Conectar
Self-medication with antibiotics (SMA) contributes significantly to the emergence of antimicrobial resistance (AMR), especially in low-income countries including Bangladesh. This study aimed to generate evidence on the self-reported prevalence of antibiotic self-medication and its determinants among indigenous people residing in Bangladesh’s Chittagong Hill Tracts (CHT) districts.
This study used a cross-sectional design with data collected through a survey using a semi-structured questionnaire.
This study was conducted from late January to early July 2021; among different indigenous group populations aged 18 years or more olders residing in the three districts of CHT.
A total of 1336 indigenous people residing in Bangladesh’s CHT districts were included.
The primary outcome measure was SMA while explanatory variables were socio-demographic characteristics, health status of participants, and knowledge of antibiotics usage and its side effects.
Among the study participants, more males (60.54%) than females (51.57%) reported using antibiotics. The SMA rate was high among individuals with education levels below secondary (over 50%) and those in the low-income group (55.19%). The most common diseases reported were cough, cold and fever, with azithromycin being the most frequently used antibiotic. Levels of education, family income, having a chronic illness and place of residence were found to be the significant predictors of having good knowledge of antibiotic use as found in the ordered logit model. Findings from a logistic regression model revealed that men had 1.6 times higher odds (adjusted OR (AOR) 1.57; 95% CI 1.12 to 2.19) of SMA than women. Participants with ≥US$893 per month family income had lowest odds (AOR 0.14; 95% CI 0.03 to 0.64) of SMA than those who earned
Male gender, family income, place of residence and knowledge of antibiotics were the significant predictors of antibiotic self-medication. Hence, it is important to streamline awareness-raising campaigns at the community level to mitigate the practice of SMA in indigenous people and ultimately address the devastating effects of Antimicrobial resistance (AMR) in Bangladesh.
by Sona M. Al Younis, Leontios J. Hadjileontiadis, Aamna M. Al Shehhi, Cesare Stefanini, Mohanad Alkhodari, Stergios Soulaidopoulos, Petros Arsenos, Ioannis Doundoulakis, Konstantinos A. Gatzoulis, Konstantinos Tsioufis, Ahsan H. Khandoker
Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (pTo investigate the challenges and opportunities for implementing smoke-free areas (SFAs) within eight area categories using the WHO Framework Convention on Tobacco Control as a framework for analysis.
This study used qualitative methods (in-depth interviews and document reviews). All transcripts from the interviews and formal documents were coded using NVivo V.11 software and analysed using an inductive thematic analysis.
Banda Aceh, Indonesia.
Seventy-three participants were interviewed, stratified by ages (18–59 years): policymakers (n=4), SFA implementers (n=33), SFA’s non-compliance prosecutors (n=2), SFA observers (n=4), communities/respected figures (n=30); and 10 documents were reviewed.
Barriers to the effective implementation of SFAs were identified: conflict of interests of Banda Aceh authorities in implementing SFA policies; inadequate monitoring, evaluation and implementation of SFAs among involved actors; inadequate public communication of SFAs to communities; and misunderstanding of ‘enclosed areas’ as SFAs. However, some important opportunities were identified: the Ministry of Education promotes SFA at schools; and smoking prohibition as part of sharia and other religions’ recommendations.
This research is the first study to examine SFAs to understand the challenges and opportunities for improving SFA policy implementation by interviewing various key respondents in Banda Aceh (religious leaders and respected figures). These key stakeholders’ roles are crucial to enhance the implementation of SFA policies in Banda Aceh (currently suboptimal) and other populous Muslim areas in Indonesia or other countries because smoking contradicts Islamic teachings and other religions’ tenets. Further, the findings propose policymakers and involved agencies strengthen public communication, execution, monitoring and evaluation, and enforcement of SFA policies in Aceh. Finally, the application of methods and results from this study to other local areas in Indonesia or other developing nations is necessary to facilitate further understanding more about the applicability, advantages and limitations of this study.
FreshRSS 