Share to Seek: The Effects of Disease Complexity on Health Information-Seeking Behavior

Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study

BACKGROUND

Nonmedical use of prescription drugs can cause overdose; this represents a serious public health crisis globally. In this digital era, social networking services serve as viable platforms for illegal acquisition of excessive amounts of medications, including prescription medications. In Japan, such illegal drug transactions have been conducted through popular flea market applications, social media, and auction websites, with most of the trades being over-the-counter (OTC) medications. Recently, an emerging unique black market, where individuals trade prescription medications-predominantly nervous system drugs-using a specific keyword ("Okusuri Mogu Mogu"), has emerged on X (formerly Twitter). Hence, these dynamic methods of illicit trading should routinely be monitored to encourage the appropriate use of medications.

OBJECTIVE

This study aimed to specify the characteristics of medications traded on X using the search term "Okusuri Mogu Mogu" and analyze individual behaviors associated with X posts, including the types of medications traded and hashtag usage.

METHODS

We conducted a cross-sectional study with publicly available posts on X between September 18 and October 1, 2022. Posts that included the term "Okusuri Mogu Mogu" during this period were scrutinized. Posts were categorized on the basis of their contents: buying, selling, self-administration, heads-up, and others. Among posts categorized as buying, selling, and self-administration, medication names were systematically enumerated and categorized using the Anatomical Therapeutic Chemical (ATC) classification. Additionally, hashtags in all the analyzed posts were counted and classified into 6 categories: medication name, mental disorder, self-harm, buying and selling, community formation, and others.

RESULTS

Out of 961 identified posts, 549 were included for analysis. Of these posts, 119 (21.7%) referenced self-administration, and 237 (43.2%; buying: n=67, 12.2%; selling: n=170, 31.0%) referenced transactions. Among these 237 posts, 1041 medication names were mentioned, exhibiting a >5-fold increase from the study in March 2021. Categorization based on the ATC classification predominantly revealed nervous system drugs, representing 82.1% (n=855) of the mentioned medications, consistent with the previous survey. Of note, the diversity of medications has expanded to include medications that have not been approved by the Japanese government. Interestingly, OTC medications were frequently mentioned in self-administration posts (odds ratio 23.6, 95% CI 6.93-80.15). Analysis of hashtags (n=866) revealed efforts to foster community connections among users.

CONCLUSIONS

This study highlighted the escalating complexity of trading of illegal prescription medication facilitated by X posts. Regulatory measures to enhance public awareness should be considered to prevent illegal transactions, which may ultimately lead to misuse or abuse such as overdose. Along with such pharmacovigilance measures, social approaches that could direct individuals to appropriate medical or psychiatric resources would also be beneficial as our hashtag analysis shed light on the formation of a cohesive or closed community among users.

Internet-Based Inquiries From Users With the Intention to Overdose With Over-the-Counter Drugs: Qualitative Analysis of Yahoo! Chiebukuro

BACKGROUND

Public concern with regard to over-the-counter (OTC) drug abuse is growing rapidly across countries. OTC drug abuse has serious effects on the mind and body, such as poisoning symptoms, and often requires specialized treatments. In contrast, there is concern about people who potentially abuse OTC drugs whose symptoms are not serious enough to consult medical institutions or drug addiction rehabilitation centers yet are at high risk of becoming drug dependent in the future.

OBJECTIVE

Consumer-generated media (CGM), which allows users to disseminate information, is being used by people who abuse (and those who are trying to abuse) OTC drugs to obtain information about OTC drug abuse. This study aims to analyze the content of CGM to explore the questions of people who potentially abuse OTC drugs.

METHODS

The subject of this research was Yahoo! Chiebukuro, the largest question and answer website in Japan. A search was performed using the names of drugs commonly used in OTC drug abuse and the keywords overdose and OD, and the number of questions posted on the content of OTC drug abuse was counted. Furthermore, a thematic analysis was conducted by extracting text data on the most abused antitussive and expectorant drug, BRON.

RESULTS

The number of questions about the content of overdose medications containing the keyword BRON has increased sharply as compared with other product names. Furthermore, 467 items of question data that met the eligibility criteria were obtained from 528 items of text data on BRON; 26 codes, 6 categories, and 3 themes were generated from the 578 questions contained in these items. Questions were asked about the effects they would gain from abusing OTC drugs and the information they needed to obtain the effects they sought, as well as about the effects of abuse on their bodies. Moreover, there were questions on how to stop abusing and what is needed when seeking help from a health care provider if they become dependent. It has become clear that people who abuse OTC drugs have difficulty in consulting face-to-face with others, and CGM is used as a means to obtain the necessary information anonymously.

