Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review

BACKGROUND

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

OBJECTIVE

We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.

METHODS

We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations.

RESULTS

In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines.

CONCLUSIONS

This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers

Introduction: The debate over the legal status of many cannabis- and hemp-derived products, including delta-8 tetrahydrocannabinol (THC), is in question. Although low concentrations of delta-8 THC are legal at the Federal level, many states have implemented their own regulations to both allow and restrict its use and sale. Of concern, sellers with unknown legal credentials have appeared online and are actively selling this product. Materials and Methods: We characterized the marketing, sale, and compliance of online delta-8 THC sellers using (1) data collected from the Twitter Application Programming Interface with delta-8 THC-related keywords; (2) unsupervised topic modeling using the Biterm Topic Model to identify clusters of tweets involved in marketing and selling; (3) inductive coding to identify marketing and selling characteristics; and (4) web forensics and simulated shopping to determine compliance with state restrictions for delta-8 THC sales. Results: In total, 110 unique hyperlinks associated with 7085 tweets that included marketing and selling activity for delta-8 THC were collected. From these links, we conducted simulated purchasing in January 2021 to identify compliant and noncompliant websites. Among the vendors, age verification was not found in over half of websites (59, 53.63%); 60 (54.55%) did not report a physical address; and 74 (65.45%) sold delta-8 products direct-to-consumer. Sixty-seven (90.54%) of detected vendors shipped delta-8 products to addresses in states that prohibit sales. Forty-three (64.18%) of Internet Protocol addresses were located within the United States; all others were international. Conclusion: Our analysis suggests that online storefronts are illegally selling and shipping cannabinoid derivatives to U.S. consumers. Further research is needed to understand downstream health and regulatory impacts from this unregulated access.

Identification and Characterization of Illegal Sales of Cannabis and Nicotine Delivery Products on Telegram Messaging Platform

INTRODUCTION

Unregulated and potentially illegal sales of tobacco, nicotine, and cannabis products have been detected on various social media platforms, e-commerce sites, online retailers, and the dark web. New end-to-end encrypted messaging services are popular among online users and present opportunities for marketing, trading, and selling of these products. The purpose of this study was to identify and characterize tobacco, nicotine, and cannabis selling activity on the messaging platform Telegram.

METHODS

The study was conducted in three phases: (1) identifying keywords related to tobacco, nicotine, and cannabis products for purposes of detecting Telegram groups and channel messages; (2) automated data collection from public Telegram groups; and (3) manual annotation and classification of messages engaged in marketing and selling products to consumers.

RESULTS

Four keywords were identified ("Nicotine," "Vape," "Cannabis," and "Smoke") that yielded 20 Telegram groups with 262 506 active subscribers. Total volume of channel messages was 43 963 unique messages that included 3094 (7.04%) marketing/selling messages. The most commonly sold products in these groups were cannabis-derived products (83.25%, n = 2576), followed by tobacco/nicotine-derived products (6.46%, n = 200), and other illicit drugs (0.77%, n = 24). A variety of marketing tactics and a mix of seller accounts were observed, though most appeared to be individual suppliers.

CONCLUSIONS

Telegram is an online messaging application that allows for custom group creation and global connectivity, but also includes unregulated activities associated with the sale of cannabis and nicotine delivery products. Greater attention is needed to conduct monitoring and enforcement on these emerging platforms for unregulated and potentially illegal cannabis and nicotine product sales direct-to-consumer.

IMPLICATIONS

Based on study results, Telegram represents an emerging platform that enables a robust cannabis and nicotine-selling marketplace. As local, state, and national tobacco control regulations continue to advance sales restrictions and bans at the retail level, easily accessible and unregulated Internet-based channels must be further assessed to ensure that they do not act as conduits for exposure and access to unregulated or illegal cannabis and nicotine products.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis

Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how attributes of social media posts (i.e., features) influence safety evaluations of mock posts selling illicit substances. 440 participants were recruited online for self-reporting use or purchase of controlled substances or prescription medicines recreationally. The following attributes were tested: drug packaging, drug offerings, profile photo of seller, payment info provided, and use of emojis. Results: Packaging was ranked the most important attribute (Average Importance =43.68, Offering=14.94, Profile=13.86, Payment=14.11, Emoji=13.41), with posts that displayed drugs in pill bottles assessed as the most safe. Attribute levels for advertising multiple drugs, having a blank profile photo, including payment information, and including emojis also ranked higher in perceived safety. Rankings were consistent across tested demographic factors (i.e., gender, age, and income). Survey results show that online pharmacies were most likely to be perceived as safe for purchasing drugs and medications. Additionally, those who were younger in age, had higher income, and identified as female were more likely to purchase from a greater number of platforms. Conclusions: These findings can assist in developing more precise content moderation for platforms seeking to address this ongoing threat to public safety.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study

BACKGROUND

Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance.

