The Australian Therapeutic Goods Administration\'s approved indications for prescription of gabapentinoids are refractory focal epilepsy and neuropathic pain. Use of gabapentinoids outside of the approved indications is common, but evidence for this is limited, especially for chronic nonspecific back pain and nonradicular leg pain. Some effects of gabapentinoids encourage their nonmedical use (e.g. euphoria, sedation, disinhibition). Widespread nonmedical use has increased the incidence of accidental and deliberate poisonings. Dependence may develop with chronic use of gabapentinoids and abrupt cessation may induce withdrawal symptoms. If the indication for continued use is unclear, gradual dose tapering as a means of deprescribing is recommended. Clinicians should consider the indication, patient characteristics and harm-benefit profile when prescribing gabapentinoids. Some people, such as those with kidney disease, have an increased risk of harm when using these drugs.

BACKGROUND: Changes in regulation for cannabis for nonmedical use (CNMU) are underway worldwide. Switzerland amended the law in 2021 allowing pilot trials evaluating regulative models for cannabis production and distribution. Simultaneously, cannabis for medical use (CMU) in psychiatry is a growing issue, asked for by patients and being discussed by medical professionals.
METHODS: From December 2021 to February 2022, we conducted an online survey of psychiatrists in Switzerland. The survey comprised questions on attitudes towards regulative models for CNMU and towards prescribing CMU for mental disorders.
RESULTS: We contacted 2010 psychiatrists in Switzerland. A total of 274 (14%) participated in the survey. Sixty-four percent agreed to a regulated legalization of CNMU, and 89% would welcome pilot trials in Switzerland assessing models for regulating CNMU with those from a French-speaking region being more skeptical. Forty-nine percent of psychiatrists agree that CMU might have a therapeutic effect in mental disorders, but 50% agree that there is not enough scientific evidence yet. Participants working in an inpatient setting or in a French-speaking region as well as those with a longer duration of practice were more skeptical on CMU for mental health.
CONCLUSIONS: Most surveyed Swiss psychiatrists favor the regulation of CNMU and the conduct of pilot trials. Despite little evidence and potential negative consequences, many participating Swiss psychiatrists agreed that cannabis could be efficacious in the treatment of some mental disorders advocating for more research in this topic.

BACKGROUND: Nonmedical use (NMU) of prescription psychotherapeutic drugs (PPD) may increase risk for significant morbidity and mortality in the overdose crisis.
OBJECTIVE: This study examines sources of PPD using real-world data from adolescents and adults reporting past 30-day NMU of PPDs.
METHODS: A convenience sample of individuals aged ≥10 years assessed for substance use disorders (SUD) treatment was analyzed using the 2014-2022 National Addictions Vigilance Intervention and Prevention Program datasets. PPD include prescription opioids, prescription tranquilizers/sedatives, and prescription stimulants.
RESULTS: Overall, among assessments of adolescents aged 10-18 years (N = 1991) and young adults aged 19-24 years (N = 15,166), \"family/friend\" (46.08-47.41 %) and \"dealer\" (33.82-42.71 %) were the most common sources. Among assessments of adults aged ≥25 years (N = 89,225), \"own prescription\" was the most common source and increased in frequency as age increased. Across all age groups, \"family/friend\" was the most frequent source for all drug classes (41.96-48.76 %) except for nonmedically used buprenorphine/methadone, for which \"own prescription\" was the most common source (51.85 %) among adults.
CONCLUSIONS: Our study demonstrates heterogeneity in sources of nonmedically used PPD across age groups. Tailored prevention strategies for different age groups and improving timely access to medical care to ensure proper treatment of chronic medical conditions including SUD are needed.

