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Pathog Glob Health ; 117(5): 437-449, 2023 07.
Artigo em Inglês | MEDLINE | ID: mdl-36562085

RESUMO

Antimicrobial resistance (AMR) poses a substantial risk to public health. In low-income and middle-income (LMICs) nations, the impact of AMR is significantly more severe. The absence of data from low-income countries (LMICs) causes this topic to be frequently overlooked. Additionally, the COVID-19 pandemic could make the AMR issue even worse. Earlier guidelines recommended antibiotic use in patients with COVID-19, even in those without bacterial coinfection. This study aims to investigate the proportion of antibiotic prescriptions in LMICs among patients with and without coronavirus disease-2019 (COVID-19), the proportion of inappropriate antibiotics, and multi-antibiotic prescribing. We followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). We retrieved data through online databases, including PubMed, Scopus, and ScienceDirect. Amongst COVID-19 patients, the meta-analytic estimate of antibiotic prescription was 0.80 (95% CI: 0.72-0.88), whereas antibiotic use among patients with non-COVID-19 infections was 0.54 (95% CI: 0.49-0.58). Half of those prescribed antibiotics (0.52, 95% CI: 0.32-0.72) are inappropriate prescriptions. In addition, we found that one-third of antibiotics prescriptions consisted of more than one antibiotic (0.32, 95% CI: 0.21-0.43). In conclusion, antibiotics are highly prescribed across LMICs, and their use is increased in patients with COVID-19. Amongst those prescriptions, inappropriate and multiple use was not uncommon. This study has several limitations, as it included two studies in an ambulatory setting, and some of the studies included in the analysis were conducted on a small scale. Nevertheless, our findings suggest that urgent action to improve prescribing practices is essential.


Assuntos
Antibacterianos , COVID-19 , Prescrições , Prevalência , Humanos , Antibacterianos/administração & dosagem , Países em Desenvolvimento , Prescrições/estatística & dados numéricos , Prescrição Inadequada
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