Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

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.

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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: 6.07.0) and lowest in Germany (1.7%, 1.52.0). Lifetime NMU of GABA analogues was highest in Germany (5.4%, 5.05.7) and lowest in France (2.2%, 1.92.5) and the UK (2.2%, 1.92.6) 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. K E Y W O R D S addiction, drug abuse, public health 1 | INTRODUCTION Non-medical use (NMU) of prescription medicines is a global issue and, while much of the attention is focused on NMU of prescription opioids, there are other drug classes that warrant investigation. 1,2 A previous study by Novak et al. investigating NMU of prescription drugs in Denmark, Germany, Great Britain, Spain and Sweden found that, while opioids were the most commonly reported medication for lifetime NMU (13.5% of respondents after weighting, compared to 10.9% for sedative drugs [benzodiazepines and tranquilisers]), sedative drugs were most commonly reported for past-year NMU (5.8% compared to 5.0% for opioids). 3 Benzodiazepines have been widely used for their sedative and anxiolytic properties globally for decades, while more recently the