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Abstract
Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The epidemiological model is required to provide evidence for public health policymakers to reduce the spread of COVID-19. Health behaviour is assumed could reduce the spread of this virus. This study purposes to construct an acceptable model of health behaviour. To achieve this goal, a Bayesian structural equation modelling (SEM) is implemented. This current study is also purposed to evaluate the performance of Bayesian SEM, including the sensitivity, adequacy, and the acceptability of parameters estimated with the result that the acceptable model is obtained. The sensitivity of the Bayesian SEM estimator is evaluated by choosing several types of prior and the model results are compared. The adequacy of the Bayesian SEM estimate is checked by doing the convergence test of the corresponding model parameters. The acceptability of the Bayesian approach and its associated algorithm in recovering the true parameters are monitored by the Bootstrap simulation study. The Bayesian SEM applies the Gibbs sample approach in estimating model parameters. This method is applied to the primary data gathered from an online survey from March to May 2020 during COVID-19 to individuals living in West Sumatera, Indonesia. It is found that health motivation is significantly related to health behaviour. Whereas socio-demographic and perceived susceptibility has no significant effect on health behaviour.
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Funding data
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Direktorat Riset Dan Pengabdian Kepada Masyarakat
Grant numbers 104/E4.1/AK.04.PT/2021
References
- Arora, T., & Grey, I. (2020). Health behaviour changes during COVID-19 and the potential consequences: A mini-review. Journal of Health Psychology, 25(9), 1155–1163. https://doi.org/10.1177/1359105320937053
- Asparouhov, T., & Muthén, B. (2021). Advances in Bayesian Model Fit Evaluation for Structural Equation Models. Structural Equation Modelling: A Multidisciplinary Journal, 28(1), 1–14. https://doi.org/10.1080/10705511.2020.1764360
- Cain, M. K., & Zhang, Z. (2019). Fit for a Bayesian: An Evaluation of PPP and DIC for Structural Equation Modelling. Structural Equation Modelling: A Multidisciplinary Journal, 26(1), 39–50. https://doi.org/10.1080/10705511.2018.1490648
- Choompunuch, B., Suksatan, W., Sonsroem, J., Kutawan, S., & In-udom, A. (2021). Stress, adversity quotient, and health behaviours of undergraduate students in a Thai university during COVID-19 outbreak. Belitung Nursing Journal, 7(1), 1–7. https://doi.org/10.33546/bnj.1276
- Conner, M., & Norman, P. (Eds.). (2007). Predicting health behaviour: Research and practice with social cognition models (2. ed., repr). Open Univ. Press.
- Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modelling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70. https://doi.org/10.1037/met0000224
- Glanz, K., Rimer, B. K., & Viswanath, K. (Eds.). (2008). Health behaviour and health education: Theory, research, and practice (4th ed). Jossey-Bass.
- Hou, W., Zhang, W., Jin, R., Liang, L., Xu, B., & Hu, Z. (2020). Risk factors for disease progression in hospitalized patients with COVID-19: A retrospective cohort study. Infectious Diseases, 52(7), 498–505. https://doi.org/10.1080/23744235.2020.1759817
- Kelava, A., Nagengast, B., & Brandt, H. (2014). A Nonlinear Structural Equation Mixture Modelling Approach for Nonnormally Distributed Latent Predictor Variables. Structural Equation Modelling: A Multidisciplinary Journal, 21(3), 468–481. https://doi.org/10.1080/10705511.2014.915379
- Khoso, P. A., Yew, V. W. C., & Mutalib, M. H. A. (2016). Comparing and Contrasting Health Behaviour With Illness Behaviour. 11(2), 12.
- Lee, S.-Y. (2007). Structural Equation Modelling: A Bayesian Approach. John Wiley & Sons, Ltd.
- Muharisa, C., Yanuar, F., & Devianto, D. (2018). Simulation Study of the Using of Bayesian Quantile Regression in Non- normal Error. Cauchy - Jurnal Matematika Murni Dan Aplikasi, 5(November), 121–126.
- Nam, E. J., Lee, E. K., & Oh, M.-S. (2018). Bayesian quantile regression analysis of Korean Jeonse deposit. Communications for Statistical Applications and Methods, 25(5), 489–499. https://doi.org/10.29220/CSAM.2018.25.5.489
- Ntzoufras, I. (2009). Bayesian modelling using WinBUGS. Wiley.
- Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The Performance of ML, GLS, and WLS Estimation in Structural Equation Modelling Under Conditions of Misspecification and Nonnormality. Structural Equation Modelling: A Multidisciplinary Journal, 7(4), 557–595. https://doi.org/10.1207/S15328007SEM0704_3
- Parekh, N., & Deierlein, A. L. (2020). Health behaviours during the coronavirus disease 2019 pandemic: Implications for obesity. Public Health Nutrition, 23(17), 3121–3125. https://doi.org/10.1017/S1368980020003031
- Perlman, S. (2020). Another Decade, Another Coronavirus. The New England Journal of Medicine, 3.
- Rahmadita, A., Yanuar, F., & Devianto, D. (2018). The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modelling Approach. CAUCHY, 5(2), 73. https://doi.org/10.18860/ca.v5i2.5039
- Suksatan, W., Choompunuch, B., Koontalay, A., Posai, V., & Abusafia, A. H. (2021). Predictors of Health Behaviours Among Undergraduate Students During the COVID-19 Pandemic: A Cross-Sectional Predictive Study. Journal of Multidisciplinary Healthcare, Volume 14, 727–734. https://doi.org/10.2147/JMDH.S306718
- Worldometers. (2021). COVID-19 Coronavirus pandemic. Https://Www.Worldometers.Info/Coronavirus/.
- Y. Thanoon, T., Adnan, R., & Saffari, S. E. (2016). Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data. Pakistan Journal of Statistics and Operation Research, 12(1), 125–140. https://doi.org/10.18187/pjsor.v12i1.952
- Yanuar, F. (2016). The Health Status Model in Urban and Rural Society in West Sumatera, Indonesia: An Approach of Structural Equation Modelling. Indian Journal of Science and Technology, 9(4). https://doi.org/10.17485/ijst/2016/v9i4/72601
- Yanuar, F., Ibrahim, K., & Aziz Jemain, A. (2013). Bayesian structural equation modelling for the health index. Journal of Applied Statistics, 40(6), 1254–1269.
- Yanuar, F., Ibrahim, K., & Jemain, A. A. (2010). On the application of structural equation modelling for the construction of a health index. Environmental Health and Preventive Medicine, 15(5), 285–291. https://doi.org/10.1007/s12199-010-0140-7
- Yanuar, F., Yozza, H., & Zetra, A. (2022). Modified Quantile Regression for Modelling the Low Birth Weight. Frontiers in Applied Mathematics and Statistics, 8, 890028. https://doi.org/10.3389/fams.2022.890028
- Zhang, X., & Savalei, V. (2016). Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modelling. Structural Equation Modelling: A Multidisciplinary Journal, 23(3), 392–408. https://doi.org/10.1080/10705511.2015.1118692
- Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G. F., & Tan, W. (2020). A Novel Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine, 382(8), 727–733. https://doi.org/10.1056/NEJMoa2001017
- Zvolensky, M. J., Garey, L., Rogers, A. H., Schmidt, N. B., Vujanovic, A. A., Storch, E. A., Buckner, J. D., Paulus, D. J., Alfano, C., Smits, J. A. J., & O’Cleirigh, C. (2020). Psychological, addictive, and health behaviour implications of the COVID-19 pandemic. Behaviour Research and Therapy, 134, 1–16. https://doi.org/10.1016/j.brat.2020.103715