Methylphenidate is an effective first-line treatment for attention-deficit/hyperactivity disorder (ADHD). However, many adverse effects of methylphenidate have been recorded from randomized clinical trials and patient-reported outcomes, but it is difficult to determine abuse from them. In the context of COVID-19, it is important to determine how drug use evaluation, as well as misuse of drugs, have been affected by the pandemic. As people share their reasons for using medication, patient sentiments, and the effects of medicine on social networking services (SNSs), the application of machine learning and SNS data can be a method to overcome the limitations. Proper machine learning models could be evaluated to validate the effects of the COVID-19 pandemic on drug use.
To analyze the effect of the COVID-19 pandemic on the use of methylphenidate, this study analyzed the adverse effects and nonmedical use of methylphenidate and evaluated the change in frequency of nonmedical use based on SNS data before and after the outbreak of COVID-19. Moreover, the performance of 4 machine learning models for classifying methylphenidate use based on SNS data was compared.
In this cross-sectional study, SNS data on methylphenidate from Twitter, Facebook, and Instagram from January 2019 to December 2020 were collected. The frequency of adverse effects, nonmedical use, and drug use before and after the COVID-19 pandemic were compared and analyzed. Interrupted time series analysis about the frequency and trends of nonmedical use of methylphenidate was conducted for 24 months from January 2019 to December 2020. Using the labeled training data set and features, the following 4 machine learning models were built using the data, and their performance was evaluated using F-1 scores: naïve Bayes classifier, random forest, support vector machine, and long short-term memory.
This study collected 146,352 data points and detected that 4.3% (6340/146,352) were firsthand experience data. Psychiatric problems (521/1683, 31%) had the highest frequency among the adverse effects. The highest frequency of nonmedical use was for studies or work (741/2016, 36.8%). While the frequency of nonmedical use before and after the outbreak of COVID-19 has been similar (odds ratio [OR] 1.02 95% CI 0.91-1.15), its trend has changed significantly due to the pandemic (95% CI 2.36-22.20). Among the machine learning models, RF had the highest performance of 0.75.
The trend of nonmedical use of methylphenidate has changed significantly due to the COVID-19 pandemic. Among the machine learning models using SNS data to analyze the adverse effects and nonmedical use of methylphenidate, the random forest model had the highest performance.

BACKGROUND: The prevalence of abuse, diversion, and web-based endorsement of tapentadol (extended-release [ER], immediate-release [IR]) has been characterized as low compared with other prescription opioids. Little is known about individual experience with tapentadol nonmedical use (NMU).
OBJECTIVE: This study aims to pilot web-based survey technologies to investigate the motivation for tapentadol NMU, sources of procurement, routes of administration, tampering methods, doses used, and impressions of tapentadol products (Nucynta and Nucynta ER).
METHODS: Recruitment flyers and banner advertisements were placed on the Bluelight website [DragonByte Technologies Ltd] with a link to a web-based survey (Qualtrics) designed to query about individuals\' lifetime tapentadol NMU. This web-based survey was followed by an interactive web-based chat (Cryptocat) with respondents who were willing to be contacted. Respondents were queried about sources for obtaining tapentadol, motives for use, routes of administration, tampering methods, drugs used in combination, tablet strengths and dosages, and reasons for continued or discontinued use. Desirability and attractiveness for NMU was rated.
RESULTS: Web-based recruitment successfully attracted difficult-to-find study participants. A total of 78 participants reported that tapentadol was obtained from friends and family (ER 11/30, 37%; IR 18/67, 27%), the internet (ER 11/30, 37%; IR 12/67, 18%) or participants\' own prescriptions from a doctor (ER 9/30, 30%; IR 17/67, 25%). It was used nonmedically for pain relief (ER 18/30, 60%; IR 33/67, 49%) and multiple psychotropic effects, including relaxation (ER 13/30, 43%; IR 29/67, 43%), reduction in depression or anxiety (ER 7/30, 23%; IR 30/67, 45%), or getting high (ER 12/30, 40%; IR 33/67, 49%). Tapentadol was primarily swallowed (ER 22/30, 73%; IR 55/67, 82%), although snorting (ER 2/30, 7%; IR 8/67, 12%) and injection (ER 2/30, 7%; IR 5/67, 8%) were also reported. The preferred dose for NMU was 100 mg (both ER and IR). The participants reported tapentadol use with benzodiazepines (ER 12/21, 57%; IR 28/47, 60%). Most participants had discontinued tapentadol NMU at the time of survey completion (ER 22/30, 73%; IR 55/67, 82%). Reasons for discontinued ER NMU included side effects (10/22, 46%) and lack of effective treatment (10/22, 46%). Reasons for discontinued IR NMU included lack of access (26/55, 47%) and better NMU options (IR 21/55, 38%). Few individuals were willing to divulge identifying information about themselves for the interactive chat (8/78, 10%), demonstrating the strength of anonymous, web-based surveys. Interactive chat supported the survey findings. A subgroup of participants (4/78, 5%) reported hallucinogenic side effects with high doses.
CONCLUSIONS: Web-based surveys can successfully recruit individuals who report drug NMU and those who are difficult to find. Tapentadol NMU appears to occur primarily for pain relief and for its psychotropic effects. Although it was liked by some, tapentadol did not receive a robust pattern of endorsement for NMU.

