Multicriteria evaluation of cancer prevention pathways integrating MICMAC structural analysis and Régnier Abacus.

Abstract

Introduction: Cancer prevention requires comprehensive approaches that consider the complexity of the social, institutional, and behavioral factors involved. This study aimed to evaluate cancer prevention pathways using a multicriteria approach, integrating the MICMAC structural analysis method and Régnier Abacus, in order to identify the most relevant and feasible strategies for strengthening public health policy.

Methods: A mixed, descriptive, prospective, and participatory study was developed, structured in four phases: (1) a literature review for the identification of variables; (2) validation and prioritization of variables with 12 experts in public health and prospective studies; (3) application of the MICMAC method for the analysis of influence and dependence between variables, complemented with the Régnier Abacus for the evaluation of desirability and feasibility of the strategies; and (4) validation of the model and reliability analysis.

Results: The structural model identified five driving variables: health education, community participation, sustainable public financing, intersectoral coordination, and institutional governance, as determining factors of the preventive system. Régnier Abacus ranked five main strategies: Strategies R1 (Community Health Education and Communication) and R4 (Strengthening Primary Health Care) obtained the highest scores for desirability and feasibility, establishing themselves as priority actions for immediate implementation. Expert consensus exceeded 80%, confirming the coherence between the structural analysis and the multi-criteria prioritization.

Conclusion: The MICMAC–Régnier approach enabled the development of a comprehensive, participatory, and validated model that guides strategic planning for cancer prevention. The findings highlight the importance of strengthening health education, primary care, and governance as cornerstones of sustainable intervention, and demonstrate the usefulness of multicriteria analysis for public health decision-making. The model is replicable in other contexts and at different levels of health policy