To compare long-term trends in wastewater data with other indicators of stimulant use in three locations and to test the reliability of estimates based on one week of sampling.
Comparison of trends in quantities (‘loads’) of stimulants or their metabolites in wastewater with trends in other indicators of stimulant use (e.g. treatment, police, population survey data).
Setting and Participants
Populations in Oslo (Norway), South-East Queensland (Australia) and Eindhoven (The Netherlands).
Wastewater data were modelled for MDMA (3,4-Methylenedioxy methamphetamine), benzoylecgonine (a metabolite of cocaine), amphetamine and methamphetamine in Oslo; benzoylecgonine in Eindhoven; and methamphetamine in South-East Queensland. Choice of stimulants modelled in each region was primarily determined by availability of useable data.
In Oslo, wastewater data, driving under the influence of drugs statistics and seizure data all suggested increasing MDMA use between 2009 and 2017. In South-East Queensland, there was an estimated 31.1% (95%CI 29.4-32.9%) annual
increase in daily loads of methamphetamine in wastewater between 2009 and 2016, compared with a 14.1% (95%CI 10.9-17.3%) annual increase in seizures. Some of the increase in wastewater can be explained by increased purity. In Eindhoven, there was no evidence of a change in cocaine consumption from wastewater, but a reduction was observed in numbers in treatment for cocaine use from 2012 to 2017. In approximately half the cases examined in Oslo, credible intervals around estimates of annual average loads from a regression model versus estimates based on a single week of sampling did not overlap.
Long-term trends in loads of stimulants in wastewater appear to be broadly consistent with trends in other indicators of stimulant use in three locations. Wastewater data should be interpreted alongside epidemiological indicators and purity data. One week of wastewater sampling may not be sufficient for valid inference about drug consumption.
- Sewage epidemiology
- Long-term trends
- Bayesian analysis