According to Pacula, drug epidemics can be divided into similar stages. In the early stage of an outbreak, the number of users increases, as new people are exposed to drugs. In the case of opioids, exposure often starts with a prescription. It might be tempting to simply blame patients for taking too much medication, or doctors for overprescribing. But we must also consider the pharmaceutical companies who market strong opioids directly to doctors. And insurance companies, who are often more likely to fund painkillers than alternatives like physiotherapy. Our modern lifestyles also play a role, with rising chronic pain associated with increases in obesity and office-based work.
One of the best ways to slow an epidemic in its early stages is to reduce the number of people who are susceptible. For drugs, this means improving education and awareness. ‘Education has been very important and very effective,’ said Pacula. Strategies that reduce the supply of drugs can also help early on. Given the multitude of drugs involved in the opioid epidemic, this means targeting all potential routes of exposure, rather than one specific medication.
Once the number of new users peaks, we enter the middle stage of a drug epidemic. At this point, there are still a lot of existing users, who may be progressing towards heavier drug use, and potentially moving on to illegal drugs as they lose their access to prescriptions. Providing treatment and preventing heavy use can be particularly effective at this stage. The aim here is to reduce the overall number of users, rather than just preventing new addictions.
In the final stage of a drug epidemic, the number of new and existing users is declining, but a group of heavy users remains. These are the people who are most at risk, having potentially switched from prescription opioids to cheaper drugs like heroin.[79] But it’s not as simple as cracking down on the illegal drug market in these later stages. The underlying problem of addiction is much deeper and wider than this. As Police Chief Paul Cell put it, ‘America can’t arrest its way out of the opioid epidemic’.[80] Nor is it just a matter of taking away access to prescription drugs. ‘There’s an addiction problem, and not just an opioid problem,’ Pacula said. ‘If you don’t provide treatment when you’re taking away the drug, you’re basically encouraging them to go to anything else.’ She pointed out that drug epidemics also come with a series of knock-on effects. ‘Even if we get the issue of misuse of opioids under control, we have some very concerning long term trends that we haven’t even started dealing with.’ One is the effect on drug users’ health. As people move from taking pills to injecting drugs, they face the risk of infections like hepatitis C and hiv. Then there is the wider social impact – on families, communities, and jobs – of having large numbers of people with drug addiction.
Because the success of different control strategies can vary between the three stages of a drug epidemic, it’s crucial to know what stage we’re currently in. In theory, it should be possible to work this out by estimating the annual numbers of new users, existing users, and heavy users. But the complexity of the opioid crisis – with its mix of prescription and illegal use, makes it very difficult to pick these things apart. There are some useful data sources – such as visits to emergency rooms and results of post-arrest drug tests – but this information has become harder to get hold of in recent years. We can’t draw a neat graph showing the different stages of drug use like we can for the Yambuku Ebola outbreak, because the data simply aren’t available. It’s a common problem in outbreak analysis: things that aren’t reported are by definition tough to analyse.
In the early stages of a disease outbreak, there are generally two main aims: to understand transmission and to control it. These goals are closely linked. If we improve our understanding of how something is spreading, we can come up with more effective control measures. We may be able to target interventions at high-risk groups, or identify other weak links in the chain of transmission.
The relationship works the other way too: control measures can influence our understanding of transmission. For diseases, as with drug use and gun violence, health centres often act as our windows onto the outbreak. It means that if health systems are weakened or overburdened, it can affect the quality of data coming in. During the Ebola epidemic in Liberia in August 2014, one dataset we were working with suggested that the number of new cases was leveling off in the capital Monrovia. At first this seemed like good news, but then we realised what was actually happening. The dataset was coming from a treatment unit that had reached capacity. The case reports hadn’t peaked because the outbreak was slowing down; they’d stopped because the unit couldn’t admit any more patients.
The interaction between understanding and control is also important in the world of crime and violence. If authorities want to know where crime is occurring, they generally have to rely on what’s being reported. When it comes to using models to predict crime, this can create problems. In 2016, statistician Kristian Lum and political scientist William Isaac published an example of how reporting might influence predictions.[81] They’d focused on drug use in Oakland, California. First they’d gathered data on drug arrests in 2010, and then plugged these into the PredPol algorithm, a popular tool for predictive policing in the US. Such algorithms are essentially translation devices, taking information about an individual or location and converting it into an estimate of crime risk.
