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Pharmaceutical pollution is a globally recognised public health and environmental issue that can negatively affect aquatic organisms, impact on drinking water quality and contribute to the spread of antimicrobial resistance. In this research, we developed the first UK multi-criteria decision support tool (DST) for medical practitioners to encourage eco-directed prescribing that considers environmental risk factors alongside medical considerations. The probabilistic DST, based on Bayesian networks, implemented a novel decision-making framework as a blueprint to predict environmental risk and inform eco-directed prescribing for an initial list of priority pharmaceuticals. The risk criteria agreed with stakeholders from both healthcare and environmental sectors included pharmaceutical physico-chemical properties; prescription and excretion rates; sewage treatment removal rates and dilution in the freshwater environment. The priority compounds were selected through surveys, facilitated discussion and voting by stakeholders across the environment, medicines regulation, prescribing, public health and pharmaceutical industry sectors. Based on clinical and environmental factors, four pharmaceuticals were selected: carbamazepine, clarithromycin, fluoxetine, and propranolol. Expert consultation and literature review identified data on the environmental exposure and hazard of selected pharmaceuticals. Data was collated into a database, following a classification system based on prescribing data (population standardised, by mass), ecotoxicological data, environmental monitoring data, and drug physicochemical properties. Scotland-wide risk simulation models were developed, with mapping to visualise risk levels in freshwater catchments. The models show a gradient of risk in Scotlands freshwaters, with greatest risk in the most highly populated areas. The project has helped to increase awareness on environmental impact of pharmaceuticals, and has progressed cross-sector activity to develop support tools to introduce environmental data into prescribing decision-making in Scotland.
Glendell et al. (Fri,) studied this question.