Antimicrobial resistance (AMR) is one of the biggest global public health challenges of the 21st century, but it does not impact all populations equally. Understanding the factors associated with overall AMR and specific resistances in distinct populations can offer insights into which interventions may be most appropriate for different global health contexts. Socioeconomic factors shape not only antibiotic consumption – the main driver of AMR in humans – but also the emergence and transmission of drug-resistant bacteria. These factors include income and income inequality, which are likely to have complex bi-directional relationships with AMR. Commonly applied univariate linear regression models fail to unravel this complexity. This project aims to use richer distributional regression approaches to explore the associations between income and inequality, and AMR at the country level, and to what extent these associations can be explained by factors that can be improved through (targeted) public health investment, such as vaccination coverage and provision of WASH. We will augment the ATLAS and GEARS databases with population-level measures of income and income inequality, vaccination and universal health coverage, measures of water, sanitation and hygiene (WASH), density of healthcare workers, and age structure, gathered previously by our team. Distributional regression models will allow us to uncover associations between income and income inequality, and commonly ignored features of the distribution of AMR in human populations, levering continuous minimum inhibitory concentrations instead of binary resistance classification. A specific type of distributional model, bivariate distributional copulas, will allow us to identify heterogeneity in the relationship between income inequality and AMR, and any synergies, trade-offs or decoupling between reducing income inequality and reducing AMR. Findings from this study can improve understanding of the complex relationship between income inequality and AMR, and ultimately inform policies to tackle these two high-priority global health and economic challenges.
Center for Global Research Data