Evidence map›Paper›PMID 41473712›Full record

ArticleFrontiers in public health2025

Evaluation and optimization of municipal-level health impact assessment policies in China based on the PMC index model.

Ying Zhou, Lin Cheng, Yanshan Zhu, Jun Yuan, Yu Ma

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Article in Frontiers in public health, 2025. 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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5 · Who and what money

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5 authors.

Ying ZhouCentral Office, Guangzhou Center for Disease Control and Prevention (Guangzhou Health Supervision Institute), Guangzhou, China.
Lin ChengCentral Office, Guangzhou Center for Disease Control and Prevention (Guangzhou Health Supervision Institute), Guangzhou, China.
Yanshan ZhuCentral Office, Guangzhou Center for Disease Control and Prevention (Guangzhou Health Supervision Institute), Guangzhou, China.
Jun YuanCentral Office, Guangzhou Center for Disease Control and Prevention (Guangzhou Health Supervision Institute), Guangzhou, China.
Yu MaCentral Office, Guangzhou Center for Disease Control and Prevention (Guangzhou Health Supervision Institute), Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to develop an integrated framework combining text mining with the Policy Modelling Consistency (PMC) index model to evaluate China's Health Impact Assessment (HIA) policies, thereby advancing the methodological and theoretical framework for policy evaluation in health governance. Methods: This study focuses on health impact assessment policies issued at the municipal level in China since 2019. Using the NVivo 11 plus text mining tool, high-frequency terms from the policies were extracted to construct a Policy Modeling Consistency (PMC) index model for health impact assessment policies. Quantitative analysis was conducted on 30 policies using nine primary variables and 39 sary variables. Results: The average PMC index score was 6.89. Among the sample policies, 11 were deemed complete and reasonable, and 19 were considered acceptable. The quality of the policies was at an acceptable level or above. The policies scored highly in terms of policy nature, content, and evaluation, but scored lower in policy timeliness, issuing authority, safeguards, and policy tools. The average concavity index was 3.11, indicating a moderate concavity level. The concavity index indicates that existing policies have established a solid foundation, ensuring the orderly advancement of the pilot program. However, the policies' intensity and the balance within their internal structure remain inadequate, suggesting that future efforts should focus on strengthening incentive mechanisms and addressing shortcomings. Conclusion: This study adapts the PMC model and validates its applicability in policy analysis for health impact assessments, providing a replicable framework for evaluating health policies in developing countries. China's municipal-level health impact assessment policies have effectively safeguarded public health security and advanced the implementation of the "Health in All Policies" approach. However, numerous challenges persist, including an inadequate legal framework, weak oversight mechanisms, imbalanced policy tools, and insufficient interregional collaboration. Based on this, it is recommended that future efforts strengthen top-level design, address the aforementioned key aspects, and systematically advance the optimization and improvement of health impact assessment policies to better align with the strategic needs of China's health development in the new era, enhance policy effectiveness, and promote the sustained improvement of public health levels.

Indexed as

Health Impact AssessmentHealth PolicyPolicy MakingChinaHumansevaluationhealth impact assessmentoptimizationpolicypolicy consistency index model

Identifiers

PMID41473712
PMCPMC12745205

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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.