Modeling social and behavioral factors associated with community participation in community-based surveillance of communicable diseases: a cross-sectional study in Kelantan, Malaysia.

Abstract

Background: This study examined the social and behavioral factors influencing community leaders' participation in community-based surveillance (CBS) of infectious diseases. We hypothesized that knowledge, attitudes, and perceptions significantly predict the behavioral likelihood of engaging in CBS activities.

Methods: A cross-sectional study was conducted among 470 schoolteachers in Kelantan, Malaysia, from March to June 2024. Participants were selected using multistage sampling, with random selection of schools followed by convenience sampling of teachers. Data were collected using the validated KAP-CBS-ID questionnaire, which was based on the Theory of Reasoned Action and Health Belief Model. Structural equation modeling was used to analyze relationships among the study variables with the maximum likelihood robust estimator.

Results: The final model demonstrated good fit (comparative fit index, 0.921; standardized root mean square residual, 0.071; root mean square error of approximation, 0.040 [90% confidence interval, 0.036-0.044]). Ten significant path relationships were identified, explaining 46.1% of the variance in behavioral likelihood. Knowledge of infectious diseases (β=0.419, p<0.001), subjective norms (β=0.235, p=0.038), and negative attitudes (β=-0.432, p<0.001) significantly influenced intention. Behavioral likelihood was directly predicted by intention (β =0.347, p<0.001), perceived susceptibility (β=0.310, p<0.001), perceived benefits (β=0.198, p=0.001), and perceived barriers (β=-0.132, p=0.008). Self-efficacy showed significant indirect effects through perceived benefits (β=0.785, p<0.001) and perceived susceptibility (β=0.765, p<0.001).

Conclusion: Knowledge and psychosocial factors significantly inf luence community engagement in CBS of infectious diseases. Public health interventions should address these interconnected determinants to strengthen early disease detection and reporting systems.