Co-developing frameworks towards environmentally directed pharmaceutical prescribing in Scotland – A mixed methods study

• Published in: The Science of the total environment 2024-12, Vol.955, p.176929, Article 176929
• Main Authors: Niemi, Lydia, Arakawa, Naoko, Glendell, Miriam, Gagkas, Zisis, Gibb, Stuart, Anderson, Claire, Pfleger, Sharon
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Catchment modelling and mapping; Consensus development; Environmental Monitoring; Environmental risk assessment; Humans; Multi-criteria decision-making; Pharmaceutical pollution; Pharmaceutical Preparations - analysis; Public health; Scotland; Water Pollutants, Chemical - analysis
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.176929

The presence of human pharmaceuticals in the aquatic environment is recognised internationally as an important public health and environmental issue. In Scotland, healthcare sustainability targets call for improvements to medicine prescribing and use to reduce healthcare's impact on the environment. This proof-of-concept study aimed to develop a framework on the environmental impact of pharmaceuticals to use as a knowledge support tool for healthcare professionals, focussing on pharmaceutical pollution. Nominal Group Technique was applied to achieve consensus on pharmaceuticals and modelling factors for the framework, working with a panel of cross-sector stakeholders. Bayesian Belief Network modelling was applied to predict the environmental impact (calculated from hazard and exposure factors) of selected pharmaceuticals, with Scotland-wide mapping for visualisation in freshwater catchments. The model calculated the pollution risk score of the individual pharmaceuticals, using the ratio of prescribed mass vs. mass that would not exceed the predicted no-effect concentration in the freshwater environment. The pharmaceuticals exhibited different risk patterns, and spatial variation of risk was evident (generally related to population density), with the most catchments predicted to exceed the pollution risk score for clarithromycin (probability >80 % in 35 of 40 modelled catchments). Simulated risk scores were compared against observed risk calculated as the ratio of measured environmental concentrations from national regulatory and research monitoring and predicted no-effect concentrations. The model generally overpredicted risk, likely due to missing factors (e.g. solid-phase sorption, temporal variation), low spatial resolution, and low temporal resolution of the monitoring data. This work demonstrates a novel, trans-disciplinary approach to develop tools aiding collation and integration of environmental information into healthcare decision-making, through application of public health, environmental science, and health services research methods. Future work will refine the framework with additional clinical and environmental factors to improve model performance, and develop electronic interfaces to communicate environmental information to healthcare professionals. [Display omitted] •The study explored environmentally informed prescribing to reduce pharmaceutical pollution.•The methods integrated public health, environmental modelling, and health services re