OBJECTIVE

The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition.

METHODS

In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug.

RESULTS

Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery.

CONCLUSIONS

The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use.

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

Background

Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.

Methods

We collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.

Results

The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.

Conclusions

NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Illicit Online Pharmacies: A Scoping Review

This scoping review presents the extent and nature of the body of literature on illicit online pharmacies (IOPs) and identifies research gaps. Using the five-step framework developed by Arksey and O'Malley, we searched PubMed, Web of Science, EMBASE, CINAHL, Science Direct and PsycInfo to retrieve relevant studies published in English in peer-reviewed journals. The search strategy identified forty-three articles that met the inclusion criteria. Ten themes were identified and categorized into five clusters: patient risk, healthcare providers, marketing and supply chain, public health and society, and policy and regulation. Research into these clusters has evolved over time and has focused increasingly on issues related to specific drugs rather than the overall phenomenon. Data collection has been dominated by convenience sampling, online searches, content analysis and surveys. Data analysis remains primarily descriptive. Gaps within the extant literature suggest an agenda for future research into regulation and enforcement; public health awareness and education; healthcare services; risks to patients and public health; patient-, price- and product-related issues; website design; social media promotion; and supply chains and logistics. We conclude that IOPs are vastly understudied and suggest an urgent need for further empirical and conclusive research.

Using Social Media to Examine Themes Surrounding Fentanyl Misuse and Risk Indicators

Background: Opioid misuse is a crisis in the United States, and synthetic opioids such as fentanyl pose risks for overdose and mortality. Individuals who misuse substances commonly seek information and support online due to stigma and legal concerns, and this online networking may provide insight for substance misuse prevention and treatment. We aimed to characterize topics in substance-misuse related discourse among members of an online fentanyl community. Method: We investigated posts on a fentanyl-specific forum on the platform Reddit to identify emergent substance misuse-related themes potentially indicative of heightened risk for overdose and other adverse health outcomes. We analyzed 27 posts and 338 comments with a qualitative codebook established using a subset of user posts via inductive and deductive methods. Posts and comments were independently reviewed by two coders with a third coder resolving discrepancies. The top 200 subreddits with the most activity by r/fentanyl members were also inductively analyzed to understand interests of r/fentanyl users. Results: Functional/quality of life impairments due to substance misuse (29%) was the most commonly occurring theme, followed by polysubstance use (27%) and tolerance/dependence/withdrawal (20%). Additional themes included drug identification with photos, substances cut with other drugs, injection drugs, and past overdoses. Media-focused subreddits and other drug focused communities were among the communities most often followed by r/fentanyl users. Conclusion: Themes closely align with DSM-V substance use disorder symptoms for fentanyl and other substances. High involvement in media-focused subreddits and other substance-misuse-related communities suggests digital platforms as acceptable for overdose prevention and recovery support interventions.

Adaptive interventions for opioid prescription management and consumption monitoring

OBJECTIVES

While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.

MATERIALS AND METHODS

Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components.

RESULTS

SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD.

DISCUSSION

The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption.

CONCLUSION

SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.

