Analyzing the collaborative development needs of grassroots centers for disease control and prevention using the Kano model: A case study of China’s Chengdu–Chongqing Economic Circle

Background: Advancing the development of centers for disease control and prevention (CDCs) has become a priority within global public health governance. However, public health governance capacity varies significantly among CDCs across different countries and regions, grassroots CDCs face particular disadvantages. Establishing stable, efficient collaborative development mechanisms among CDCs across diverse regions to maximize overall effectiveness and ensure sustainable development represents a critical public health science issue. Objective: This study aims to provide scientific references and a theoretical foundation for the coordinated development of grassroots CDCs within the Chengdu–Chongqing Economic Circle (CCEC) and the construction of public health systems. Methods: A questionnaire for collaborative development needs indicators in grassroots CDCs, comprising 4 primary needs and 13 secondary needs, was developed through literature review, the Delphi expert consultation method, and the Kano model. Analysis focused on questionnaires collected from eight grassroots CDCs within the CCEC. The importance of needs was ranked using the better–worse coefficient and satisfaction sensitivity analysis. Results: Analysis of the 110 valid questionnaires showed that for the must-be attribute, satisfaction sensitivity ranked as follows: performance compensation (0.883)> talent exchange and scientific research and innovation cooperation (0.824)> public health emergency rescue mechanism (emergency material reserve and cross-regional material mobilization; 0.817)> cross-regional case monitoring, investigation, and tracking (0.775). Regarding the one-dimensional attribute, the satisfaction sensitivity ranking was joint risk assessment and emergency command (0.937)> business archive co-construction and sharing mechanism (emergency response plan, and technical scheme) (0.909)> regional co-construction and sharing between the university and the local area (0.832). For the attractive attribute, the satisfaction sensitivity ranking was regional monitoring and early-warning information management system (0.922)> community chronic disease prevention and service (0.804)> coordinated transfer and diversion diagnosis and treatment of patient with infectious diseases within the region (0.734). However, the collaborative release and interaction mechanism of social integrated media information, public health collaborative governance entities, and the construction of a cross-regional expert database constitute indifferent attributes. Conclusions: This study provides preliminary scientific evidence for the precise allocation of public health resources and the establishment of localized collaborative development mechanisms. Simultaneously, the research methodology and analytical framework offer new theoretical references for similar studies in other regions globally.