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.

BackgroundIn 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.ObjectiveThe 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.MethodsThe 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.ResultsThere 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.ConclusionsOur 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.

To estimate prevalence of last 12-month nonmedical use (NMU) of benzodiazepines and Z-drugs (the nonbenzodiazepine hypnotics zaleplon, zolpidem and zopiclone) in the UK. Methods: Data were collected using the Non-Medical Use of Prescription Drugs survey with poststratification weighting applied to be representative of the UK population (≥16 years). Participants were questioned about whether they had nonmedically used benzodiazepines and/or Z-drugs in the last 12-months and from where they had obtained the drug (including via a prescription, or illicitly from a friend/family member, a dealer or via the internet). Additional questions were asked about last 12-month use of illicit drugs (cannabis, cocaine, 3,4-methylenedioxymethylamphetamine [MDMA], non-pharmaceutical amphetamine, crack cocaine and/or heroin). Results: The study included 10 006 eligible participants representing approximately 52 927 000 UK adults. The estimated prevalence of past 12-month NMU of any benzodiazepine and/or Z-drug was 1.2% (95% confidence interval: 1.0-1.5) corresponding to approximately 635 000 adults; amongst this group only an estimated 4.6% (1.2-8.0) had NMU of both a benzodiazepine and a Z-drug. The highest prevalence of NMU for only Z-drugs was among those who had used heroin in the last 12-months (5.4%, 2.7-10.5), whilst the highest prevalence of NMU for only benzodiazepines was among those who had used illicit stimulants in the last 12-months: cocaine (5.9%, 3.8-8.9), amphetamine (5.6%, 3.1-10.0) and MDMA (5.2%, 3.1-8.8). The drug non-medically used was more commonly acquired without than with a prescription for both only benzodiazepines (70.2%, 59.4-81.1 compared to 51.3%, 41.5-64.6) and only Z-drugs (75.6%, 61.6-89.7 compared to 33.9%, 16.9-51.0). Conclusion: There is little overlap between benzodiazepine and Z-drug NMU suggesting distinct nonmedical use of the drugs; future studies need to explore whether this relates to personal preference, drug availability or other factors. A significant proportion are acquiring these drugs for NMU without a prescription, so without guidance and monitoring from a medical practitioner. While the dangers of mixing benzodiazepines and heroin/other opioids are well documented, there is a The authors confirm that the PI for this paper is Paul I. Dargan and that he had direct clinical responsibility for patients.