Too often, patients drop out early from opioid agonist therapy and lose the many benefits it offers. Is there some way to intervene early and retain patients?
A new study in Drug and Alcohol Dependence suggests that, indeed, there may be.
A group headed by Leigh V. Panlilio, PhD, a staff fellow at the National Institute on Drug Abuse Intramural Research Program, investigated whether certain measurement methods might help explain why people drop out—and what could be done to keep them in treatment.
Those measurement methods are known as intensive longitudinal methods (see box below). They involve repeated daily assessments that provide unique clues about how certain factors can affect drug use.
Intensive Longitudinal Methods and EMA
Intensive longitudinal methods (ILMs), also called daily diaries, involve recording data for the same subjects repeatedly, over time, usually as self-reports in the field.
Ecological momentary assessment (EMA) is an intensive longitudinal method that is used to study the relationship between psychosocial variables—such as mental distress and anxiety sensitivity—and behavior. EMA minimizes the problem of relying on memory of past events or feelings. The reason: participants can be prompted frequently, at specific intervals; or randomly; or at critical times, such as when a coworker’s criticism leads to anger, causing immediate automatic prompts to the participant to respond to queries on a smartphone.
The current investigation is part of a large longitudinal study of 238 participants being treated for opioid use disorder in Baltimore, Maryland. This current study zeroed in on stress, drug craving, and environment-related effects on drug use. Investigative tools included multiple daily electronic diary entries, in which participants using cellphone apps self-reported craving, stress, and mood. The authors know of no previous studies using intensive longitudinal methods to study retention in the treatment of substance use disorders.
This study is a secondary analysis—a reanalysis of data from a previous study. It has two arms: Methadone-Buprenorphine (81.5%), and Office-Based Opioid Treatment (18.5%). Participants’ medication was methadone (47%) or buprenorphine-naloxone (53%). Clinic visits were scheduled for 5 to 7 days/week, or 2 days/week, respectively. A protocol change from the more rigorous ongoing study, the reduced schedule was “driven by budgetary/staffing concerns,” according to the authors. Enrollment ran from July 2009 through September 2017.
Participants used smartphones as electronic diaries. How they did so was meticulously planned. Random prompts told participants when to enter data on mood, stress, hassles, and drug use; end-of-day reports queried them about, among other things, stress of the day, and experiences with each of 32 potential hassles, such as money, family, or work.
Investigators examined the smartphone data, determined which factors led to dropout, and targeted those factors with medications and other interventions, with an eye on increasing retention.
Participants also underwent a semi-structured interview—the Addiction Severity Index (ASI)—with a focus on psychological or emotional problems, such as anxiety, depression, or a history of drug use.
At the study’s close, participants were classified by outcome:
- Completed (remained for the designated length, 16 to 18 weeks)
- Dropped out
- Unable to comply with EMA self-report requirements
Variables Affecting Dropping Out
The final model for Dropping Out was statistically significant, and contained seven variables.
Factors that increased the risk of dropping out:
- More “hassles” (a measure of stress) reported in end-of-day EMA
- Higher levels of cocaine craving reported in randomly prompted EMA
- Recent history of emotional abuse
- Recent history of being bothered frequently by psychological problems
Factors that decreased the risk of dropping out:
- Treatment with methadone rather than buprenorphine
- Higher self-ratings of positive mood in randomly prompted EMA
- More years of polydrug use
The greatest effects were seen with hassles and medication type.
The Dropped Out group had higher levels of craving, stress, and negative mood than the Completers.
In the Discussion section of the paper the 12 authors, affiliated with highly ranked institutions, draw on their knowledge of the opioid field to offer insights and interpretations that make this publication especially valuable.
For example, they remark that alpha-2 adrenergic agonists, such as lofexidine and clonidine, have been used to treat stress-related conditions, and “should be considered for use as an adjunct medication in treating opioid use disorder.” But they add that the question of whether clonidine reduces the level of dropout could depend in part on the time of treatment initiation and the length of abstinence—showing a “clear need for research specifically designed to test the effects of clonidine and other medications on stress-induced dropout starting from the outset of opioid agonist therapy.”
Specific steps the authors recommend for improving retention include directing greater attention to stress, craving, and mood early in treatment and as treatment progresses; and including medication and counseling in the treatment plan, especially for people taking buprenorphine.
They also suggest using EMA data obtained from patients as they start opioid agonist therapy, and tracking and identifying patients with problems arising from stress and mood. These patients, they point out, could benefit the most from antidepressant medication or behavioral therapy.
As for counseling, the authors mention using contingency management to decrease drug use—“the one type of intervention that was effective in improving outcome.” Another recommendation: examine the potential of contingency management with interventions targeting psychological problems.
This nicely done study offers more information than we can include; see the publication for details.
Panlilio LV, Stull SW, Kowalczyk WJ, et al. Stress, craving and mood as predictors of early dropout from opioid agonist therapy [Epub ahead of print July 16 2019]. Drug Alcohol Depend. 2019;202:200-208. doi:10.1016/j.drugalcdep.2019.05.026