How Motivations for Using Buprenorphine Products Differ From Using Opioid Analgesics: Evidence from an Observational Study of Internet Discussions Among Recreational Users

Buprenorphine use and setting type among reproductive-aged women self-reporting nonmedical prescription opioid use

INTRODUCTION

Screening for opioid misuse and treatment for opioid use disorder are critical for reducing morbidity and mortality. We sought to understand the extent of self-reported past 30-day buprenorphine use in various settings among women of reproductive age with self-reported nonmedical prescription opioid use being assessed for substance use problems.

METHODS

The study collected data from individuals being assessed for substance use problems using the Addiction Severity Index-Multimedia Version in 2018-2020. We stratified the sample of 10,196 women ages 12-55 self-reporting past 30-day nonmedical prescription opioid use by buprenorphine use and setting type. We categorized setting types as: buprenorphine in specialty addiction treatment, buprenorphine in office-based opioid treatment, and diverted buprenorphine. We included each woman's first intake assessment during the study period. The study assessed number of buprenorphine products, reasons for using buprenorphine, and sources of buprenorphine procurement. The study calculated frequency of reasons for using buprenorphine to treat opioid use disorder outside of a doctor-managed treatment, overall and by race/ethnicity.

RESULTS

Overall, 25.5 % of the sample used buprenorphine in specialty addiction treatment, 6.1 % used buprenorphine prescribed in office-based treatment, 21.7 % used diverted buprenorphine, and 46.7 % reported no buprenorphine use during the past 30 days. Among women who reported using buprenorphine to treat opioid use disorder, but not as part of a doctor-managed treatment, 72.3 % could not find a provider or get into a treatment program, 21.8 % did not want to be part of a program or see a provider, and 6.0 % reported both; a higher proportion of American Indian/Alaska Native women (92.1 %) reported that they could not find a provider or get into a treatment program versus non-Hispanic White (78.0 %), non-Hispanic Black (76.0 %), and Hispanic (75.0 %) women.

CONCLUSIONS

Appropriate screening for nonmedical prescription opioid use to assess need for treatment with medication for opioid use disorder is important for all women of reproductive age. Our data highlight opportunities to improve treatment program accessibility and availability and support the need to increase equitable access for all women.

Endogenous opiates and behavior: 2020

This paper is the forty-third consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2020 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Anhedonia and Substance Use Disorders by Type, Severity, and With Mental Health Disorders

OBJECTIVES

Anhedonia can accompany substance use disorders (SUDs); its severity may vary by substance type, severity of SUD symptoms, or psychiatric comorbidity. The goal of this study was to clarify the contribution of each.

METHODS

Data were from participants aged 18 to 65 years in the National Epidemiologic Survey on Alcohol and Related Conditions III (n = 30,999; 51% women), a cross-sectional, nationally representative sample reporting lifetime DSM-5 symptoms and lifetime anhedonia. We used logistic regression to test how anhedonia was associated with specific SUDs and psychiatric disorders in respondents with one lifetime diagnosis. We used latent class analysis to assess the association of anhedonia with patterns of comorbidity in all respondents.

RESULTS

Opioid use disorder (OUD) had the greatest odds of anhedonia relative to other SUDs (ORs [95% CIs]): mild alcohol use disorder (AUD) ( 3.33 [1.74, 6.38]), moderate/severe AUD ( 2.73 [1.41, 5.30]), and cannabis use disorder ( 3.21 [1.43, 7.19]), though not significantly greater than stimulant use disorder ( 2.44 [.88, 6.73]). Anhedonia was more likely in mood disorders and posttraumatic stress disorder (PTSD) than in any SUD, except for PTSD versus OUD (OR [95% CIs] = .98 [.47, 2.02]). In latent class analysis analyses, the poly disorder class, which included SUDs and other diagnoses, had greater odds of anhedonia than the Poly SUD (ORs [95% CIs] = 1.62 [1.25, 2.09] and AUD 2.89 [2.40, 3.48]) classes.

CONCLUSIONS

People with OUD or a lifetime history of mood disorder or PTSD may be most likely to present to SUD treatment with anhedonia.

Alternative use of buprenorphine among people who use opioids in three U.S. Cities

Background: Buprenorphine is an effective treatment for opioid use disorder, yet some persons are concerned with its "alternative use" (i.e., any use unintended by the prescriber). There is limited evidence on the factors associated with alternative use of buprenorphine (AUB); in this study, we examined correlates of recent (past 6 months) AUB. Methods: Multivariable logistic regression was used to analyze survey data from a multi-site, cross-sectional study of people who use drugs (PWUD) (N = 334) in Baltimore, Maryland; Boston, Massachusetts; and Providence, Rhode Island. Results: One-fifth (20%) of the sample reported recent AUB. In adjusted analyses, significant negative correlates of AUB were female gender (adjusted odds ratio [aOR] 0.48, 95% confidence intervals [CI] 0.24-0.95), recent emergency room visit (aOR 0.45, 95% CI 0.23-0.89), and recent injection drug use (aOR 0.41, 95% CI 0.19-0.88). Significant positive correlates were alternative use of other prescription opioids (aOR 8.32, 95% CI 4.22-16.38), three or more overdoses in the past year (aOR 3.74, 95% CI 1.53-9.17), recent buprenorphine use as prescribed (aOR 2.50, 95% CI 1.12-5.55), and recent residential rehabilitation treatment (aOR 3.71, 95% CI 1.50-9.16). Conclusions: Structural and behavioral correlates of AUB may help identify PWUD at high risk of overdose with unmet treatment needs.

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

BACKGROUND

Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research.

OBJECTIVE

This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017.

METHODS

Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs.

RESULTS

The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03).

CONCLUSIONS

Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.