Fighting AMR: Close schools and use antibiotics

The global dynamics of antimicrobial resistance (AMR) are extremely complex, but we usually focus on the selective pressure created by antibiotic consumption and spread of resistant strains. The brave ones amongst us (or the ignorant) try to disentangle all the facets of global AMR dynamics, and even attempt to quantify the relative contribution of each of these factors. Well, some brave investigators tried to do just that and published their findings in Lancet Planet Health. Perfect Journal Club material.

They constructed a database with data from up to 103 countries on antibiotic use, climate, infrastructure, Gross Domestic Product per capita, education, public spending on healthcare and quality of governance (more specifically the countries position at the international corruption index list). There were two endpoints: E. coli AMR and aggregate AMR (including AMR in E. coli, Klebsiella spp and MRSA). In a cross-sectional design each country had a score for resistance and scores for all the determinants, to play with. For all obvious problems with interpretation of univariable correlations (i.e. confounding, confounding and confounding), we go straight to the multivariable analyses, which were restricted to 73 countries. Surprisingly, or not, antibiotic usage was not significantly associated with both E. coli and aggregate AMR. Yet, an increase in healthcare expenditure, improved quality of governance (i.e., less corruption) and infrastructure were significantly associated with less AMR and higher average educational levels were associated with more AMR.

So, what does that mean? Should we close schools in order to lower the average education level to subsequently get less AMR? And does the absence of evidence for an association between antibiotic use and AMR prove evidence of absence for this relationship?

The authors concluded that antibiotic consumption only explains a portion of AMR levels (safe conclusion!), but also that their findings should have major policy implications. And this turned on the alarm bells among the PhD candidates. How perfect were the data on antibiotic use in low- and middle-income countries, where most of the resistance was? What role did ecological fallacy play? This may happen when we deduce inferences about individuals on the basis of trends observed in the group to which these individuals belong, which may lead to wrong conclusions. For example, if you would conclude that an observed association between higher educational levels and higher AMR implies that “more affluent and better educated people probably use more antibiotics”.

The overall feeling during the Journal club was that the authors are to be applauded for their one-health (one-world, actually!) approach and for thinking outside the current paradigm box. Yet, they also felt that the authors go way too far in inferring causality based on statistical associations. They actually interpreted model coefficients as if they could predict effects of interventions, such as an expected decrease in E. coli AMR of 18.6% for every standard deviation improvement in the infrastructure score.

No sensible person will question the beneficial effects of the proposed interventions (i.e. improve sanitation, reduce corruption), but it is very questionable whether – inferred from this cross-sectional study – these changes will reduce AMR. For teaching and scientific reasons we warn the world against allocating healthcare budgets based on results of imperfect cross-sectional studies, but for altruistic reasons we favor the recommendations coming from these analyses.

Denise van Hout is a PhD candidate in the department of Infectious DiseasesEpidemiology.



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