title: Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part B: placing back-calculations in a formal statistical framework.
abstract: Concentrations of metabolites of illicit drugs in sewage water can be measured with great accuracy and precision, thanks to the development of sensitive and robust analytical methods. Based on assumptions about factors including the excretion profile of the parent drug, routes of administration and the number of individuals using the wastewater system, the level of consumption of a drug can be estimated from such measured concentrations. When presenting results from these 'back-calculations', the multiple sources of uncertainty are often discussed, but are not usually explicitly taken into account in the estimation process. In this paper we demonstrate how these calculations can be placed in a more formal statistical framework by assuming a distribution for each parameter involved, based on a review of the evidence underpinning it. Using a Monte Carlo simulations approach, it is then straightforward to propagate uncertainty in each parameter through the back-calculations, producing a distribution for instead of a single estimate of daily or average consumption. This can be summarised for example by a median and credible interval. To demonstrate this approach, we estimate cocaine consumption in a large urban UK population, using measured concentrations of two of its metabolites, benzoylecgonine and norbenzoylecgonine. We also demonstrate a more sophisticated analysis, implemented within a Bayesian statistical framework using Markov chain Monte Carlo simulation. Our model allows the two metabolites to simultaneously inform estimates of daily cocaine consumption and explicitly allows for variability between days. After accounting for this variability, the resulting credible interval for average daily consumption is appropriately wider, representing additional uncertainty. We discuss possibilities for extensions to the model, and whether analysis of wastewater samples has potential to contribute to a prevalence model for illicit drug use.
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK. Electronic address: hayley.jones@bristol.ac.uk.
authors.forename: Hayley E
authors.initials: HE
authors.lastname: Jones
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
authors.forename: Matthew
authors.initials: M
authors.lastname: Hickman
authors.affiliation: Department of Chemistry, University of Bath, Faculty of Science, Bath BA2 7AY, UK.
authors.forename: Barbara
authors.initials: B
authors.lastname: Kasprzyk-Hordern
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
authors.forename: Nicky J
authors.initials: NJ
authors.lastname: Welton
authors.affiliation: Mass Spectrometry Business Unit, Shimadzu, Wharfside, Manchester M17 1GP, UK.
authors.forename: David R
authors.initials: DR
authors.lastname: Baker
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
authors.forename: A E
authors.initials: AE
authors.lastname: Ades
mesh_terms: D001499:Bayes Theorem; D004363:Drug Utilization / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000379:methods*; D013287:Illicit Drugs / Q000032:analysis*; D009010:Monte Carlo Method; D004364:Pharmaceutical Preparations / Q000032:analysis*; D012722:Sewage; D015813:Substance Abuse Detection / Q000379:methods; D019966:Substance-Related Disorders / Q000453:epidemiology; D014865:Waste Disposal, Fluid; D062065:Wastewater / Q000737:chemistry* / Q000706:statistics & numerical data; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D013287:Illicit Drugs; D004364:Pharmaceutical Preparations; D012722:Sewage; D062065:Wastewater; D014874:Water Pollutants, Chemical
keywords: Bayesian modelling; Illicit drugs; Monte Carlo simulation; Sewage epidemiology; Uncertainty propagation