ACKNOWLEDGEMENTS
I would like to take this moment to extend my utmost appreciation for all the support provided by my supervisor, Dr. Russell de Souza. He assisted me along the way and ensured that I was always on the right path to achieve all my goals and checkpoints in every circumstance. I would also like to thank my committee for providing me with fantastic support: Ms. Laura Banfield for always being there to help solve any problem in this process, and Dr. Zain Chagla for providing a plethora of knowledge from the technical perspective of infectious disease and being so patient. Special thanks to Dr. Alexandra Mayhew for her support in our prevalence meta-analysis. Finally, I would like to thank my family, my wife, Dr. Sanjida Rowshan Anannya, for whom I am here today, and my parents, siblings, and in-laws; you are always there for me in every walk of life. You are why I have gotten to where I am today and are my daily inspiration. / Background: Bacteria is a major cause of many infectious diseases, and the treatment for these diseases is antibiotics designed to kill or subdue the growth of the bacteria. However, bacteria evolve, and if an antibiotic prescription is not the right antibiotic for the right patient at the right time with the correct dose and the right route, Antimicrobial Resistance (AMR) may result. During this pandemic, the use of antibiotics to treat hospitalized COVID-19 patients without any bacterial coinfection threatens the effectiveness of antibiotic treatment for current and future bacterial infections.
Methods: A systematic search was conducted of the Embase, Medline, Web of Science, and Cochrane Library databases by generating search terms using the concepts of “COVID-19,” “Bacterial Coinfection,” “Secondary bacterial infection,” and “Antimicrobial resistance” to identify studies that reported the prevalence of antibiotic prescription for the treatment of COVID-19 in hospitalized patients with and without bacterial coinfection. The pooled estimate of the percentage of the total and confirmed appropriate antibiotic prescriptions provided to hospitalized COVID-19 patients was generated using a random effect meta-analysis with inverse variance weighting.
Result: Of 157,623 participants from 29 studies included in our review, 67% (CI 64% to 71%, P<0.00001) were prescribed antibiotics, among which 80% (CI 76% to 83%, P<0.00001) prescriptions were given for the COVID-19 patients without any bacterial coinfections. The use of antibiotics varied during the pre-immunosuppressive period (before 16 June 2020) and post-immunosuppressive period of the pandemic and between the High-Income Countries and Upper and Lower Middle-Income Countries.
Conclusion: This Systematic Review and Meta-analysis finds greater than expected use of antibiotics to treat hospitalized COVID-19 patients without bacterial coinfections, which can worsen AMR globally. Clear and concrete guidelines for the use of antibiotic prescriptions to treat COVID-19 patients, strict monitoring, and compliance with Antimicrobial Stewardship are needed to prevent over-prescription. / Thesis / Master in Advanced Studies (MAS) / Bacteria is a major cause of many infectious diseases. Before the discovery of Antibiotics in 1928, hundreds of thousands of people used to die due to infectious diseases caused by bacteria. While Antibiotics are essential to treat bacterial infectious diseases, overuse or misuse can accelerate Antibiotic Resistance, a phenomenon when bacteria change and/or develop the ability to escape the drugs designed to kill them. Self-medication, availability of antibiotics without a prescription, and inappropriate dosing of antibiotics can worsen the situation. During the COVID-19 pandemic, antibiotics were commonly prescribed as part of the treatment regime for COVID-19, even when a clear bacterial infection was not identified. In our Systematic Review and Meta-analysis, we aimed to see the frequency of antibiotic prescriptions to treat hospitalized COVID-19 patients without any bacterial coinfections.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27928 |
| Date | January 2022 |
| Creators | Rabbi, Fazle |
| Contributors | de Souza, Russell, Global Health |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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