Online Pharmacies Selling Prescription Drugs: Systematic Review

Introduction: The patronage of online pharmacies is rapidly growing, driven by the convenience and cheaper costs of purchasing prescription drugs electronically, especially under the lockdown situation. However, there are issues regarding the quality of the prescription drugs sold online and the legitimacy of online pharmacies. The use of prescription drugs without the supervision of a licensed health care practitioner may potentially harm consumers. Objectives: This systematic review was conducted to improve the body of knowledge on three main aspects of online pharmacies: (1) type and characteristics of the online pharmacies selling drugs; (2) the quality of pharmaceutical drugs purchased online; and (3) the characteristics of consumers of online pharmacies. Methods: Based on a pre-defined search strategy, PubMed and Scopus were utilised to search articles written in the English language published between January 2009 and February 2020. Studies focusing on the sale of prescription drugs were included. The terms used for the literature search were “online pharmacy”, “internet pharmacy”, “e-pharmacy”, “prescription”, “quality”, “medication safety”, and “counterfeit medicine”. These terms were used alone and in combination with Boolean operators. The institutional webpages including the World Health Organization (WHO) and the United States Food and Drug Administration (USFDA) were also examined for any additional studies. No methodological limitations in terms of study design were applied. A standardised data collection form was used to compile the data. Results: Based on the inclusion and exclusion criteria, a total of 46 articles were eligible and included in the final analysis. There were 27 articles on types and characteristic of online pharmacies, 13 articles on the quality of prescription drugs sold from online pharmacies, and 11 articles on consumers purchasing prescription drugs from online pharmacies. Readers should note that five articles discussed both the types and characteristics of online pharmacies, and the quality of the drugs sold from the outlets. The response rate (products received out of the number of orders) ranged from 20% to 100%, whereas the proportion of consumers buying prescription drugs online ranged from 2.3% to 13%. Reasons for online purchase of prescription drugs include the difficulty of obtaining a prescription for certain medications such as opioid analgesics, cheaper cost, since the costs associated with seeing a physician to obtain a prescription are reduced, and the need to obtain drugs such as opioid analgesics and benzodiazepine for misuse. Conclusions: Almost half of the online pharmacies are not properly regulated and fraudulent issues were uncovered. To address this issue, stricter regulation by World Health Organization and implementation should be carried out together with frequent monitoring of the licensure system and pharmacy verification on every online pharmacy, this would reduce the number of illegal or illegitimate online pharmacy.

An unsupervised machine learning approach for the detection and characterization of illicit drug-dealing comments and interactions on Instagram

Background: Growing use of social media has led to the emergence of virtual controlled substance and illicit drug marketplaces, prompting calls for action by government and law enforcement. Previous studies have analyzed Instagram drug selling via posts. However, comments made by users involving potential drug selling have not been analyzed. In this study, we use unsupervised machine learning to detect and classify prescription and illicit drug-related buying and selling interactions on Instagram. Methods: We used over 1,000 drug-related hashtags on Instagram to collect a total of 43,607 Instagram comments between February 1st, 2019 and May 31st, 2019 using data mining approaches in the Python programming language. We then used an unsupervised machine learning approach, the Biterm Topic Model (BTM), to thematically summarize Instagram comments into distinct topic groupings, which were then extracted and manually annotated to detect buying and selling comments. Results: We detected 5,589 comments from sellers, prospective buyers, and online pharmacies from 531 unique posts. The vast majority (99.7%) of comments originated from drug sellers and online pharmacies. Key themes from comments included providing contact information through encrypted third-party messaging platforms, drug availability, and price inquiry. Commonly offered drugs for sale included scheduled controlled substances such as Adderall and Xanax, as well as illicit hallucinogens and stimulants. Comments from prospective buyers of drugs most commonly included inquiries about price and availability. Conclusions: We detected prescription controlled substances and other illicit drug selling interactions via Instagram comments to posts. We observed that comments were primarily used by sellers offering drugs, and typically not by prospective buyers interacting with sellers. Further research is needed to characterize these "social" drug marketplace interactions on this and other popular social media platforms.

Investigating Individuals’ Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data

Background

Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.

Objective

The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals’ beliefs and perceptions.

Methods

We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels.

Results

LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. “Emotion and beliefs” had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by “physical activity” (124,251/896,867, 13.85%) and “daily life” (80,730/896,867, 9%), while “food and drink,” “weather,” and “not being understood” had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within “emotion and beliefs,” 113,562/157,563 (72%) had negative sentiment.

Conclusions

The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals’ perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.

Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions

Introduction

The counterfeit phenomenon is a largely under-reported issue, with potentially large burden for healthcare. The market for counterfeit drugs used in sexual medicine, most notably type 5 phosphodiesterase inhibitors (PDE5i), is rapidly growing.

Aims

To report the health risks associated with the use of counterfeit medications, the reasons driving their use, and the strategies enacted to contain this phenomenon.

Methods

A systematic scoping review of the literature regarding counterfeit PDE5i was carried between January and June 2021, then updated in August 2021.

Main Outcome Measure

We primarily aimed to clarify the main drivers for counterfeit PDE5i use, the health risks associated, and the currently available strategies to fight counterfeiters.

