“Antibiotics kill things very quickly” - consumers’ perspectives on non-prescribed antibiotic use in Saudi Arabia

Abstract Background In recent decades, the Kingdom of Saudi Arabia has seen an exponentially growing antibiotic resistance, which is exacerbated by the use of antibiotics without a prescription and other various factors. However, no published data are available on factors influencing non-prescription use of antibiotics among the general public in Saudi Arabia using an in-depth interview technique. Methods Semi-structured interviews were carried out with 40 Saudi participants from the Eastern Province of Saudi Arabia, selected via snowball sampling technique. Participants were enrolled based on the following inclusion criteria: 18 years of age or older and had self-medicated themselves with antibiotics in the past two years. Data collection was continued until data saturation was attained. Interviews were audiotaped, transcribed verbatim and analysed using NVivo 10 software. Results Participants (80% female) had a mean (SD) age of 30 years (10.2). Self-medication with antibiotics was associated with various inappropriate antibiotic use behaviours and negative outcomes such as antibiotic resistance, treatment failures and adverse events. Interviews revealed that different reasons contribute to the rise of self-medication with antibiotics, ranging from difficulty accessing healthcare services, participant’s cultural beliefs and practices, lack of knowledge about antibiotics and antibiotic resistance, and weak regulatory enforcement. Conclusions The findings of the present study will aid in generating data that may provide an insight when designing future interventions to promote public health awareness regarding safe and effective use of antibiotics

Self-medication and self-prescription with antibiotics in the Middle East—do they really happen? A systematic review of the prevalence, possible reasons, and outcomes

Objectives: There has been no review on the prevalence, possible causes, and clinical outcomes of self-medication with antibiotics (SMA) in the Middle East. Methods: Databases were searched (January 2000 through June 2016) for articles on SMA among adults aged ≥18 years living in the Middle East. A hand search for relevant citations and key journals was also performed. Results: Twenty-two studies were found. The prevalence of SMA ranged from 19% to 82%. Age, sex, and educational and income levels were the main determinants of SMA. Socio-cultural, economic, and regulatory factors were the most commonly cited reasons for SMA. Penicillins were the antibiotics most commonly used; the antibiotics were obtained mainly via stored leftover drugs, pharmacies without prescriptions, and friends/relatives. SMA was mainly for upper respiratory tract problems. The primary sources of drug information included relatives/friends and previous successful experience. Inappropriate drug use such as wrong indication, short and long duration of treatment, sharing of antibiotics, and storing antibiotics at home for use at a later time were reported. Negative and positive outcomes of SMA were identified. Conclusions: It is important to understand the links between different factors promoting SMA and to assess the changing trends in order to derive strategies aimed at reducing drug-related health risks

Beta blockers may be protective in COVID-19; findings of a study to develop an interpretable machine learning model to assess COVID-19 disease severity in light of clinical findings, medication history, and patient comorbidities

The coronavirus disease 2019 (COVID-19) has overwhelmed healthcare systems and continues to pose a significant threat worldwide. Predicting disease severity would enhance treatment provision and resource allocation. Although multiple studies were conducted to assess COVID-19's severity using machine learning (ML) models, few studies focus on patient medication history and comorbidities. In this study, ML algorithms were trained using a comprehensive dataset comprising medication history, comorbidities, and clinical findings. Patient data was gathered from King Fahad University Hospital (KFUH) in Saudi Arabia (IRB#: 2021-05-480). The dataset comprised 622 positive COVID-19 with 49 features. Three experiments were conducted to train four ML algorithms, including random forest (RF), gradient boosting machine (GMB), extreme gradient boost (XGBoost), and extra trees (ET). Findings revealed that GBM outperformed other models with 96.30% accuracy, 95.80% precision, 97.64% recall, and 96.69% F-score, with 23 features. Moreover, the permutation feature importance technique suggested that the five most influential features for forecasting disease severity were “CRP level”, “CO2 level”, “SrCr”, “Tocilizumab”, and “Age”. In addition, the shapley additive explanation (SHAP) recommended that the “D-Dimer level”, “CrCl”, and “Hypertension” were also influential. The development of an effective GBM model has the potential to aid medical specialists in the assessment of disease severity. While several models take into account patient presentation and laboratory findings, this study is unique in its scope, considering a far more comprehensive patient profile. The developed model was able to accurately predict features that have been clinically shown to correlate with disease severity. Of interest the model was able to identify a pattern of association between the use of certain medications such and disease severity. We report that the use of beta blockers may be associated with reduced severity, whereas the use of immune modulating drugs namely tocilizumab appeared to be associated with poor disease outcomes in this patient population