ArticleIJID regions2026
Predicting measles elimination through surveillance-response and health system determinants: a village-level structural equation modeling study in Cirebon Regency, Indonesia.
Article in IJID regions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Measles elimination remains a major public health challenge despite the widespread availability of effective vaccines. Rising outbreaks in some countries suggest that high immunization coverage alone may not be enough to stop transmission. However, evidence remains limited regarding how surveillance-response, health system capacity, nutritional status, and community vulnerability interact to influence measles elimination within a single epidemiological framework. This study aimed to develop and validate an integrated epidemiological prediction model for measles elimination using structural equation modeling-partial least squares (SEM-PLS) at the village level in Cirebon Regency, Indonesia. Methods: A cross-sectional study was conducted using secondary data from 424 village administrative units in Cirebon Regency during 2022-2024. Variables included measles immunization coverage, nutritional status, village vulnerability, health resources, surveillance-response performance, suspected measles cases, confirmed measles cases, and transmission incidence. Data were analyzed using SEM-PLS to estimate the direct and indirect pathways linking surveillance-response, immunization, nutritional status, health system capacity, community vulnerability, and transmission to measles elimination. Results: The SEM-PLS model explained 56.9% of the variance in measles elimination. Surveillance-response performance emerged as the principal mediating mechanism linking health system capacity with reductions in suspected cases, confirmed cases, and transmission. Measles elimination was significantly associated with confirmed cases ( Conclusion: Measles elimination should be viewed not solely as a vaccination outcome but as the product of an integrated epidemiological system in which surveillance-response, health system capacity, nutritional status, and community vulnerability interact to interrupt transmission. The proposed predictive model provides a practical framework for evidence-based elimination planning and more adaptive, evidence-informed measles elimination policies in endemic settings.
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