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Background Loperamide is a mu-opioid receptor agonist that is available as an over-the-counter anti-motility agent in the US and UK; recommended maximum doses of 12-16 mg/day. Anecdotal reports of non-medical use (NMU) have increased over the past decade with supra-therapeutic doses (70-800 mg/day) associated with cardiotoxicity. Little data exists on the prevalence of loperamide NMU. Aim The aim of this study was to determine the prevalence of loperamide NMU in the UK and US and to describe characteristics of non-medical loperamide users. Design The Researched, Abuse, Diversion and Addiction Related Surveillance (RADARS®) Survey of Nonmedical Use of Prescription Drugs (NMURx) was utilized to study NMU of loperamide among the adult population in the UK and US in 2017. The RADARS® NMURx is anonymous and self-administered online. Methods A total of 40,029 completed surveys were included (10,019 from the UK and 30,010 from the US). Respondents were asked questions about medical and NMU of loperamide, frequency of and reasons for NMU, route of use problematic drug use markers, and demographics. Results Prevalence of lifetime loperamide use (95% CI) and lifetime NMU of loperamide were: UK 28.5% (27.67-29.4), and 0.66% (0.5-0.8), respectively; US 33.7% (33.1-34.2), and 5.19% (4.9-5.5), respectively. Problematic drug use markers were elevated in those who reported NMU of loperamide in both the UK and US, however high-risk use was more prevalent in the UK than in the US. Conclusion NMU of loperamide is common. In the current international environment of opioid addiction involving both therapeutic and illicit opioids, awareness of the NMU of loperamide is important. [ABSTRACT FROM AUTHOR]

Objective: Daily cannabis users develop tolerance to some drug effects, but the extent to which this diminishes driving impairment is uncertain. This study compared the impact of acute cannabis use on driving performance in occasional and daily cannabis users using a driving simulator., Methods: We used a within-subjects design to observe driving performance in adults age 25 to 45 years with different cannabis use histories. Eighty-five participants (43 males, 42 females) were included in the final analysis: 24 occasional users (1 to 2 times per week), 31 daily users and 30 non-users. A car-based driving simulator (MiniSim™, National Advanced Driving Simulator) was used to obtain two measures of driving performance, standard deviation of lateral placement (SDLP) and speed relative to posted speed limit, in simulated urban driving scenarios at baseline and 30 min after a 15 min ad libitum cannabis smoking period. Participants smoked self-supplied cannabis flower product (15% to 30% tetrahydrocannabinol (THC). Blood samples were collected before and after smoking (30 min after the start of smoking). Non-users performed the same driving scenarios before and after an equivalent rest interval. Changes in driving performance were analyzed by repeated measures general linear models., Results: Mean whole blood THC cannabinoids concentrations post smoking were use THC = 6.4 ± 5.6 ng/ml, THC-COOH = 10.9 ± 8.79 ng/mL for occasional users and THC = 36.4 ± 37.4 ng/mL, THC-COOH = 98.1 ± 90.6 ng/mL for daily users. On a scale of 0 to 100, the mean post-use score of subjective high was similar in occasional users and daily users (52.4 and 47.2, respectively). In covariate-adjusted analysis, occasional users had a significant increase in SDLP in the straight road segment from pre to post compared to non-users; non-users decreased by a mean of 1.1 cm (25.5 cm to 24.4 cm) while occasional users increased by a mean of 1.9 cm (21.7 cm to 23.6 cm; p = 0.02). Daily users also increased adjusted SDLP in straight road segments from baseline to post-use (23.2 cm to 25.0 cm), but the change relative to non-users was not statistically significant (p = 0.08). The standardized mean difference in unadjusted SDLP from baseline to post-use in the straight road segments comparing occasional users to non-users was 0.64 (95% CI 0.09 - 1.19), a statistically significant moderate increase. When occasional users were contrasted with daily users, the baseline to post changes in SDLP were not statistically significant. Daily users exhibited a mean decrease in baseline to post-use adjusted speed in straight road segments of 1.16 mph; a significant change compared to slight speed increases in the non-users and occasional