COVID-19 treatments sold online without prescription requirements in the United States

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online

BACKGROUND

The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors.

OBJECTIVE

The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations.

METHODS

We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases.

RESULTS

Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%).

CONCLUSIONS

While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

The characteristics and operations of "online pharmacies" investigated in relation to medicines popularised during the coronavirus pandemic: a cross-sectional study

Aim: To explore the characteristics and operations of online pharmacies in relation to medications that gained widespread popularity and increased demand during the COVID-19 pandemic. Furthermore, to assess and compare practices between legitimate and "rogue" online pharmacies. Methods: Fifteen COVID-19-pandemic-related medications were investigated through a UK-based online search. We systematically assessed the credibility of 116 retrieved online pharmacies using the factors: operational location, range of medicines sold, prescription requirements for Prescription Only Medicines (POMs), information exchange, payment/delivery, user-friendliness, legitimacy. Descriptive analysis was conducted, and legitimacy status (legitimate vs. illegitimate/rogue) was tested against relevant safety indicators using a chi-square test. Results: Out of 116 "online pharmacies," 55 (47%) were confirmed as "rogue," 47 (41%) were verified as legitimate by the General Pharmaceutical Council (GPhC), and 14 (12%) were verified by other bodies but not by the GPhC. A total of 93 declared their "apparent" physical location of operation on the webpage of which 63 (67.7%, n = 93) declared a location that did not match their actual location according to the online pharmacy's server (through their IP address). All 15 medicines analysed were readily available for purchase. A high percentage of online pharmacies offered POMs to the public (93.1%, n = 116). Only 23 out of the 116 online pharmacies assessed required a prescription for providing POMs, with only four of these verified as legitimate by the GPhC register, while most of the legitimate pharmacies (44 out of 47) offered online consultations as an alternative option instead of a prescription. Controlled medicines were offered by 28 online pharmacies 27 of which were deemed as rogue. Rogue online pharmacies were significantly more likely to guarantee refunds for medication, not require prescriptions for POMs, and not require an online consultation to obtain POMs. Discussion: Findings reveal easy access to rogue online pharmacies, posing patient safety risks. We also found legitimate online pharmacies often offer online consultations without requiring prescriptions for POMs, raising concerns about inadequate safety checks. This emphasises the need for improved regulations for both types of online pharmacies, especially during public health crises.

Moderating Effect of eHealth Literacy on the Associations of Coronaphobia With Loneliness, Irritability, Depression, and Stigma in Chinese Young Adults: Bayesian Structural Equation Model Study

BACKGROUND

The COVID-19 pandemic has led to an increase in known risk factors for mental health problems. Although medical information available through the internet and smartphones has greatly expanded, people's ability to seek, eschew, and use reliable web-based medical information and services to promote their mental health remains unknown.

OBJECTIVE

This study aims to explore the associations between coronaphobia and 4 frequently reported mental health problems, loneliness, irritability, depression, and stigma, during the COVID-19 pandemic and to assess the moderating effects of eHealth literacy (eHL) on the adjustment of these relationships in Chinese young adults.

METHODS

The data used in this study were collected from a web-based survey of the general Chinese population, aged between 18 and 30 years, conducted in China between December 2022 and January 2023. A nonprobability snowball sampling method was used for data collection. A Bayesian structural equation model (BSEM) using parameter expansion was used to estimate the moderating effect of eHL on the relationship between coronaphobia and psychological problems. The posterior mean and 95% highest density intervals (HDIs) were estimated.

RESULTS

A total of 4119 participants completed the questionnaire and provided valid responses. Among them, 64.4% (n=2653) were female and 58.7% (n=2417) were rural residents. All measures showed statistically significant but minor-to-moderate associations (correlation coefficients ranged from -0.04 to 0.65). Significant heterogeneity was observed between rural and urban residents at the eHL level, and coronaphobia was observed. The BSEM results demonstrated that eHL was a significant moderator in reducing the negative effects of coronaphobia on loneliness (posterior mean -0.0016, 95% HDI -0.0022 to -0.0011), depression (posterior mean -0.006, 95% HDI -0.0079 to -0.004), stigma (posterior mean -0.0052, 95% HDI -0.0068 to -0.0036), and irritability (posterior mean -0.0037, 95% HDI -0.0052 to -0.0022). The moderating effects of eHL varied across the rural and urban subsamples.

CONCLUSIONS

Using BSEM, this study demonstrated that improving eHL can significantly mitigate the negative effects of coronaphobia on 4 COVID-19-related mental health problems in Chinese young adults. Future eHL initiatives should target rural communities to ensure equal access to information and resources that can help protect their mental health during the pandemic.

Public awareness of online pharmacies, consumers' motivating factors, experience and satisfaction with online pharmacy services, and current barriers and motivators for non-consumers: The case of Saudi Arabia

Purpose

Online pharmacies (OPs) represent a growing field that plays a major role in providing pharmaceutical services in Saudi Arabia (SA). Thus, investigating public awareness of this option and assessing consumers' experiences and satisfaction, as well as opportunities and barriers for OPs, were the main aims of this study.

