Development of a household-level surveillance system for liver fluke infection risk behaviors using digital technology and spatial risk mapping in Uthumphon Phisai district, Sisaket province
Keywords:
liver fluke, household-level surveillance, digital technology, spatial risk mapping, H-OV GIS ModelAbstract
Liver fluke infection remains an important public health problem in northeastern Thailand, particularly in communities where consumption of raw or undercooked freshwater fish persists. This mixed-methods study aimed to develop a household-level surveillance system for liver fluke infection risk behaviors using digital technology and spatial risk mapping in Uthumphon Phisai District, Sisaket Province. The study was conducted across 19 subdistricts from May 2025 to April 2026. The quantitative sample consisted of 327 household representatives from liver fluke control model villages, while 42 key informants participated in the qualitative component. Research instruments included questionnaires, in-depth interview guidelines, a satisfaction assessment form, and a spatial data recording system. Data were analyzed using descriptive statistics, correlation analysis, multiple regression, paired t-test, content analysis, and geographic information system analysis. The findings showed that knowledge, attitude, and readiness or participation in the digital health surveillance system jointly predicted liver fluke infection risk behaviors, explaining 16.9% of the variance. Knowledge of liver fluke disease and readiness or participation in the digital health surveillance system were significant predictors. The developed system, called the H-OV GIS Model, consisted of a household database, QR Code or Line OA-based assessment, risk scoring and classification, spatial risk mapping, surveillance dashboard, alert and follow-up system, and community feedback mechanism. Spatial analysis identified five high-risk subdistricts and fourteen moderate-risk subdistricts. After implementation, knowledge, attitude, and participation scores significantly increased, while risk behavior scores significantly decreased at the .001 level. Overall satisfaction with the system was high. The H-OV GIS Model can transform household-level behavioral risk data into actionable spatial information to support risk screening, follow-up of at-risk households, home-visit planning, and area-based public health decision-making.
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