users (p = 0.02 and p = 0.01, respectively)., Conclusion: We observed a decrement in driving performance assessed by SDLP after acute cannabis smoking that was statistically significant only in the occasional users in comparison to the nonusers. Direct contrasts between the occasional users and daily users in SDLP were not statistically significant. Daily users drove slower after cannabis use as compared to the occasional use group and non-users. The study results do not conclusively establish that occasional users exhibit more driving impairment than daily users when both smoke cannabis ad libitum., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose: Understanding potential bias due to rarity of the outcome is important when monitoring newly approved drugs and drugs with low availability to the general public. Although there is an increasing use of online surveys to investigate health outcomes, the limits of inference due to drug availability have not been studied. The goal of this study was to quantify the relationship between dispensing of prescription drugs and estimates of use in an online general population survey., Methods: An online repeated, cross-sectional survey from 2018 to 2020 was used to estimate the number of adults in the United States who used prescription drugs in the general population and compared to estimated number of prescriptions dispensed over an equivalent time period. Joinpoint regression was used to quantify thresholds. A sample of respondents was retested to estimate reliability statistics., Results: A model with a single threshold was the best fit, with the estimated threshold of 565 000 (95% CI: 9500-11 600 000) prescriptions dispensed per year. Above the threshold, there was a significant association between dispensing and estimates (p < 0.001); below the threshold, the relationship was not significant (p = 0.912). Above the threshold, responses were more reliable than random chance, and reliability steadily increased with increased dispensing., Conclusions: These results suggest the threshold demarcates two distinct pharmacoepidemiological paradigms when investigating drug use in general population surveys. Dispensing can be used as a guide to determine the epidemiological paradigm that is best suited., (© 2021 John Wiley & Sons Ltd.)

Aims: We investigated the prevalence of non-medical use (NMU) of benzodiazepines and GABA analogues in Europe., Methods: Data were collected using the online Non-Medical Use of Prescription Drugs (NMURx) survey from France, Germany, Italy, Spain and the UK., Results: The study included 55 223 eligible surveys which, after post-stratification weights were applied, represented approximately 260 million European adults. Lifetime NMU of benzodiazepines was highest in Spain (6.5%, 95% CI: 6.0-7.0) and lowest in Germany (1.7%, 1.5-2.0). Lifetime NMU of GABA analogues was highest in Germany (5.4%, 5.0-5.7) and lowest in France (2.2%, 1.9-2.5) and the UK (2.2%, 1.9-2.6) While no notable difference was observed for France or the UK, there was a higher prevalence of last 12-month NMU of benzodiazepines compared to GABA analogues in Italy (2.4 times higher) and Spain (3.0 times higher) and a higher prevalence of NMU of GABA analogues compared to benzodiazepines in Germany (2.6 times higher)., Conclusion: This study shows that there is variation in NMU of benzodiazepines and GABA analogues among countries. Of particular interest is the high incidence of GABA analogue NMU in Germany and benzodiazepine NMU in Spain. Further research to identify factors and motivations responsible for the higher prevalence observed are essential to inform public health policies in those countries., (© 2020 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Background and Aims: Mitragyna speciosa ('kratom') contains mu opioid partial agonists. It is widely available, and occasionally used as a home remedy for opioid use disorder. The Drug Enforcement Agency considers kratom a drug of concern; however, prevalence of use and role in drug misuse are unknown. This study aimed to characterize kratom use in the United States., Design: Cross-sectional Survey of Non-Medical Use of Prescription Drugs (NMURx) Program, 2018 third quarter and 2019 first quarter., Setting: A validated non-probability online survey in the United States., Participants: A total of 59 714 respondents aged 18 years or older, weighted to represent the adult US population (n = 252 063 