As policies legalizing adult cannabis use increase in the United States, understanding and characterizing the proportion of individuals consuming cannabis for medical and nonmedical purposes is important to inform targeted health education efforts. Data came from 7672 adults (> = 18 years) with past month cannabis use who responded to questions about reason for cannabis use on the 2018 Behavioral Risk Factor Surveillance System survey in 14 states. State and aggregated data were analyzed using weighted frequencies for descriptive analyses, and prevalence ratios were computed to identify demographic and substance use characteristics associated with medical only use or use for both medical and nonmedical reasons (vs. nonmedical use). Overall, 28.6% (95% CI: 26.7, 30.4) of adults who use cannabis reported using cannabis medically, 34.2% (95% CI: 32.3, 36.2) nonmedically, and 37.2% (95% CI: 35.2, 39.2) both medically and nonmedically. Characteristics associated with medical only use (compared with nonmedical only use) included being female; aged > 45 years; out of work, a homemaker, or unable to work; having daily or near daily cannabis use; having past month cigarette use; having no past month alcohol use; self-reporting poor health; and ever having been diagnosed with certain chronic diseases. Medical only use of cannabis is the least prevalent reason for use; use for both medical and nonmedical reasons is the most prevalent. Monitoring reasons for cannabis use can aid states in understanding differences between medical and nonmedical cannabis users, providing context to use patterns, and targeting health education messages to appropriate audiences.

As prescriptions for gabapentin have increased in recent years, nonmedical use and risk of adverse outcomes (e.g., hospitalizations and overdose) have been identified, particularly in association with opioids, including opioid agonist medications (OAMs) buprenorphine and methadone. However, there is a lack of systematic, nationwide data assessing the relationship between the nonmedical use of gabapentin and OAMs.
Data were sourced from two nationwide opioid surveillance programs of treatment-seeking individuals with opioid use disorder (OUD). Both programs utilized an identical serial, cross sectional survey of 12,792 new entrants to one of 163 substance use treatment programs for OUD in 46 states and the District of Columbia from January 2019 to December 2020.
Past month nonmedical use of gabapentin was endorsed by 9.3% of the sample. Of those using gabapentin nonmedically, 64.1% also endorsed nonmedical use of an OAM, including concomitant use of methadone (35.3%), and buprenorphine (49.0%). Concomitant nonmedical use of gabapentin and OAMs was more prevalent (versus nonmedical use of gabapentin alone) in the Southern region, among those living in a street dwelling, those with chronic pain and healthcare professionals.
Nonmedical use of gabapentin in people with OUD appears to frequently coincide with nonmedical use of OAMs. As prescriptions and off-label use of gabapentin increase, provider education should include the risks of concomitant gabapentin and OAM use, particularly amongst buprenorphine prescribers. Future research should investigate motivations (e.g., OUD self-management) for nonmedical use of gabapentin and OAMs within the context of OUD treatment access and retention.