Results

One hundred thirty-one records were considered for the present scoping review. Production of fake PDE5i is highly lucrative and the lacking awareness of the potential health risks makes it a largely exploitable market by counterfeiters. Adulteration with other drugs, microbial contamination and unreliable dosages make counterfeit medications a cause of worry also outside of the sexual medicine scope. Several laboratory techniques have been devised to identify and quantify the presence of other compounds in counterfeit medications. Strategies aimed at improving awareness, providing antitampering packaging and producing non-falsifiable products, such as the orodispersible formulations, are also described.

Clinical implications

Improving our understanding of the PDE5i counterfeit phenomenon can be helpful to promote awareness of this issue and to improve patient care.

Strengths & Limitations

Despite the systematic approach, few clinical studies were retrieved, and data concerning the prevalence of counterfeit PDE5i use is not available on a global scale.

Conclusion

The counterfeit phenomenon is a steadily growing issue, with PDE5i being the most counterfeited medication with potentially large harmful effects on unaware consumers.

Sansone A, Cuzin B, and Jannini EA. Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions. Sex Med 2021;9:100437.

Online medication purchasing during the Covid-19 pandemic: potential risks to patient safety and the urgent need to develop more rigorous controls for purchasing online medications, a pilot study from the United Arab Emirates

Background

Since the WHO announced that Covid-19 had become a global pandemic, online pharmacies have emerged as an extremely popular way to purchase medication due to the quarantine measures introduced by numerous countries to prevent the virus's spread.

Aim

The aim of this study was to collect information regarding the extent of online medication purchasing in the UAE and to assess the factors that motivating the purchase of medications from the internet.

Method

A convenience sampling of people living in the UAE was used to conduct an online descriptive cross-sectional study. Respondents were solicited using the social media platforms WhatsApp and Facebook, whereby they were asked to fill in a validated web-based questionnaire. The number of people buying medications from online pharmacies was calculated using a percentage with 95% CIs.

Results

131 respondents (31.2%) [95% CI: 26.7–35.6] stated that they purchased medication via the Internet after Covid-19 was classed as a pandemic. It was found that those respondents most likely to have purchased medication via the Internet were male, single, and older and with a high school education.

Conclusion

More research should be conducted to investigate and compare the self-medication and associated risk factors between online pharmacies and community pharmacies. Moreover, regulatory bodies need to make and implement changes to the regulations that govern the sale and use of medications during COVID-19.

Demystifying the Dark Web Opioid Trade: Content Analysis on Anonymous Market Listings and Forum Posts

Background

Opioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid crisis. The role of anonymous online marketplaces and forums that resemble eBay or Amazon, where anyone can post, browse, and purchase opioid commodities, has become increasingly important in opioid trading. Therefore, a greater understanding of anonymous markets and forums may enable public health officials and other stakeholders to comprehend the scope of the crisis. However, to the best of our knowledge, no large-scale study, which may cross multiple anonymous marketplaces and is cross-sectional, has been conducted to profile the opioid supply chain and unveil characteristics of opioid suppliers, commodities, and transactions.

Objective

We aimed to profile the opioid supply chain in anonymous markets and forums via a large-scale, longitudinal measurement study on anonymous market listings and posts. Toward this, we propose a series of techniques to collect data; identify opioid jargon terms used in the anonymous marketplaces and forums; and profile the opioid commodities, suppliers, and transactions.

Methods

We first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. We then developed a suite of opioid domain–specific text mining techniques (eg, opioid jargon detection and opioid trading information retrieval) to recognize information relevant to opioid trading activities (eg, commodities, price, shipping information, and suppliers). Subsequently, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums.

Results

A total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers’ IDs and 2778 unique opioid buyers’ IDs. Our study characterized opioid suppliers (eg, activeness and cross-market activities), commodities (eg, popular items and their evolution), and transactions (eg, origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand.

Conclusions

The results provide insight into opioid trading in the anonymous markets and forums and may prove an effective mitigation data point for illuminating the opioid supply chain.

The convergence of social media and other communication technologies in the promotion of illicit and controlled drugs

Some social media platforms have strict regulations regarding the promotion of illicit and controlled drug on their sites. This study attempts to examine whether social media outlets like Twitter, Flickr and Tumblr have implemented practical measures to stop the active promotion of such drugs. We examined over 2.6 million social media posts taken from these three platforms. By focusing on keyword searches around mobile apps and communication means, we found evidence of ongoing opioid drug promotion, especially on Twitter followed by Flickr and Tumblr; we discuss our approach which effectively identifies posts related to the promotion of opioids and controlled drugs.

