Estimating Markov Transition Probabilities Between Health States in the Social Security Malaysia (SOCSO) Dataset
Shamshimah Samsuddin1, Noriszura Ismail2
1Shamshimah Samsuddin, School of Mathematical Sciences, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia.
2Noriszura Ismail, Centre for Actuarial Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
Manuscript received on 06 September 2019 | Revised Manuscript received on 15 September 2019 | Manuscript Published on 26 October 2019 | PP: 278-282 | Volume-8 Issue-11S2 September 2019 | Retrieval Number: K104309811S219/2019©BEIESP | DOI: 10.35940/ijitee.K1043.09811S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Occupational injury represents a considerable part of injury burden to the society as it may affect workers in their most productive years. The objective of this paper is to estimate the Markov transition probabilities of a worker’s health states over time using the Counting Method (CM) and the Proportional Odds Model (POM), focusing on disability among the Social Security Organization (SOCSO) contributors in Malaysia. Four health states namely active/work (A), temporary disability (T), permanent disability (P) and death (D) are considered, where the transition probabilities are estimated at yearly intervals based on age, gender, year and disability category.
Keywords: Occupational Injury, Markov Transition Probabilities, Counting Method, Proportional Odds Model.
Scope of the Article: Security, Trust and Privacy