Research on China’s population health policy: an analysis based on the PMC index model

With China’s profound demographic transition, population health issues have emerged as critical determinants of sustainable national development. Since 2001, the Chinese government has implemented numerous population health policies; however, the absence of a systematic, standardized, and quantifiable evaluation framework has constrained the scientific rigor and effectiveness of policy assessment. This study aims to develop a Policy Modeling Consistency (PMC) Index model tailored to China’s context, enabling systematic quantitative evaluation of population health policies to identify strengths and limitations, thereby establishing an evidence base for policy optimization.This study employs text mining combined with the PMC Index model to construct an evaluation framework comprising 9 primary variables and 40 secondary variables. Twelve representative population health policies were quantitatively assessed, with results visualized through PMC surface maps to analyze internal policy consistency and multidimensional performance.The evaluation revealed that 8 policies achieved “excellent” ratings (PMC index: 6.00-7.31), while 4 received “good” ratings (PMC index: 5.59–5.78), indicating generally high quality with notable variations across China’s population health policies. Most policies demonstrated consistent strengths in timeliness, evaluation mechanisms, and transparency. However, significant disparities emerged in policy functions, target populations, and policy objectives.Through development of the PMC Index model, this study provides systematic, multidimensional quantitative evaluation of China’s population health policies, introducing novel insights and methodological approaches for policy optimization. The findings offer substantial theoretical and practical contributions toward enhancing the scientific precision and effectiveness of China’s population health policies, ultimately promoting improved population health outcomes.