Association of Drugs for Sale on the Internet and Official Health Indicators: Darknet Parsing and Correlational Study
Notice bibliographique
Résumé
BACKGROUND: Studying illicit drug circulation and its effects on population health is complicated due to the criminalization of trade and consumption. Illicit drug markets have evolved with IT, moving digital to the "darknet." Previous research has analyzed darknet market listings and customer reviews. Research tools include public health surveys and medical reports but lack neutral data on drugs' spread and impact. This study fills this gap with an analysis of the volume of drugs traded on the darknet market. OBJECTIVE: We aimed to use the dark web data and officially published indicators to identify the most vulnerable regions of Russia and the correlations between the pairs of variables to measure how illicit drug trade can affect population well-being. METHODS: We web-parsed the Hydra darknet drug marketplace using Python code. The dataset encompassed 3045 individual sellers marketing 6721 unique products via 58,563 distinct postings, each representing specific quantities sold in different Russian regions during 2019. In the second stage, we collected 31 variables from official sources to compare officially collected data with darknet data about amounts and types of selling drugs in every 85 regions of Russia. The health-related data were obtained from official published sources-statistical yearbooks. Maps, diagrams, correlation matrixes, and applied observational statistical methods were used. RESULTS: In 2019, a minimum of 124 kilograms of drugs circulated daily in small batches on the Russian darknet. Cannabis dominated the market, being 10 times more prevalent than opiates, and cannabis products' higher availability in the region is correlated with a lower incidence of opiate overdoses. The "grams of opiates in the region" variable is significantly correlated with drug overdose deaths (r=.41; P=.003), HIV-positive cases due to drug use (r=.51; P=.002), and drug court convictions in Russia (r=.39; P=.004). The study identified significant correlations between opiate sales on the darknet and higher rates of HIV among injection drug users (r=.47; P=.003). Conversely, regions with higher cannabis sales exhibited significant negative correlations with indicators of harmful drug use (r=-.52; P=.002) and its prevalence (r=-.49; P=.001). These findings suggest regional variations in drug sales on the darknet may be associated with differing public health outcomes. These indicators accurately reflect regional drug issues, though some official statistics may be incomplete or biased. CONCLUSIONS: Our findings point to varying levels of risk associated with different types of drugs sold on the darknet, but further research is needed to explore these relationships in greater depth. The study's findings highlight the importance of considering regional variations in darknet drug sales when developing public health strategies. The significant correlations between drug sales data and public health indicators suggest that region-specific interventions could be more effective in addressing the diverse challenges posed by illicit drug use.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».