Substandard and falsified antibiotics are associated with antimicrobial resistance: a retrospective country-level analysis
- PMID: 40461054
- PMCID: PMC12142163
- DOI: 10.1136/bmjgh-2024-017078
Substandard and falsified antibiotics are associated with antimicrobial resistance: a retrospective country-level analysis
Abstract
Objective: Substandard and falsified (SF) medicines pose a significant public health concern due to their elusive nature and potentially dire consequences, including the exacerbation of antimicrobial resistance (AMR). This study aims to quantitatively assess the relationship between the prevalence of SF antibiotics and AMR.
Methods: We combine the Infectious Diseases Data Observatory database (1962-2019), with Global Bacterial Antimicrobial Resistance Burden Estimates (2019) to investigate whether the prevalence of SF antibiotics is positively associated with AMR. Using a quasi-binomial regression, we analyse 257 unique observations across 63 countries and 7 antibiotic classes between 1992 and 2019. We adjust the model for socioeconomic, environmental, health-related, governance and livestock production factors.
Findings: We find that the prevalence of SF antibiotics is positively associated with AMR, after controlling for Gross Domestic Product per capita, population density, particulate matter 2.5, cardiovascular death rate, human antibiotic consumption, regulatory quality, livestock production. The prevalence of SF antibiotics is also positively associated with deaths per 100 000 people attributable to or associated with AMR. This is robust to adding more covariates and country fixed effects as well as dropping countries with a limited number of observations.
Conclusion: AMR ranks among the top global health threats, presenting a multifaceted challenge affecting humans, animals and environment. This study sheds light on the possible relationship between SF antibiotics and AMR. While the prevalence of SF antibiotics appears to be associated with AMR, further research and more representative data are needed to determine the extent to which this association could be explained by a direct causal relationship.
Keywords: Global Health; Health systems; Public Health.
© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.
Conflict of interest statement
Competing interests: None declared.
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