Introduction: The consequences of antimicrobial resistance (AMR) are observed across different socio-economic strata and geographical regions; however, its key drivers and consequences are exacerbated by persisting economic and social inequalities. The AMR Quadripartite and the United Nations (UN) High-Level Political Forum have both empasized the importance of intersectoral actions addressing structual inequalities, as well as the need for sustained political commitment and policy predictability.
Methods: An ecological study was carried out based on secondary data collection, where the association between systemic (including community and hospital-based) antibiotic use (SABU) and thirty-three (N=33) indicators—corresponding to economic, developmental, political conditions, health systems status and inequality—for Hungary were assessed, over the period of 2010–2022. Data on SABU, expressed as defined daily doses per 1,000 inhabitants per day (DDD/1,000 inhabitants/day), were obtained from the European Centre for Disease Prevention and Control (ECDC) ESAC-Net database, while other indicators were sourced from the Hungarian Central Statistical Office (KSH), WHO Health for All, UN Human Development Reports, Our World in Data, and the World Bank DataBank databases, respectively. Statistical analyses (descriptive statistics, Spearman’s rho [ρ]) were conducted using jamovi version 2.4.5. The study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines.
Results: SABU showed negative correlations with the mean number of years in education (rₛ=−0.688; p=0.009), number of nurses and midwives/10,000 inhabitants (rₛ=−0.564; p=0.044), gross domestic product (GDP) per capita (rₛ=−0.523; p=0.067), political stability index (rₛ=−0.529; p=0.062), gross national product (GNP) (rₛ=−0.502; p=0.081), and the Gini coefficient (rₛ=−0.484; p=0.094). Conversely, positive correlations were observed between SABU and the human rights index (rₛ=0.565; p=0.044) and with the proportion of the population living in poverty (rₛ=0.517; p=0.070).
Conclusions: Incorporating social stratifiers and the perspectives of intersectional is warranted in AMR-related data collection (e.g., surveillance), policy design and mitigation of AMR-related inequalities.
