Proportional Odds Model for Health States Analysis
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2019-01-18 
https://doi.org/10.14419/ijet.v8i1.7.25956
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health status, disability, repeated responses, Markov Chain Model, Proportional Odds Model -
Abstract
Employees’ health status is one of the key issues that should be considered in ensuring the economic growth of a country. Information on employees’ health status is useful in social and economic studies, especially in issues related to work-related disabilities and deaths. The estimation of disability probability involves a challenging method as a disabled employee may move from one state to another (from temporary to permanent, or vice versa), or from one event to another (from disabled to active, or to death), implying that repeated responses may be obtained at different time points in the relevant longitudinal studies. Markov Chain Model can be used to analyze repeated measurements, or ordinal responses in a longitudinal data, and to compare between one health states to another. The main objective of this study is to estimate the Markov transition probabilities between health states using the Proportional Odds Model (POM) based on the dataset obtained from Social Security Organization, Malaysia (SOCSO). The results show that female employees in age group 55-59 have the highest probability of remaining in active state (A), while male employees in age group 15-19 have the lowest probability of remaining in active state (A), or have the highest risk of transitioning from healthy state to disability or death states.
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References
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- Ganjali, M. Fitting Transition Models to Longitudinal Ordinal Response Data Using Available Software. In Proceedings of The 8th International ConferenceoOn Teaching Statistics (Icots8). 2010.[2] Bender, R. And U. Grouven, Ordinal Logistic Regression In Medical Research. Journal of The Royal College of Physicians of London, 1997. 31(5): P. 546-551.[3] Agresti, A. And M. Kateri, Categorical Data Analysis, In International Encyclopedia of Statistical Science. 2011, Springer. P. 206-208.[4] Manan, N.B., H. Hashim, And M.A. Mohd. Modification of The Current Malaysian No-Claim Discount System Using Markov Chains. In Business Engineering and Industrial Applications Colloquium (Beiac), 2013 Ieee. 2013. IEEE.[5] Zakaria, N.Z. And S.M. Deni, Application Of Alternative Geometric Distribution and Markov Chain Models For Fitting Sequences of Wet and Dry Days in Peninsular Malaysia. International Journal of Engineering and Management Research (Ijemr), 2016. 6(1): P. 110-119.[6] Mohamad, N., S. Deni, And A. Ul-Saufie, Application of The First Order of Markov Chain Model in Describing The PM10 Occurrences in Shah Alam And Jerantut, Malaysia. Pertanika Journal of Science & Technology, 2018. 26(1).[7] Cole, B.F., et al., A Multistate Markov Chain Model for Longitudinal, Categorical Qualityâ€Ofâ€Life Data Subject to Nonâ€Ignorable Missingness. Statistics in Medicine, 2005. 24(15): P. 2317-2334.[8] Jung, J., Estimating Markov Transition Probabilities Between Health States in The HRS Dataset. Indiana University, 2006.[9] Mccullagh, P., Regression Models for Ordinal Data. Journal of The Royal Statistical Society. Series B (Methodological), 1980: P. 109-142.[10] Steyerberg, E.W. And M.J. Eijkemans, Prognostic Modeling With Logistic Regression Analysis. Network, 2000. 10: P. 11.[11] Abreu, M.N.S., A.L. Siqueira, and W.T. Caiaffa, Ordinal Logistic Regression in Epidemiological Studies. Revista De Saude Publica, 2009. 43(1): P. 183-194.[12] Abreu, M.N.S., Et Al., Ordinal Logistic Regression Models: Application in Quality of Life Studies. Cadernos De Saúde Pública, 2008. 24: P. S581-S591.[13] Malaysia, Akta Keselamatan Sosial Pekerja 1969, 1969.[14] Samsuddin, S. And N. Ismail. Multi-State Markov Model for Disability: A Case of Malaysia Social Security (Socso). In Innovations Through Mathematical and Statistical Research: Proceedings of the 2nd International Conference On Mathematical Sciences and Statistics (ICMSS2016). 2016. AIP Publishing.[15] Samsuddin, S. And N. Ismail, Isu Hilang Upaya Dikalangan Pencarum Perkeso di Malaysia. Malaysia Labour Review, 2015. 11(No. 2): P. 85-94.[16] Samsuddin, S. And N. Ismail. Transition Probabilities of Health States for Workers in Malaysia Using A Markov Chain Model. In AIP Conference Proceedings. 2017. AIP Publishing.[17] Rusli, N.M., Z. Ibrahim, And R.M. Janor. Predicting Students’ Academic Achievement: Comparison Between Logistic Regression, Artificial Neural Network, And Neuro-Fuzzy. In Information Technology, 2008. ITSIM2008. International Symposium On. 2008. IEEE.[18] Rahman, H.A.A., et al. Comparison Of Predictive Models to Predict Survival of Cardiac Surgery Patients. In Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference On. 2012.IEEE.
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How to Cite
Samsuddin, S., Ismail, N., & ., . (2019). Proportional Odds Model for Health States Analysis. International Journal of Engineering & Technology, 8(1.7), 56-61. https://doi.org/10.14419/ijet.v8i1.7.25956Received date: 2019-01-16
Accepted date: 2019-01-16
Published date: 2019-01-18