A county-level indicator framework for assessing schistosomiasis transmission risk in post-transmission-interruption China.

BACKGROUND

In China's post-transmission-interruption stage of schistosomiasis control, infection signals are often sparse, and conventional surveillance indicators focused on infection detection may not adequately capture residual risk across heterogeneous ecological settings. This study aimed to develop an operational indicator framework for assessing schistosomiasis transmission risk and supporting routine risk assessment and management in this stage.

METHODS

A county-level indicator framework for schistosomiasis transmission risk assessment was developed through a two-round Delphi consultation and weighted using a combined Delphi-entropy weight method. Using longitudinal data from six pilot counties during 2020-2024, an annual composite risk index (R) was calculated by weighted linear aggregation and classified into risk levels with trapezoidal fuzzy membership functions. Robustness was evaluated by perturbing the subjective preference coefficient (α) and examining the consistency of county-year rankings across scenarios using Spearman's rank correlation.

RESULTS

The final framework comprised three first-level, twelve second-level, and thirty-nine third-level indicators spanning biological, environmental, and social domains. Across the six pilot counties, R ranged from 0.18 to 0.44 and the overall risk level was predominantly low. Nonetheless, distinct county-level risk profiles were observed between lake/marshland and mountainous settings. Risk rankings remained highly consistent under α perturbations (ρ = 0.909-0.998), indicating good robustness of the assessment results.

CONCLUSIONS

This framework translates multidimensional determinants relevant to the re-establishment of schistosomiasis transmission into interpretable county-level risk profiles. It provides an operational tool to support targeted surveillance and more efficient allocation of control resources in low-endemicity contexts.