800)., Measurements: In addition to prevalence of past-year kratom and other drug use, behavior proportions were estimated. The Drug Abuse Screening Test (DAST-10) estimated consequences of drug abuse., Findings: The estimated prevalence of past-year kratom use in the adult US population was 0.8% [95% confidence interval (CI) = 0.7-0.9], representing 2 031 803 adults. Life-time prevalence was 1.3% (95% CI = 1.2-1.4), representing 3 353 624 adults. Kratom users were younger (mean 35 years, P < 0.001), with higher proportions of males (61.0 versus 48.6%, P < 0.001), students (14.1 versus 7.5%, P < 0.001) and health-care professionals (9.7 versus 4.5%, P < 0.001) and fewer bachelor's/advanced degree graduates (33.4 versus 42.6%, P < 0.001) compared with non-users. Results were inconclusive on whether there was a difference in kratom use by race, household income or employment status. Among those with past-year kratom use, 36.7% (95% CI = 32.1-41.3) non-medically used prescription opioids, 21.7% (95% CI = 18.0-25.5) used illicit opioids, 54.4% (95% CI = 49.5-59.3) used another illicit drug and 67.1% (95% CI = 62.5-71.8) used cannabis. The DAST-10 profile was more often substantial/severe in kratom users (21 versus 1%, P < 0.001) compared with non-users., Conclusions: Estimated United States past-year prevalence of kratom use is 0.8%, and kratom users tend to have more serious substance abuse profiles than non-users or users of cannabis, alcohol or cigarettes. To our knowledge, this is the first description of kratom use at the national level., (© 2020 Society for the Study of Addiction.)

Background: Loperamide is a mu-opioid receptor agonist that is available as an over-the-counter anti-motility agent in the US and UK; recommended maximum doses of 12-16 mg/day. Anecdotal reports of non-medical use (NMU) have increased over the past decade with supra-therapeutic doses (70-800 mg/day) associated with cardiotoxicity. Little data exists on the prevalence of loperamide NMU., Aim: The aim of this study was to determine the prevalence of loperamide NMU in the UK and US and to describe characteristics of non-medical loperamide users., Design: The Researched, Abuse, Diversion and Addiction Related Surveillance (RADARS® ) Survey of Nonmedical Use of Prescription Drugs (NMURx) was utilized to study NMU of loperamide among the adult population in the UK and US in 2017. The RADARS® NMURx is anonymous and self-administered online., Methods: A total of 40,029 completed surveys were included (10,019 from the UK and 30,010 from the US). Respondents were asked questions about medical and NMU of loperamide, frequency of and reasons for NMU, route of use problematic drug use markers, and demographics., Results: Prevalence of lifetime loperamide use (95% CI) and lifetime NMU of loperamide were: UK 28.5% (27.67-29.4), and 0.66% (0.5-0.8), respectively; US 33.7% (33.1-34.2), and 5.19% (4.9-5.5), respectively. Problematic drug use markers were elevated in those who reported NMU of loperamide in both the UK and US, however high-risk use was more prevalent in the UK than in the US., Conclusion: NMU of loperamide is common. In the current international environment of opioid addiction involving both therapeutic and illicit opioids, awareness of the NMU of loperamide is important., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: In rapidly changing fields such as the study of drug use, the need for accurate and timely data is paramount to properly inform policy and intervention decisions. Trends in drug use can change rapidly by month, and using study designs with flexible modules could present advantages. Timely data from online panels can inform proactive interventions against emerging trends, leading to a faster public response. However, threats to validity from using online panels must be addressed to create accurate estimates., Objective: The objective of this study was to demonstrate a comprehensive methodological approach that optimizes a nonprobability, online opt-in sample to provide timely, accurate national estimates on prevalence of drug use., Methods: The Survey of Non-Medical Use of Prescription Drugs Program from the Researched Abuse, Diversion and Addiction Related Surveillance (RADARS) System is an online, cross-sectional survey on drug use in the United States, and several best practices were implemented. To optimize final estimates, two best practices were investigated in detail: exclusion of respondents showing careless