Research Article | 09 Jun 2025

Community participation in community-based surveillance of infectious diseases: A structural equation modeling approach based on the theory of reasoned action

Ahmed Azeez Hasan1 ORCID, Anis Kausar Ghazali1 ORCID, Norsa’adah Bachok1 ORCID, Najib Majdi Yacoob1 ORCID, Suhaily Mohd Hairon2 ORCID, Nur Amira M. Nadir3 ORCID, and Fatimah Muhd Shukri4 ORCIDShow more

1. Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia.

2. Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia.

3. Department of Ophthalmology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia.

4. Department of Film Studies, Culture Heritage and National Cinema, Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Kelantan, Malaysia.

Corresponding author: Anis Kausar Ghazali (anisyo@usm.my)

Received: 2025-02-13, Accepted: 2025-05-06, Published: 2025-06-09

VETERINARY WORLD | pg no. 178-185 | Vol. 11, Issue 1 | DOI: 10.14202/vetworld.2025.178-185
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Abstract

          
Background and Aim: Community-based surveillance (CBS) is a critical mechanism for early detection of infectious diseases. Understanding the behavioral drivers of CBS participation is essential to strengthening community engagement. This study employed structural equation modeling (SEM) based on the theory of reasoned action (TRA) to investigate the impact of knowledge, subjective norms (SN), and attitudes on the intention and behavioral likelihood (BL) of participating in CBS activities.
Materials and Methods: A cross-sectional survey was conducted among 470 schoolteachers selected through a multi-stage mixed sampling strategy across Kelantan, Malaysia. A structured questionnaire assessing sociodemographic factors, knowledge, attitudes, and perceptions toward CBS was developed and validated. Confirmatory factor analysis and SEM were employed with model parameters estimated using the robust maximum likelihood (MLR) approach. Model fit was assessed using comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) indices.
Results: The final model demonstrated good fit (CFI = 0.923; TLI = 0.913; RMSEA = 0.045; SRMR = 0.070). Knowledge (β = 0.335) and SN (β = 0.296) positively influenced intention to participate in CBS, whereas negative attitudes (β = −0.313) showed a significant negative association. Intention significantly predicted BL (β = 0.633). The model explained 40% of the variance in intention and 43% in BL. Intention mediated the effects of knowledge, norms, and attitudes on behavioral engagement.
Conclusion: Knowledge, positive SN, and reduced negative attitudes are pivotal in fostering community participation in CBS initiatives. Intention emerged as a critical mediator linking cognitive and normative beliefs to actual behavioral engagement. These findings provide actionable insights for designing targeted interventions that enhance CBS participation and strengthen infectious disease surveillance at the community level.

         
Keywords: community participation, community-based surveillance, infectious diseases, structural equation modeling, theory of reasoned action.