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

Int. J. One Health Vol.11 No.1 Article - 17

Research Article

International Journal of One Health, 11(1): 178-185

https://doi.org/10.14202/IJOH.2025.178-185

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

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.

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.

How to cite this article: Hasan AA, Ghazali AK, Bachok N, Yacoob NM, Hairon SM, Nadir NAM, and Shukri FM (2025) Community participation in community-based surveillance of infectious diseases: A structural equation modeling approach based on the theory of reasoned action, Int. J. One Health, 11(1): 178-185.

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

Corresponding author: Anis Kausar Ghazali E-mail: anisyo@usm.my

DOI: 10.14202/IJOH.2025.178-185

Copyright: Hasan, et al. This article is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.