Evidence map›Paper›PMID 38805262›Full record

ArticleJMIR formative research2024

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

Hayase Hakariya, Natsuki Yokoyama, Jeonse Lee, Arisa Hakariya, Tatsuki Ikejiri

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hayase HakariyaInterfaculty Institute of Biochemistry, University of Tuebingen, Tuebingen, Germany.ORCID https://orcid.org/0000-0002-9121-4551
Natsuki YokoyamaLaboratory for Human Nature, Cultures and Medicine, Shiga, Japan.ORCID https://orcid.org/0009-0007-9723-2754
Jeonse LeeLaboratory for Human Nature, Cultures and Medicine, Shiga, Japan.ORCID https://orcid.org/0009-0001-0245-4677
Arisa HakariyaLaboratory for Human Nature, Cultures and Medicine, Shiga, Japan.ORCID https://orcid.org/0009-0009-8629-3484
Tatsuki IkejiriLaboratory for Human Nature, Cultures and Medicine, Shiga, Japan.ORCID https://orcid.org/0009-0006-3624-6135

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNonmedical 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.

objectiveThis 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.

methodsWe 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.

resultsOut 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.

conclusionsThis 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.

Indexed as

antipsychoticscross-sectional studyillegal tradingJapanJapanesemedicationoverdosepharmaceuticalpharmaceutical drugspharmacovigilanceprescription drugprescription medicationpsychiatricSNSsocial networkingsocial networking servicesocial supportsupport

Identifiers

PMID38805262
PMCPMC11167319

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.