Evidence map›Paper›PMID 38111908›Full record

ArticleRisk management and healthcare policy2023

Study on the Relationship Between the Number of Adverse Drug Reactions of Essential Drugs and Visits: Based on Vector Autoregressive Model.

Wan Tang, Hongdou Chen, Zixin Zhang, Gang Wu, Yanquan Lin

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2023. 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
0.2field-weighted citation impact, top 41% of its field
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 citations in OpenAlex.

  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 at 1 institution in 1 country.

Wan TangPharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People's Republic of China.ORCID 0000-0001-7041-7715
Hongdou ChenPharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People's Republic of China.
Zixin ZhangPharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People's Republic of China.
Gang WuPharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People's Republic of China.
Yanquan LinPharmacy, Suqian Hospital of Nanjing Drum-Tower Hospital Group, Suqian, People's Republic of China.
Xuzhou Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To analyse the relationship between the adverse drug reactions (ADRs) of essential drugs and visits, based on the recorded annual increase in ADRs associated with essential medicines in China, to provide a reliable theoretical basis for further analysis and optimization of the safety of essential drugs. Methods: The data of adverse reactions of essential drugs in China from 2011 to 2020, time series analysis was conducted, and vector autoregressive (VAR) model was established. The relationship between the number of ADRs and visits was explored empirically through Granger causality test, impulse response function and variance decomposition. Results: There was a long-term cointegration relationship and one-way causality between the number of visits and ADRs caused by essential medicines. In the initial stage, the ADR response to the number of visits increased sharply, but with an increase in the number of lag periods, the impact remained basically stable, even showing a slight decreasing trend. Conclusion: The number of visits impacts ADRs caused by essential medicines, but this impact remains basically stable after reaching a certain level.

Indexed as

adverse drug reactionsessential medicinestime-series analysisvector autoregressive model

Identifiers

PMID38111908
PMCPMC10726950
OpenAlexW4389766755

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.