From an Infection Prevention Perspective.

A cardiac implantable electronic device (CIED) is indicated for patients with severely reduced ejection fraction or with life-threatening cardiac arrhythmias. Infection related to a CIED is one of the most feared complications of this life-saving device. The rate of CIED infection has been estimated to be between 2 and 25; though evidence shows that this rate continues to rise with increasing expenditure to the patient as well as healthcare systems. Multiple risk factors have been attributed to the increased rates of CIED infection and host comorbidities as well as procedure related risks. Infection prevention efforts are being developed as defined bundles in numerous hospitals around the country given the increased morbidity and mortality from CIED related infections. This paper aims at reviewing the various infection prevention measures employed at hospitals and also highlights the areas that have relatively less established evidence for efficacy.