The Impact of Population Changes on Healthcare Demands using Nonhomogeneous Markov Model
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Published:
May 28, 2019
Keywords:
Markov Model Elderly Population Aging Healthcare Logistics Nonhomogeneous Model
Main Article Content
Abstract
The study of the change of population structure is vital because there is continuous growth of number of elderly both in the national and the international. The purpose of this research is to study the effect of population change on the healthcare demand using nonhomogeneous Markov models to estimate the number of population and inpatients in the next ten years based on genders and age groups. The results show that 22 % of the population are likely to become the elderly in 2025 and the number of inpatients are increasing. This leads to the growing demand of long-term care and healthcare staff . This demand data can be used to prepare budgets for managing the change of resource requirement for long-term care management.
Article Details
How to Cite
[1]
บุราณศรี น. and จิตตะมัย พ., “The Impact of Population Changes on Healthcare Demands using Nonhomogeneous Markov Model”, RMUTI Journal, vol. 12, no. 3, pp. 48–63, May 2019.
Section
Research article
References
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[3] Lugo-Palacios, D. G., and Cairns, J. (2015). Using Ambulatory Care Sensitive Hospitalisations to Analyse the Effectiveness of Primary Care Services in Mexico. Social Science & Medicine. Vol. 144, pp. 59-68. DOI: 10.1016/j.socscimed.2015.09.010
[4] Craig, B. A. and Newton, M. A. (1997). Modeling the History of Diabetic Retinopathy. Case Studies in Bayesian Statistics. Springer. pp. 305-323. DOI: 10.1007/978-1-4612-2290-3_7
[5] Pérez-Ocón, R., Ruiz-Castro, J. E., and Gámiz-Pérez, M. L. (2000). Markov Models with Lognormal Transition Rates in the Analysis of Survival Times. Test. Vol. 9, Issue 2, pp. 353-370. DOI: 10.1007/BF02595740
[6] Vassiliou, P. C. (1997). The Evolution of the Theory of Non‐Homogeneous Markov Systems. Applied Stochastic Models and Data Analysis. Vol. 13, Issue 3‐4, pp. 159-176
[7] Kalbfleisch, J. D., Lawless, J. F., and Vollmer, W. M. (1983). Estimation in Markov Models from Aggregate Data. Biometrics. Vol. 39, No. 4, pp. 907-919. DOI: 10.2307/2531326
[8] Van Der Plas, A. P. (1983). On the Estimation of the Parameters of Markov Probability Models Using Macro Data. The Annals of Statistics. Vol. 11, No. 1, pp. 78-85
[9] Kalbfleisch, J. D. and Lawless, J. F. (1984). Least-Squares Estimation of Transition Probabilities from Aggregate Data. The Canadian Journal of Statistics / La Revue Canadienne de Statistique. Vol. 12, No. 3, pp. 169-182. DOI: 10.2307/3314745
[10] Lawless, J. and McLeish, D. (1984). The Information in Aggregate Data from Markov Chains. Biometrika. Vol. 71, No. 3, pp. 419-430. DOI: 10.2307/2336552
[11] McLeish, D. L. (1984). Estimation for Aggregate Models: The Aggregate Markov Chain. The Canadian Journal of Statistics / La Revue Canadienne de Statistique. Vol. 12, No. 4, pp. 265-282. DOI: 10.2307/3314810
[12] Davis, B. A., Heathcote, C. R., and O’Neill, T.J. (2002). Estimating and Interpolating a Markov Chain from Aggregate Data. Biometrika. Vol. 89, Issue 1, pp. 95-110. DOI: 10.1093/biomet/89.1.95
[13] Smith, S. K., Tayman, J., and Swanson, D. A. (2006). State and Local Population Projections. Methodology and Analysis. Springer Netherlands. DOI: 10.1007/0-306-47372-0
[14] VI, M. (1970). Methods of Measuring Internal Migration. United Nations Publication, New York.
[15] MacRae, E. C. (1977). Estimation of Time-Varying Markov Processes with Aggregate Data. Econometrica. Vol. 45, No. 1, pp. 183-198. DOI: 10.2307/1913295
[16] Anderson, T. W. and Goodman, L. A. (1957). Statistical Inference About Markov Chains. The Annals of Mathematical Statistics. Vol. 28, Number 1, pp. 89-110.
[17] Agresti, A. and Kateri, M. (2011). Categorical Data Analysis. Springer, Berlin. DOI: 10.1007/978-3-642-04898-2_161