A new study from the University of Warwick in the UK has found that efforts to reduce the use of antibiotics in low-to-middle-income countries could be impeded by fears around leaving infectious diseases untreated and poorly enforced antibiotic supply controls.
The research is one of the most detailed qualitative analyses of the context of clinical trials in antimicrobial resistance ever conducted. It calls for the routine collection of social data alongside clinical trials to help tailor the local appropriateness of clinical interventions and help researchers interpret their findings.
The researchers analysed clinical trials in Myanmar, Thailand and Vietnam and identified disparities relating to the effectiveness of a five-minute finger-prick blood test to reduce antibiotic prescriptions for fever patients in primary healthcare. They found that if antibiotics were over-abundant or if healthcare workers were worried about deadly infectious diseases, they were less likely to follow the guidance provided by the biomarker test. They also found that if long and dangerous journeys prevented patients from follow-up visits to primary health centres or if they struggled to understand the purpose of the test, then patients may be more likely to ignore the results and buy antibiotics without prescriptions from local grocery stores and pharmacies.
Lead author Dr Marco Haenssgen said: “An example of how context affected clinical adherence relates to the strong antibiotic policies and the ways to manage patients without antibiotics in Thailand. Some doctors had a surprising oversupply of antibiotics to the extent that they almost felt they needed to prescribe to get rid of the surplus medicine. This was of course not the only way in which clinician adherence varied, but it shows how the same AMR intervention might or might not work, and how we need to tailor our interventions specifically for each country – one size doesn't fit all contexts.
“For researchers, more contextual data from clinical trials means that we will be able to carry out meta-analyses to identify which contextual factor (e.g. poverty, complementary health policy) matters for the successful operation of a new intervention. That would then inform a design toolkit for clinicians that can guide them in identifying appropriate interventions or advocating for changes in policy.”
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