Evidence map›Paper›PMID 39206179›Full record

ArticleFrontiers in medicine2024

Game-theoretic analysis of governance and corruption in China's pharmaceutical industry.

Xi Wang, Tao Zhang, Hanxiang Gong, Jinghua Li, Baoling Wu, Baoxin Chen, Shufang Zhao

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xi Wang *Faculty of Humanities and Social Sciences, Macau Polytechnic University, Macau, Macau SAR, China.
Tao Zhang *Faculty of Humanities and Social Sciences, Macau Polytechnic University, Macau, Macau SAR, China.
Hanxiang GongThe Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.
Jinghua LiSchool of Public Health, Guangzhou Medical University, Guangzhou, Guangdong, China.
Baoling WuFaculty of Humanities and Social Sciences, Macau Polytechnic University, Macau, Macau SAR, China.
Baoxin ChenPingshan Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Shufang ZhaoFaculty of Humanities and Social Sciences, Macau Polytechnic University, Macau, Macau SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the rapid development of China's pharmaceutical industry, issues of corruption and regulatory effectiveness have become increasingly prominent, posing critical challenges to public health safety and the industry's sustainable development. Methods: This paper adopts a bounded rationality perspective and employs a game-theoretic evolutionary approach to establish a tripartite evolutionary game model involving pharmaceutical companies, third-party auditing organizations, and health insurance regulatory agencies. It analyzes the stable strategies of the parties involved and the sensitivity of key parameters within this tripartite game system. Results: The study reveals that adherence to health insurance regulations by pharmaceutical companies, refusal of bribes by third-party auditing organizations, and the implementation of lenient regulations by health insurance agencies can form an effective governance equilibrium. This equilibrium state contributes to reducing corruption in the pharmaceutical industry, balancing the interests of all parties, and promoting healthy industry development. Discussion: Pharmaceutical companies must balance compliance costs against the risks of non-compliance benefits while maximizing profits; third-party auditing organizations need to choose between fulfilling their duties and accepting bribes, considering their economic benefits and professional reputation; health insurance regulatory agencies adjust their strategies between strict and lenient regulation to maximize social welfare. The paper suggests enhancing policy support, strengthening compliance supervision, improving audit independence, and adjusting regulatory strategies to optimize governance in the pharmaceutical industry. Additionally, the research highlights the role of collaborative efforts among the three parties in achieving sustainable governance. Furthermore, the study conducts a numerical simulation analysis to demonstrate the impact of various parameters on the evolutionary stability of the system, providing practical insights into the implementation of regulatory policies. This research offers new insights for policy formulation and governance in China's pharmaceutical sector, providing significant reference value for guiding the industry's sustainable development.

Indexed as

corruptionevolutionary game theorygovernancepharmaceutical industrypublic health safetyregulatory strategies

Identifiers

PMID39206179
PMCPMC11349649

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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.