Quantitative Evaluation of China's Policies for Disease Control Supervisors in Medical Institutions: A PMC Index Model Approach

Background: The disease control supervisor system in medical institutions is a central institutional innovation of China's disease control reform, designed to integrate medical treatment with disease prevention. Since its pilot launch in 2023, the system has expanded rapidly, but the design quality of the pilot policy documents remains unevaluated. This study uses the Policy Modeling Consistency (PMC) index model to evaluate these pilot policies and provide evidence for policy refinement ahead of nationwide scale-up. Methods: We collected 24 pilot policy texts issued across 24 pilot regions in China between 2023 and 2025 from official government sources and screened them against predefined inclusion and exclusion criteria. We constructed a PMC evaluation framework with nine primary variables and 46 secondary variables. Each secondary variable was binary-coded (1 = satisfied, 0 = otherwise), and primary-variable scores and the overall PMC index were calculated under an equal-weight assumption. Policies were classified into quality levels according to standard PMC rating criteria, and a grouped 3D projection-smooth matrix supported horizontal, vertical, and cross-group comparisons. Results: The PMC indices of the 24 policies ranged from 5.04 to 7.82, with a mean of 6.33; four policies were rated Excellent and twenty were rated Great. Policy Tool (X7, mean 0.83) was the strongest dimension, and Policy Aim (X2, 0.57) and Policy Function (X6, 0.60) were the weakest. Policy Function showed the greatest inter-policy divergence (range 0.75). Policy Evaluation (X9) most sharply distinguished Excellent from Great policies (0.95 vs. 0.68), and the completeness of evaluation-related elements largely determined policy quality. Conclusions: The pilot policies of the disease control supervisor system are generally well designed but strategically incomplete: operational specificity is strong, while long-term objectives, function clarification, and incentive design are weak. Future refinements should strengthen policy evaluation elements, articulate long-term aims, build a dedicated workforce, and embed supervision within integrated care structures.