Hey Google! will New Zealand vote to legalise cannabis? Using Google Trends data to predict the outcome of the 2020 New Zealand cannabis referendum

BACKGROUND

New Zealand held a referendum on the legalisation of recreational cannabis in October 2020. Polls preceding the referendum provided contrasting outcomes. We investigated whether internet search data from Google Trends could provide an alternative estimate of the referendum outcome.

METHODS

We assessed various methods for accessing Google Trends data, downloading search probability data for google.com searches from New Zealand via trends.google.com, PyTrends and Google Trends Extended for Health. We used daily data for the three months prior to the final referendum date, and hourly data for the final week. We defined two smaller time frames each from daily and hourly data, allowing comparisons over the entire time frames, and progressively closer to the end. Using the selected keyword combination of 'cannabis referendum yes/no' we calculated the proportions of 'yes' and 'no' searches for each time frame/data source combination, aiming for a prediction within 2% of the final result.

RESULTS

Data from different sources varied slightly. The method used to aggregate search probabilities over the selected time frame (mean/median) resulted in changes in the predicted outcome for hourly-, but not daily data. On 20 October we predicted the 'no' vote at 51.9%-55.4% for daily-, and 60% for hourly data when aggregated using the median, but only 49% for mean hourly data. Hourly data performed poorly at predicting the final 51.2% 'no' result, while predictions based on mean daily data for the full voting period provided the best prediction, differing by 0.1-0.2%.

CONCLUSION

Predictions based on Google Trends data broadly agreed with polling predictions, but the exact method used affected the eventual prediction. While polls are subject to influence from methodological considerations (e.g., sampling), it is clear that Google Trends data can be used to make a prediction, but do not present a magic bullet solution to polling problems.

Developing a framework regarding a complex risk based methodology in the evaluation of hazards associated with medicinal products sourced via the internet

Today, the increasing number of illicit internet pharmacies is a global phenomenon, however, the size of the online pharmaceutical market is still relatively unknown and the dubious quality of products is questionable and warrants investigation. Descriptive data from this black market channel are derived from studies analyzing the online availability of different medications procured over the internet and their methodology is quite heterogeneous. Our aim was to develop a comprehensive and specific risk assessment for selecting high patient safety risk medications from the online pharmaceutical market. A rapid tool was developed based upon the two quality and safety standard resolutions in pharmaceutical practice, published by the European Directorate for the Quality of Medicines, and was illustrated on eye drops. We developed five dimensions in support of the risk assessment including intrinsic, extrinsic and potential risks of counterfeiting. The five criteria were integrated in a comprehensively weighted risk-scoring format. The probability of procuring the product from the internet was also assessed based on the number of relevant links within the first twenty search engine results and the cost of the products. With the application of the tool a dorzolamide & timolol combination eye drop represented the highest overall patient safety risk score. In consideration of our literature review of the past 20 years, there is no current, standardized methodology to effectively identify pharmaceutical products associated with high patient safety risks. Notably, the fully comprehensive analysis of the internet pharmaceutical market and the test purchase of all online available medicines is unrealistic. Therefore, we developed a method to aid online surveillance researches and targeted international organizational led joint actions against the uncontrolled sale of falsified and substandard medications (e.g.: Operation Pangea).

Characteristics of online pharmacies selling Adderall

OBJECTIVES

Adderall (amphetamine-dextroamphetamine) is a controlled substance with harmful adverse effects if abused or misused. We assessed the availability of Adderall from common search engines, and evaluated the safety and marketing characteristics of online pharmacies selling Adderall.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

From December 2019 to February 2020, the phrase "buy Adderall online" was queried in four search engines: Google (N = 100), Bing (N = 100), Yahoo (N = 50) and DuckDuckGo (N = 50). Online pharmacies that claimed to sell Adderall and had unique Uniform Resource Locators, were active, free-access, and in English language were included.

OUTCOME MEASURES

Online pharmacies were categorized as rogue, unclassified, or legitimate on the basis of LegitScript classifications. Safety and marketing characteristics, and costs were collected.

