Bayesian structural equation modeling for the health index

Abstract

There are many factors which could influence the level of health of an individual. These factors are interactive and their overall effects on health are usually measured by an index which is called as health index. The health index could also be used as an indicator to describe the health level of a community. Since the health index is important, many research have been done to study its determinant. The main purpose of this study is to model the health index of an individual based on classical structural equation modeling (SEM) and Bayesian SEM. For estimation of the parameters in the measurement and structural equation models, the classical SEM applies the robust-weighted least-square approach, while the Bayesian SEM implements the Gibbs sampler algorithm. The Bayesian SEM approach allows the user to use the prior information for updating the current information on the parameter. Both methods are applied to the data gathered from a survey conducted in Hulu Langat, a district in Malaysia. Based on the classical and the Bayesian SEM, it is found that demographic status and lifestyle are significantly related to the health index. However, mental health has no significant relation to the health index.

Keywords:

• health index
• structural equation modeling
• Bayesian SEM
• Gibbs sampler
• prior information

Acknowledgements

The authors thank the Associate Professor Khalib A Latiff and Mr Khairul Yusof from the Department of Community Health, Medical Faculty, UKM, who furnished us the health survey data used in this study. This research was financially supported by Directorate General of Higher Education, Ministry of National Education, Indonesia. In addition, a partial support was also obtained from UKM under the research grant with the code UKM-ST-06-FRGS0011-2007. We also thank several anonymous referees for their constructive comments which have improved the final version of this paper.