Evidence map›Paper›PMID 41891108›Full record

ArticleSubstance abuse and rehabilitation2026

Shifting Drug Landscapes in China: A Multilevel Analysis of Traditional vs New Psychoactive Substance Use and Interregional Differences.

Xiaochen Ma, Zepeng Chen, Danqi Yao, Hang Su, Min Su

Abstract read
In one paragraph

Article in Substance abuse and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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.

Xiaochen MaDepartment of Preventive Medicine, Shantou University Medical College, Shantou, Guangdong, People's Republic of China.
Zepeng ChenDepartment of Pathology, Shantou University Medical College, Shantou, Guangdong, People's Republic of China.
Danqi YaoDepartment of Pathology, Shantou University Medical College, Shantou, Guangdong, People's Republic of China.
Hang SuCollege of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an, Shanxi, People's Republic of China.
Min SuDepartment of Pathology, Shantou University Medical College, Shantou, Guangdong, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: China's drug landscape is rapidly evolving, yet existing research remains fragmented, lacking a comprehensive national perspective. This study analyzes current drug use patterns, trends, and regional differences in China, providing critical insights to guide effective anti-drug policies. Methods: We conducted a comprehensive multilevel analysis of secondary data of drug use data from the China Drug Situation Report (2005-2023) and 34 academic articles (1990-2021). Our analysis includes descriptive statistics, time series, regional differences, and population-specific trends. Results: This study identifies a declining trend in traditional drug use, while the use of new synthetic drugs and new psychoactive substances (NPS) is increasing. Strong negative correlations were found between law enforcement intensity and overall drug use ( Conclusion: The analysis delineates a clear shift in China's drug landscape from traditional drugs to NPS, with concentrations in southern, coastal, and adolescent demographics. These patterns suggest that effective policy responses should be regionally tailored and prioritize youth prevention in economically advanced areas. Future research is needed to verify these associations and explore underlying causal mechanisms.

Indexed as

21st centuryadolescentsChinadrug usenew psychoactive substancesregional differences

Identifiers

PMID41891108
PMCPMC13016123

What OpenQuestion holds

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Registered trials

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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.