RESULTS

Of the 62 online pharmacies found to sell Adderall, 61 were rogue or unclassified. Across all rogue and unclassified online pharmacies, prescriptions were not required (100%), pharmacist services were not offered (100%), and quantity limits were not placed on the number of Adderall purchases (100%). Rogue and unclassified online pharmacies appealed to cost, offering price discounts (61%), bulk discounts (67%), and coupon codes (70%). Contrary to their claims, cheaper prices were available for all formulations and dosages of Adderall from GoodRx than from these online pharmacies. Rogue and unclassified online pharmacies promoted and enabled the illicit purchase of Adderall, appealing to privacy (74%), offering purchase through cryptocurrency (74%), and claiming registration or accreditation of their sites (33%).

CONCLUSION

Rogue online pharmacies are pervasive in search engine results, enabling the illicit purchase of Adderall without a prescription. Consumers are at risk of purchasing Adderall, a medication with high abuse potential, from unsafe sources. Law enforcement, regulatory agencies, and search engines should work to further protect consumers from unregistered and illegitimate online pharmacies selling Adderall.

Managing Illicit Online Pharmacies: Web Analytics and Predictive Models Study

BACKGROUND

Online pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily).

OBJECTIVE

This study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs.

METHODS

We first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor.

RESULTS

We found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM).

CONCLUSIONS

Our prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.

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.

Novel Opioids: Systematic Web Crawling Within the e-Psychonauts' Scenario

Background

A wide range of novel psychoactive substances (NPSs) are regularly searched and discussed online by e-psychonauts. Among NPSs, the range of prescription/non-prescription opioids (fentanyl and non-fentanyl analogs) and herbal derivatives currently represents a challenge for governments and clinicians.

Methods

Using a web crawler (i.e., NPS.Finder^®), the present study aimed at assessing psychonaut fora/platforms to better understand the online situation regarding opioids.

Results

The open-web crawling/navigating software identified some 426 opioids, including 234 fentanyl analogs. Of these, 176 substances (162 were very potent fentanyls, including two ohmefentanyl and seven carfentanyl analogs) were not listed in either international or European NPS databases.

Conclusion

A web crawling approach helped in identifying a large number, indeed higher than that listed by European/international agencies, of unknown opioids likely to possess a significant misuse potential. Most of these novel/emerging substances are still relatively unknown. This is a reason of concern; each of these analogs potentially presents with different toxicodynamic profiles, and there is a lack of docking, preclinical, and clinical observations. Strengthening multidisciplinary collaboration between clinicians and bioinformatics may prove useful in better assessing public health risks associated with opioids.

Using machine learning to predict opioid misuse among U.S. adolescents

This study evaluated prediction performance of three different machine learning (ML) techniques in predicting opioid misuse among U.S. adolescents. Data were drawn from the 2015-2017 National Survey on Drug Use and Health (N = 41,579 adolescents, ages 12-17 years) and analyzed in 2019. Prediction models were developed using three ML algorithms, including artificial neural networks, distributed random forest, and gradient boosting machine. The performance of the ML prediction models was compared with performance of the penalized logistic regression. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used as metrics of prediction performance. We used the AUPRC as the primary measure of prediction performance given that it is considered more informative for assessing binary classifiers on imbalanced outcome variable than AUROC. The overall rate of opioid misuse among U.S. adolescents was 3.7% (n = 1521). Prediction performance was similar across the four models (AUROC values range from 0.809 to 0.815). In terms of the AUPRC, the distributed random forest showed the best performance in prediction (0.172) followed by penalized logistic regression (0.162), gradient boosting machine (0.160), and artificial neural networks (0.157). Findings suggest that machine learning techniques can be a promising technique especially in the prediction of outcomes with rare cases (i.e., when the binary outcome variable is heavily lopsided) such as adolescent opioid misuse.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study

Background

Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug dealers selling controlled substances, opioids, and other illicit drugs. Previous studies and news investigations have reported the use of popular social media platforms as conduits for opioid sales. This study uses deep learning to detect illicit drug dealing on the image and video sharing platform Instagram.

Objective

The aim of this study was to develop and evaluate a machine learning approach to detect Instagram posts related to illegal internet drug dealing.

Methods

In this paper, we describe an approach to detect drug dealers by using a deep learning model on Instagram. We collected Instagram posts using a Web scraper between July 2018 and October 2018 and then compared our deep learning model against 3 different machine learning models (eg, random forest, decision tree, and support vector machine) to assess the performance and accuracy of the model. For our deep learning model, we used the long short-term memory unit in the recurrent neural network to learn the pattern of the text of drug dealing posts. We also manually annotated all posts collected to evaluate our model performance and to characterize drug selling conversations.

