Understanding Community Engagement in Infectious Disease Surveillance: A Multi-Theoretical Predictive Model

Abstract

INTRODUCTION: Community-based surveillance (CBS) plays a crucial role in early detection and response to communicable disease outbreaks. Understanding the determinants of community participation in CBS is essential for developing effective public health strategies. This study aimed to identify and quantify the key determinants of community participation in communicable disease surveillance, utilising factors from the Theory of Planned Behaviour and Health Belief Model to form a comprehensive predictive model. MATERIALS AND METHODS: A cross-sectional study design was used, with a sample of 470 participants interviewed to assess their likelihood of CBS participation using the KAP-CBS-ID questionnaire. Multiple linear regression analysis was utilised to test the relationships between the predictor variables and the likelihood of engaging in CBS. RESULTS: The final model demonstrated good predictive capacity, accounting for 51.7% of the variance in behavioural likelihood (F= 99.49, p<0.001). Five predictors showed significant relationships with CBS participation behaviour. Perceived susceptibility showed the highest predictive power (β=0.255, p<0.001), followed by perceived benefits (β=0.231, p<0.001), intention to accept involvement (β=0.209, p<0.001), and subjective norms (β=0.202, p<0.001). There was a significant negative association between attitudes toward participation (β=-0.104, p=0.002). Knowledge of infectious diseases, self-efficacy, and perceived barriers were not significant predictors and were excluded from the final model. CONCLUSION: A multi-theoretical approach to understand CBS participation is supported empirically by these findings. Initiatives to increase CBS participation should emphasize community susceptibility to disease outbreaks, explain the advantages, foster positive social norms, and address unfavourable perceptions of surveillance practices.