Participants and methods

In this cross-sectional study, adult participants (≥18 years) in SA completed a three-part, custom-designed online questionnaire. The first section collected information on participants' demographic characteristics, their awareness of the existence of OPs, and history of OP purchases. The second section explores customer satisfaction levels and motivating factors. Finally, the third section investigated non-consumers' reasons for not purchasing from OPs and sought information about services that could motivate them to make future purchase decisions.

Results

In total, 487 participants completed the questionnaire; they were mostly female (65.7%) and younger than 40 years (57.1%). Among all the respondents, 89.3% were aware of the existence of OPs, and 60.2% purchased from OPs in the past. Most were satisfied with the product quality (92.7%), completeness of order delivery (91.2%), and condition of the product and packaging (89.3%). Furthermore, 99.2% of respondents indicated that they would continue to purchase from OPs. Customers' main motivational factors included saving time (85.5%), offers and discounts (83.6%), and variety of products (82.1%). Among non-consumers, the main reasons for not purchasing from OPs included a personal preference to visit a community pharmacy (87.2%), the ability to talk to pharmacists directly (83.6%), and the vicinity of a pharmacy (80.0%).

Conclusions

These findings confirm the increasing level of awareness regarding the existence of OPs in SA. Overall, OP customers expressed satisfaction with the services provided. Nevertheless, various areas of improvement have emerged, such as improved delivery time and providing medical consultation services. Increasing public awareness of OP services provided is essential considering their significant role in reforming the healthcare system in SA.

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.

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

BACKGROUND

COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

OBJECTIVE

We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

METHODS

We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

RESULTS

We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

CONCLUSIONS

This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study

BACKGROUND

Antimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription.

OBJECTIVE

The aim of this paper is to identify and characterize "no prescription" online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys.

METHODS

We first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants.

RESULTS

A total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants.

CONCLUSIONS

Our study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients.

Online Pharmacy Accessibility of Imatinib, An Oral Chemotherapy Medication

BACKGROUND

Since prices of imatinib (Gleevec) remain high, patients on oral chemotherapy are looking for alternative methods to access this life-saving medication. We assessed the accessibility of imatinib through online pharmacies and analyzed each website for medication safety, price, and marketing tactics.

METHODS

We searched the term "buy imatinib online" using 4 commonly used internet search engines (Google, Bing, Yahoo!, and DuckDuckGo) and screened web pages displayed in the first 10 pages. Websites were included if they were published in English, sold imatinib, were free to access, and offered shipping in the United States. Websites were classified using LegitScript's categorization as "certified," "unclassified," "unapproved," or "rogue." We analyzed information on websites' patient safety characteristics, marketing techniques, pricing, domain registration information, and IP addresses.

RESULTS

Of the 44 online pharmacies identified, only 3 (7%) were certified, and the remainder were classified as rogue (52%; n=23), unapproved (30%; n=13), or unclassified (11%; n=5). Thirteen online pharmacies (30%; 9 rogue, 4 unclassified) sold imatinib without a prescription. Nearly one-quarter (n=10) of online pharmacies selling imatinib did not include drug-related warnings on their websites, and nearly half (n=21) did not limit the purchasable quantity. More than three-quarters (n=34) of online pharmacies selling imatinib did not offer pharmacist consultations, even though nearly all websites extended offers to speak with sales associates (91%; n=40). Most online pharmacies selling imatinib claimed price discounts (95%; n=42), but fewer provided bulk discounts (23%; n=10) or coupons (34%; n=15). One-third of rogue pharmacies selling imatinib (n=7) claimed to be registered or accredited on their websites.

CONCLUSIONS

The lack of safety measures taken by illegitimate online pharmacies endangers patient safety because they allow patients to purchase imatinib without appropriate evaluation for response, drug interactions, and adverse effects. Healthcare providers need to be aware of this practice and should assure patient access to imatinib through safe and legitimate pharmacies.

Luteolin Potentially Treating Prostate Cancer and COVID-19 Analyzed by the Bioinformatics Approach: Clinical Findings and Drug Targets

Coronavirus disease 2019 (COVID-19) is a serious epidemic, characterized by potential mutation and can bring about poor vaccine efficiency. It is evidenced that patients with malignancies, including prostate cancer (PC), may be highly vulnerable to the SARS-CoV-2 infection. Currently, there are no existing drugs that can cure PC and COVID-19. Luteolin can potentially be employed for COVID-19 treatment and serve as a potent anticancer agent. Our present study was conducted to discover the possible drug target and curative mechanism of luteolin to serve as treatment for PC and COVID-19. The differential gene expression of PC cases was determined via RNA sequencing. The application of network pharmacology and molecular docking aimed to exhibit the drug targets and pharmacological mechanisms of luteolin. In this study, we found the top 20 up- and downregulated gene expressions in PC patients. Enrichment data demonstrated anti-inflammatory effects, where improvement of metabolism and enhancement of immunity were the main functions and mechanism of luteolin in treating PC and COVID-19, characterized by associated signaling pathways. Additional core drug targets, including MPO and FOS genes, were computationally identified accordingly. In conclusion, luteolin may be a promising treatment for PC and COVID-19 based on bioinformatics findings, prior to future clinical validation and application.