Exploring the evidence base for national and regional policy interventions to combat resistance

The effectiveness of existing policies to control antimicrobial resistance is not yet fully understood. A strengthened evidence base is needed to inform effective policy interventions across countries with different income levels and the human health and animal sectors. We examine three policy domains—responsible use, surveillance, and infection prevention and control—and consider which will be the most effective at national and regional levels. Many complexities exist in the implementation of such policies across sectors and in varying political and regulatory environments. Therefore, we make recommendations for policy action, calling for comprehensive policy assessments, using standardised frameworks, of cost-effectiveness and generalisability. Such assessments are especially important in low-income and middle-income countries, and in the animal and environmental sectors. We also advocate a One Health approach that will enable the development of sensitive policies, accommodating the needs of each sector involved, and addressing concerns of specific countries and regions

Molecular characterization of extended spectrum β -lactamases enterobacteriaceae causing lower urinary tract infection among pediatric population.

The β-lactam antibiotics have traditionally been the main treatment of Enterobacteriaceae infections, nonetheless, the emergence of species producing β- Lactamases has rendered this class of antibiotics largely ineffective. There are no published data on etiology of urinary tract infections (UTI) and antimicrobial resistance profile of uropathogens among children in Qatar. The aim of this study is to determine the phenotypic and genotypic profiles of antimicrobial resistant Enterobacteriaceae among children with UTI in Qatar. Bacteria were isolated from 727 urine positive cultures, collected from children with UTI between February and June 2017 at the Pediatric Emergency Center, Doha, Qatar. Isolated bacteria were tested for antibiotic susceptibility against sixteen clinically relevant antibiotics using phoenix and Double Disc Synergy Test (DDST) for confirmation of extended-spectrum beta-lactamase (ESBL) production. Existence of genes encoding ESBL production were identified using polymerase chain reaction (PCR). Statistical analysis was done using non-parametric Kappa statistics, Pearson chi-square test and Jacquard's coefficient. 201 (31.7%) of samples were confirmed as Extended Spectrum β -Lactamases (ESBL) Producing Enterobacteriaceae. The most dominant pathogen was 166 (83%) followed by 22 (11%). Resistance was mostly encoded by CTX-M (59%) genes, primarily CTX-MG1 (89.2%) followed by CTX-MG9 (7.7%). 37% of isolated bacteria were harboring multiple genes (2 genes or more). isolates were categorized into 11 clusters, while were grouped into five clonal clusters according to the presence and absence of seven genes namely TEM, SHV, CTX-MG1, CTX-MG2, CTX-MG8 CTX-MG9 CTX-MG25. Our data indicates an escalated problem of ESBL in pediatrics with UTI, which mandates implementation of regulatory programs to reduce the spread of ESBL producing Enterobacteriaceae in the community. The use of cephalosporins, aminoglycosides (gentamicin) and trimethoprim/sulfamethoxazole is compromised in Qatar among pediatric population with UTI, leaving carbapenems and amikacin as the therapeutic option for severe infections caused by ESBL producers

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from June 14 to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization

Neonatal sepsis 1991-2001 Prevalent bacterial agents and antimicrobial susceptibilities in Bahrain

contribution to the definition of the role of fungal infection among neonates at ris

Trends in antibiotic sensitivity pattern and molecular detection of tet(O)-mediated tetracycline resistance in Campylobacter jejuni isolates from human and poultry sources

This study was conducted to determine the trends in Campylobacter antibiotic resistance occurring in our setting and to assess the differences in the isolates using patterns of plasmid profiles. One hundred Campylobacter jejuni strains of human and poultry origin isolated in 2002-2003 (phase A) and 2005-2006 (phase B) in the Kingdom of Bahrain were evaluated. Susceptibility to erythromycin, ciprofloxacin and tetracycline was determined, and plasmid extraction and polymerase chain reaction detection of the tet(O) gene was carried out. A single erythromycin-resistant isolate was identified, in sharp contrast to the high ciprofloxacin resistance which also showed an increment in phase B. Tetracycline resistance was higher in chicken (80.9%) compared to human (41.3%) isolates (P < 0.01). Most isolates harbored two plasmids (23 kb and 35 kb) with significant correlation between tetracycline resistance and plasmid carriage in chicken isolates. The findings show continued effectiveness of erythromycin for campylobacteriosis but an increasing trend of high ciprofloxacin and tetracycline resistance. Tetracycline resistance is most likely due to the transfer of plasmids carrying the tet(O) gene between isolates