Stopping Opioid Addiction Before It Starts
MIE Assistant Professor Muhammad Noor E. Alam is working with DMSB/Bouve Professor Gary Young, Bouve Clinical Assistant Professor Alyssa Peckham, and CSSH Associate Professor Alicia Modestino to determine methods of reducing the number of opioid addictions in the country before they even start. Photo by iStock.
Northeastern researchers in business, health sciences, engineering, and public policy are working together to answer these questions in an effort to curb opioid addiction before it starts.
More than 47,000 people in the United States died from opioid overdoses in 2017, according to the Centers for Disease Control and Prevention. And approximately 40 percent of those deaths involved prescription opioids. The National Safety Council reports that Americans today are more likely to die from an opioid overdose than in a car accident, drowning, or fire.
“This is an important social issue,” says Gary Young, one of four Northeastern professors who are collaborating to tackle the scourge of opioids under the direction of the Center for Health Policy and Healthcare Research at Northeastern. “Everyone is worried about opioids.”
A less addictive pain medication
Young, who directs the Center for Health Policy and Healthcare Research, is leading an effort to evaluate how doctors prescribe Suboxone.
“Some believe it’s one of the most important treatments out there,” says Young, who is also a professor of strategic management and healthcare systems. “We see that it can be very, very effective for treating opioid use disorder, but we don’t really know how it’s being used in practice.”
Suboxone contains a mixture of buprenorphine, which is an opioid, and naloxone, which blocks opioid receptors, says Alyssa Peckham, an assistant clinical professor of pharmacy who is part the research team. The drug is intended to help patients with opioid use disorder to manage their cravings. But the team’s early research shows that doctors may also be prescribing it to treat pain, in place of other opioids such as oxycodone.
“What we’ve seen so far in our preliminary analyses is clinicians are particularly likely to prescribe Suboxone to a patient that they suspect is more likely to become addicted to oxycodone,” Young says.
While the Federal Drug Administration has approved the use of Suboxone only to treat opioid use disorder, doctors are allowed to use their discretion when they prescribe drugs. It may be that Suboxone could be used to treat pain to keep individuals from becoming addicted to opioids in the first place, says Young, whose research team has received funding from Centers for Disease Control and Prevention and the Massachusetts Department of Public Health to find out.
“We need to have a better sense of how Suboxone is being prescribed and what’s happening to these people over an extended period of time, as they undergo treatment with Suboxone,” Young says.
A better way to predict addiction
Muhammad Noor E Alam, an assistant professor of mechanical and industrial engineering, is trying to help doctors make the best decisions when prescribing pain medication. He and and doctoral student Muhammad Mahmudul Hasan are working to create a machine learning algorithm to evaluate how likely a patient is to become addicted to opioids.
“Physicians will supply patient information and the model will predict how likely this person is going to be to abuse opioids,” says Alam, whose research is supported by the Global Resilience Institute at Northeastern. “Based on this prediction, doctors can choose an appropriate medication, appropriate doses, and appropriate frequency of refill.”
Doctors already have tools to evaluate patients, but they are based on simple interviews or forms that patients fill out. Research has shown that they aren’t very efficient or effective, Hasan says.
“This kind of interview doesn’t cover the entire story,” Hasan says. “When you’re using a data-driven approach, you are leveraging the whole treasure trove of data that actually tells the patient’s previous history: how our patient actually progresses towards opiate dependency, what are the underlying reasons, the clinical story behind that.”
The researchers started with roughly 12,000 possible factors that could contribute to opioid abuse disorder. They eventually winnowed them down to 30. Using those, the algorithm accurately predicted which individuals would develop opioid use disorder 94 percent of the time. But the researchers still want to refine and improve the framework for even better accuracy.
“Before we can put it into practice, first of all, we have to convince physicians,” Alam says. “We are trying to make this model more interpretable, more useful to doctors.”
Getting leftover opioids out of the medicine cabinet
Alicia Modestino is heading a project to determine how best to encourage patients to return their unused opioids.
“If you had surgery or you injured your back, it’s still the case that we’re probably giving you too much medication, that you won’t need it all,” says Modestino, who is an associate professor of public policy, urban affairs, and economics. “And then what happens is that you keep the rest in your medicine cabinet.”
If opioids are lying around, someone in the household may use them to self-medicate and potentially become addicted or sell the pills to someone else, says Modestino. But many people don’t recognize the dangers. And even if they do, they may not know how to safely dispose of opioid medications.
Modestino hopes that a little information, possibly paired with a small financial incentive, will help get unused opioid medication off the street. She and her fellow researchers are working with the Abdul Latif Jameel Poverty Action Lab at the Massachusetts Institute of Technology to conduct a yearlong study testing ways to encourage patients to return their extra pills to community pharmacies.
“Pharmacists are often filling these prescriptions, but they are not included in the solutions for opioids,” Modestino says. “Most of the interventions have been focused on the physicians or public awareness. They’re excited to be able to make a difference.”
When individuals pick up their opioid prescriptions on one of several randomly-selected days, they will receive additional information from their pharmacist and a small informational card. The card will explain that the medication is addictive and that they probably have more pills than they need. They will be told that they can bring their extra pills back to the same pharmacy, some of which will offer them a $10 gift card for the trouble of returning the medication.
The study will track how many pills are returned at different locations and whether a monetary incentive drives patients to return their leftover opioids. If their results are promising, the researchers will plan to expand it to other community health providers around Massachusetts.
“If you can get people to bring it back without giving them a financial incentive, then great. That’s a cheap program to run,” Modestino says. “But it could be the case that information alone gets you nothing. And that if you really want these pills off the street, $10 for however many pills you get back is probably a pretty good deal, given how addictive they are.”