or improbable responding patterns and calibration of weights. The approach in this work was to cumulatively implement each method, which improved key estimates during the third quarter 2018 survey launch. Cutoffs for five exclusion criteria were tested. Using a series of benchmarks, average relative bias and changes in bias were calculated for 33 different weighting variable combinations., Results: There were 148,274 invitations sent to panelists, with 40,021 who initiated the survey (26.99%). After eligibility assessment, 20.23% (29,998/148,274) of the completed questionnaires were available for analysis. A total of 0.52% (157/29,998) of respondents were excluded based on careless or improbable responses; however, these exclusions had larger impacts on lower volume drugs. Number of exclusions applied were negatively correlated to total dispensing volume by drug (Spearman ρ=-.88, P<.001). A weighting scheme including three demographic and two health characteristics reduced average relative bias by 31.2%. After weighting, estimates of drug use decreased, reflecting a weighted sample that had healthier benchmarks than the unweighted sample., Conclusions: Our study illustrates a new approach to using nonprobability online panels to achieve national prevalence estimates for drug abuse. We were able to overcome challenges with using nonprobability internet samples, including misclassification due to improbable responses. Final drug use and health estimates demonstrated concurrent validity to national probability-based drug use and health surveys. Inclusion of multiple best practices cumulatively improved the estimates generated. This method can bridge the information gap when there is a need for prompt, accurate national data., (©Joshua Curtis Black, Karilynn Rockhill, Alyssa Forber, Elise Amioka, K Patrick May, Colleen M Haynes, Nabarun Dasgupta, Richard C Dart. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.10.2019.)

There is concern in the UK about nonmedical use (NMU) of alprazolam (Xanax). We investigated the epidemiology of alprazolam NMU compared with diazepam using data from the Survey of Non-Medical Use of Prescription Drugs (NMURx) programme (collected 28 September-1 December 2017). The survey included 10 019 respondents and was weighted by age, sex and region to represent 52 927 659 UK adults. The estimated national prevalence of lifetime NMU of alprazolam was 0.32% (95% confidence interval: 0.19-0.46), and 1.30% (1.06-1.54) for diazepam. The prevalence of NMU in the last 90 days was significantly different when split by age category for alprazolam (P < .001), but not for diazepam (P = .262) with alprazolam NMU being more common among younger adults (age 16-24 years: 0.37%; age 25-34 years: 0.14%; 35 years or older: 0.01%). Further research is needed to fully understand the motivations of alprazolam NMU and to monitor whether the popularity of alprazolam will rise., (© 2019 The British Pharmacological Society.)

Background: The objective of this study was to develop an algorithm for identifying surgical site infections (SSIs) using independent variables from electronic health record data and outcomes from the American College of Surgeons National Surgical Quality Improvement Program to supplement manual chart review., Methods: We fit 3 models to data from patients undergoing operations at the University of Colorado Hospital between 2013 and 2015: a similar model reported previously in the literature, a comprehensive model with 136 possible predictors, and a combination of those. All models used a generalized linear model with a lasso penalty. Several techniques for handling imbalance in the outcome were also used: Youden's J statistic to optimize the probability cutoff and sampling techniques combined with Youden's J. The models were then tested on data from patients undergoing operations during 2016., Results: Two hundred thirty of 6,840 patients (3.4%) had an SSI. The comprehensive model fit to the full set of training data performed the best, achieving 90% specificity, 80% sensitivity, and an area under the receiver operating characteristic curve of 0.89., Conclusions: We identified a model that accurately identified SSIs. The framework presented can be easily implemented by other American College of Surgeons National Surgical Quality Improvement Program-participating hospitals to develop models for enhancing surveillance of SSIs., (Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)