Evidence map›Paper›PMID 38968168›Full record

ArticlePloS one2024

The impact of the Drug Marketing Authorization Holder system on sustainable improvement of innovation quality in the pharmaceutical manufacturing industry in China-An empirical study using synthetic control methods.

Qiang Liu, Su Wang, Zhe Huang

Abstract read
In one paragraph

Article in PloS one, 2024. 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

3 authors.

Qiang LiuSchool of Business Administration, Shenyang Pharmaceutical University, Shenyang, Liaoning, China.ORCID 0000-0002-1052-6378
Su WangSchool of Business Administration, Shenyang Pharmaceutical University, Shenyang, Liaoning, China.
Zhe HuangSchool of Business Administration, Shenyang Pharmaceutical University, Shenyang, Liaoning, China.ORCID 0000-0002-3170-9341

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn November 2015, China launched a pilot program for its Drug Marketing Authorization Holder (MAH) system, aiming to integrate production and research and development resources to spur drug innovation. While many scholars have examined the theoretical relationship between the MAH system and pharmaceutical manufacturing innovation, empirical studies have been scarce.

methodsThis study uses inter-provincial panel data on China's pharmaceutical manufacturing industry from 2009 to 2019, along with synthetic control methods, to offer a robust analysis of the impact of the MAH paradigm on innovation quality in pilot provinces. We treat the 2015 MAH system implementation in ten provinces as a natural experiment and account for the mediating effects of R&D investments, allowing us to discern the underlying mechanisms.

resultsOur findings demonstrate a significant positive effect of China's MAH system on innovation quality in the pharmaceutical manufacturing industry. Specifically, the system is most effective in Jiangsu, Guangdong, and Shandong provinces. We also identify R&D investments as playing a mediating role in facilitating the favorable impact of the MAH system on innovation quality.

conclusionOur study highlights the crucial importance of the MAH system in promoting innovation quality in China's pharmaceutical manufacturing industry, providing vital empirical support for its adoption and further enhancement. This research also offers practical insights for policymakers and R&D decision-makers in the pharmaceutical sector.

Indexed as

Drug IndustryChinaHumansMarketing

Identifiers

PMID38968168
PMCPMC11226080

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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.