Modeling the determinants of community engagement in community-based surveillance of infectious diseases: Applying...

Community-Based Surveillance (CBS) refers to involving community members in the detection and reporting of diseases within their communities for timely and effective response. Hence, it is necessary to study the factors that influence community participation in CBS.

This study aims to utilize the Health Belief Model (HBM) to identify predictors of community engagement in CBS activities in Kelantan state, Malaysia.

Perceived Benefit (BEN), Perceived Barriers (BARR), Perceived Susceptibility (SUS), and Behavioral Likelihood (BL) were assessed using a validated questionnaire (KAP-CBS-ID). A Covariance-based Structural Equation Modeling (SEM) approach was used to understand the relationships between the study variables.

The model demonstrated a good fit (RMSEA = 0.048, 90% CI [0.042, 0.054]; CFI = 0.935; TLI = 0.925; SRMR = 0.078) and explained 40.5% of the variance in the behavioral likelihood of engaging in CBS. Self-efficacy (SE) emerged as a strong direct predictor of participation. SUS showed both direct and indirect (mediated) effects on behavioral likelihood to participate in CBS, with the indirect effect occurring through SE. Similarly, BEN influenced BL indirectly through SE. The perceived barriers, on the other hand, had a significant negative direct effect on participation (BL). The effects of SE, BARR, and SUS on participation in CBS were substantial.

Public health interventions should focus on improving community self-efficacy to participate in CBS initiatives, as well as raising awareness of disease susceptibility, highlighting the benefits of CBS, and addressing participation barriers to increase community engagement in surveillance systems.