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Countries worldwide have deployed mass COVID-19 vaccination drives, but there are people who are hesitant to receive the vaccine. Studies assessing the factors associated with COVID-19 vaccination hesitancy are inconclusive. This study aimed to assess the global prevalence of COVID-19 vaccination hesitancy and determine the potential factors associated with such hesitancy. We performed an organized search for relevant articles in PubMed, Scopus, and Web of Science. Extraction of the required information was performed for each study. A single-arm meta-analysis was performed to determine the global prevalence of COVID-19 vaccination hesitancy; the potential factors related to vaccine hesitancy were analyzed using a Z-test. A total of 56 articles were included in our analysis. We found that the global prevalence of COVID-19 vaccination hesitancy was 25%. Being a woman, being a 50-year-old or younger, being single, being unemployed, living in a household with five or more individuals, having an educational attainment lower than an undergraduate degree, having a non-healthcare-related job and considering COVID-19 vaccines to be unsafe were associated with a higher risk of vaccination hesitancy. In contrast, living with children at home, maintaining physical distancing norms, having ever tested for COVID-19, and having a history of influenza vaccination in the past few years were associated with a lower risk of hesitancy to COVID-19 vaccination. Our study provides valuable information on COVID-19 vaccination hesitancy, and we recommend special interventions in the sub-populations with increased risk to reduce COVID-19 vaccine hesitancy.

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Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

The high rates of bacterial infections affect the economy worldwide by contributing to the increase in morbidity and treatment costs. The present cross-sectional study was carried out to evaluate the prevalence of bacterial infection in urinary tract infection (UTI) patients and to evaluate the antimicrobial resistance rate (AMR) in a Tertiary Care Hospital in Lahore, Pakistan. The study was conducted for the period of one year from January 2020 to December 2020. A total of 1899 different clinical samples were collected and examined for bacterial cultures using standard procedures. Samples were inoculated on different culture media to isolate bacterial isolates and for identification and susceptibility testing. A total of 1107/1899 clinical samples were positive for Staphylococcus aureus (S. aureus), Pseudomonas aeruginosa (P. aeruginosa), Escherichia coli (E. coli) and other bacterial isolates. Methicillin-resistant S. aureus (MRSA) prevalence was 16.93% from these positive cases. MRSA strains were found to be highly resistant to amikacin, clindamycin, fusidic acid, gentamicin and tobramycin, while highest sensitivity was noted against vancomycin (100%) and linezolid (100%). MRSA and high rates of multidrug resistance (MDR) pose a serious therapeutic burden to critically ill patients. A systematic and concerted effort is essential to rapidly identify high-risk patients and to reduce the burden of AMR.

Implementing the IMA was associated with significant reductions in VAP rates in ICUs of Saudi Arabia.

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