Canada is experiencing an epidemic of opioid-related mortality, with increasing yet heterogeneous fatality patterns from illicit/synthetic (e.g., fentanyl) opioids. The present study examined whether differential provincial reductions in medical opioid dispensing following restrictive regulations (post-2010) were associated with differential contributions of fentanyl to opioid mortality. Annual provincial opioid dispensing totals in defined daily doses/1000 population/day, and change rates in opioid dispensing for the 10 provinces for (1) 2011-2018 and (2) \"peak-year\" to 2018 were derived from a pan-Canadian pharmacy-based dispensing panel. Provincial contribution rates of fentanyl to opioid-related mortality (2016-2019) were averaged. Correlation values (Pearson\'s R) between provincial changes in opioid dispensing and the relative fentanyl contributions to mortality were computed for the two scenarios. The correlation between province-based changes in opioid dispensing (2011-2018) and the relative contribution of fentanyl to total opioid deaths (2016-2019) was -0.70 (t = 2.75; df = 8; p = 0.03); the corresponding correlation for opioid dispensing changes (\"peak-year\" to 2018) was -0.59 (t = -2.06; df = 8; p = 0.07). Provincial reductions in medical opioid dispensing indicated (near-)significant correlations with fentanyl contribution rates to opioid-related death totals. Differential reductions in pharmaceutical opioid availability may have created supply voids for nonmedical use, substituted with synthetic/toxic (e.g., fentanyl) opioids and leading to accelerated opioid mortality. Implications of these possible unintended adverse consequences warrant consideration for public health policy.

Use of prescription opioids \'beyond the bounds\' of medical guidance can lead to opioid dependence. Yet recent efforts to predict extra-medical use of prescription pain relievers (EMPPR) have relied on electronic medical or pharmacy records. Because peak incidence of EMPPR occurs during adolescence- a time of relative health- administrative data may be inadequate. In this study, with data from a United States (US) population sample, we develop and internally validate an EMPPR prediction model. We analyzed data from 234,593 individuals aged 12-to-17-years, as sampled by the US National Survey of Drug Use and Health, 2004-2018, an annual cross-sectional survey. We encoded 14 predictors with onset prior to EMPPR initiation, including age, sex, and facets of drug and psychiatric history. We ranked these predictors by clinical utility before sequentially adding each to a regularized logistic regression model. On held-out test data (n = 23,685), the model performs well with 14 predictors, with an area under the precision recall curve (AUPRC) is 0.155. The area under the receiver operator curve (AUC) is 0.819, exceeding a recent benchmark on this dataset. Results are robust to survey redesign that occurred in 2015, and are not moderated by past-year use of medical services. In conclusion, while selection of predictors is limited to those with known timing prior to initiation of EMPPR rather than any cross-sectional variable, this model discriminates well. Good classification occurs even with a small set of clinically available predictors- age, a history of depression and alcohol, cigarette, and cannabis use.

the nonmedical use of prescribed medications among adolescents has increased significantly in recent years. We aimed to identify the patterns of benzodiazepine nonmedical use and its evolution during the decade 2006-2016 among immigrant and native-born adolescent populations.
we used individualized secondary data retrieved from the 2006-2016 Spanish State Survey on Drug Use in Secondary Education (ESTUDES) of the school-aged population. Using logistic multivariate regression models, we estimated the independent effect of each of these variables on nonmedical use. Two models were generated: one for immigrant adolescents and one for native-born adolescents.
during the decade 2006-2016, 2.81% of native-born and 3.36% of immigrant adolescent students made nonmedical use of benzodiazepines. Gender and socioeconomic status were found to be related to the nonmedical use of benzodiazepines. Consumption of illegal psychoactive substances, other than marijuana, was the variable of greatest value (aOR = 6.00, 95% CI 3.89-9.27). Perceived risks and drug availability were found to be predictors for the nonmedical use of benzodiazepines in both immigrant and native-born adolescents.
in Spain, patterns of benzodiazepine nonmedical use among immigrant and native-born adolescents are similar. The results of this study refute certain stereotypes related to consumption of substances among immigrant adolescents, identifying them as a risk group.