CONCLUSIONS

On CGM, people who abused or tried to abuse OTC drugs were asking questions about their abuse expectations and anxieties. In addition, when they became dependent, they sought advice to quit their abuse. CGM was used to exchange information about OTC drug abuse, and many questions on anxieties and hesitations were posted. This study suggests that it is necessary to produce and disseminate information on OTC drug abuse, considering the situation of those who abuse or are willing to abuse OTC drugs. Support from pharmacies and drugstores would also be essential to reduce opportunities for OTC drug abuse.

CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions

General public, often called consumers, are increasingly seeking health information online. To be satisfactory, answers to health-related questions often have to go beyond informational needs. Automated approaches to consumer health question answering should be able to recognize the need for social and emotional support. Recently, large scale datasets have addressed the issue of medical question answering and highlighted the challenges associated with question classification from the standpoint of informational needs. However, there is a lack of annotated datasets for the non-informational needs. We introduce a new dataset for non-informational support needs, called CHQ-SocioEmo. The Dataset of Consumer Health Questions was collected from a community question answering forum and annotated with basic emotions and social support needs. This is the first publicly available resource for understanding non-informational support needs in consumer health-related questions online. We benchmark the corpus against multiple state-of-the-art classification models to demonstrate the dataset's effectiveness.

Association of multimorbidity with the use of health information technology

Objective

To examine the association of multimorbidity with health information technology use among adults in the USA.

Methods

We used cross-sectional study design and data from the Health Information National Trends Survey 5 Cycle 4. Health information technology use was measured with ten variables comprising access, recent use, and healthcare management. Unadjusted and adjusted logistic and multinomial logistic regressions were used to model the associations of multimorbidity with health information technology use.

Results

Among adults with multimorbidity, health information technology use for specific purposes ranged from 37.8% for helping make medical decisions to 51.7% for communicating with healthcare providers. In multivariable regressions, individuals with multimorbidity were more likely to report general use of health information technology (adjusted odds ratios  =  1.48, 95% confidence intervals  =  1.01-2.15) and more likely to use health information technology to check test results (adjusted odds ratios  =  1.85, 95% confidence intervals  =  1.33-2.58) compared to adults with only one chronic condition, however, there were no significant differences in other forms of health information technology use. We also observed interactive associations of multimorbidity and age on various components of health information technology use. Compared to younger adults with multimorbidity, older adults (≥ 65 years of age) with multimorbidity were less likely to use almost all aspects of health information technology.

Conclusion

Health information technology use disparities by age and multimorbidity were observed. Education and interventions are needed to promote health information technology use among older adults in general and specifically among older adults with multimorbidity.

Lurking or active? The influence of user participation behavior in online mental health communities on the choice and evaluation of doctors

BACKGROUND

Web-based psychological counseling sites have become an important source of health information and expert assistance. Although many studies have suggested the feasibility and effectiveness of online consultation, there is an insufficient understanding of the influence of the distinction of users' participation behaviors online on health behavior decision-making.

OBJECTIVE

This study aimed to investigate whether and how the differences in the online participation behaviors of users affect their doctor selection and evaluation characteristics.

METHODS

First, we collected information from 7,781 paid consultation clients from a professional mental health service platform in China. Effective indicators and variables were formed through data cleaning and classification. Next, we used a mixed methods research approach that included qualitative text analysis (topic and sentiment) and quantitative statistical analysis (ANOVA).

RESULTS

The ANOVA results show that differences in online participation behaviors (diving, searching and socializing) have a significant impact on doctor selection based on consultation price (F_7,780=6.05; P = 0.00), online service volume (F_7,780=4.76; P = 0.00), online reputation (F_7,780=4.30; P = 0.01) and online answers (F_7,780=5.76; P = 0.00). When evaluating doctors, the frequency of reviews (F_7,780=69.62; P = 0.00) and the average length of the text (F_7,780=15.33; P = 0.00) were significantly different among users. Two of the three topics, namely, service attitude (F_7,780=28.63; P = 0.00) and self-expression (F_7,780=40.83; P = 0.00), had significant effects. In addition, our results show that differences in participating behaviors have a significant impact on both the positive (F_7,780=7.30; P = 0.00) and negative (F_7,780=9.44; P = 0.00) emotions involved in evaluating doctors.

CONCLUSIONS

Our findings provide preliminary insights for establishing the relationship between users' online information behavior and health decision-making. Further research should be conducted to verify the validity of the results and help apply them to the design of personalized customized services for the users in an online health community.

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.