Results

From the 12,857 posts we collected, we detected 1228 drug dealer posts comprising 267 unique users. We used cross-validation to evaluate the 4 models, with our deep learning model reaching 95% on F1 score and performing better than the other 3 models. We also found that by removing the hashtags in the text, the model had better performance. Detected posts contained hashtags related to several drugs, including the controlled substance Xanax (1078/1228, 87.78%), oxycodone/OxyContin (321/1228, 26.14%), and illicit drugs lysergic acid diethylamide (213/1228, 17.34%) and 3,4-methylenedioxy-methamphetamine (94/1228, 7.65%). We also observed the use of communication applications for suspected drug trading through user comments.

Conclusions

Our approach using a combination of Web scraping and deep learning was able to detect illegal online drug sellers on Instagram, with high accuracy. Despite increased scrutiny by regulators and policymakers, the Instagram platform continues to host posts from drug dealers, in violation of federal law. Further action needs to be taken to ensure the safety of social media communities and help put an end to this illicit digital channel of sourcing.

Opioids and the Internet: Convergence of Technology and Policy to Address the Illicit Online Sales of Opioids

The United States is in the midst of an opioid public health emergency, one that is also influenced by a convergence of Internet-based technology, health policy, and the need for stakeholder collaboration and action around the need to combat the illicit online sales of opioids by illegal online pharmacies and digital drug dealers. This risk is not new, however, with calls to actively reduce online opioid availability as online pharmacies use a growing array of digital channels, including search engines, social media platforms, and the dark Web. In response, the US Food and Drug Administration convened a special June 2018 summit bringing together technology companies, government agencies, researchers, and advocacy groups with the goal of collaboratively developing and implementing solutions to tackle the problem. Yet after this meeting, stakeholders remain fragmented in approaches despite the availability of technology that can detect, classify, and report illicit sellers who are in direct violation of Federal law. Despite ongoing challenges, advances in data science and the resources and expertise technology companies can contribute will be a key factor in ensuring that the Internet helps end and not fuel the public health emergency of opioid abuse.

Consumers Turning to the Internet Pharmacy Market: Cross-Sectional Study on the Frequency and Attitudes of Hungarian Patients Purchasing Medications Online

Background

During the past two decades, the internet has become an accepted way to purchase products and services. Buying medications online are no exception. Besides its benefits, several patient safety risks are linked to the purchase of medicines outside the traditional supply chain. Although thousands of internet pharmacies are accessible on the web, the actual size of the market is unknown. Currently, there is limited data available on the use of internet pharmacies, the number, and attitude of people obtaining medications and other health products from the internet.

Objective

This study aims to gather information on the frequency and attitudes of patients purchasing medications online in a nationally representative sample of outpatients. Attitudes towards main supply chain channels, perceived benefits, and disadvantages of influencing online medication purchase are evaluated.

Methods

A cross-sectional explorative study using a personally administered survey was conducted in a representative sample of Hungarian outpatients in 2018.

Results

A total of 1055 outpatients completed the survey (response rate 77.23%). The mean age was 45 years, and 456 (43.22%) reported having chronic health conditions. The majority (872/1055, 82.65%) of the respondents were aware that medications could be obtained online, but only 44 (4.17%) used the internet for previous medication purchases. Attitudes towards the different pharmaceutical supply chain retail channels showed significant differences (P<.001), respondents accepted retail pharmacy units as the most appropriate source of medications while rejected internet pharmacies. Respondents were asked to evaluate 9 statements regarding the potential benefits and disadvantages about the online medicine purchase, and based on the computed relative attitude rate there is a weak still significant tendency toward rejection (P<.001). Correspondence of demographic factors, internet usage behavior, and prospective online drug purchase attitude was evaluated. Respondents who use the internet more and purchase goods online will be more likely to buy medications online. Furthermore, youth and education will determine the medication purchase behavior.

Conclusions

Many patients will purchase medications on the internet in the future. Currently, there is an increased risk of patients buying products from illegal sites because these dominate the global online pharmacy market. Consequently, improved patient-provider communication and promotion campaigns are needed to inform the public about the safe use of internet pharmacies, as these initiatives can directly prevent patient safety threats.