When you think about addictions research and public health modelling, raw sewage probably isn't the first thing that comes to mind.
However, a recent article in The Sunday Times (paywall) highlighted plans to expand the UK Government's use of wastewater testing to help understand the illicit tobacco market. I was interviewed for the article to explain how wastewater-based epidemiology (WBE) works, and how it is already being used to monitor drug use through the Home Office's Wastewater Analysis for Narcotics Detection (WAND) programme.
WAND currently monitors substances including cocaine, heroin and MDMA in wastewater. The planned expansion would bring tobacco into this existing programme, with wastewater testing used alongside information on legitimate tobacco sales to help estimate the scale of the illicit market. This approach is not entirely new: Australia's National Wastewater Drug Monitoring Program already includes tobacco alongside other substances.
Before joining the Sheffield Addictions Research Group (SARG) to work on synthetic population modelling, my academic background focused on wastewater analysis. I spent several years extracting and analysing target chemicals from environmental samples, so this is an area of research I know well.
The announcement also got me thinking about how wastewater data could complement the population modelling work we are doing in SARG. So, how does wastewater testing actually work, and what could it add?
How wastewater analysis works
At its core, wastewater-based epidemiology is like taking a collective health sample for an entire community.
When someone smokes, drinks, or takes a drug, their body breaks down that substance and flushes out chemical markers known as metabolites. Automated samplers collect untreated sewage at local treatment plants at regular intervals. In a lab, these samples are then filtered and passed through high-precision instruments that separate and identify specific molecules and measure how much is present.
Importantly, WBE can also tell us something about whether nicotine use involves tobacco. Chemicals such as anabasine and anatabine are found in the tobacco plant, so they can indicate tobacco use. Other chemicals, such as cotinine and hydroxycotinine, are produced when the body processes nicotine. These can be detected after using tobacco, vapes or nicotine pouches, so they indicate nicotine use more generally.
It is an efficient way of gathering information about a population. For example, monitoring just 50 wastewater treatment sites under the UK's WAND programme captures data for roughly 32% of the population in England and Scotland.
What wastewater testing can – and can't – tell us
Until relatively recently, estimates of substance use have relied heavily on national surveys. For tobacco and nicotine use, official estimates for England use the Annual Population Survey to produce estimates at a local authority level.
Surveys are valuable, but they have limitations. People may under-report their substance use, particularly when they are reluctant to disclose it. Survey data can also take time to collect and analyse, and may not provide the level of geographical detail needed to understand patterns at a local level.
Wastewater offers a different way of measuring consumption across a population. But it has an important limitation: it cannot tell us who is using the substances.
A wastewater sample can tell us about the amount of a substance being consumed in an area, but it cannot tell us whether that consumption is coming from younger or older people, different socioeconomic groups, or people using different products.
This is where I think wastewater analysis could become particularly interesting alongside the population modelling we do in SARG.
Bringing wastewater data and population modelling together
Synthetic population modelling combines data from sources such as the Census and national health surveys to estimate health and risk behaviours across different groups and geographic areas. In SARG, we use these models to explore how these behaviours might change over time or in response to specific public health policies and interventions.
For example, as part of the Local Health and Global Profits programme, we are developing a synthetic population for England to help understand how health and risk behaviours vary across different local areas. We are also using these methods to estimate smoking prevalence in neighbourhoods across Wales and mapping the data directly to local stop-smoking services.
Wastewater data could provide a complementary source of information about tobacco and nicotine use across a population. Bringing these approaches together could have several potential benefits.
- First, wastewater data could provide a way of comparing synthetic population estimates with biological data from the communities they represent. It could also help identify changes in consumption patterns without having to wait for annual survey data.
- Second, synthetic population models could provide some of the demographic context that wastewater data lacks. They could help us explore which groups or areas might be contributing to the patterns seen in wastewater, and how those patterns may vary across a population.
There is still work to do to understand how these approaches could best be combined. But as the ways people use nicotine continue to change, including the growing use of vapes and nicotine pouches alongside traditional tobacco smoking, having different ways of measuring consumption will become increasingly important.
For me, the growing interest in wastewater testing highlights an opportunity to bring together two different ways of understanding population health: biological data from wastewater and computational models that help us understand how behaviours vary across different groups and places.
While this is an area of great interest to me and the wider team, there are practical challenges to carrying out this kind of work at the scale required, including access to laboratory space and resources. If you are interested in collaborating with SARG in this area, we would be very interested to hear from you.